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Approximately 90 days of the SARS-CoV-2 (COVID-19) spreading originally from Wuhan, China, and across the globe has led to a widespread chain of events with imminent threats to the fragile relationship between community health and economic health. Despite near hourly reporting on this crisis, there has been no regular, updated, or accurate reporting of hospitalizations for COVID-19. It is known that many test-positive individuals may not develop symptoms or have a mild self-limited viral syndrome consisting of fever, malaise, dry cough, and constitutional symptoms. However some individuals develop a more fulminant syndrome including viral pneumonia, respiratory failure requiring oxygen, acute respiratory distress syndrome requiring mechanical ventilation, and in substantial fractions leading to death attributable to COVID-19. The pandemic is evolving in a clustered, non-inform fashion resulting in many hospitals with preparedness but few or no cases, and others that are completely overwhelmed. Thus, a considerable risk of spread when personal protection equipment becomes exhausted and a large fraction of mortality in those not offered mechanical ventilation are both attributable to a crisis due to maldistribution of resources. The pandemic is amenable to self-reporting through a mobile phone application that could obtain critical information on suspected cases and report on the results of self testing and actions taken. The only method to understand the clustering and the immediate hospital resource needs is mandatory, uniform, daily reporting of hospital censuses of COVID-19 cases admitted to hospital wards and intensive care units. Current reports of hospitalizations are delayed, uncertain, and wholly inadequate. This paper urges all the relevant stakeholders to take up self-reporting and reporting of hospitalizations of COVID-19 as an urgent task in combating this devastating pandemic. [ABSTRACT FROM AUTHOR]

Epidemics of communicable diseases place a huge burden on public health infrastructures across the world. Producing accurate and actionable forecasts of infectious disease incidence at short and long time scales will improve public health response to outbreaks. However, scientists and public health officials face many obstacles in trying to create such real-time forecasts of infectious disease incidence. Dengue is a mosquito-borne virus that annually infects over 400 million people worldwide. We developed a real-time forecasting model for dengue hemorrhagic fever in the 77 provinces of Thailand. We created a practical computational infrastructure that generated multi-step predictions of dengue incidence in Thai provinces every two weeks throughout 2014. These predictions show mixed performance across provinces, out-performing seasonal baseline models in over half of provinces at a 1.5 month horizon. Additionally, to assess the degree to which delays in case reporting make long-range prediction a challenging task, we compared the performance of our real-time predictions with predictions made with fully reported data. This paper provides valuable lessons for the implementation of real-time predictions in the context of public health decision making. [ABSTRACT FROM AUTHOR]

Italian medical history in the age of positivism showed a strong interest in epidemics. This can be seen in Alfonso Corradi's monumental Annali (1865-1895) and in works of other 19th-century historians who addressed major public health issues in the newly unified country. Local history was also widely practiced in Italy, and it was instrumental in discovering and publishing a wealth of documentation on past epidemic and endemic diseases, as well as on measures such as quarantines that were invented or introduced in the peninsula as early as the late Middle Ages. The way Italian historians looked at epidemics in the 20th century was shaped by politics, religion and literature more than by demography, epidemiology, or technical knowledge in the medical field. This article and its accompanying bibliography will focus on regional historiography and deal with the history of plague, smallpox, cholera and malaria, and other diseases addressed in works published after the 1980s. [ABSTRACT FROM AUTHOR]

Background: AIDS is a worrying public health issue in China and lacks timely and effective surveillance. With the diffusion and adoption of the Internet, the ‘big data’ aggregated from Internet search engines, which contain users’ information on the concern or reality of their health status, provide a new opportunity for AIDS surveillance. This paper uses search engine data to monitor and forecast AIDS in China. Methods: A machine learning method, artificial neural networks (ANNs), is used to forecast AIDS incidences and deaths. Search trend data related to AIDS from the largest Chinese search engine, Baidu.com, are collected and selected as the input variables of ANNs, and officially reported actual AIDS incidences and deaths are used as the output variable. Three criteria, the mean absolute percentage error, the root mean squared percentage error, and the index of agreement, are used to test the forecasting performance of the ANN method. Results: Based on the monthly time series data from January 2011 to June 2017, this article finds that, under the three criteria, the ANN method can lead to satisfactory forecasting of AIDS incidences and deaths, regardless of the change in the number of search queries. Conclusions: Despite the inability to self-detect HIV/AIDS through online searching, Internet-based data should be adopted as a timely, cost-effective complement to a traditional AIDS surveillance system. [ABSTRACT FROM AUTHOR]

