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Background: Over 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015-2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission., Methodology/principal Findings: We combined information from Zika-related Google searches, Twitter microblogs, and the HealthMap digital surveillance system with historical Zika suspected case counts to track and predict estimates of suspected weekly Zika cases during the 2015-2016 Latin American outbreak, up to three weeks ahead of the publication of official case data. We evaluated the predictive power of these data and used a dynamic multivariable approach to retrospectively produce predictions of weekly suspected cases for five countries: Colombia, El Salvador, Honduras, Venezuela, and Martinique. Models that combined Google (and Twitter data where available) with autoregressive information showed the best out-of-sample predictive accuracy for 1-week ahead predictions, whereas models that used only Google and Twitter typically performed best for 2- and 3-week ahead predictions., Significance: Given the significant delay in the release of official government-reported Zika case counts, we show that these Internet-based data streams can be used as timely and complementary ways to assess the dynamics of the outbreak., Competing Interests: The authors have declared that no competing interests exist.

Using geo-referenced case data, we present spatial and spatio-temporal cluster analyses of the early spread of the 2013-2015 chikungunya virus (CHIKV) in Dominica, an island in the Caribbean. Spatial coordinates of the locations of the first 417 reported cases observed between December 15th, 2013 and March 11th, 2014, were captured using the Global Positioning System (GPS). We observed a preponderance of female cases, which has been reported for CHIKV outbreaks in other regions. We also noted statistically significant spatial and spatio-temporal clusters in highly populated areas and observed major clusters prior to implementation of intensive vector control programs suggesting early vector control measures, and education had an impact on the spread of the CHIKV epidemic in Dominica. A dynamical identification of clusters can lead to local assessment of risk and provide opportunities for targeted control efforts for nations experiencing CHIKV outbreaks.

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change. Author summary: Mosquito vectors are strongly affected by temperature, and their distributions are likely to shift under climate change. Lab studies show that mosquito abundance has a unimodal response to temperature with thermal optima, upper and lower thermal limits. However, it remains unknown how mosquito laboratory-derived thermal responses relate to the thermal responses of mosquitoes in nature. We used a global database of field-collected mosquito occurrences, geospatial environmental covariates, and species distribution models to estimate the relationship between temperature and probability of mosquito occurrence. We found that thermal minima (r = 0.87) and, to a lesser degree, thermal optima (r = 0.69) estimated from laboratory studies were correlated with those from the species distribution models. For most species, we did not detect thermal maxima. These results suggest that laboratory studies and field-based machine learning studies are complementary. Together, they can help to better understand vector response to climate change. [ABSTRACT FROM AUTHOR]

Background: Seasonal fluctuations in weather are recognized as factors that affect both Aedes (Ae.) aegypti mosquitoes and the diseases they carry, such as dengue fever. The El Niño-Southern Oscillation (ENSO) is widely regarded as one of the most impactful atmospheric phenomena on Earth, characterized by the interplay of shifting ocean temperatures, trade wind intensity, and atmospheric pressure, resulting in extensive alterations in climate conditions. In this study, we investigate the influence of ENSO and local weather conditions on the spatio-temporal variability of Ae. aegypti infestation index. Methods: We collected seasonal entomological survey data of immature forms of Ae. aegypti mosquitoes (Breteau index), as well as data on temperature, rainfall and the Oceanic Niño Index (ONI) for the period 2008–2018 over the 645 municipalities of the subtropical State of São Paulo (Brazil). We grounded our analytical approach on a Bayesian framework and we used a hierarchical spatio-temporal model to study the relationship between ENSO tracked by ONI, seasonal weather fluctuations and the larval index, while adjusting for population density and wealth inequalities. Results: Our results showed a relevant positive effect for El Niño on the Ae. aegypti larval index. In particular, we found that the number of positive containers would be expected to increase by 1.30-unit (95% Credible Intervals (CI): 1.23 to 1.37) with El Niño events (i.e., ≥ 1°C, moderate to strong) respect to neutral (and weak) events. We also found that seasonal rainfall exceeding 153.12 mm appears to have a notable impact on vector index, leading potentially to the accumulation of ample water in outdoor discarded receptacles, supporting the aquatic phase of mosquito development. Additionally, seasonal temperature above 23.30°C was found positively associated to the larval index. Although the State of São Paulo as a whole has characteristics favourable to proliferation of the vector, there were specific areas with a greater tendency for mosquito infestation, since the most vulnerable areas are predominantly situated in the central and northern regions of the state, with hot spots of abundance in the south, especially during El Niño events. Our findings also indicate that social disparities present in the municipalities contributes to Ae. aegypti proliferation. Conclusions: Considering the anticipated rise in both the frequency and intensity of El Niño events in the forthcoming decades as a consequence of climate change, the urgency to enhance our ability to track and diminish arbovirus outbreaks is crucial. Author summary: Dengue fever is a dramatically expanding disease transmitted by mosquitoes. Earlier research has demonstrated the susceptibility of dengue-carrying mosquitoes to weather conditions, with rainfall and temperature significantly impacting dengue transmission. While there is evidence of a connection between weather factors, the El Niño-Southern Oscillation (ENSO) and the risk of dengue, their impact on the behaviour of Aedes (Ae.) aegypti mosquitoes, which is the main disease vectors, remains poorly comprehended. In this study we analyse municipality-specific time series of a larval index of Ae. aegypti population in the State of São Paulo (southern Brazil) from 2008 to 2018. The main goal was to explore how intra- and inter-annual changes in weather conditions and ENSO periodic fluctuations affected mosquito abundance. We found a strong and significant coherence between the temporal pattern of mosquito abundance and El Niño phases, rainfall and temperature, showing the role that climate plays in driving infestation periodicity. Specifically, we identified a relationship between Ae. aegypti infestation and the strength of the ENSO phenomenon: periods characterized by El Niño phase (moderate to strong) were associated with a higher abundance of Ae. aegypti mosquito larvae. Additionally, we found that wealth inequalities increase opportunities for Ae. aegypti proliferation. Our discoveries carry significant implications for vector control strategies, as they offer valuable insights for categorizing areas based on spatial risk, enabling the prioritization of intensified vector control measures. Given the anticipated rise in the frequency of El Niño events due to climate change, these findings suggest that arbovirus outbreaks could also see an increase, and that El Niño occurrences might serve as a predictive tool for such outbreaks. [ABSTRACT FROM AUTHOR]

Background: Hantavirus pulmonary syndrome (HPS) is a life threatening disease transmitted by the rodent Oligoryzomys longicaudatus in Chile. Hantavirus outbreaks are typically small and geographically confined. Several studies have estimated risk based on spatial and temporal distribution of cases in relation to climate and environmental variables, but few have considered climatological modeling of HPS incidence for monitoring and forecasting purposes., Methodology: Monthly counts of confirmed HPS cases were obtained from the Chilean Ministry of Health for 2001-2012. There were an estimated 667 confirmed HPS cases. The data suggested a seasonal trend, which appeared to correlate with changes in climatological variables such as temperature, precipitation, and humidity. We considered several Auto Regressive Integrated Moving Average (ARIMA) time-series models and regression models with ARIMA errors with one or a combination of these climate variables as covariates. We adopted an information-theoretic approach to model ranking and selection. Data from 2001-2009 were used in fitting and data from January 2010 to December 2012 were used for one-step-ahead predictions., Results: We focused on six models. In a baseline model, future HPS cases were forecasted from previous incidence; the other models included climate variables as covariates. The baseline model had a Corrected Akaike Information Criterion (AICc) of 444.98, and the top ranked model, which included precipitation, had an AICc of 437.62. Although the AICc of the top ranked model only provided a 1.65% improvement to the baseline AICc, the empirical support was 39 times stronger relative to the baseline model., Conclusions: Instead of choosing a single model, we present a set of candidate models that can be used in modeling and forecasting confirmed HPS cases in Chile. The models can be improved by using data at the regional level and easily extended to other countries with seasonal incidence of HPS.

Dengue is a common and growing problem worldwide, with an estimated 70-140 million cases per year. Traditional, healthcare-based, government-implemented dengue surveillance is resource intensive and slow. As global Internet use has increased, novel, Internet-based disease monitoring tools have emerged. Google Dengue Trends (GDT) uses near real-time search query data to create an index of dengue incidence that is a linear proxy for traditional surveillance. Studies have shown that GDT correlates highly with dengue incidence in multiple countries on a large spatial scale. This study addresses the heterogeneity of GDT at smaller spatial scales, assessing its accuracy at the state-level in Mexico and identifying factors that are associated with its accuracy. We used Pearson correlation to estimate the association between GDT and traditional dengue surveillance data for Mexico at the national level and for 17 Mexican states. Nationally, GDT captured approximately 83% of the variability in reported cases over the 9 study years. The correlation between GDT and reported cases varied from state to state, capturing anywhere from 1% of the variability in Baja California to 88% in Chiapas, with higher accuracy in states with higher dengue average annual incidence. A model including annual average maximum temperature, precipitation, and their interaction accounted for 81% of the variability in GDT accuracy between states. This climate model was the best indicator of GDT accuracy, suggesting that GDT works best in areas with intense transmission, particularly where local climate is well suited for transmission. Internet accessibility (average ∼ 36%) did not appear to affect GDT accuracy. While GDT seems to be a less robust indicator of local transmission in areas of low incidence and unfavorable climate, it may indicate cases among travelers in those areas. Identifying the strengths and limitations of novel surveillance is critical for these types of data to be used to make public health decisions and forecasting models.

Background: Dengue is a growing problem both in its geographical spread and in its intensity, and yet current global distribution remains highly uncertain. Challenges in diagnosis and diagnostic methods as well as highly variable national health systems mean no single data source can reliably estimate the distribution of this disease. As such, there is a lack of agreement on national dengue status among international health organisations. Here we bring together all available information on dengue occurrence using a novel approach to produce an evidence consensus map of the disease range that highlights nations with an uncertain dengue status., Methods/principal Findings: A baseline methodology was used to assess a range of evidence for each country. In regions where dengue status was uncertain, additional evidence types were included to either clarify dengue status or confirm that it is unknown at this time. An algorithm was developed that assesses evidence quality and consistency, giving each country an evidence consensus score. Using this approach, we were able to generate a contemporary global map of national-level dengue status that assigns a relative measure of certainty and identifies gaps in the available evidence., Conclusion: The map produced here provides a list of 128 countries for which there is good evidence of dengue occurrence, including 36 countries that have previously been classified as dengue-free by the World Health Organization and/or the US Centers for Disease Control. It also identifies disease surveillance needs, which we list in full. The disease extents and limits determined here using evidence consensus, marks the beginning of a five-year study to advance the mapping of dengue virus transmission and disease risk. Completion of this first step has allowed us to produce a preliminary estimate of population at risk with an upper bound of 3.97 billion people. This figure will be refined in future work.

Background: A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics., Methodology/principal Findings: Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003-2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99., Conclusions/significance: Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance.

Background: Dengue virus (DENV) is endemic to many parts of the world and has serious health and socioeconomic effects even in high-income countries, especially with rapid changes in the climate globally. We explored the literature on dengue vector control methods used in high-income, city settings and associations with dengue incidence, dengue prevalence, or mosquito vector densities. Methods: Studies of any design or year were included if they reported effects on human DENV infection or Aedes vector indices of dengue-specific vector control interventions in high-income, city settings. Results: Of 24 eligible sources, most reported research in the United States (n = 8) or Australia (n = 5). Biocontrol (n = 12) and chemical control (n = 13) were the most frequently discussed vector control methods. Only 6 sources reported data on the effectiveness of a given method in reducing human DENV incidence or prevalence, 2 described effects of larval and adult control on Aedes DENV positivity, 20 reported effectiveness in reducing vector density, using insecticide, larvicide, source reduction, auto-dissemination of pyriproxyfen and Wolbachia, and only 1 described effects on human-vector contact. Conclusions: As most studies reported reductions in vector densities, rather than any effects on human DENV incidence or prevalence, we can draw no clear conclusions on which interventions might be most effective in reducing dengue in high-income, city areas. More research is needed linking evidence on the effects of different DENV vector control methods with dengue incidence/prevalence or mosquito vector densities in high-income, city settings as this is likely to differ from low-income settings. This is a significant evidence gap as climate changes increase the global reach of DENV. The importance of community involvement was clear in several studies, although it is impossible to tease out the relative contributions of this from other control methods used. Author summary: Dengue virus is present in many parts of the world and has serious health and socioeconomic effects even in high-income countries, especially with rapid changes in the climate globally. In this study, we explored the literature on dengue vector control methods used in high-income, city settings and associations with number of human dengue infections, and the density of mosquitoes. We found 24 papers with relevant results. Most of these described studies in the United States or Australia. Most were about various forms of biological or chemical control of the mosquitoes. Few papers discussed effects on human dengue infection. We were unable to draw any clear conclusions on how effective mosquito control methods were as we could identify little research on this subject in this specific setting. More research is needed on this topic, particularly as climate change will make more areas of the world vulnerable to dengue infection. [ABSTRACT FROM AUTHOR]

