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Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures., Competing Interests: AC has received payment from Pfizer for teaching of mathematical modelling of infectious diseases. All other authors have declared that no competing interests exist. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2023 Bhatia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Symptomatic testing programmes are crucial to the COVID-19 pandemic response. We sought to examine United Kingdom (UK) testing rates amongst individuals with test-qualifying symptoms, and factors associated with not testing. We analysed a cohort of untested symptomatic app users (N = 1,237), nested in the Zoe COVID Symptom Study (Zoe, N = 4,394,948); and symptomatic respondents who wanted, but did not have a test (N = 1,956), drawn from a University of Maryland survey administered to Facebook users (The Global COVID-19 Trends and Impact Survey [CTIS], N = 775,746). The proportion tested among individuals with incident test-qualifying symptoms rose from ~20% to ~75% from April to December 2020 in Zoe. Testing was lower with one vs more symptoms (72.9% vs 84.6% p<0.001), or short vs long symptom duration (69.9% vs 85.4% p<0.001). 40.4% of survey respondents did not identify all three test-qualifying symptoms. Symptom identification decreased for every decade older (OR = 0.908 [95% CI 0.883-0.933]). Amongst symptomatic UMD-CTIS respondents who wanted but did not have a test, not knowing where to go was the most cited factor (32.4%); this increased for each decade older (OR = 1.207 [1.129-1.292]) and for every 4-years fewer in education (OR = 0.685 [0.599-0.783]). Despite current UK messaging on COVID-19 testing, there is a knowledge gap about when and where to test, and this may be contributing to the ~25% testing gap. Risk factors, including older age and less education, highlight potential opportunities to tailor public health messages. The testing gap may be ever larger in countries that do not have extensive, free testing, as the UK does., Competing Interests: The authors have read the journal’s policy and the authors of this manuscript have the following competing interests. AM, JCP, CH, JW are employees of Zoe Global Ltd. TDS is a consultant to Zoe Global Ltd. DAD and ATC previously served as investigators on a clinical trial of diet and lifestyle using a separate smartphone application that was supported by Zoe Global. ATC reports grants from Massachusetts Consortium on Pathogen Readiness, during the conduct of the study; personal fees from Pfizer Inc., personal fees from Boehringer Ingelheim, personal fees from Bayer Pharma AG, outside the submitted work. DAD reports grants from National Institutes of Health, grants from MassCPR, grants from American Gastroenterological Association during the conduct of the study. All other authors have declared that no competing interests exist., (Copyright: © 2022 Graham et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Limitations in laboratory diagnostic capacity impact population surveillance of COVID-19. It is currently unknown whether participatory surveillance tools for COVID-19 correspond to government-reported case trends longitudinally and if it can be used as an adjunct to laboratory testing. The primary objective of this study was to determine whether self-reported COVID-19-like illness reflected laboratory-confirmed COVID-19 case trends in Ontario Canada., Methods: We retrospectively analyzed longitudinal self-reported symptoms data collected using an online tool-Outbreaks Near Me (ONM)-from April 20th, 2020, to March 7th, 2021 in Ontario, Canada. We measured the correlation between COVID-like illness among respondents and the weekly number of PCR-confirmed COVID-19 cases and provincial test positivity. We explored contemporaneous changes in other respiratory viruses, as well as the demographic characteristics of respondents to provide context for our findings., Results: Between 3,849-11,185 individuals responded to the symptom survey each week. No correlations were seen been self-reported CLI and either cases or test positivity. Strong positive correlations were seen between CLI and both cases and test positivity before a previously documented rise in rhinovirus/enterovirus in fall 2020. Compared to participatory surveillance respondents, a higher proportion of COVID-19 cases in Ontario consistently came from low-income, racialized and immigrant areas of the province- these groups were less well represented among survey respondents., Interpretation: Although digital surveillance systems are low-cost tools that have been useful to signal the onset of viral outbreaks, in this longitudinal comparison of self-reported COVID-like illness to Ontario COVID-19 case data we did not find this to be the case. Seasonal respiratory virus transmission and population coverage may explain this discrepancy., Competing Interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests: IIB has consulted to BlueDot, a social benefit corporation that tracks the spread of emerging infectious diseases. DNF reports personal consultant fees from Pfizer, AstraZeneca, and Seqirus, outside the submitted work. This does not alter our adherence to PLOS ONE policies on sharing data and materials

Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by one to three weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the twelve continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions., Competing Interests: The authors have declared that no competing interests exist.