We show that many structured epidemic models may be described using a straightforward product structure in this paper. Such products, derived from products of directed graphs, may represent useful refinements including geographic and demographic structure, age structure, gender, risk groups, or immunity status. Extension to multistrain dynamics, that is, pathogen heterogeneity, is also shown to be feasible in this framework. Systematic use of such products may aid in model development and exploration, can yield insight, and could form the basis of a systematic approach to numerical structural sensitivity analysis. [ABSTRACT FROM AUTHOR]

Epidemic curves are used by decision makers and the public to infer the trajectory of the COVID-19 pandemic and to understand the appropriateness of response measures. Symptom onset date is commonly used to date incident cases on the epidemic curve in public health reports and dashboards; however, third-party trackers date cases by the date they were publicly reported by the public health authority. These two curves create very different impressions of epidemic progression. On April 1, 2020, the epidemic curve based on public reporting date for Ontario, Canada showed an accelerating epidemic, whereas the curve based on a proxy variable for symptom onset date showed a rapidly declining epidemic. This illusory downward trend is a feature of epidemic curves anchored using date variables earlier in time than the date a case was publicly reported, such as the symptom onset date. Delays between the onset of symptoms and the detection of a case by the public health authority mean that recent days will always have incomplete case data, creating a downward bias. Public reporting date is not subject to this bias and can be used to visualize real-time epidemic curves meant to inform the public and decision makers. [ABSTRACT FROM AUTHOR]

Background We describe the epidemiological trends and report and review the public health restrictions implemented during the first wave of the COVID-19 pandemic in Ireland. Methods The study reviewed confirmed cases of COVID-19 notified from 1 March to 18 July 2020. Data were obtained from the national COVID-19 Data Hub, the National Health Protection Surveillance Centre, the National Contact Management Programme and the European Centre for Disease Prevention and Control. Results A total of 25 617 cases were notified during the study period. Weekly cases and deaths peaked in mid-April 2020 at 5701 and 316, respectively. Mean number of close contacts per case was lowest at 0.7 in April, rising to 6.6 by July. Outbreak settings shifted from travel and workplace in March, to healthcare in April. Restrictions implemented on 12 March extended to full lockdown on 27 March. Phased relaxation of restrictions commenced 18 May. Effective suppression of community transmission of COVID-19 was achieved by June 2020. Conclusion Lockdown is a crude population-level restriction effective in controlling COVID-19. Phased relaxation of restrictions in Ireland, however, led to an immediate increase in mean number of contacts per case, which facilitates viral transmission unless individual-level restrictions are adhered to. This demonstrates a limitation of lockdown as a long-term mechanism of pandemic control. [ABSTRACT FROM AUTHOR]