Dengue viruses (DENV) are endemic in the US territories of Puerto Rico, American Samoa, and the US Virgin Islands, with focal outbreaks also reported in the states of Florida and Hawaii. However, little is known about the intensity of dengue virus transmission over time and how dengue viruses have shaped the level of immunity in these populations, despite the importance of understanding how and why levels of immunity against dengue may change over time. These changes need to be considered when responding to future outbreaks and enacting dengue management strategies, such as guiding vaccine deployment. We used catalytic models fitted to case surveillance data stratified by age from the ArboNET national arboviral surveillance system to reconstruct the history of recent dengue virus transmission in Puerto Rico, American Samoa, US Virgin Islands, Florida, Hawaii, and Guam. We estimated average annual transmission intensity (i.e., force of infection) of DENV between 2010 and 2019 and the level of seroprevalence by age group in each population. We compared models and found that assuming all reported cases are secondary infections generally fit the surveillance data better than assuming all cases are primary infections. Using the secondary case model, we found that force of infection was highly heterogeneous between jurisdictions and over time within jurisdictions, ranging from 0.00003 (95% CrI: 0.00002–0.0004) in Florida to 0.08 (95% CrI: 0.044–0.14) in American Samoa during the 2010–2019 period. For early 2020, we estimated that seropositivity in 10 year-olds ranged from 0.09% (0.02%–0.54%) in Florida to 56.3% (43.7%–69.3%) in American Samoa. In the absence of serological data, age-specific case notification data collected through routine surveillance combined with mathematical modeling are powerful tools to monitor arbovirus circulation, estimate the level of population immunity, and design dengue management strategies. Author summary: Viruses transmitted by Aedes mosquitoes are a substantial public health burden globally. In the US, the increasing number of outbreaks in recent decades and the co-circulation of the four dengue viruses present a risk of experiencing sequential infections, which can result in more severe disease. However, reported cases only represent a proportion of all infections, so the intensity of dengue circulation in US jurisdictions and the level of population immunity are largely unknown. Combining dengue surveillance data stratified by age with mathematical models enables direct estimation of transmission intensity and previous exposure. We found that dengue virus transmission intensity was highest in American Samoa and Puerto Rico. The US Virgin Islands also showed patterns of endemic transmission, while Hawaii and Guam present more sporadic outbreak profiles. These estimates can help improve risk assessment in these locations and guide interventions to the populations who most need them. [ABSTRACT FROM AUTHOR]

Introduction: Schistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microscopists. An automated digital microscope with artificial intelligence (Schistoscope), offers a potential solution. This field study aimed to validate the diagnostic performance of the Schistoscope for detecting and quantifying Schistosoma haematobium eggs in urine compared to conventional microscopy and to a composite reference standard (CRS) consisting of real-time PCR and the up-converting particle (UCP) lateral flow (LF) test for the detection of schistosome circulating anodic antigen (CAA). Methods: Based on a non-inferiority concept, the Schistoscope was evaluated in two parts: study A, consisting of 339 freshly collected urine samples and study B, consisting of 798 fresh urine samples that were also banked as slides for analysis with the Schistoscope. In both studies, the Schistoscope, conventional microscopy, real-time PCR and UCP-LF CAA were performed and samples with all the diagnostic test results were included in the analysis. All diagnostic procedures were performed in a laboratory located in a rural area of Gabon, endemic for S. haematobium. Results: In study A and B, the Schistoscope demonstrated a sensitivity of 83.1% and 96.3% compared to conventional microscopy, and 62.9% and 78.0% compared to the CRS. The sensitivity of conventional microscopy in study A and B compared to the CRS was 61.9% and 75.2%, respectively, comparable to the Schistoscope. The specificity of the Schistoscope in study A (78.8%) was significantly lower than that of conventional microscopy (96.4%) based on the CRS but comparable in study B (90.9% and 98.0%, respectively). Conclusion: Overall, the performance of the Schistoscope was non-inferior to conventional microscopy with a comparable sensitivity, although the specificity varied. The Schistoscope shows promising diagnostic accuracy, particularly for samples with moderate to higher infection intensities as well as for banked sample slides, highlighting the potential for retrospective analysis in resource-limited settings. Trial registration: NCT04505046 ClinicalTrials.gov. Author summary: Assessment of schistosomiasis control programs is a crucial step to understanding the success rate of these control programs. The Schistoscope: an AI-powered automated digital microscope could overcome the limitations of conventional microscopy in endemic resource limited settings as well as in settings lacking microscopy experts. In this study, we carried out an extensive validation of the Schistoscope's diagnostic performance for diagnosis of urogenital schistosomiasis compared to conventional microscopy as well as more accurate diagnostic tests such as real-time PCR and the up-converting particle (UCP) lateral flow (LF) test for the detection of circulating anodic antigen (CAA) on freshly collected urines. We also assessed the performance of the Schistoscope for the diagnosis of schistosomiasis on banked sample slides, using a simple and sustainable storage method, for approximately two years. Having a tool that can prospectively and retrospectively analyse samples in an easy and sustainable way could facilitate schistosomiasis control programs in settings with little or no access to microscopists. Overall, we found the Schistoscope to be as good as conventional microscopy for the diagnosis of schistosomiasis, and given its downstream advantages of digital health, it would serve as a valuable diagnostic/screening tool in resource limited endemic settings. [ABSTRACT FROM AUTHOR]

Background: Montelukast has shown potential as a candidate treatment for dengue. This study aimed to evaluate the efficacy and safety of montelukast in preventing dengue with warning signs. Methods: This multicenter, randomized, double-blind, placebo-controlled trial enrolled adult participants with NS1 antigenemia in Thailand. The participants were randomly assigned to receive either oral montelukast (10 mg) or a placebo for 10 days or until all symptoms resolved. Results: Between January 2021 and June 2023, 358 participants were enrolled and randomly assigned (1:1) to receive either montelukast or placebo. The incidence rate of warning signs in the montelukast group and the placebo group were 9.5% and 7.8% per day, respectively. There was no difference between the two groups (HR 1.36; 95%CI 0.94–1.96, P = 0.105). No statistically significant differences were observed in the incidence rate of severe dengue, hemoconcentration, thrombocytopenia, admission, or recovery from dengue. Neither dengue shock, nor mortality occurred. The montelukast group exhibited a decreased incidence rate of transaminase elevations (0.7% vs 1.4% per day, HR: 0.48, 95%CI 0.25–0.90, P = 0.023). Conclusion: Oral montelukast does not reduce the incidence of warning signs among patients with dengue. Nevertheless, the observed decrease in transaminase elevations warrants further investigation to evaluate the potential effect of montelukast. Clinical Trials Registration: Clinicaltrials.gov, NCT04673422, registered on 9 December 2020 Author summary: Although there is no specific treatment for dengue to date, montelukast shows promise as a potential therapeutic agent. An open-label study previously showed a 71% reduction in dengue shock among patients with dengue who were administered montelukast. However, concerns were raised regarding the clinical study's lack of a power analysis or predefined statistical difference. Because of its affordability, relatively safe profile, and widespread availability, exploring its role in preventing dengue complications holds great significance. This study is a double-blind clinical trial with pragmatic design assessing the efficacy of montelukast in dengue. In contrast to the previous open-label study, montelukast does not reduce the incidence of warning signs among patients with dengue. Nevertheless, a reduction in the incidence of transaminase elevations was observed. In conclusion, montelukast should not be used for the prevention of warning signs in patients with dengue. However, the observed decrease in transaminase elevations warrants further investigation to evaluate the potential effect of montelukast. [ABSTRACT FROM AUTHOR]

Dengue virus (DENV) infection causes dengue fever, the most prevalent arthropod-transmitted viral disease worldwide. Viruses are acellular parasites and obligately rely on host cell machinery for reproduction. Previous studies have indicated metabolomic changes in endothelial cell models and sera of animal models and patients with dengue fever. To probe the immunometabolic mechanism of DENV infection, here, we report the metabolomic landscape of a human macrophage cell model of DENV infection and its antibody-dependent enhancement. DENV infection of THP-1-derived macrophages caused 202 metabolic variants, of which amino acids occupied 23.7%, fatty acids 21.78%, carbohydrates 10.4%, organic acids 13.37%, and carnitines 10.4%. These metabolomic changes indicated an overall anabolic signature, which was characterized by the global exhaustion of amino acids, increases of cellular fatty acids, carbohydrates and pentoses, but decreases of acylcarnitine. Significant activation of metabolic pathways of glycolysis, pentose phosphate, amino acid metabolism, and tricarboxylic acid cycle collectively support the overall anabolism to meet metabolic demands of DENV replication and immune activation by viral infection. Totally 88 of 202 metabolic variants were significantly changed by DENV infection, 36 of which met the statistical standard (P<0.05, VIP>1.5) of differentially expressed metabolites, which were the predominantly decreased variants of acylcarnitine and the increased variants of fatty acids and carbohydrates. Remarkably, 11 differentially expressed metabolites were significantly distinct between DENV only infection and antibody-dependent enhancement of viral infection. Our data suggested that the anabolic activation by DENV infection integrates the viral replication and anti-viral immune activation. Author summary: Dengue fever, a widespread viral disease transmitted by mosquitoes, is caused by the dengue virus (DENV). In our study, we explored how DENV infection affects human macrophages, crucial immune cells in our body. By analyzing the metabolic changes within these cells during DENV infection and its antibody-dependent enhancement, we uncovered a metabolic landscape characterized by significant alterations. Our findings revealed a shift towards utilization of amino acids and increased production of carbohydrates and fatty acids, indicating an overall boost in cellular activity—a signature response to the virus's replication demands and immune system activation triggered by the infection. Specifically, we identified 88 metabolic changes induced by DENV, with 36 showing notable statistical significance, highlighting decreased acylcarnitine and increased fatty acids and carbohydrates as prominent alterations. Importantly, our study identified 11 distinct metabolites significantly different between DENV infection alone and its enhancement by antibodies. These results strongly suggest that the virus not only facilitates its replication but also orchestrates anabolic processes that integrate viral growth with the host immune responses. Understanding these metabolic dynamics during DENV infection could offer valuable insights for potential therapeutic interventions in combating this prevalent and impactful disease. [ABSTRACT FROM AUTHOR]

Mayaro virus (MAYV) is a mosquito-borne Alphavirus that is widespread in South America. MAYV infection often presents with non-specific febrile symptoms but may progress to debilitating chronic arthritis or arthralgia. Despite the pandemic threat of MAYV, its true distribution remains unknown. The objective of this study was to clarify the geographic distribution of MAYV using an established risk mapping framework. This consisted of generating evidence consensus scores for MAYV presence, modeling the potential distribution of MAYV in select countries across Central and South America, and estimating the population residing in areas suitable for MAYV transmission. We compiled a georeferenced compendium of MAYV occurrence in humans, animals, and arthropods. Based on an established evidence consensus framework, we integrated multiple information sources to assess the total evidence supporting ongoing transmission of MAYV within each country in our study region. We then developed high resolution maps of the disease's estimated distribution using a boosted regression tree approach. Models were developed using nine climatic and environmental covariates that are related to the MAYV transmission cycle. Using the output of our boosted regression tree models, we estimated the total population living in regions suitable for MAYV transmission. The evidence consensus scores revealed high or very high evidence of MAYV transmission in several countries including Brazil (especially the states of Mato Grosso and Goiás), Venezuela, Peru, Trinidad and Tobago, and French Guiana. According to the boosted regression tree models, a substantial region of South America is suitable for MAYV transmission, including north and central Brazil, French Guiana, and Suriname. Some regions (e.g., Guyana) with only moderate evidence of known transmission were identified as highly suitable for MAYV. We estimate that approximately 58.9 million people (95% CI: 21.4–100.4) in Central and South America live in areas that may be suitable for MAYV transmission, including 46.2 million people (95% CI: 17.6–68.9) in Brazil. Our results may assist in prioritizing high-risk areas for vector control, human disease surveillance and ecological studies. Author summary: We used a comprehensive, georeferenced compendium of Mayaro virus (MAYV) occurrence to model the suitability for MAYV in Central and South America. Our boosted regression tree model incorporated 195 MAYV occurrence locations and 9 gridded environmental covariates to generate a 5 x 5 km continuous surface of MAYV suitability across Central and South America. Using this distribution model, we estimated the total population residing in areas that are suitable for MAYV transmission. Furthermore, we developed evidence consensus scores for each country in our study region that synthesized a variety of sources to assess the overall evidence of known MAYV transmission. Our study provides a contemporary estimate of MAYV distribution using a well-established disease mapping framework. This information provides an evidence base that can guide disease surveillance (including human cases and ecological studies) and vector control efforts in Central and South America. This is especially useful in regions with high predicted MAYV suitability but only moderate evidence of current MAYV transmission (e.g., Guyana and Suriname). [ABSTRACT FROM AUTHOR]