Background: Syndromic surveillance through web or phone-based polling has been used to track the course of infectious diseases worldwide. Our study objective was to describe the characteristics, symptoms, and self-reported testing rates of respondents in three different COVID-19 symptom surveys in Canada., Methods: This was a cross-sectional study using three distinct Canada-wide web-based surveys, and phone polling in Ontario. All three sources contained self-reported information on COVID-19 symptoms and testing. In addition to describing respondent characteristics, we examined symptom frequency and the testing rate among the symptomatic, as well as rates of symptoms and testing across respondent groups., Results: We found that over March- April 2020, 1.6% of respondents experienced a symptom on the day of their survey, 15% of Ontario households had a symptom in the previous week, and 44% of Canada-wide respondents had a symptom in the previous month. Across the three surveys, SARS-CoV-2-testing was reported in 2-9% of symptomatic responses. Women, younger and middle-aged adults (versus older adults) and Indigenous/First nations/Inuit/Métis were more likely to report at least one symptom, and visible minorities were more likely to report the combination of fever with cough or shortness of breath., Interpretation: The low rate of testing among those reporting symptoms suggests significant opportunity to expand testing among community-dwelling residents of Canada. Syndromic surveillance data can supplement public health reports and provide much-needed context to gauge the adequacy of SARS-CoV-2 testing rates., Competing Interests: WJ Schatten is a paid employee of Forum Research. This commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials. II Bogoch has consulted to BlueDot, a social benefit corporation that tracks the spread of emerging infectious diseases. The remaining authors have no disclosures.

Background: Media reporting on communicable diseases has been demonstrated to affect the perception of the public. Communicable disease reporting related to foreign-born persons has not yet been evaluated., Objective: Examine how political leaning in the media affects reporting on tuberculosis (TB) in foreign-born persons., Methods: HealthMap, a digital surveillance platform that aggregates news sources on global infectious diseases, was used. Data was queried for media reports from the U.S. between 2011-2019, containing the term "TB" or "tuberculosis" and "foreign born", "refugee (s)," or "im (migrants)." Reports were reviewed to exclude duplicates and non-human cases. Each media source was rated using two independent media bias indicators to assess political leaning. Forty-six non-tuberculosis reports were randomly sampled and evaluated as a control. Two independent reviewers performed sentiment analysis on each report., Results: Of 891 TB-associated reports in the US, 46 referenced foreign-born individuals, and were included in this analysis. 60.9% (28) of reports were published in right-leaning news media and 6.5% (3) of reports in left-leaning media, while 39.1% (18) of the control group reports were published in left- leaning media and 10.9% (5) in right-leaning media (p < .001). 43% (20) of all study reports were posted in 2016. Sentiment analysis revealed that right-leaning reports often portrayed foreign-born persons negatively., Conclusion: Preliminary data from this pilot suggest that political leaning may affect reporting on TB in US foreign-born populations. Right-leaning news organizations produced the most reports on TB, and the majority of these reports portrayed foreign-born persons negatively. In addition, the control group comprised of non-TB, non-foreign born reports on communicable diseases featured a higher percentage of left-leaning news outlets, suggesting that reporting on TB in foreign-born individuals may be of greater interest to right-leaning outlets. Further investigation both in the U.S. and globally is needed., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Between August and November 2017, Madagascar reported nearly 2500 cases of plague; the vast majority of these cases were pneumonic, resulting in early exponential growth due to person-to-person transmission. Though plague is endemic in Madagascar, cases are usually bubonic and thus result in considerably smaller annual caseloads than those observed from August-November 2017., Methods: In this study, we consider the transmission dynamics of pneumonic plague in Madagascar during this time period, as well as the role of control strategies that were deployed to curb the outbreak and their effectiveness., Results: When using data from the beginning of the outbreak through late November 2017, our estimates for the basic reproduction number range from 1.6 to 3.6, with a mean of 2.4. We also find two distinctive periods of "control", which coincide with critical on-the-ground interventions, including contact tracing and delivery of antibiotics, among others., Discussion: Given these results, we conclude that existing interventions remain effective against plague in Madagascar, despite the atypical size and spread of this particular outbreak.