Social distancing is an effective strategy to mitigate the impact of infectious diseases. If sick or healthy, or both, predominantly socially distance, the epidemic curve flattens. Contact reductions may occur for different reasons during a pandemic including health-related mobility loss (severity of symptoms), duty of care for a member of a high-risk group, and forced quarantine. Other decisions to reduce contacts are of a more voluntary nature. In particular, sick people reduce contacts consciously to avoid infecting others, and healthy individuals reduce contacts in order to stay healthy. We use game theory to formalize the interaction of voluntary social distancing in a partially infected population. This improves the behavioral micro-foundations of epidemiological models, and predicts differential social distancing rates dependent on health status. The model's key predictions in terms of comparative statics are derived, which concern changes and interactions between social distancing behaviors of sick and healthy. We fit the relevant parameters for endogenous social distancing to an epidemiological model with evidence from influenza waves to provide a benchmark for an epidemic curve with endogenous social distancing. Our results suggest that spreading similar in peak and case numbers to what partial immobilization of the population produces, yet quicker to pass, could occur endogenously. Going forward, eventual social distancing orders and lockdown policies should be benchmarked against more realistic epidemic models that take endogenous social distancing into account, rather than be driven by static, and therefore unrealistic, estimates for social mixing that intrinsically overestimate spreading. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic and the coming transition to a postpandemic world where COVID-19 will likely remain as an endemic disease present a host of challenges and opportunities in epidemiologic research. The scale and universality of this disruption to life and health provide unique opportunities to study phenomena and health challenges in all branches of epidemiology, from the obvious infectious disease and social consequences to less clear impacts on chronic disease and cancer. If we are to both take advantage of the largest natural experiment of our lifetimes and provide evidence to inform the numerous public health and clinical decisions being made every day, we must act quickly to ask critical questions and develop new methods for answering them. In doing so, we should build on each of our strengths and expertise and try to provide new insights rather than become yet another voice commenting on the same set of questions with limited evidence. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has caused more than 1,000,000 reported deaths globally, of which more than 200,000 have been reported in the United States as of October 1, 2020. Public health interventions have had significant impacts in reducing transmission and in averting even more deaths. Nonetheless, in many jurisdictions, the decline of cases and fatalities after apparent epidemic peaks has not been rapid. Instead, the asymmetric decline in cases appears, in most cases, to be consistent with plateau- or shoulder-like phenomena--a qualitative observation reinforced by a symmetry analysis of US state-level fatality data. Here we explore a model of fatality-driven awareness in which individual protective measures increase with death rates. In this model, fast increases to the peak are often followed by plateaus, shoulders, and lag-driven oscillations. The asymmetric shape of model-predicted incidence and fatality curves is consistent with observations from many jurisdictions. Yet, in contrast to model predictions, we find that population-level mobility metrics usually increased from low levels before fatalities reached an initial peak. We show that incorporating fatigue and long-term behavior change can reconcile the apparent premature relaxation of mobility reductions and help understand when post-peak dynamics are likely to lead to a resurgence of cases. [ABSTRACT FROM AUTHOR]

Background: Following the epidemiologic and demographic transition, non-communicable disease mortality is the leading cause of death in Iran. Projecting mortality trend can provide valuable tools for policy makers and planners. In this article, we have estimated the trend of non-communicable disease mortality during 2001–2015 and have projected it until 2030 at national and subnational levels in Iran. Methods: The data employed was gathered from the Iranian death registration system and using the Spatio-temporal model, the trends of 4 major categories of non-communicable diseases (cancers, cardiovascular diseases, asthma and COPD, and diabetes) by 2030 were projected at the national and subnational levels. Results: The results indicated that age standardized mortality rate for cancers, CVDs, and Asthma and COPD will continue to decrease in both sexes (cancers: from 81.8 in 2015 to 45.2 in 2030, CVDs: 307.3 to 173.0, and Asthma and COPD: from 52.1 to 46.6); however, in terms of diabetes, there is a steady trend in both sexes at national level (from 16.6 to 16.5). Age standardized mortality rates for cancers and CVDs, in males and females, were high in all provinces in 2001. The variation between the provinces is clearer in 2015, and it is expected to significantly decrease in all provinces by 2030. Conclusion: Generally, the age standardized mortality rate from NCDs will decrease by 2030. Of course, given the experience of the past two decades in Iran, believing that the mortality rate will decrease may not be an easy notion to understand. However hard to believe, this decrease may be the result of better management of risk factors and early detection of patients due to more comprehensive care in all segments of society, as well as improved literacy and awareness across the country. [ABSTRACT FROM AUTHOR]