Background: The timely identification of severe dengue in peadiatric patients is of utmost importance, as any delay in diagnosis could lead to an irreversible state of shock potentially leading to fatal consequences. The primary aim of our study was to characterize dengue severity in paediatric patients based on initial symptoms, signs, and laboratory investigation of their presentation in the emergency department. Methodology: We conducted a retrospective data retrieval from the medical records of 254 paediatric patients who had been diagnosed with confirmed cases of dengue fever. The clinical characteristics were compared between severe and non-severe dengue. Multiple logistic regression analysis was utilised to elucidate the variables that exhibited associations with severe dengue. Results: A total of 254 paediatric patients were included, among whom 15.4% (n = 39) were diagnosed with severe dengue. Multiple logistic regression analysis identified lethargy, systolic blood pressure (SBP) below 90 mmHg, capillary refilled time (CRT) longer than 2 seconds, ascites, and hepatomegaly were independently associated with severe dengue. Conclusion: In paediatric patients, severe dengue is associated with specific clinical indicators, including lethargy, low systolic blood pressure, prolonged capillary refill time (CRT), and the presence of ascites and hepatomegaly. Identifying these clinical features early is crucial for primary care physicians, as it enables accurate diagnosis and timely intervention to manage severe dengue effectively. Author summary: Cheras is a suburban area located in the Klang Valley region of Malaysia. In recent years, the population of Cheras has grown rapidly due to urbanization and economic development in the surrounding areas. Cheras has a relatively high population density (N = 135,823, 8,489 / km2) with a mix of high-rise apartment buildings, landed residential properties, and commercial areas. The area is well-connected to other parts of the Klang Valley via major highways and public transportation, which also contributes to the population density. The present study documented clinical and laboratory profiles of 254 paediatric patients with dengue who presented in the emergency department between November 2018 to November 2019 at the only hospital in Cheras, a prevalent dengue area. Patients had an average age of 13 years old, and the male-to-female ratio was nearly balanced. More than 15% had severe dengue manifestation. We found the independent clinical risk factors for severe dengue in paediatric patients include lethargy, systolic blood pressure (SBP) below 90 mmHg, capillary refilled time (CRT) longer than 2 seconds, ascites, and hepatomegaly. Early recognition of these manifestations will assist primary care physicians in making correct diagnosis with timely intervention and management. The presence of dengue in a high-density population and rapidly growing zone warrants further holistic actions to reduce its impact on children's health. [ABSTRACT FROM AUTHOR]

Vector-borne diseases remain a significant public health threat in many regions of the world. Traditional vector surveillance and control methods have relied on active and passive surveillance programs, which are often costly and time-consuming. New internet-based vector surveillance systems have shown promise in removing some of the cost and labor burden from health authorities. We developed and evaluated the effectiveness of a new internet-based surveillance system, "AlertaChirimacha", for detecting Triatoma infestans (known locally by its Quechua name, Chirimacha), the Chagas disease vector, in the city of Arequipa, Peru. In the first 26 months post-implementation, AlertaChirimacha received 206 reports of residents suspecting or fearing triatomines in their homes or neighborhoods, of which we confirmed, through pictures or inspections, 11 (5.3%) to be Triatoma infestans. After microscopic examination, none of the specimens collected were infected with Trypanosoma cruzi. AlertaChirimacha received 57% more confirmed reports than the traditional surveillance system and detected 10% more infested houses than active and passive surveillance approaches combined. Through in-depth interviews we evaluate the reach, bilateral engagement, and response promptness and efficiency of AlertaChirimacha. Our study highlights the potential of internet-based vector surveillance systems, such as AlertaChirimacha, to improve vector surveillance and control efforts in resource-limited settings. This approach could decrease the cost and time horizon for the elimination of vector-mediated Chagas disease in the region. Author summary: Insects that spread diseases are still a problem in many parts of the world. Monitoring and controlling them can be expensive and time-consuming, but new internet-based methods have shown promise in reducing workload and costs. We developed AlertaChirimacha, an internet-based surveillance system to detect Triatoma infestans, a species of insect that spreads Chagas disease. AlertaChirimacha was tested in Arequipa, Peru, and in the 26 months after it was implemented, it received 206 reports from residents who suspected they had these insects in their homes, 11 were confirmed by our team. AlertaChirimacha detected more infested homes than the traditional methods combined. AlertaChirimacha was also effective in reaching people, getting them to engage, and respond to reports. This approach can improve efforts to control insect-borne diseases in places with limited resources and make it less expensive to eliminate vector-mediated Chagas disease. [ABSTRACT FROM AUTHOR]

Background: Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control. Methods: We collected the daily number of HFMD cases among children aged 0–14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period. The LSTM, Seq2Seq, Seq2Seq-Luong and Seq2Seq-Shih models were used to perform multi-step prediction of HFMD through multi-input multi-output. We evaluated the models in terms of overall prediction performance, the time delay and intensity of detection peaks. Results: From 2011 to 2017, HFMD in Chengdu showed seasonal trends that were consistent with temperature, air pressure, rainfall, relative humidity, and PM10. The Seq2Seq-Shih model achieved the best performance, with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705 for the 2-day to 15-day predictions, respectively. Meanwhile, the Seq2Seq-Shih model is able to detect peaks in the next 15 days with a smaller time delay. Conclusions: The deep learning Seq2Seq-Shih model achieves the best performance in overall and peak prediction, and is applicable to HFMD multi-step prediction based on environmental factors. Author summary: Hand, foot and mouth disease (HFMD) remains a serious public health concern in China. It is important to predict trends and understand peaks in the number of cases in advance for its prevention and control. The aim of this study was to consider the influence of meteorology and air pollution on the transmission of HFMD and establish multi-step prediction models for HFMD. In this study, we compared the performance of the Shih attention mechanism with the Luong attention mechanism, Seq2Seq model and LSTM model for future multi-day HFMD prediction based on multi-input multi-output. It was found that the Seq2Seq-Shih model performed best in predicting the trend for the next 2 days-15 days with RMSE, sMAPE and PCC values of 13.943~22.192, 17.880~27.937, and 0.887~0.705, respectively. Meanwhile, it was able to predict the peak within 15 days earlier by 17 days. This is the first study to use Seq2Seq models and perform daily multi-step prediction of HFMD. This study demonstrates the benefit of the Shih attention mechanism in multivariate time series multi-step prediction of infectious diseases. [ABSTRACT FROM AUTHOR]

Background: Aedes-borne arboviruses cause both seasonal epidemics and emerging outbreaks with a significant impact on global health. These viruses share mosquito vector species, often infecting the same host population within overlapping geographic regions. Thus, comparative analyses of the virus evolutionary and epidemiological dynamics across spatial and temporal scales could reveal convergent trends. Methodology/Principal findings: Focusing on Mexico as a case study, we generated novel chikungunya and dengue (CHIKV, DENV-1 and DENV-2) virus genomes from an epidemiological surveillance-derived historical sample collection, and analysed them together with longitudinally-collected genome and epidemiological data from the Americas. Aedes-borne arboviruses endemically circulating within the country were found to be introduced multiple times from lineages predominantly sampled from the Caribbean and Central America. For CHIKV, at least thirteen introductions were inferred over a year, with six of these leading to persistent transmission chains. For both DENV-1 and DENV-2, at least seven introductions were inferred over a decade. Conclusions/Significance: Our results suggest that CHIKV, DENV-1 and DENV-2 in Mexico share evolutionary and epidemiological trajectories. The southwest region of the country was determined to be the most likely location for viral introductions from abroad, with a subsequent spread into the Pacific coast towards the north of Mexico. Virus diffusion patterns observed across the country are likely driven by multiple factors, including mobility linked to human migration from Central towards North America. Considering Mexico's geographic positioning displaying a high human mobility across borders, our results prompt the need to better understand the role of anthropogenic factors in the transmission dynamics of Aedes-borne arboviruses, particularly linked to land-based human migration. Author summary: Mexico is endemic to several Aedes-borne arboviruses relevant to global health, and ranks within the top five countries in the Americas that report the highest case numbers. Our study provides a general overview of arbovirus introduction, spread and establishment patterns in North and Central America, and should be of interest to both local health and global authorities. Moreover, it sets to explore the paradigm of convergence at different scales in independent virus populations, represented by comparable epidemiological and evolutionary trends in Aedes-borne arboviruses sharing ecological niches. Our results represent important advances in the study of mosquito-borne viruses listed as a threat to global health, specifically applied to key countries within the developing world. [ABSTRACT FROM AUTHOR]

Background: Snakebite envenoming is a well-known medical emergency in the Terai of Nepal in particular. However, there is an epidemiological knowledge gap. The news media data available online provide substantial information on envenomings. Assessing this information can be a pristine approach for understanding snakebite epidemiology and conducting knowledge-based interventions. We firstly analyzed news media-reported quantitative information on conditions under which bites occur, treatment-seeking behavior of victims, and outcomes of snakebite envenomings in Nepal. Methodology/Principal findings: We analyzed 308 Nepalese snakebite envenomed cases reported in 199 news media articles published between 2010 and 2022 using descriptive statistics, Wilcoxon, and Chi-square tests to know why and how victims were bitten, their treatment-seeking behavior, and the outcomes. These envenomated cases known with substantial information represented 48 districts (mostly located in the Terai region) of Nepal. These envenomings mostly occurred in residential areas affecting children. Generally, envenomings among males and females were not significantly different. But, in residential areas, females were more envenomed than males. Further, victims' extremities were often exposed to venomous snakebites while their active status and these episodes often occurred at night while victims were passive during snakebites indoors and immediate surroundings of houses. Snakebite deaths were less among referred than non-referred cases, males than females, and while active than passive conditions of victims. Conclusion/Significance: The most of reported envenomed patients were children, and most envenomings were due to cobra bites. Consultation with traditional healers complicated snakebite management. In most cases, deaths that occur without medical interventions are a severe snakebite consequence in Nepal. Further, several deaths in urban areas and mountains and higher hills of Nepal suggest immediate need of snakebite management interventions in the most affected districts. Therefore, there is an urgent need to immediately admit Nepalese snakebite victims to nearby snakebite treatment centers without adopting non-recommended prehospital interventions. The strategies for preventing snakebite and controlling venom effects should also include hilly and mountain districts where snakebite-associated deaths are reported. Author summary: Worldwide, 1.8 to 2.7 million snakebite envenomings occur annually resulting in 81,000 to 138,000 deaths. Annually, 1,000 to 3,225 deaths occur in Nepal. However, there is an epidemiological knowledge gap. Since news media data are typically available in near real-time, the news media data can be analyzed to understand the epidemiological situations in Nepal like poor resource country. Therefore, we analyzed news media-reported snakebite envenomings that occurred in the last 13 years. The median age of envenomed patients was 19 y. People were envenomated mainly during natural human activities such as sleeping. Cobras, kraits, Russell's Viper, Mountain Pitviper, and Green Pitvipers were involved in snakebites. Overall, three-fourth of victims who consulted traditional healers died. Forty-four cases being referred to higher centers were died on the way to higher healthcare centers or in healthcare institutions during treatment. Dependency on traditional snakebite healers, the long distance between snakebite localities and snakebite treatment centers, and snakebite occurred at night were the major barriers to accessing healthcare facilities by people bitten by snakes in Nepal. In this country, people inhabiting 48 districts are at risk of venomous snakebites. [ABSTRACT FROM AUTHOR]

Internet-based surveillance methods for vector-borne diseases (VBDs) using “big data” sources such as Google, Twitter, and internet newswire scraping have recently been developed, yet reviews on such “digital disease detection” methods have focused on respiratory pathogens, particularly in high-income regions. Here, we present a narrative review of the literature that has examined the performance of internet-based biosurveillance for diseases caused by vector-borne viruses, parasites, and other pathogens, including Zika, dengue, other arthropod-borne viruses, malaria, leishmaniasis, and Lyme disease across a range of settings, including low- and middle-income countries. The fundamental features, advantages, and drawbacks of each internet big data source are presented for those with varying familiarity of “digital epidemiology.” We conclude with some of the challenges and future directions in using internet-based biosurveillance for the surveillance and control of VBD. [ABSTRACT FROM AUTHOR]

Background: Dengue fever is an infectious disease that is imported into Shanghai, China and requires prevention and control measures. Controlling the vector Aedes albopictus through insecticide use is a key approach to dengue control. However, the rapid evolution of insecticide resistance in Ae. albopictus has raised concerns about the failure of dengue control efforts. Knockdown resistance (kdr) caused by point mutations in the voltage-gated sodium channel (VGSC) gene is a primary mechanism of pyrethroid resistance. In this study, we investigated the kdr mutations of Ae. albopictus in Shanghai and evaluated the trend in its evolution. Methodology/principal findings: We collected 17 populations of Ae. albopictus from 15 districts in Shanghai in 2020, extracted genomic DNA from individual mosquitoes, and amplified Domain II, III, and IV in VGSC using PCR. Following sequencing, we obtained 658 VGSC sequences. We detected the nonsynonymous mutations V1016G, I1532T, and F1534S/C/I, among which V1016G and F1534C/I were reported in Shanghai for the first time and F1534I was a novel mutant allele in Ae. albopictus. The overall mutation frequency was 84.65%, with individual mutation frequencies ranging from 46.81% to 100%, excluding the Fengxian District population, which had a frequency of 0%. The V1016G and I1532T mutation types accounted for 7.14% and 3.42%, respectively. The mutant allele at codon 1534 accounted for 63.98% of all mutations, including TCC/S (62.77%), TGC/C (1.06%), and ATC/I (0.15%). We identified and classified five intron types in Domain III by length, including A (83 bp, 12.07%), B (68 bp, 87.30%), C (80 bp, 0.16%), D (72 bp, 0.16%), and E (70 bp, 0.31%). Individuals with intron B had a significant mutation tendency at codon 1534 relative to intron A (chi-square test, p < 0.0001). We found no correlation between mutation frequency and the amount of pyrethroid used (Pearson correlation, p = 0.4755). Conclusions/significance: In recent years, kdr mutations in the Ae. albopictus population in Shanghai have rapidly evolved, as evidenced by an increase in mutation types and significantly increased mutation frequency. The F1534I/ATC mutant allele was found to be a novel mutation, F1534C/TGC was reported for the first time in Shanghai, and intron B in Domain III was significantly associated with mutation frequency at codon 1534. Continuous monitoring of resistance changes and strict regulation of insecticide use are required. Author summary: Aedes albopictus is the primary dengue fever vector in Shanghai, China. Given the lack of an effective vaccine, vector control remains the key prevention method for this infectious disease. Pyrethroids are the preferred insecticide for vector control owing to their high efficiency and low toxicity; however, vector resistance has reached a high level under selection pressure. Pyrethroids target the voltage-gated sodium channel (VGSC), and mutations in the coding region of the VGSC gene can result in knockdown resistance (kdr), one of the primary mosquito resistance mechanisms. This study explored kdr mutations in 17 Ae. albopictus populations collected from 15 districts in Shanghai in 2020. The overall mutation frequency was alarmingly high, reaching 84.65%. Mutant VGSC alleles, including V1016G, I1532T, and F1534S/C/I, were detected, with V1016G and F1534C/I being reported for the first time in Shanghai and F1534I being a novel mutant allele in Ae. albopictus. Additionally, five types of introns in Domain III were identified, with intron B type found to be significantly associated with mutation frequency at codon 1534. The rapid evolution trend of kdr mutations is concerning and indicates a growing risk of mosquito-borne disease outbreaks. Therefore, continuous monitoring of resistance changes and strict regulation of insecticide use are necessary. [ABSTRACT FROM AUTHOR]