Competing Interests: The authors have declared that no competing interests exist.

Dengue is a mosquito-borne disease that threatens over half of the world's population. Despite being endemic to more than 100 countries, government-led efforts and tools for timely identification and tracking of new infections are still lacking in many affected areas. Multiple methodologies that leverage the use of Internet-based data sources have been proposed as a way to complement dengue surveillance efforts. Among these, dengue-related Google search trends have been shown to correlate with dengue activity. We extend a methodological framework, initially proposed and validated for flu surveillance, to produce near real-time estimates of dengue cases in five countries/states: Mexico, Brazil, Thailand, Singapore and Taiwan. Our result shows that our modeling framework can be used to improve the tracking of dengue activity in multiple locations around the world.

Introduction: Although many studies have investigated the probability of Ebola virus disease (EVD) outbreaks while other studies have simulated the size and speed of EVD outbreaks, few have investigated the environmental and population-level predictors of Ebola transmission once an outbreak is underway. Identifying strong predictors of transmission could help guide and target limited public health resources during an EVD outbreak. We examined several environmental and population-level demographic predictors of EVD risk from the West African epidemic., Methods: We obtained district-level estimates from the World Health Organization EVD case data, demographic indicators obtained from the Demographic and Health surveys, and satellite-derived temperature, rainfall, and land cover estimates. A Bayesian hierarchical Poisson model was used to estimate EVD risk and to evaluate the spatial variability explained by the selected predictors., Results:  We found that districts had greater risk of EVD with increasing proportion of households not possessing a radio (RR 2.79, 0.90-8.78; RR 4.23, 1.16-15.93), increasing rainfall (RR 2.18; 0.66-7.20; 5.34, 1.20-23.90), and urban land cover (RR 4.87, 1.56-15.40; RR 5.74, 1.68-19.67)., Discussion:  The finding of radio ownership and reduced EVD transmission risk suggests that the use of radio messaging for control and prevention purposes may have been crucial in reducing the EVD transmission risk in certain districts, although this association requires further study. Future research should examine the etiologic relationships between the identified risk factors and human-to-human transmission of EVD with a focus on factors related to population mobility and healthcare accessibility, which are critical features of epidemic propagation and control.

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.

Introduction: Data from social media have been shown to have utility in augmenting traditional approaches to public health surveillance. Quantifying the representativeness of these data is needed for making accurate public health inferences., Methods: We applied machine-learning methods to explore spatial and temporal dengue event reporting trends on Twitter relative to confirmed cases, and quantified associations with sociodemographic factors across three Brazilian states (São Paulo, Rio de Janeiro, and Minas Gerais) at the municipality level., Results: Education and income were positive predictors of dengue reporting on Twitter. In contrast, municipalities with a higher percentage of older adults, and males were less likely to report suspected dengue disease on Twitter. Overall, municipalities with dengue disease tweets had higher mean per capita income and lower proportion of individuals with no primary school education., Conclusions: These observations highlight the need to understand population representation across locations, age, and racial/ethnic backgrounds in studies using social media data for public health research. Additional data is needed to assess and compare data representativeness across regions in Brazil.

As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors-sampling the most connected, random, and friends of random individuals-in three complex social networks-a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals-early and accurate detection of epidemic emergence and peak, and general situational awareness-we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R0). For diseases with a low R0, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R0 is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information.