The unexpected early cessation of the recent West Africa Ebola outbreak demonstrated shortcomings of popular forecasting approaches and has not been fully understood yet. A popular hypothesis is that public health interventions mitigated the spread, such as ETUs and safe burials. We investigate whether risk heterogeneity within the population could serve as an alternative explanation. We introduce a model for spread in heterogeneous host population that is particularly well suited for early predictions due to its simplicity and ease of application. Furthermore, we explore the conditions under which the observed epidemic trajectory can be explained without taking into account the effect of public health interventions. While the obtained fits closely match the total case count time series, closer inspection of sub-population results made us conclude that risk heterogeneity is unlikely to fully explain the early cessation of Ebola; other factors such as behavioral changes and other interventions likely played a major role. More accurate predictions in a future scenario require models that allow for early sub-exponential growth, as well as access to additional data on patient occupation (risk level) and location, to allow identify local phenomena that influence spreading behavior. [ABSTRACT FROM AUTHOR]

Peter Godfrey-Faussett and colleagues present six epidemiological metrics for tracking progress in reducing the public health threat of HIV. [ABSTRACT FROM AUTHOR]

Background: Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New 'big data' approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures.Methods: We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national 'stay at home' lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis.Results: Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15-30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted.Conclusions: The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment. [ABSTRACT FROM AUTHOR]

In the 2013-2016 west Africa outbreak of Ebola Virus Disease (EVD), most of the planned clinical trials failed to reach a conclusion within the time frame of the epidemic. The performance of clinical trial designs for the evaluation of one or more experimental treatments in the specific context of an ongoing epidemic with changing case fatality rates (CFR) and unpredictable case numbers is unclear. We conduct a comprehensive evaluation of commonly used two- and multi-arm clinical trial designs based on real data, which was recorded during the 2013-16 EVD epidemic in west Africa. The primary endpoint is death within 14 days of hospitalization. The impact of the recruitment start times relative to the time course of the epidemic on the operating characteristics of the clinical trials is analysed. Designs with frequent interim analyses with the possibility of early stopping are shown to outperform designs with only a single analysis not only in terms of average time to conclusion and average sample size, but also in terms of the probability of reaching any conclusion at all. Historic control designs almost always result in substantially inflated false positive rates, when the case fatality rate changes over time. Response-adaptive randomization may be a compromise between the goal of scientific validity and the ethical goal of minimizing the number of patients allocated to ineffective treatments. [ABSTRACT FROM AUTHOR]

Background: During the West Africa Ebola outbreak, cultural practices have been described as hindering response efforts. The acceptance of control measures improved during the outbreak, but little is known about how and why this occurred. We conducted a qualitative study in two administrative districts of Sierra Leone to understand Ebola survivor, community, and health worker perspectives on Ebola control measures. We aimed to gain an understanding of community interactions with the Ebola response to inform future intervention strategies. Methodology/Principal findings: Participants (25 survivors, 24 community members, and 16 health workers) were recruited purposively. A flexible participatory method gathered data through field notes and in-depth, topic-led interviews. These were analysed thematically with NVivo10© by open coding, constant comparison, and the principles of grounded theory. The primary theme, ‘when Ebola is real’, centred on denial, knowledge, and acceptance. Ebola was denied until it was experienced or observed first-hand and thus health promotion was more effective if undertaken by those directly exposed to Ebola rather than by mass media communication. Factors that enabled acceptance and engagement with control measures included: access to good, proximate care and prevention activities; seeing that people can survive infection; and the co-option of trusted or influential local leadership, with bylaws implemented by community leaders being strongly respected. All participants noted that dignity, respect, and compassion were key components of effective control measures. Conclusions: Successful control approaches need strong community leadership, with the aim of achieving collective understanding between communities and health workers. Health promotion for communities at risk is best conducted through people who have had close interaction with or who have survived Ebola as opposed to reliance on broad mass communication strategies. [ABSTRACT FROM AUTHOR]