Previous studies suggested that dengue was associated with an increased risk of several autoimmune diseases. However, this association still needs to be explored due to the limitations of these studies. A population-based cohort study was conducted using national health databases in Taiwan and included 63,814 newly diagnosed, laboratory-confirmed dengue patients between 2002 and 2015 and 1:4 controls (n = 255,256) matched by age, sex, area of residence and symptom onset time. Multivariate Cox proportional hazard regression models were used to investigate the risk of autoimmune diseases after dengue infection. Dengue patients had a slightly higher risk of overall autoimmune diseases than non-dengue controls (aHR 1.16; P = 0.0002). Stratified analyses by specific autoimmune diseases showed that only autoimmune encephalomyelitis remained statistically significant after Bonferroni correction for multiple testing (aHR 2.72; P < 0.0001). Sixteen (0.025%) dengue patients and no (0%) controls developed autoimmune encephalomyelitis in the first month of follow-up (HR >9999, P < 0.0001), but the risk between groups was not significantly different thereafter. Contrary to previous studies, our findings showed that dengue was associated with an increased short-term risk of a rare complication, autoimmune encephalomyelitis, but not associated with other autoimmune diseases. Author summary: Dengue fever is a mosquito-borne tropical disease caused by the dengue virus. Antibodies produced during dengue infection can cross-react with several self-antigens to the tissues of the body and contribute to thrombocytopenia, coagulopathy, and vascular leakage in severe dengue. Autoimmunity might occur after dengue infection and cause some autoimmune diseases. Previous studies suggested some autoimmune diseases might be associated with dengue infection. Contrary to previous studies, our findings showed that dengue was associated with an increased short-term risk of a rare complication, autoimmune encephalomyelitis, but not associated with other autoimmune diseases. [ABSTRACT FROM AUTHOR]

Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the 'Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies' ('CHARMS') framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (10.3%), both climate change and demographic factors (10.3%), vector factors (5.1%), and demographic factors (5.1%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations. Author summary: Dengue is considered as a major public health challenge and a life-threatening disease affecting people worldwide. Over the past decades, numerous forecast models have been developed to predict dengue incidence using various factors based on different geographical locations. Dengue transmission appears to be highly sensitive to climate variability and change, however quantitative models used to assess the relationship between climate change and dengue often differ due to their distribution assumptions, the nature of the relationship and the spatial and/or temporal dynamics of the response. We performed a systematic review to examine current literature surrounding existing quantitative models based on development methodology, predictor variable used and model performance. Our analysis demonstrates several shortcomings in current modelling practice, and advocates for the use of real time primary predictor data, the incorporation of non-climatic parameters as predictors and more comprehensive reporting of model development techniques and validation. This review collates methodological approaches adopted in the modelling practices in the field across current literature. This will provide an evidence-based framework for upgrading future modelling practice to develop more accurate predictive models with robust techniques. In turn, this also provided an opportunity for the effective distribution of limited public health resources to prepare for demand. [ABSTRACT FROM AUTHOR]

Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study. Author summary: Dengue fever is a vector-borne viral disease endemic to tropical and subtropical countries. The virus is transmitted by female Aedes mosquitoes and affects approximately 100 million people every year. Although most infections are mild or asymptomatic, some may cause severe symptoms, leading to a higher risk of death. In the affected countries, the challenges associated with preventing and controlling dengue outbreaks have highlighted the need for novel tools. In this context, using statistical tools with climate and epidemiological information makes it possible to provide timely information to public health officials about the risk of dengue outbreaks, allowing the optimization of resources and preventive and non-reactive decision-making. [ABSTRACT FROM AUTHOR]

The live attenuated tetravalent CYD-TDV vaccine (Dengvaxia) is effective but has scarcely been used due to safety concerns among seronegative recipients. Rapid diagnostic tests (RDTs) which can accurately determine individual dengue serostatus are needed for use in pre-vaccination screening. This study aimed to determine the performance of existing RDTs (which have been designed to detect levels of immunoglobulin G, IgG, associated with acute post-primary dengue) when repurposed for detection of previous dengue infection (where concentrations of IgG are typically lower). A convenience sample of four-hundred-and-six participants (including 217 children) were recruited in the community. Whole blood was collected by phlebotomy and tested using Bioline Dengue IgG/IgM (Abbott) and Standard Q Dengue IgM/IgG (SD Biosensor) RDTs in the field. Serum samples from the same individuals were also tested at National Health Laboratory. The Panbio indirect IgG ELISA was used as a reference test. Reference testing determined that 370 (91.1%) participants were dengue IgG seropositive. Both assays were highly specific (100.0%) but had low sensitivity (Bioline = 21.1% and Standard Q = 4.6%) when used in the field. Sensitivity was improved when RDTs were used under laboratory conditions, and when assays were allowed to run beyond manufacturer recommendations (and read at a delayed time-point), but specificity was reduced. Efforts to develop RDTs with high sensitivity and specificity for prior dengue infection which can be operationalised for pre-vaccination screening are ongoing. Performance of forthcoming candidate assays should be tested under field conditions with blood samples, as well as in the laboratory. Author summary: Dengvaxia is effective but has scarcely been used due to safety concerns among seronegative recipients. Rapid diagnostic tests (RDTs) which can accurately determine individual dengue serostatus are therefore needed for pre-vaccination screening. This study evaluated two commercially available RDTs during a community seroprevalence survey in Timor-Leste. Field conditions were intended to replicate a pre-vaccination screening setting. Both assays were specific but had low sensitivity, and performance was found to be inferior when compared to previous studies which were conducted in laboratories using stored serum samples. Future candidate assays must be evaluated under field conditions before they can safely be used in pre-vaccination screening. [ABSTRACT FROM AUTHOR]

Acute febrile illness is a common problem managed by clinicians and health systems globally, particularly in the Tropics. In many regions, malaria is a leading and potentially deadly cause of fever; however, myriad alternative etiologies exist. Identifying the cause of fever allows optimal management, but this depends on many factors including thorough knowledge of circulating infections. Arboviruses such as dengue (DENV) cause fever and may be underdiagnosed in sub-Saharan Africa where malaria is a major focus. We examined cases of fever in western Cameroon that tested negative for malaria and found 13.5% (13/96) were due to DENV, with 75% (9/12) of these being DENV serotype 2 infections. Two complete DENV2 genomes were obtained and clustered closely to recent isolates from Senegal and Burkina Faso. The seroprevalence of DENV in this region was 24.8% (96/387). Neutralizing antibodies to DENV2 were detected in all (15/15) seropositive samples tested. Chikungunya (CHIKV) is an arthritogenic alphavirus that is transmitted by Aedes mosquitoes, the same principal vector as DENV. The seroprevalence for CHIKV was 15.7% (67/427); however, CHIKV did not cause a single case of fever in the 96 subjects tested. Of note, being seropositive for one arbovirus was associated with being seropositive for the other (Χ2 = 16.8, p<0.001). Taken together, these data indicate that Aedes-transmitted arboviruses are endemic in western Cameroon and are likely a common but underappreciated cause of febrile illness. This work supports the need for additional study of arboviruses in sub-Saharan Africa and efforts to improve diagnostic capacity, surveillance systems, and arbovirus prevention strategies. Author summary: Acute illness with fever is common but can be challenging for clinicians to manage, particularly in resource-limited settings. In sub-Saharan Africa, malaria is a major cause of fever, but other causes of fever are poorly documented or monitored, which impairs optimal medical care to patients and implementation of public health interventions to control leading causes of disease. Viruses transmitted by mosquitoes are a prevalent and expanding problem throughout the tropics and beyond; however, there is concern that these infections frequently go undetected in sub-Saharan Africa. We discovered a previously unrecognized outbreak of dengue virus in western Cameroon by testing remnant samples from over 400 patients that presented with fever. Our results indicate that dengue has circulated in this region for decades with little recognition. This study adds important information about causes of fever in sub-Saharan Africa and advocates for increasing investment in surveillance systems and prevention strategies for mosquito-borne viruses. [ABSTRACT FROM AUTHOR]

Background: Understanding the underlying risk of infection by dengue virus from surveillance systems is complicated due to the complex nature of the disease. In particular, the probability of becoming severely sick is driven by serotype-specific infection histories as well as age; however, this has rarely been quantified. Island communities that have periodic outbreaks dominated by single serotypes provide an opportunity to disentangle the competing role of serotype, age and changes in surveillance systems in characterising disease risk. Methodology: We develop mathematical models to analyse 35 years of dengue surveillance (1979–2014) and seroprevalence studies from French Polynesia. We estimate the annual force of infection, serotype-specific reporting probabilities and changes in surveillance capabilities using the annual age and serotype-specific distribution of dengue. Principal findings: Eight dengue epidemics occurred between 1979 and 2014, with reporting probabilities for DENV-1 primary infections increasing from 3% to 5%. The reporting probability for DENV-1 secondary infections was 3.6 times that for primary infections. We also observed heterogeneity in reporting probabilities by serotype, with DENV-3 having the highest probability of being detected. Reporting probabilities declined with age after 14 y.o. Between 1979 and 2014, the proportion never infected declined from 70% to 23% while the proportion infected at least twice increased from 4.5% to 45%. By 2014, almost half of the population had acquired heterotypic immunity. The probability of an epidemic increased sharply with the estimated fraction of susceptibles among children. Conclusion/Significance: By analysing 35 years of dengue data in French Polynesia, we characterised key factors affecting the dissemination profile and reporting of dengue cases in an epidemiological context simplified by mono-serotypic circulation. Our analysis provides key estimates that can inform the study of dengue in more complex settings where the co-circulation of multiple serotypes can greatly complicate inference. Author summary: Characterising the true extent of dengue circulation and the level of population immunity is essential to assess the burden of disease, evaluate epidemic risk and organise prevention strategies against future epidemics. However, this is difficult in a context where most people who are infected by dengue virus (DENV) only have mild symptoms which may not be reported to surveillance systems. In this article, we develop a mathematical model to evaluate the fraction of unreported dengue infections from case data. The key idea is to introduce reporting probabilities that depend on the infecting serotype and the infection history of patients. These factors are known to contribute to variations in the severity of symptoms and hence the reporting probabilities, but have rarely been taken into account in model frameworks to study population immunity from the case data. Using the developed model, we study long-term dengue virus immunity in French Polynesia. [ABSTRACT FROM AUTHOR]

Background: Aedes aegypti is the major vector of dengue that threatens public health in tropical and subtropical regions. Pyrethroid-based control strategies effectively control this vector, but the repeated usage of the same insecticides leads to resistance and hampers control efforts. Therefore, efficient and prompt monitoring of insecticide resistance in local mosquito populations is critical for dengue control. Methodology/Principal finding: We collected Ae. aegypti in southern Taiwan in March and October 2016. We analyzed the voltage-gated sodium channel (vgsc) genotypes of parentals (G0) and G1 adults after cypermethrin insecticide bioassay. Our results showed that four VGSC mutations (S989P, V1016G, F1534C, and D1763Y) associated with resistance were commonly detected in field-collected Ae. aegypti. The frequencies of these four mutations in the local mosquito population were significantly higher in October (0.29, 0.4, 0.27 and 0.11) than in March (0.09, 0.16, 0.18 and 0.03). Specific vgsc combined genotypes composed of the one to four such mutations (SGFY/SGFY, SVCD/SVCD, SGFY/PGFD, SVCD/SGFY, PGFD/PGFD, and SVCD/PGFD) shifted towards higher frequencies in October, implying their resistance role. In addition, the cypermethrin exposure bioassay data supported the field observations. Moreover, our study observed an association between the resistance level and the proportion of resistance genotypes in the population. Conclusions/Significance: This is the first study to demonstrate the role of four-locus vgsc genotypes in resistance evaluation in a local Ae. aegypti population in Taiwan. This alternative method using resistance-associated genotypes as an indicator of practically insecticide resistance monitoring is a useful tool for providing precise and real-time information for decision makers. Author summary: Dengue outbreaks occur annually in Taiwan, and pyrethroid insecticides are commonly used to reduce mosquito density. Insecticide resistance of mosquitoes is commonly observed in the field and threatens vector control programs. Here, we analyzed the association between the combined vgsc genotype and resistance phenotype based on field surveillance data in March and October and a cypermethrin exposure bioassay. Resistance-attributable specific vgsc genotypes were proposed. Using the combined vgsc genotype rather than each vgsc allele is recommended for better resistance prediction to provide real-time information for control program managers. [ABSTRACT FROM AUTHOR]