We present a machine learning-based methodology capable of providing real-time ("nowcast") and forecast estimates of influenza activity in the US by leveraging data from multiple data sources including: Google searches, Twitter microblogs, nearly real-time hospital visit records, and data from a participatory surveillance system. Our main contribution consists of combining multiple influenza-like illnesses (ILI) activity estimates, generated independently with each data source, into a single prediction of ILI utilizing machine learning ensemble approaches. Our methodology exploits the information in each data source and produces accurate weekly ILI predictions for up to four weeks ahead of the release of CDC's ILI reports. We evaluate the predictive ability of our ensemble approach during the 2013-2014 (retrospective) and 2014-2015 (live) flu seasons for each of the four weekly time horizons. Our ensemble approach demonstrates several advantages: (1) our ensemble method's predictions outperform every prediction using each data source independently, (2) our methodology can produce predictions one week ahead of GFT's real-time estimates with comparable accuracy, and (3) our two and three week forecast estimates have comparable accuracy to real-time predictions using an autoregressive model. Moreover, our results show that considerable insight is gained from incorporating disparate data streams, in the form of social media and crowd sourced data, into influenza predictions in all time horizons.

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.

Participatory systems for surveillance of acute respiratory infection give real-time information about infections circulating in the community, yet to-date are limited to self-reported syndromic information only and lacking methods of linking symptom reports to infection types. We developed the GoViral platform to evaluate whether a cohort of lay volunteers could, and would find it useful to, contribute self-reported symptoms online and to compare specimen types for self-collected diagnostic information of sufficient quality for respiratory infection surveillance. Volunteers were recruited, given a kit (collection materials and customized instructions), instructed to report their symptoms weekly, and when sick with cold or flu-like symptoms, requested to collect specimens (saliva and nasal swab). We compared specimen types for respiratory virus detection sensitivity (via polymerase-chain-reaction) and ease of collection. Participants were surveyed to determine receptivity to participating when sick, to receiving information on the type of pathogen causing their infection and types circulating near them. Between December 1 2013 and March 1 2014, 295 participants enrolled in the study and received a kit. Of those who reported symptoms, half (71) collected and sent specimens for analysis. Participants submitted kits on average 2.30 days (95 CI: 1.65 to 2.96) after symptoms began. We found good concordance between nasal and saliva specimens for multiple pathogens, with few discrepancies. Individuals report that saliva collection is easiest and report that receiving information about what pathogen they, and those near them, have is valued and can shape public health behaviors. Community-submitted specimens can be used for the detection of acute respiratory infection with individuals showing receptivity for participating and interest in a real-time picture of respiratory pathogens near them.

Background: While formal reporting, surveillance, and response structures remain essential to protecting public health, a new generation of freely accessible, online, and real-time informatics tools for disease tracking are expanding the ability to raise earlier public awareness of emerging disease threats. The rationale for this study is to test the hypothesis that the HealthMap informatics tools can complement epidemiological data captured by traditional surveillance monitoring systems for meningitis due to Neisseria meningitides (N. meningitides) by highlighting severe transmissible disease activity and outbreaks in the United States., Methods: Annual analyses of N. meningitides disease alerts captured by HealthMap were compared to epidemiological data captured by the Centers for Disease Control's Active Bacterial Core surveillance (ABCs) for N. meningitides. Morbidity and mortality case reports were measured annually from 2010 to 2013 (HealthMap) and 2005 to 2012 (ABCs)., Findings: HealthMap N. meningitides monitoring captured 80-90% of alerts as diagnosed N. meningitides, 5-20% of alerts as suspected cases, and 5-10% of alerts as related news articles. HealthMap disease alert activity for emerging disease threats related to N. meningitides were in agreement with patterns identified historically using traditional surveillance systems. HealthMap's strength lies in its ability to provide a cumulative "snapshot" of weak signals that allows for rapid dissemination of knowledge and earlier public awareness of potential outbreak status while formal testing and confirmation for specific serotypes is ongoing by public health authorities., Conclusions: The underreporting of disease cases in internet-based data streaming makes inadequate any comparison to epidemiological trends illustrated by the more comprehensive ABCs network published by the Centers for Disease Control. However, the expected delays in compiling confirmatory reports by traditional surveillance systems (at the time of writing, ABCs data for 2013 is listed as being provisional) emphasize the helpfulness of real-time internet-based data streaming to quickly fill gaps including the visualization of modes of disease transmission in outbreaks for better resource and action planning. HealthMap can also contribute as an internet-based monitoring system to provide real-time channel for patients to report intervention-related failures.