Epidemic transitions are an important feature of infectious disease systems. As the transmissibility of a pathogen increases, the dynamics of disease spread shifts from limited stuttering chains of transmission to potentially large scale outbreaks. One proposed method to anticipate this transition are early-warning signals (EWS), summary statistics which undergo characteristic changes as the transition is approached. Although theoretically predicted, their mathematical basis does not take into account the nature of epidemiological data, with are typically aggregated into periodic case reports and subject to reporting error. The viability of EWS for epidemic transitions therefore remains uncertain. Here we demonstrate that most EWS can predict emergence even when calculated from imperfect data. We quantify performance using the area under the curve (AUC) statistic, a measure of how well an EWS distinguishes between numerical simulations of an emerging disease and one which is stationary. Values of the AUC statistic are compared across a range of different reporting scenarios. We find that different EWS respond to imperfect data differently. The mean, variance and first differenced variance all perform well unless reporting error is highly overdispersed. The autocorrelation, autocovariance and decay time perform well provided that the aggregation period of the data is larger than the serial interval and reporting error is not highly overdispersed. The coefficient of variation, skewness and kurtosis are found to be unreliable indicators of emergence. Overall, we find that seven of ten EWS considered perform well for most realistic reporting scenarios. We conclude that imperfect epidemiological data is not a barrier to using EWS for many potentially emerging diseases. [ABSTRACT FROM AUTHOR]

Population structure, spatial diffusion, and climatic conditions mediate the spatiotemporal spread of seasonal influenza in temperate regions. However, much of our knowledge of these dynamics stems from a few well-studied countries, such as the United States (US), and the extent to which this applies in different demographic and climatic environments is not fully understood. Using novel data from Norway, Sweden, and Denmark, we applied wavelet analysis and non-parametric spatial statistics to explore the spatiotemporal dynamics of influenza transmission at regional and international scales. We found the timing and amplitude of epidemics were highly synchronized both within and between countries, despite the geographical isolation of many areas in our study. Within Norway, this synchrony was most strongly modulated by population size, confirming previous findings that hierarchical spread between larger populations underlies seasonal influenza dynamics at regional levels. However, we found no such association when comparing across countries, suggesting that other factors become important at the international scale. Finally, to frame our results within a wider global context, we compared our findings from Norway to those from the US. After correcting for differences in geographic scale, we unexpectedly found higher levels of synchrony in Norway, despite its smaller population size. We hypothesize that this greater synchrony may be driven by more favorable and spatially uniform climatic conditions, although there are other likely factors we were unable to consider (such as reduced variation in school term times and differences in population movements). Overall, our results highlight the importance of comparing influenza spread at different spatial scales and across diverse geographic regions in order to better understand the complex mechanisms underlying disease dynamics. [ABSTRACT FROM AUTHOR]

Accurate and reliable predictions of infectious disease dynamics can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models have been developed for this task, using different model structures, covariates, and targets for prediction. Experience has shown that the performance of these models varies; some tend to do better or worse in different seasons or at different points within a season. Ensemble methods combine multiple models to obtain a single prediction that leverages the strengths of each model. We considered a range of ensemble methods that each form a predictive density for a target of interest as a weighted sum of the predictive densities from component models. In the simplest case, equal weight is assigned to each component model; in the most complex case, the weights vary with the region, prediction target, week of the season when the predictions are made, a measure of component model uncertainty, and recent observations of disease incidence. We applied these methods to predict measures of influenza season timing and severity in the United States, both at the national and regional levels, using three component models. We trained the models on retrospective predictions from 14 seasons (1997/1998–2010/2011) and evaluated each model’s prospective, out-of-sample performance in the five subsequent influenza seasons. In this test phase, the ensemble methods showed average performance that was similar to the best of the component models, but offered more consistent performance across seasons than the component models. Ensemble methods offer the potential to deliver more reliable predictions to public health decision makers. [ABSTRACT FROM AUTHOR]