Background: Colombia has one of the highest burdens of arboviruses in South America. The country was in a state of hyperendemicity between 2014 and 2016, with co-circulation of several Aedes-borne viruses, including a syndemic of dengue, chikungunya, and Zika in 2015. Methodology/Principal findings: We analyzed the cases of dengue, chikungunya, and Zika notified in Colombia from January 2014 to December 2018 by municipality and week. The trajectory and velocity of spread was studied using trend surface analysis, and spatio-temporal high-risk clusters for each disease in separate and for the three diseases simultaneously (multivariate) were identified using Kulldorff's scan statistics. During the study period, there were 366,628, 77,345 and 74,793 cases of dengue, chikungunya, and Zika, respectively, in Colombia. The spread patterns for chikungunya and Zika were similar, although Zika's spread was accelerated. Both chikungunya and Zika mainly spread from the regions on the Atlantic coast and the south-west to the rest of the country. We identified 21, 16, and 13 spatio-temporal clusters of dengue, chikungunya and Zika, respectively, and, from the multivariate analysis, 20 spatio-temporal clusters, among which 7 were simultaneous for the three diseases. For all disease-specific analyses and the multivariate analysis, the most-likely cluster was identified in the south-western region of Colombia, including the Valle del Cauca department. Conclusions/Significance: The results further our understanding of emerging Aedes-borne diseases in Colombia by providing useful evidence on their potential site of entry and spread trajectory within the country, and identifying spatio-temporal disease-specific and multivariate high-risk clusters of dengue, chikungunya, and Zika, information that can be used to target interventions. Author summary: Dengue, chikungunya, and Zika are diseases transmitted to humans by the bite of infected Aedes mosquitoes. Between 2014 and 2016 chikungunya and Zika viruses started causing outbreaks in Colombia, one of the countries historically most affected by dengue. We used case counts of the diseases by municipality and week to study the spread trajectory of chikungunya and Zika within Colombia's territory, and to identify space-time high-risk clusters, i.e., the areas and time periods that dengue, chikungunya, and Zika were more present. Chikungunya and Zika spred similarly in Colombia, but Zika spread was faster. The Atlantic coast, a famous touristic destination in the country, was likely the place of entry of chikungunya and Zika in Colombia. The south-western region was identified as a high-risk cluster for all three diseases in separate and simultaneously. This region has a favorable climate for the Aedes mosquitoes and other characteristics that facilitate the diseases' transmission, such as social deprivation and high population mobility. Our results provide useful information on the locations that should be prioritized for interventions to prevent the entry of new diseases transmitted by Aedes and to reduce the burden of dengue, chikungunya and Zika where they are established. [ABSTRACT FROM AUTHOR]

Background: Dengue is the world's most prevalent mosquito-borne viral disease. It is endemic in many tropical and subtropical countries and represents a significant global health burden. The first reports of dengue virus (DENV) circulation in the South West Indian Ocean (SWIO) islands date back to the early 1940s; however, an increase in DENV circulation has been reported in the SWIO in recent years. The aim of this review is to trace the history of DENV in the SWIO islands using available records from the Comoros, Madagascar, Mauritius, Mayotte, Seychelles, and Reunion. We focus in particular on the most extensive data from Reunion Island, highlighting factors that may explain the observed increasing incidence, and the potential shift from one-off outbreaks to endemic dengue transmission. Methods: Following the PRISMA guidelines, the literature review focused queried different databases using the keywords "dengue" or "Aedes albopictus" combined with each of the following SWIO islands the Comoros, Madagascar, Mauritius, Mayotte, Seychelles, and Reunion. We also compiled case report data for dengue in Mayotte and Reunion in collaboration with the regional public health agencies in these French territories. References and data were discarded when original sources were not identified. We examined reports of climatic, anthropogenic, and mosquito-related factors that may influence the maintenance of dengue transmission independently of case importation linked to travel. Findings and conclusions: The first report of dengue circulation in the SWIO was documented in 1943 in the Comoros. Then not until an outbreak in 1976 to 1977 that affected approximately 80% of the population of the Seychelles. DENV was also reported in 1977 to 1978 in Reunion with an estimate of nearly 30% of the population infected. In the following 40-year period, DENV circulation was qualified as interepidemic with sporadic cases. However, in recent years, the region has experienced uninterrupted DENV transmission at elevated incidence. Since 2017, Reunion witnessed the cocirculation of 3 serotypes (DENV-1, DENV-2 and DENV-3) and an increased number of cases with severe forms and deaths. Reinforced molecular and serological identification of DENV serotypes and genotypes circulating in the SWIO as well as vector control strategies is necessary to protect exposed human populations and limit the spread of dengue. [ABSTRACT FROM AUTHOR]

Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly. Author summary: To respond quickly to disease outbreaks, policymakers need rapid data on the number of new infections. However, for many diseases, such data is very delayed, due to the administrative work required to record each case in a disease surveillance system. This is a problem for data on chikungunya, a mosquito-borne disease which is a growing threat in Brazil. In Rio de Janeiro, delays in chikungunya cases being recorded average four weeks. These delays are sometimes longer and sometimes shorter. In stark contrast to chikungunya data, data on what people are searching for on Google is available almost immediately. People suffering from chikungunya might search on Google for information about the disease. Here, we investigate whether rapidly available Google data can help generate quick estimates of the number of chikungunya cases in Rio de Janeiro in the previous week. Our model uses a Bayesian methodology to help account for the varying delays in the chikungunya data. We show that including Google search data in the model reduces both the error and uncertainty of the chikungunya case count estimates, in particular during epidemics. Our method could be used to help policymakers to respond more quickly to future chikungunya epidemics. [ABSTRACT FROM AUTHOR]

Widespread application of insecticide remains the primary form of control for Chagas disease in Central America, despite only temporarily reducing domestic levels of the endemic vector Triatoma dimidiata and having little long-term impact. Recently, an approach emphasizing community feedback and housing improvements has been shown to yield lasting results. However, the additional resources and personnel required by such an intervention likely hinders its widespread adoption. One solution to this problem would be to target only a subset of houses in a community while still eliminating enough infestations to interrupt disease transfer. Here we develop a sequential sampling framework that adapts to information specific to a community as more houses are visited, thereby allowing us to efficiently find homes with domiciliary vectors while minimizing sampling bias. The method fits Bayesian geostatistical models to make spatially informed predictions, while gradually transitioning from prioritizing houses based on prediction uncertainty to targeting houses with a high risk of infestation. A key feature of the method is the use of a single exploration parameter, α, to control the rate of transition between these two design targets. In a simulation study using empirical data from five villages in southeastern Guatemala, we test our method using a range of values for α, and find it can consistently select fewer homes than random sampling, while still bringing the village infestation rate below a given threshold. We further find that when additional socioeconomic information is available, much larger savings are possible, but that meeting the target infestation rate is less consistent, particularly among the less exploratory strategies. Our results suggest new options for implementing long-term T. dimidiata control. Author summary: Effective public health interventions for the control and elimination of neglected tropical diseases require an efficient use of resources while still causing long-term disease reduction at the community level. To use resources to best effect, areas most in need of control efforts must be identified. However, strategies for correctly identifying these areas are rarely known due to the complex environmental, biological, and cultural factors shaping disease spread. In turn, incorrect prioritization of control targets can cause the intervention to have no lasting effect. We address this tradeoff between efficiency and efficacy by adapting control priorities throughout an intervention, targeting areas of high uncertainty during the initial stages while shifting to areas of greatest risk at later stages. In the context of controlling Triatoma dimidiata, the primary vector of Chagas disease in several countries in Latin America, our methods provide a means of targeting only a subset of homes for insecticide and housing improvements, while still reducing a village's overall infestation rate below the critical threshold. [ABSTRACT FROM AUTHOR]

Background: Transmission patterns and genetic diversity of dengue virus (DENV) circulating in Africa remains poorly understood. Circulation of the DENV serotype 1 (DENV1) in Angola was detected in 2013, while DENV serotype 2 (DENV2) was detected for the first time in 2018. Here, we report results from molecular and genomic investigations conducted at the Ministry of Health national reference laboratory (INIS) in Angola on suspected dengue cases detected between January 2017 and February 2019. Methods: A total of 401 serum samples from dengue suspected cases were collected in 13 of the 18 provinces in Angola. Of those, 351 samples had complete data for demographic and epidemiological analysis, including age, gender, province, type of residence, clinical symptoms, as well as dates of onset of symptoms and sample collection. RNA was extracted from samples and tested for DENV-RNA by two distinct real time RT-PCR protocols. On-site whole genome nanopore sequencing was performed for PCR+ cases. Bayesian coalescent models were used to estimate date and origin of outbreak emergence, as well as population growth rates. Results: Molecular diagnosis shows that 66 out of 351 (19%) suspected cases were DENV-RNA positive across 5 provinces in Angola. DENV PCR+ cases were diagnosed more frequently in urban sites compared to rural sites. Of the DENV positive samples, most were collected within 6 days of symptom onset. 93% of infections were confirmed by serotype-specific RT-PCR as DENV2 and 1 case (1.4%) was confirmed as DENV1. Six CHIKV RT-PCR positive cases were also detected during the study period, including 1 co-infection with DENV1. Most cases (87%) were detected in Luanda during the rainy season between April and October. Symptoms associated with severe dengue were observed in 11 patients, including 2 with a fatal outcome. On-site nanopore genome sequencing followed by genetic analysis revealed an introduction of DENV2 Cosmopolitan genotype (also known as DENV2-II genotype) possibly from India in or around October 2015, at least 1.5 years before its detection in the country. Coalescent models suggest relatively moderately rapid epidemic growth rates and doubling times, and a moderate expansion of DENV2 in Angola during the studied period. Conclusion: This study describes genomic, epidemiological and demographic characteristic of predominately urban transmission of DENV2 in Angola. We also find co-circulation of DENV2 with DENV1 and CHIKV and report several RT-PCR confirmed severe dengue cases in the country. Increasing dengue awareness in healthcare professional, expanding the monitorization of arboviral epidemics across the country, identifying most common mosquito breeding sites in urban settings, implementing innovative vector control interventions and dengue vaccination campaigns could help to reduce vector presence and DENV transmission in Angola. Author summary: Angola has experienced dengue, yellow fever and Zika virus outbreaks between 2013–2017. To improve the detection of arbovirus outbreaks in Angola, we set up the National Arbovirus Laboratory of Surveillance Programme at the National Institute of Health Research (INIS), Ministry of Health, Angola, which officially kick-started arbovirus surveillance activities in early 2017. In this study we tested samples collected from patients with suspected dengue cases January 2017 to February 2019. We report molecular and genomic findings on DENV2 transmission in Angola. On-site whole genome sequencing and subsequent phylogenetic analysis indicate a single introduction of the DENV2 Cosmopolitan genotype (also known as DENV2-II genotype) from Southern Asia, probably as a result of international travel, followed by moderate virus population growth. In Angola, most detections occurred in urban areas, although we detected DENV2 in 5 of the 18 provinces in Angola. This work emphasizes the importance of maintaining an active arbovirus surveillance programme throughout the country to investigate DENV transmission patterns and the risk of hyperendemicity in Angola. [ABSTRACT FROM AUTHOR]