Background: Prior to the 2007-2010 Q fever epidemic in the Netherlands, the seroprevalence of antibodies against Coxiella burnetii in the general population was 1.5%, which is low compared to other countries. We aimed to determine the seroprevalence after the Q fever epidemic among people living in the affected area, compare the seroprevalence with the incidence of Q fever notifications during the 2007-2010 Q fever epidemic, and to identify farm exposures associated with having antibodies against C. burnetii.Methods: During the period March 2014-February 2015, residents aged 18-70 years from two provinces were invited by general practitioners to complete a questionnaire on their symptoms and personal characteristics and to submit a blood sample. We used the mandatory provincial database of livestock licences to calculate distance to farms/farm animals for each participant. To compare ELISA-positive participants for C. burnetii antibodies with those who were negative, we calculated prevalence ratios (PR) using binominal regression. We compared the C. burnetii seroprevalence in the period March 2014-February 2015 with the incidence of Q fever notifications during the 2007-2010 Q fever epidemic at municipal level by calculating the Spearman correlation coefficient.Results: Of the 2296 participants (response rate: 34%), 6.1% (n = 139, 95% CI 5.1-7.1%) had C. burnetii antibodies (range in municipalities: 1.7-14.1%). C. burnetii seroprevalence was higher in individuals living within 1000 m of goat farms (PR 3.0; 95% CI 1.4-6.4) or within 1000 m of > 50 goats (PR 1.9; 95% CI 1.2-3.0). Seroprevalence increased with decreasing distance to the closest goat farm that was infected during the epidemic years (< 500 m, PR 9.5, 95% CI 2.8-32; 500-1000 m, PR 4.5, 95% CI 2.6-7.7; 1000-1500 m, PR 2.2, 95% CI 1.1-4.3, 1500-2000 m, PR 1.2, 95% CI 0.6-2.5; > 2000 reference group). There was no significant correlation between C. burnetii seroprevalence and Q fever incidence during the 2007-2010 epidemic (r s  = 0.42, p = 0.156).Conclusions: Results showed a remarkable spatial variation in C. burnetii seroprevalence in a relatively small livestock dense area. It confirms previous evidence that the Q fever epidemic was primarily the result of airborne C. burnetii transmission from Q fever affected goat farms. [ABSTRACT FROM AUTHOR]

Background: Chickenpox is a common contagious disease that remains an important public health issue worldwide. Over 90% of unvaccinated individuals become infected, but infection occurs at different ages in different parts of the world. Many people have been infected by 20 to 30 years of age in China, and adults and pregnant women who become infected often develop severe infection. Furthermore, a mortality rate of 2-3 per 100,000 infected persons has been reported. In this study, we explore the temperature-dependent transition of patterns of reported chickenpox cases in two large subtropical climate cities, Wuhan and Hong Kong, China, to aid in the prediction of epidemics and preparation for the effects of climatic changes on epidemiology of chickenpox in China.Methods: We used a time series analysis comprising a spectral analysis based on the maximum entropy method in the frequency domain and the nonlinear least squares method in the time domain. Specifically, the following time series data were analyzed: data of reported chickenpox cases and meteorological data, including the mean temperature, relative humidity and total rainfall in Wuhan and Hong Kong from January 2008 to June 2015.Results: The time series data of chickenpox for both Wuhan and Hong Kong have two peaks per year, one in winter and another in spring, indicating a bimodal cycle. To investigate the source of the bimodal cycle of the chickenpox data, we defined the contribution ratio of the 1-year cycle, Q 1, and the 6-month cycle, Q 2, as the contribution of the amplitude of a 1-year cycle and a 6-month cycle, respectively, to the entire amplitude of the time-series data. The Q 1 values of Wuhan and Hong Kong were positively correlated with the annual mean temperature and rainfall of each city. Conversely, the Q 2 values of Wuhan and Hong Kong were negatively correlated with the annual mean temperature and rainfall of Wuhan and Hong Kong.Conclusion: Our results showed that the mean temperature and rainfall have a significant influence on the incidence of chickenpox, and might be important predictors of chickenpox incidence in Wuhan and Hong Kong. [ABSTRACT FROM AUTHOR]