The Applying Wolbachia to Eliminate Dengue (AWED) trial was a parallel cluster randomised trial that demonstrated Wolbachia (wMel) introgression into Ae. aegypti populations reduced dengue incidence. In this predefined substudy, we compared between treatment arms, the relative abundance of Ae. aegypti and Ae. albopictus before, during and after wMel-introgression. Between March 2015 and March 2020, 60,084 BG trap collections yielded 478,254 Ae. aegypti and 17,623 Ae. albopictus. Between treatment arms there was no measurable difference in Ae. aegypti relative abundance before or after wMel-deployments, with a count ratio of 0.96 (95% CI 0.76, 1.21) and 1.00 (95% CI 0.85, 1.17) respectively. More Ae. aegypti were caught per trap per week in the wMel-intervention arm compared to the control arm during wMel deployments (count ratio 1.23 (95% CI 1.03, 1.46)). Between treatment arms there was no measurable difference in the Ae. albopictus population size before, during or after wMel-deployment (overall count ratio 1.10 (95% CI 0.89, 1.35)). We also compared insecticide resistance phenotypes of Ae. aegypti in the first and second years after wMel-deployments. Ae. aegypti field populations from wMel-treated and untreated arms were similarly resistant to malathion (0.8%), permethrin (1.25%) and cyfluthrin (0.15%) in year 1 and year 2 of the trial. In summary, we found no between-arm differences in the relative abundance of Ae. aegypti or Ae. albopictus prior to or after wMel introgression, and no between-arm difference in Ae. aegypti insecticide resistance phenotypes. These data suggest neither Aedes abundance, nor insecticide resistance, confounded the epidemiological outcomes of the AWED trial. Author summary: Dengue is a mosquito-borne viral disease and a major public health problem in the tropical and subtropical world. It is caused by any of the four dengue virus serotypes. In a previously published randomised clinical trial, called the AWED trial, we demonstrated that releases of Aedes aegypti mosquitoes infected with the insect bacterium Wolbachia can reduce the case incidence of dengue by 77%. In this current study, we compared the abundance of Ae. aegypti mosquitoes in the neighbourhoods where Wolbachia-infected mosquitoes were released versus the untreated neighbourhoods. This was important to do so that scientists could understand the mechanism for how Wolbachia releases reduced dengue incidence. Between March 2015 and March 2020, we did not observe any differences in Ae. aegypti abundance before or after Wolbachia-deployments in the AWED trial area. There was also no difference in the abundance of the related mosquito, Ae. albopictus, before, during or after wMel-deployment. We also compared insecticide resistance characteristics amongst Ae. aegypti in the first and second years after Wolbachia -deployments and found no difference between mosquitoes from Wolbachia-treated and untreated neighbourhoods. These data suggest neither Aedes abundance, nor insecticide resistance, were confounding sources to the epidemiological outcomes of the AWED trial. [ABSTRACT FROM AUTHOR]

Background: Yersinia pestis is the causative agent of plague, a zoonosis associated with small mammals. Plague is a severe disease, especially in the pneumonic and septicemic forms, where fatality rates approach 100% if left untreated. The bacterium is primarily transmitted via flea bite or through direct contact with an infected host. The 2017 plague outbreak in Madagascar resulted in more than 2,400 cases and was highlighted by an increased number of pneumonic infections. Standard diagnostics for plague include laboratory-based assays such as bacterial culture and serology, which are inadequate for administering immediate patient care for pneumonic and septicemic plague. Principal findings: The goal of this study was to develop a sensitive rapid plague prototype that can detect all virulent strains of Y. pestis. Monoclonal antibodies (mAbs) were produced against two Y. pestis antigens, low-calcium response V (LcrV) and capsular fraction-1 (F1), and prototype lateral flow immunoassays (LFI) and enzyme-linked immunosorbent assays (ELISA) were constructed. The LFIs developed for the detection of LcrV and F1 had limits of detection (LOD) of roughly 1–2 ng/mL in surrogate clinical samples (antigens spiked into normal human sera). The optimized antigen-capture ELISAs produced LODs of 74 pg/mL for LcrV and 61 pg/mL for F1 when these antigens were spiked into buffer. A dual antigen LFI prototype comprised of two test lines was evaluated for the detection of both antigens in Y. pestis lysates. The dual format was also evaluated for specificity using a small panel of clinical near-neighbors and other Tier 1 bacterial Select Agents. Conclusions: LcrV is expressed by all virulent Y. pestis strains, but homologs produced by other Yersinia species can confound assay specificity. F1 is specific to Y. pestis but is not expressed by all virulent strains. Utilizing highly reactive mAbs, a dual-antigen detection (multiplexed) LFI was developed to capitalize on the diagnostic strengths of each target. Author summary: Immunoassays were developed for the detection of two Y. pestis proteins, LcrV and F1, which have been characterized as potential biomarkers of plague. A total of twenty-two high affinity mAbs were isolated from BALB/c mice immunized with recombinant LcrV, F1 and F1-LcrV fusion protein via hybridoma technology. MAbs were characterized by Western blots, ELISA, and surface plasmon resonance. Antigen-capture LFIs and ELISAs were developed using the mAbs and optimized for analytical sensitivity. Prototype LFIs were evaluated to detect LcrV and F1 in surrogate clinical samples. A multiplexed LFI detecting both LcrV and F1 was assessed against a panel of Y. pestis isolates, clinically relevant near neighbors, and other bacterial Select Agents indicating high assay specificity. The prototype immunoassays will now need to be validated with multiple clinical matrices (i.e., whole blood), patient samples, and a larger specificity panel. [ABSTRACT FROM AUTHOR]

Background: Crimean-Congo Haemorrhagic Fever (CCHF) is a tick-borne viral zoonotic disease distributed across several continents and recognized as an ongoing health threat. In humans, the infection can progress to a severe disease with high fatality, raising public health concerns due to the limited prophylactic and therapeutic options available. Animal species, clinically unaffected by the virus, serve as viral reservoirs and amplifier hosts, and can be a valuable tool for surveillance. Little is known about the occurrence and prevalence of Crimean-Congo Haemorrhagic Fever Virus (CCHFV) in Cameroon. Knowledge on CCHFV exposure and the factors associated with its presence in sentinel species are a valuable resource to better understand transmission dynamics and assess local risks for zoonotic disease emergence. Methods and findings: We conducted a CCHFV serological survey and risk factor analysis for animal level seropositivity in pastoral and dairy cattle in the North West Region (NWR) and the Vina Division (VD) of the Adamawa Region in Cameroon. Seroprevalence estimates were adjusted for sampling design-effects and test performance. In addition, explanatory multivariable logistic regression mixed-effects models were fit to estimate the effect of animal characteristics, husbandry practices, risk contacts and ecological features on the serological status of pastoral cattle. The overall seroprevalence was 56.0% (95% CI 53.5–58.6) and 6.7% (95% CI 2.6–16.1) among pastoral and dairy cattle, respectively. Animals going on transhumance had twice the odds of being seropositive (OR 2.0, 95% CI 1.1–3.8), indicating that animal movements could be implicated in disease expansion. From an ecological perspective, absolute humidity (OR 0.6, 95% CI 0.4–0.9) and shrub density (OR 2.1, 95% CI 1.4–3.2) were associated with seropositivity, which suggests an underlying viral dynamic connecting vertebrate host and ticks in a complex transmission network. Conclusions: This study demonstrated high seroprevalence levels of CCHFV antibodies in cattle in Cameroon indicating a potential risk to human populations. However, current understanding of the underlying dynamics of CCHFV locally and the real risk for human populations is incomplete. Further studies designed using a One Health approach are required to improve local knowledge of the disease, host interactions and environmental risk factors. This information is crucial to better project the risks for human populations located in CCHFV-suitable ecological niches. Author summary: Crimean-Congo haemorrhagic fever poses an important threat for public health, particularly in endemic locations in Eurasia and parts of Africa. Despite its long-standing recognition, CCHF is considered a neglected disease, with sporadic official case reports and evidence of serological circulation in remote rural areas and less frequently, in peri-urban settings in sub-Saharan Africa. However, the drivers associated with its emergence and the spatial-temporal patterns linked to its circulation and spread are not well-understood. In this study, we identified a high proportion of cattle with CCHFV antibodies suggestive of high levels of viral circulation in the North West and the Adamawa Regions in Cameroon. In pastoral cattle, there was a positive relationship between going on transhumance, older age, higher shrub density and lower absolute humidity and CCHFV individual seropositivity. In contrast, non-indigenous breeds reported a lower risk of being seropositive. These results can be interpreted in connection to the underlying dynamics of CCHFV cycle and underline potential mechanisms linked to disease expansion. From a public health perspective, high levels of exposure to CCHFV in cattle highlight the possibility of unrecognized human infection and therefore, emphasize on the need to remain vigilant to possible disease risks for local groups involved in pastoralism and to dairy smallholders across the region. [ABSTRACT FROM AUTHOR]

The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people's lives and severely strain healthcare systems. In the absence of a reliable vaccine against dengue or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, scholarship on the topic had initially focused on prediction systems at the national- and state-levels, and much remains to be done at the finer spatial resolutions at which health policy interventions often occur. We develop a methodological framework to assess and compare dengue incidence estimates at the city level, and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that both random forest-based models and LASSO regression-based models effectively leverage these multiple data sources to produce accurate predictions, and that while the performance between them is comparable on average, the former method produces fewer extreme outliers, and can thus be considered more robust. For real-time predictions that assume long delays (6–8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of dengue incidence, whereas for predictions that assume short delays (1–3 weeks), in which the error rate is halved (as measured by relative RMSE), short-term and seasonal autocorrelation are the dominant predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different demographic, geographic and epidemic characteristics. Author summary: As the incidence of infectious diseases like dengue continues to increase throughout the world, tracking their spread in real time poses a significant challenge to local and national health authorities. Accurate incidence data are often difficult to obtain as outbreaks emerge and unfold, both due the partial reach of serological surveillance (especially in rural areas), and due to delays in reporting, which result in post-hoc adjustments to what should have been real-time data. Thus, a range of 'nowcasting' tools have been developed to estimate disease trends, using different mathematical and statistical methodologies to fill the temporal data gap. Over the past several years, researchers have investigated how to best incorporate internet search data into predictive models, since these can be obtained in real-time. Still, most such models have been regression-based, and have tended to underperform in cases when epidemiological data are only available after long reporting delays. Moreover, in tropical countries, attention has increasingly turned from testing and applying models at the national level to models at higher spatial resolutions, such as states and cities. Here, we develop machine learning models based on both LASSO regression and on random forest ensembles, and proceed to apply and compare them across 20 cities in Brazil. We find that our methodology produces meaningful and actionable disease estimates at the city level with both underlying model classes, and that the two perform comparably across most metrics, although the ensemble method produces fewer outliers. We also compare model performance and the relative contribution of different data sources across diverse geographic, demographic and epidemic conditions. [ABSTRACT FROM AUTHOR]

Background: Yellow fever (YF) is an arboviral disease which is endemic to Brazil due to a sylvatic transmission cycle maintained by infected mosquito vectors, non-human primate (NHP) hosts, and humans. Despite the existence of an effective vaccine, recent sporadic YF epidemics have underscored concerns about sylvatic vector surveillance, as very little is known about their spatial distribution. Here, we model and map the environmental suitability of YF's main vectors in Brazil, Haemagogus spp. and Sabethes spp., and use human population and NHP data to identify locations prone to transmission and spillover risk. Methodology/Principal findings: We compiled a comprehensive set of occurrence records on Hg. janthinomys, Hg. leucocelaenus, and Sabethes spp. from 1991–2019 using primary and secondary data sources. Linking these data with selected environmental and land-cover variables, we adopted a stacked regression ensemble modelling approach (elastic-net regularized GLM, extreme gradient boosted regression trees, and random forest) to predict the environmental suitability of these species across Brazil at a 1x1 km resolution. We show that while suitability for each species varies spatially, high suitability for all species was predicted in the Southeastern region where recent outbreaks have occurred. By integrating data on NHP host reservoirs and human populations, our risk maps further highlight municipalities within the region that are prone to transmission and spillover. Conclusions/Significance: Our maps of sylvatic vector suitability can help elucidate potential locations of sylvatic reservoirs and be used as a tool to help mitigate risk of future YF outbreaks and assist in vector surveillance. Furthermore, at-risk regions identified from our work could help disease control and elucidate gaps in vaccination coverage and NHP host surveillance. Author summary: Yellow fever virus (YFV) is an arbovirus transmitted to humans from mosquitoes and can lead to severe disease and death. Recent sporadic outbreaks coupled with low vaccination coverage have highlighted the importance of mosquito surveillance for preventing future outbreaks and potential virus spillover into dense urban areas. Yet, very little is known about the spatial distribution of mosquitoes known to transmit YFV and the factors that contribute to their environmental suitability in Brazil. We compiled an occurrence database of primary and secondary mosquito vectors belonging to Haemagogus and Sabethes species' collected between 1991–2019 and integrated this data with environmental and land-use data to predict their spatial suitability at 1x1km resolution. Using this information, we identified suitable regions for their co-existence. We overlaid this information with human population density and locations of non-human primate host reservoirs to identify areas at risk of transmission and spillover. Our study provides high-resolution mapping tools to assist with mosquito and arbovirus surveillance which is especially useful in low-resource settings. [ABSTRACT FROM AUTHOR]

Background: Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings: We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. Conclusions/Significance: Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders. Author summary: Dengue is one of the most important arbovirus infections in the world and its public health, societal and economic burden is increasing. Although the majority of dengue cases are asymptomatic or mild, severe disease forms can lead to death. For this reason, early diagnosis and monitoring of dengue are crucial to decrease mortality. However, most endemic regions still rely on traditional monitoring methods, despite the growing availability of novel data sources and data-driven methods based on real-world data, Big Data, and machine learning algorithms. In this systematic review, we identified and analyzed studies that used these novel approaches for dengue monitoring and/or prediction. We found that novel data streams, such as Internet search engines and social media platforms, and machine learning methods can be successfully used to improve dengue management, but are still vastly ignored in real life. These approaches should be combined with traditional methods to help stakeholders better prepare for each outbreak and improve early responsiveness. [ABSTRACT FROM AUTHOR]

The article discusses the challenges of monitoring and controlling dengue, a major public health problem in many endemic areas. Due to the lack of a vaccine, dengue control relies on controlling the mosquito vector. However, formal surveillance systems have limitations, such as delays in reporting and hierarchical reporting structures. To address these drawbacks, complementary informal surveillance tools have been developed, including event-based surveillance systems and the analysis of real-time search queries. The authors of the article apply searchstream surveillance techniques to monitor dengue and find a strong correlation between dengue-related search volumes and case counts reported by traditional surveillance systems. However, there are concerns about model over-fitting and the potential for misleading spikes in search volumes. The authors conclude that web-based surveillance tools provide an additional tool in the surveillance toolbox, with advantages and limitations, and can help improve situational awareness of dengue activity. [Extracted from the article]

Improving our understanding of Mayaro virus (MAYV) ecology is critical to guide surveillance and risk assessment. We conducted a PRISMA-adherent systematic review of the published and grey literature to identify potential arthropod vectors and non-human animal reservoirs of MAYV. We searched PubMed/MEDLINE, Embase, Web of Science, SciELO and grey-literature sources including PAHO databases and dissertation repositories. Studies were included if they assessed MAYV virological/immunological measured occurrence in field-caught, domestic, or sentinel animals or in field-caught arthropods. We conducted an animal seroprevalence meta-analysis using a random effects model. We compiled granular georeferenced maps of non-human MAYV occurrence and graded the quality of the studies using a customized framework. Overall, 57 studies were eligible out of 1523 screened, published between the years 1961 and 2020. Seventeen studies reported MAYV positivity in wild mammals, birds, or reptiles and five studies reported MAYV positivity in domestic animals. MAYV positivity was reported in 12 orders of wild-caught vertebrates, most frequently in the orders Charadriiformes and Primate. Sixteen studies detected MAYV in wild-caught mosquito genera including Haemagogus, Aedes, Culex, Psorophora, Coquillettidia, and Sabethes. Vertebrate animals or arthropods with MAYV were detected in Brazil, Panama, Peru, French Guiana, Colombia, Trinidad, Venezuela, Argentina, and Paraguay. Among non-human vertebrates, the Primate order had the highest pooled seroprevalence at 13.1% (95% CI: 4.3–25.1%). From the three most studied primate genera we found the highest seroprevalence was in Alouatta (32.2%, 95% CI: 0.0–79.2%), followed by Callithrix (17.8%, 95% CI: 8.6–28.5%), and Cebus/Sapajus (3.7%, 95% CI: 0.0–11.1%). We further found that MAYV occurs in a wide range of vectors beyond Haemagogus spp. The quality of evidence behind these findings was variable and prompts calls for standardization of reporting of arbovirus occurrence. These findings support further risk emergence prediction, guide field surveillance efforts, and prompt further in-vivo studies to better define the ecological drivers of MAYV maintenance and potential for emergence. Author summary: Mayaro virus (MAYV) is an emerging tropical public health threat in the Americas. We conducted a georeferenced, quality-graded systematic review to evaluate the current evidence regarding MAYV occurrence in non-human vertebrates and arthropods. Overall, 57 studies were eligible out of 1523 screened, published between the years 1961 and 2020. Seventeen studies reported MAYV positivity in wild mammals, birds, or reptiles and five studies reported MAYV positivity in domestic animals. MAYV positivity was reported in 12 orders of wild-caught vertebrates, most frequently in the orders Charadriiformes and Primate. Our systematic review identified 12 orders of wild-caught vertebrates and seven mosquito genera with evidence of MAYV occurrence. Primates had the highest pooled MAYV seroprevalence according to a seroprevalence meta-analysis. The graded quality of evidence behind these findings was variable and prompts calls for standardization of reporting of MAYV and perhaps other emerging arbovirus occurrence in animals and vectors. This study provides important information for public health authorities and disease ecologists concerned with the growing threat of MAYV in Latin America. Our analysis provides a foundation for future laboratory and field studies focused on the MAYV transmission cycle. [ABSTRACT FROM AUTHOR]

Background: Targeting interventions to areas that have recently experienced cases of disease is one strategy to contain outbreaks of infectious disease. Such case-area targeted interventions (CATI) have become an increasingly popular approach for dengue control but there is little evidence to suggest how precisely targeted or how recent cases need to be, to mount an effective response. The growing interest in the development of prophylactic and therapeutic drugs for dengue has also given new relevance for CATI strategies to interrupt transmission or deliver early treatment. Methods/Principal findings: Here we develop a patch-based mathematical model of spatial dengue spread and fit it to spatiotemporal datasets from Singapore. Simulations from this model suggest CATI strategies could be effective, particularly if used in lower density areas. To maximise effectiveness, increasing the size of the radius around an index case should be prioritised even if it results in delays in the intervention being applied. This is partially because large intervention radii ensure individuals receive multiple and regular rounds of drug dosing or vector control, and thus boost overall coverage. Given equivalent efficacy, CATIs using prophylactic drugs are predicted to be more effective than adult mosquito-killing vector control methods and may even offer the possibility of interrupting individual chains of transmission if rapidly deployed. CATI strategies quickly lose their effectiveness if baseline transmission increases or case detection rates fall. Conclusions/Significance: These results suggest CATI strategies can play an important role in dengue control but are likely to be most relevant for low transmission areas where high coverage of other non-reactive interventions already exists. Controlled field trials are needed to assess the field efficacy and practical constraints of large operational CATI strategies. Author summary: In resource limited settings there is a pressing need for more efficient, more targeted ways of controlling transmission and preventing outbreaks. One option is to use case-area targeted interventions (CATI) that are focussed on areas that have recently reported disease cases. The effectiveness of such CATI strategies is highly dependent on how the disease spreads. Despite CATI strategies being widely used to control the vector-transmitted disease dengue, little evidence underpins its effectiveness. In this analysis we formulate a mathematical model designed to test the effectiveness of CATI strategies for dengue control in Singapore- a best case test scenario for the approach. Simulation from this model suggested CATI are likely to be effective for dengue, but need to have large (250m+) radii around index cases and may not be suitable in higher transmission areas. These results, when combined with limited field evidence of efficacy, suggest that CATI strategies are unlikely to be universally applicable dengue control tools. Only once high coverage with other (non-reactive) interventions has been achieved and comprehensive rapid disease surveillance has been established are CATI strategies likely to become efficient methods of disease control. [ABSTRACT FROM AUTHOR]

Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the GSE28405, GSE38246, and GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein–protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent. Author summary: Dengue fever is a mosquito borne viral disease caused by a single stranded RNA virus with four serotypes. DENV infection can cause various diseases, such as breakbone fever, haemorrhagic fever, and shock syndrome. As one of the most viral diseases leading to incidence rate and mortality in animal arthropods, Dengue fever has become an increasingly serious global health threat. However, the pathogenesis of dengue fever has not been fully elucidated. In this study, we used bioinformatics analysis to identify potential biomarkers associated with dengue fever and elucidate their underlying mechanisms. Finally, we predicted that IFI44L and IFI6 might be potential biomarkers of DENV infection. This finding provides a promising target for the treatment of dengue fever to a certain extent. In addition, the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein–protein interaction (PPI) network were implemented to analyze the key differentially expressed genes after DENV infection, and the related mechanisms were illuminated by this study. [ABSTRACT FROM AUTHOR]

The mosquito Aedes aegypti is the primary vector of many disease-causing viruses, including dengue (DENV), Zika, chikungunya, and yellow fever. As consequences of climate change, we expect an increase in both global mean temperatures and extreme climatic events. When temperatures fluctuate, mosquito vectors will be increasingly exposed to temperatures beyond their upper thermal limits. Here, we examine how DENV infection alters Ae. aegypti thermotolerance by using a high-throughput physiological 'knockdown' assay modeled on studies in Drosophila. Such laboratory measures of thermal tolerance have previously been shown to accurately predict an insect's distribution in the field. We show that DENV infection increases thermal sensitivity, an effect that may ultimately limit the geographic range of the virus. We also show that the endosymbiotic bacterium Wolbachia pipientis, which is currently being released globally as a biological control agent, has a similar impact on thermal sensitivity in Ae. aegypti. Surprisingly, in the coinfected state, Wolbachia did not provide protection against DENV-associated effects on thermal tolerance, nor were the effects of the two infections additive. The latter suggests that the microbes may act by similar means, potentially through activation of shared immune pathways or energetic tradeoffs. Models predicting future ranges of both virus transmission and Wolbachia's efficacy following field release may wish to consider the effects these microbes have on host survival. Author summary: Changes in global climate, which include higher temperatures and more frequent extreme temperature events, are expected to cause dramatic shifts in the distributions of infectious diseases. The geographic range of the mosquito Aedes aegypti continues to expand, risking greater incidence of viral diseases including dengue (DENV), Zika, chikungunya, and yellow fever. One emerging solution to control these viruses is the release of the insect bacterium Wolbachia, whose infection in mosquitoes reduces virus transmission to humans. However, the effects of rising temperatures on the efficacy of this tool are unclear. Here, we studied whether DENV and Wolbachia can alter the thermal sensitivity of the mosquito Ae. aegypti by using a heat-based physiological assay. We demonstrate that, separately, DENV and Wolbachia infections increase mosquito thermal sensitivity, causing more rapid death when mosquitoes are exposed to extreme heat. The impacts of the microbes on mosquito thermal sensitivity were similar but not additive, suggesting they effect the mosquito in similar ways. Our work demonstrates that future global projections of DENV transmission risk and of Wolbachia's potential efficacy may need to consider the impact of these microbes on vector survival. [ABSTRACT FROM AUTHOR]

Background: Dengue is a major public health problem in Thailand, but data are often focused on certain dengue-endemic areas. Methods: To better understand dengue epidemiology and clinical characteristics in Thailand, a fever surveillance study was conducted among patients aged 1–55 years, who presented with non-localized febrile illness at Bang Phae Community Hospital in Ratchaburi province, Thailand from October 2011 to September 2016. Results: Among 951 febrile episodes, 130 were dengue-confirmed. Individuals aged 10–14 years were mostly affected, followed by those 15–19 years-of-age, with about 15% of dengue-confirmed cases from adults 25 years and older. There were annual peaks of dengue occurrence between June-November. Most prevalent serotype in circulation was DENV-2 in 2012, DENV-3 in 2014, and DENV-4 & -3 in 2015. Among dengue cases, 65% were accurately detected using the dengue NS1 RDT. Detection rate was similar between secondary and primary dengue cases where 66% of secondary vs. 60% of primary dengue cases had positive results on the NS1 RDT. Among dengue cases, 66% were clinically diagnosed with suspected dengue or DHF, prior to lab confirmation. Dengue was positively associated with rash, headache, hematemesis and alterations to consciousness, when compared to non-dengue. Dengue patients were 10.6 times more likely to be hospitalized, compared to non-dengue cases. Among dengue cases, 95 were secondary and 35 were primary infections. There were 8 suspected DHF cases and all were identified to be secondary dengue. Secondary dengue cases were 3.5 times more likely to be hospitalized compared to primary dengue cases. Although the majority of our dengue-positive patients were secondary dengue cases, with few patients showing manifestations of DHF, our dengue cases were mostly mild disease. Even among children < 10 years-of-age, 61% had secondary infection and the rate of secondary infection increased with age. Conclusion: While the majority of dengue-confirmed cases were children, almost three-quarters of dengue-confirmed cases in this study were secondary dengue. Our study results consistent with previous data from the country confirm the hyperendemic transmission of DENV in Thailand, even in the non-epidemic years. With various interventions becoming available for dengue prevention and control, including dengue vaccines, decision-making on future implementation strategies should be based on such burden of disease data. Author summary: Dengue is a well-known public health problem in Thailand. To identify epidemiologic and clinical patterns of dengue in Thailand, we studied 951 febrile patients with fever, aged 1–55 years, who attended Bang Phae district hospital in Ratchaburi province, Thailand. Patients were tested with a rapid test for dengue, and further tests were carried out on paired blood samples taken 10–21 days apart. Overall, 14% of the febrile episodes identified between October 2011 and September 2016 were dengue-confirmed. Teenagers were mostly affected with only few adults. Dengue peaks occurred between June-November, but there was no large epidemic during the study period. DENV type 2 was the main serotype in circulation in 2012, DENV type 3 in 2014, and DENV types 4 and 3 in 2015. Dengue cases were 11 times more likely to be hospitalized than non-dengue cases. Dengue patients were more likely to present with rash, headache, hematemesis and alterations to consciousness, compared to non-dengue. Two thirds of the dengue cases were clinically diagnosed with suspected dengue or DHF, even without lab confirmation. Secondary dengue cases were about three times more common than primary cases. Even among children < 10 years-of-age, 61% of the patients were of secondary infection. Among dengue cases, 65% were accurately detected using the dengue RDT. This was not different between secondary and primary dengue cases. All 8 patients clinically diagnosed with DHF were secondary dengue cases. Although most of our dengue-positive patients were secondary dengue cases, we only observed few patients showing DHF. Thus, we concluded that our dengue cases were mostly of mild dengue illness. Our study results support the previous data confirming the high burden of dengue in Thailand and such information would help to make decisions on future implementation of various tools for dengue prevention and control. [ABSTRACT FROM AUTHOR]