Background: One major consequence of economic development in South-East Asia has been a rapid expansion of rubber plantations, in which outbreaks of dengue and malaria have occurred. Here we explored the difference in risk of exposure to potential dengue, Japanese encephalitis (JE), and malaria vectors between rubber workers and those engaged in traditional forest activities in northern Laos PDR. Methodology/Principal findings: Adult mosquitoes were collected for nine months in secondary forests, mature and immature rubber plantations, and villages. Human behavior data were collected using rapid participatory rural appraisals and surveys. Exposure risk was assessed by combining vector and human behavior and calculating the basic reproduction number (R0) in different typologies. Compared to those that stayed in the village, the risk of dengue vector exposure was higher for those that visited the secondary forests during the day (odds ratio (OR) 36.0), for those living and working in rubber plantations (OR 16.2) and for those that tapped rubber (OR 3.2). Exposure to JE vectors was also higher in the forest (OR 1.4) and, similar when working (OR 1.0) and living in the plantations (OR 0.8). Exposure to malaria vectors was greater in the forest (OR 1.3), similar when working in the plantations (OR 0.9) and lower when living in the plantations (OR 0.6). R0 for dengue was >2.8 for all habitats surveyed, except villages where R0≤0.06. The main malaria vector in all habitats was Anopheles maculatus s.l. in the rainy season and An. minimus s.l. in the dry season. Conclusions/Significance: The highest risk of exposure to vector mosquitoes occurred when people visit natural forests. However, since rubber workers spend long periods in the rubber plantations, their risk of exposure is increased greatly compared to those who temporarily enter natural forests or remain in the village. This study highlights the necessity of broadening mosquito control to include rubber plantations. [ABSTRACT FROM AUTHOR]

Background: In a new influenza pandemic, travel data such as arrival times of cases seeded by the originating country can be regarded as a combination of the epidemic size and the mobility networks of infections connecting the originating country with other regions. It can be a complete and timely source for estimating the basic reproduction number (R 0 ), a key indicator of disease transmissibility.Method: In this study, we developed a likelihood-based method using arrival times of infected cases in different countries to estimate R 0 for influenza pandemics. A simulation was conducted to assess the performance of the proposed method. We further applied the method to the outbreak of the influenza pandemic A/H1N1 in Mexico.Results: In the numerical application, the estimated R 0 was equal to 1.69 with a 95% confidence interval (1.65, 1.73). For the simulation results, the estimations were robust to the decline of travel rate and other parameter assumptions. Nevertheless, the estimates were moderately sensitive to the assumption of infectious duration. Generally, the findings were in line with other relevant studies.Conclusions: Our approach as well as the estimate is potential to assist officials in planning control and prevention measures. Improved coordination to streamline or even centralize surveillance of imported cases among countries will thus be beneficial to public health. [ABSTRACT FROM AUTHOR]

Background: Brucellosis, one of the world's most important zoonosis, has been re-emerging in China. Shanxi Province, located in northern China, where husbandry development has been accelerated in recent years, has a rather high incidence of human brucellosis but drew little attention from the researchers. This study aimed to describe the changing epidemiology of human brucellosis in Shanxi Province from 2005 to 2014 and explore high-risk towns and space-time clusters for elucidating the necessity of decentralizing disease control resource to township level in epidemic regions, particularly in hotspot areas. Methods: We extracted data from the Chinese National Notifiable Infectious Disease Reporting System to describe the incidence and spatiotemporal distribution of human brucellosis in Shanxi Province. Geographic information system was used to identify townships at high risk for the disease. Space-Time Scan Statistic was applied to detect the space-time clusters of human brucellosis during the past decade. Results: From 2005 to 2014, a total of 50,002 cases of human brucellosis were recorded in Shanxi, with a male-tofemale ratio of 3.9:1. The reported incidence rate increased dramatically from 7.0/100,000 in 2005 to 23.5/100,000 in 2014, with an average annual increase of 14.5%. There were still 33.8% cases delaying diagnosis in 2014. The proportion of the affected towns increased from 31.5% in 2005 to 82.5% in 2014. High-risk towns spread from the north to the center and then south of Shanxi Province, which were basins and adjacent highlands suitable for livestock cultivation. During the past decade, there were 55 space-time clusters of human brucellosis detected in high risk towns; the clusters could happen in any season. Some clusters' location maintained stable over time. Conclusions: During the last decade, Shanxi province's human brucellosis epidemic had been aggravated and high-risk areas concentrated in some towns located in basins and adjacent highlands. Space-time clusters existed and some located steadily over time. Quite a few cases still missed timely diagnosis. Greater resources should be allocated and decentralized to mitigate the momentum of rise and improve the accessibility of prompt diagnosis treatment in the high-risk townships. [ABSTRACT FROM AUTHOR]