Crimean-Congo haemorrhagic fever (CCHF) is a severe tick-borne viral zoonosis endemic to parts of Africa, Europe, the Middle East and Central Asia. Human cases are reported annually in South Africa, with a 25% case fatality rate since the first case was recognized in 1981. We investigated CCHF virus (CCHFV) seroprevalence and risk factors associated with infection in cattle and humans, and the presence of CCHFV in Hyalomma spp. ticks in central South Africa in 2017–18. CCHFV IgG seroprevalence was 74.2% (95%CI: 64.2–82.1%) in 700 cattle and 3.9% (95%CI: 2.6–5.8%) in 541 farm and wildlife workers. No veterinary personnel (117) or abattoir workers (382) were seropositive. The prevalence of CCHFV RNA was significantly higher in Hyalomma truncatum (1.6%) than in H. rufipes (0.2%) (P = 0.002). Seroprevalence in cattle increased with age and was greater in animals on which ticks were found. Seroprevalence in cattle also showed significant geographic variation. Seroprevalence in humans increased with age and was greater in workers who handled livestock for injection and collection of samples. Our findings support previous evidence of widespread high CCHFV seroprevalence in cattle and show significant occupational exposure amongst farm and wildlife workers. Our seroprevalence estimate suggests that CCHFV infections are five times more frequent than the 215 confirmed CCHF cases diagnosed in South Africa in the last four decades (1981–2019). With many cases undiagnosed, the potential seriousness of CCHF in people, and the lack of an effective vaccine or treatment, there is a need to improve public health awareness, prevention and disease control. Author summary: Crimean-Congo haemorrhagic fever (CCHF) is an infection caused by a tick-borne virus endemic to a large geographic area, that is asymptomatic in animals but presents a significant health risk to humans with no effective vaccine. Serological surveys were carried out in rural populations and animals shortly after the first reported case in South Africa in 1981, but little is known about the current seroprevalence of CCHFV in humans and animals, and tick infection rates. We conducted a survey in 2017–18 to obtain recent estimates and examined exposure factors in an area of South Africa where cases are known to occur. We observed a high seroprevalence in cattle of 74.2%. Farm and wildlife workers at risk of occupational exposure to CCHFV had a seroprevalence of 3.9%, which was higher than previously reported. No evidence of CCHFV infection was found in abattoir workers or veterinary professionals in that area. CCHFV RNA was detected in both species of Hyalomma ticks but with a higher prevalence (1.6%) in H. truncatum. This study suggests that CCHFV is widespread in South Africa, that exposure to CCHFV is more common than generally believed in animal-related occupations and that increased public health awareness of risk factors associated with CCHFV infection are required to reduce morbidity and mortality from CCHF. [ABSTRACT FROM AUTHOR]

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 33% of pairwise correlation in outbreak timing, and 10% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the "normalized burn ratio," experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of "adaptive models" rather than "one-size-fits-all" models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil. Author summary: Dengue virus spreads through mosquitoes in many tropical and subtropical parts of the world, including Brazil. Each year, dengue virus causes seasonal outbreaks that vary in magnitude and timing across the country. This variation makes tailoring preparation efforts for fine spatio-temporal scales challenging. In this study, we described four properties of historical dengue time series at the mesoregion level, the Brazilian subdivision below state, and examined how they varied across the country. We found that the duration and timing of seasonal outbreaks are largely driven by climate factors, while relational properties, i.e., the similarity in outbreak timing and magnitude between two mesoregions, are explained by a mix of mobility patterns and climate similarities. Surprisingly, we found that remote sensing derived products and movement inferred through Twitter were adequate proxies for climate and mobility patterns respectively. Knowledge of how dengue outbreaks differ across the country and the factors that may influence specific outbreak properties may be important for improving efforts to build forecasting and prediction models. [ABSTRACT FROM AUTHOR]

Background: As the three major arthropod-borne viruses, dengue virus (DENV), chikungunya virus (CHIKV), and zika virus (ZIKV) are posing a growing threat to global public health and socioeconomic development. Our study aimed to systematically review the global seroprevalences of these arboviruses from existing publications. Methods: Articles published between Jan 01, 2000 and Dec 31, 2019 in the databases of Embase, Pubmed and Web of Science were searched and collected. Countries or areas with known local presence of Aedes vector mosquitoes were included. Random effects model was utilized to estimate the pooled seroprevalences and the proportion of inapparent infection. Results: Out of 1375, a total of 133 articles involving 176,001 subjects were included for our analysis. The pooled seroprevalences of DENV, CHIKV and ZIKV were 38%, 25% and 18%, respectively; and their corresponding proportions of inapparent infections were 80%, 40% and 50%. The South-East Asia Region had the highest seroprevalences of DENV and CHIKV, while the Region of the Americas had the highest seroprevalence of ZIKV. The seroprevalences of DENV and CHIKV were similar when comparing developed and developing countries, urban and rural areas, or among different populations. In addition, we observed a decreased global seroprevalences in the new decade (2010–2019) comparing to the decade before (2000–2009) for CHIKV. For ZIKV, the positive rates tested with the nucleic acid detection method were lower than those tested with the antibody detection method. Lastly, numerous cases of dual seropositivity for CHIKV and DENV were reported. Conclusions: Our results revealed a varied prevalence of arbovirus infections in different geographical regions and countries, and the inapparent infection accounted an unneglected portion of infections that requires more attention. This study will shed lights on our understanding of the true burden of arbovirus infections and promote appropriate vaccination in the future. Author summary: DENV, CHIKV, and ZIKV infections are the three major neglected tropical diseases continually posing threats to the public health and the socioeconomic development in recent decades. Given a high fraction of inapparent infections of these viruses, it is necessary to obtain informative knowledge of the seroepidemiology of DENV, CHIKV, and ZIKV worldwide, so as to estimate the true burden of these diseases and evaluate effects on vaccine efficacy and uptake. Given the fact that the reported infection rates may vary greatly due to the measurement, criteria of inclusion, and etc, a systematic review with meta-analysis is essential to collect and summarize the seroepidemiologic evidence of these arboviruses. In this study, the geographical distributions of DENV, CHIKV, and ZIKV and the proportions of their inapparent infections were analyzed, which revealed an overall high disease burden to the world, especially in specific regions. The epidemiological features of these arboviruses were also characterized via comparisons made in different countries/areas, populations, and time periods. In addition, evidence on the co-infection of DENV and CHIKV was assessed. [ABSTRACT FROM AUTHOR]

Background: Aedes albopictus is one of the most invasive species in the world as well as the important vector for mosquito-borne diseases such as dengue fever, chikungunya fever and zika virus disease. Chemical control of mosquitoes is an effective method to control mosquito-borne diseases, however, the wide and improper application of insecticides for vector control has led to serious resistance problems. At present, there have been many reports on the resistance to pyrethroid insecticides in vector mosquitoes including deltamethrin to Aedes albopictus. However, the fitness cost and vector competence of deltamethrin resistant Aedes albopictus remain unknown. To understand the impact of insecticide resistant mosquito is of great significance for the prevention and control mosquitoes and mosquito-borne diseases. Methodology/Principal findings: A laboratory resistant strain (Lab-R) of Aedes albopictus was established by deltamethrin insecticide selecting from the laboratory susceptible strain (Lab-S). The life table between the two strains were comparatively analyzed. The average development time of Lab-R and Lab-S in larvae was 9.7 days and 8.2 days (P < 0.005), and in pupae was 2.0 days and 1.8 days respectively (P > 0.05), indicating that deltamethrin resistance prolongs the larval development time of resistant mosquitoes. The average survival time of resistant adults was significantly shorter than that of susceptible adults, while the body weight of resistant female adults was significantly higher than that of the susceptible females. We also compared the vector competence for dengue virus type-2 (DENV-2) between the two strains via RT-qPCR. Considering the results of infection rate (IR) and virus load, there was no difference between the two strains during the early period of infection (4, 7, 10 day post infection (dpi)). However, in the later period of infection (14 dpi), IR and virus load in heads, salivary glands and ovaries of the resistant mosquitoes were significantly lower than those of the susceptible strain (IR of heads, salivary glands and ovaries: P < 0.005; virus load in heads and salivary glands: P < 0.05; virus load in ovaries: P < 0.001). And then, fourteen days after the DENV-2-infectious blood meal, females of the susceptible and resistant strains were allow to bite 5-day-old suckling mice. Both stains of mosquito can transmit DENV-2 to mice, but the onset of viremia was later in the mice biting by resistant group as well as lower virus copies in serum and brains, suggesting that the horizontal transmission of the resistant strain is lower than the susceptible strain. Meanwhile, we also detected IR of egg pools of the two strains on 14 dpi and found that the resistant strain were less capable of vertical transmission than susceptible mosquitoes. In addition, the average survival time of the resistant females infected with DENV-2 was 16 days, which was the shortest among the four groups of female mosquitoes, suggesting that deltamethrin resistance would shorten the life span of female Aedes albopictus infected with DENV-2. Conclusions/Significance: As Aedes albopictus developing high resistance to deltamethrin, the resistance prolonged the growth and development of larvae, shorten the life span of adults, as well as reduced the vector competence of resistant Aedes albopictus for DENV-2. It can be concluded that the resistance to deltamethrin in Aedes albopictus is a double-edged sword, which not only endow the mosquito survive under the pressure of insecticide, but also increase the fitness cost and decrease its vector competence. However, Aedes albopictus resistant to deltamethrin can still complete the external incubation period and transmit dengue virus, which remains a potential vector for dengue virus transmission and becomes a threat to public health. Therefore, we should pay high attention for the problem of insecticide resistance so that to better prevent and control mosquito-borne diseases. Author summary: Worldwide invasion and expansion of Aedes albopictus, the main vector of dengue, chikungunya, and Zika viruses, has become a serious concern in global public health. With the large use of insecticides, especially the most commonly used pyrethroid insecticides, the emergence and development of resistance in Aedes albopictus present vector control challenges. However, it is not clear whether the resistance would affect the fitness cost and vector competence of Aedes albopictus. In this study, a laboratory resistant strain of Aedes albopictus was established by selecting the susceptible strain of Aedes albopictus with deltamethrin. Comparing the resistant strain with the susceptible strain, we found that deltamethrin resistance increased the fitness cost and reduced the vector competence of DENV-2 in Aedes albopictus. These latest findings shared the light for dengue disease prevention and vector control strategies. [ABSTRACT FROM AUTHOR]

Introduction: Crimean-Congo haemorrhagic fever (CCHF) is a tick-borne, zoonotic viral disease that causes haemorrhagic symptoms. Despite having eight confirmed outbreaks between 2013 and 2017, all within Uganda's 'cattle corridor', no targeted tick control programs exist in Uganda to prevent disease. During a seven-month-period from July 2018-January 2019, the Ministry of Health confirmed multiple independent CCHF outbreaks. We investigated to identify risk factors and recommend interventions to prevent future outbreaks. Methods: We defined a confirmed case as sudden onset of fever (≥37.5°C) with ≥4 of the following signs and symptoms: anorexia, vomiting, diarrhoea, headache, abdominal pain, joint pain, or sudden unexplained bleeding in a resident of the affected districts who tested positive for Crimean-Congo haemorrhagic fever virus (CCHFv) by RT-PCR from 1 July 2018–30 January 2019. We reviewed medical records and performed active case-finding. We conducted a case-control study and compared exposures of case-patients with age-, sex-, and sub-county-matched control-persons (1:4). Results: We identified 14 confirmed cases (64% males) with five deaths (case-fatality rate: 36%) from 11 districts in western and central region. Of these, eight (73%) case-patients resided in Uganda's 'cattle corridor'. One outbreak involved two case-patients and the remainder involved one. All case-patients had fever and 93% had unexplained bleeding. Case-patients were aged 6–36 years, with persons aged 20–44 years more affected (AR: 7.2/1,000,000) than persons ≤19 years (2.0/1,000,000), p = 0.015. Most (93%) case-patients had contact with livestock ≤2 weeks before symptom onset. Twelve (86%) lived <1 km from grazing fields compared with 27 (48%) controls (ORM-H = 18, 95% CI = 3.2-∞) and 10 (71%) of 14 case-patients found ticks attached to their bodies ≤2 weeks before symptom onset, compared to 15 (27%) of 56 control-persons (ORM-H = 9.3, 95%CI = 1.9–46). Conclusions: CCHF outbreaks occurred sporadically during 2018–2019, both within and outside 'cattle corridor' districts of Uganda. Most cases were associated with tick exposure. The Ministry of Health should partner with the Ministry of Agriculture, Animal Industry and Fisheries to develop joint nationwide tick control programs and strategies with shared responsibilities through a One Health approach. Author summary: Uganda has had multiple Crimean-Congo haemorrhagic fever outbreaks since 2013 when the first outbreak was confirmed. Tick exposure has been identified as the major risk factor by our study and this finding was similar with other studies done during outbreaks in Uganda. However, Uganda still lacks national tick control guidelines and indiscriminate use of acaricides (pesticides specially for ticks) has been observed widely. This has been cited to influence increased tick resistance to acaricides. Our study might not indicate whether tick resistance to acaricides has increased tick populations in Uganda however it is imperative that tick control is considered in efforts of prevention and control of CCHF outbreaks. We therefore recommend improved tick control in Uganda through national regulations on acaricide distribution and use, development of strategies to reduce tick resistance to acaricides in the country, and more community-based engagement of tick control in livestock management. [ABSTRACT FROM AUTHOR]