Respiratory infectious disease epidemics and pandemics are recurring events that levy a high cost on individuals and society. The health-protective behavioral response of the public plays an important role in limiting respiratory infectious disease spread. Health-protective behaviors take several forms. Behaviors can be categorized as pharmaceutical (e.g., vaccination uptake, antiviral use) or non-pharmaceutical (e.g., hand washing, face mask use, avoidance of public transport). Due to the limitations of pharmaceutical interventions during respiratory epidemics and pandemics, public health campaigns aimed at limiting disease spread often emphasize both non-pharmaceutical and pharmaceutical behavioral interventions. Understanding the determinants of the public’s behavioral response is crucial for devising public health campaigns, providing information to parametrize mathematical models, and ultimately limiting disease spread. While other reviews have qualitatively analyzed the body of work on demographic determinants of health-protective behavior, this meta-analysis quantitatively combines the results from 85 publications to determine the global relationship between gender and health-protective behavioral response. The results show that women in the general population are about 50% more likely than men to adopt/practice non-pharmaceutical behaviors. Conversely, men in the general population are marginally (about 12%) more likely than women to adopt/practice pharmaceutical behaviors. It is possible that factors other than pharmaceutical/non-pharmaceutical status not included in this analysis act as moderators of this relationship. These results suggest an inherent difference in how men and women respond to epidemic and pandemic respiratory infectious diseases. This information can be used to target specific groups when developing non-pharmaceutical public health campaigns and to parameterize epidemic models incorporating demographic information. [ABSTRACT FROM AUTHOR]

The human immunodeficiency virus (HIV) epidemic in South Africa rapidly developed into a major pandemic. Here we analyse the development of the epidemic in a rural area of the country. The data used were collected between 1992 and 2013 in a longitudinal population survey, the Agincourt Health and Demographic Surveillance Study, in the northeast of the country. Throughout the period of study mortality rates were similar in all villages, suggesting that there were multiple index cases evenly spread geographically. These were likely to have been returning migrant workers. For those aged below 39 years the HIV mortality rate was higher for women, above this age it was higher for men. This indicates the protective effect of greater access to HIV testing and treatment among older women. The recent convergence of mortality rates for Mozambicans and South Africans indicates that the former refugee population are being assimilated into the host community. More than 60% of the deaths occurring in this community between 1992 and 2013 could be attributed directly or indirectly to HIV. Recently there has been an increasing level of non-HIV mortality which has important implications for local healthcare provision. This study demonstrates how evidence from longitudinal analyses can support healthcare planning. [ABSTRACT FROM AUTHOR]

: Dengue is a public health concern across the globe, and an escalating problem in the Americas. As part of a wider programme (covering Latin America and South East Asia) to characterize the epidemiology of dengue in dengue endemic areas, we undertook a systematic literature review to assess epidemiological trends (incidence, timing and duration of outbreaks/epidemics, age and sex distribution, serotype distribution, seroprevalence and disease severity) for dengue across the French Territories of the Americas (FTA), in French Guiana, Guadeloupe, Martinique, Saint Martin and Saint Barthélemy between 2000 and 2012 (CRD42012002341: ). Of 413 relevant data sources identified, 45 were eligible for inclusion. A large proportion of the available data were from national surveillance reports, and 12 publications were from peer-reviewed journals. During the review period, 3–5 epidemics were identified in each of the island territories and French Guiana, and epidemics were often associated with a shift in the predominant circulating dengue virus serotype. Substantial gaps in epidemiological knowledge were identified. In particular, information regarding dengue virus genotype distribution, seroprevalence and age distribution of dengue were lacking. Additionally, much of the available data were from epidemic years; data from inter-epidemic periods were sparse. Nevertheless, the available epidemiological data showed that dengue is endemic across the FTA and suggest an evolution towards hyperendemicity, highlighting the need to continue the efforts with the existing surveillance programmes to assist in planning an effective vaccination programme once a dengue vaccine is deployed. Protocol registration: PROSPERO [ABSTRACT FROM AUTHOR]

Introduction: Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings: We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance: These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. [ABSTRACT FROM AUTHOR]