The West Africa Ebola virus epidemic now appears to be coming to an end. In the proposed model, we simulate changes in population behavior that help to explain the observed transmission dynamics. We introduce an EVD transmission model accompanied by a model of social mobilization. The model was fit to Lofa County, Liberia through October 2014, using weekly counts of new cases reported by the US CDC. In simulation studies, we analyze the dynamics of the disease transmission with and without population behavior change, given the availability of beds in Ebola treatment units (ETUs) estimated from observed data. Only the model scenario that included individuals' behavioral change achieved a good fit to the observed case counts. Although the capacity of the Lofa County ETUs greatly increased in mid-August, our simulations show that the expansion was insufficient to alone control the outbreak. Modeling the entire outbreak without considering behavior change fit the data poorly, and extrapolating from early data without taking behavioral changes into account led to a prediction of exponential outbreak growth, contrary to the observed decline.  Education and awareness-induced behavior change in the population was instrumental in curtailing the Ebola outbreak in Lofa County and is likely playing an important role in stopping the West Africa epidemic altogether.

In this commentary, we consider the relationship between early outbreak changes in the observed reproductive number of Ebola in West Africa and various media reported interventions and aggravating events. We find that media reports of interventions that provided education, minimized contact, or strengthened healthcare were typically followed by sustained transmission reductions in both Sierra Leone and Liberia. Meanwhile, media reports of aggravating events generally preceded temporary transmission increases in both countries. Given these preliminary findings, we conclude that media reported events could potentially be incorporated into future epidemic modeling efforts to improve mid-outbreak case projections.

Background: Global outbreak detection and reporting have generally improved for a variety of infectious diseases and geographic regions in recent decades. Nevertheless, lags in outbreak reporting remain a threat to the global human health and economy. In the time between first occurrence of a novel disease incident and public notification of an outbreak, infected individuals have a greater possibility of traveling and spreading the pathogen to other nations. Shortening outbreak reporting lags has the potential to improve global health by preventing local outbreaks from escalating into global epidemics., Methods: Reporting lags between the first record and the first public report of an event were calculated for 318 outbreaks occurring 1996-2009. The influence of freedom of the press, Internet usage, per capita health expenditure, and cell phone subscriptions, on the timeliness of outbreak reporting was evaluated., Results: Freer presses and increasing Internet usage correlate with reduced time between the first record of an outbreak and the public report. Increasing Internet usage reduced the expected reporting lag from more than one month in nations without Internet users to one day in those where 75 of 100 people use the Internet., Conclusion: Advances in technology and the emergence of more open and free governments are associated with to improved global infectious disease surveillance.

Digital disease detection tools are technologically sophisticated, but dependent on digital information, which for many areas suffering from high disease burdens is simply not an option. In areas where news is often reported in local media with no digital counterpart, integration of local news information with digital surveillance systems, such as HealthMap (Boston Children's Hospital), is critical. Little research has been published in regards to the specific contribution of local health-related articles to digital surveillance systems. In response, the USAID PREDICT project implemented a local media surveillance (LMS) pilot study in partner countries to monitor disease events reported in print media. This research assessed the potential of LMS to enhance digital surveillance reach in five low- and middle-income countries. Over 16 weeks, select surveillance system attributes of LMS, such as simplicity, flexibility, acceptability, timeliness, and stability were evaluated to identify strengths and weaknesses in the surveillance method. Findings revealed that LMS filled gaps in digital surveillance network coverage by contributing valuable localized information on disease events to the global HealthMap database. A total of 87 health events were reported through the LMS pilot in the 16-week monitoring period, including 71 unique reports not found by the HealthMap digital detection tool. Furthermore, HealthMap identified an additional 236 health events outside of LMS. It was also observed that belief in the importance of the project and proper source selection from the participants was crucial to the success of this method. The timely identification of disease outbreaks near points of emergence and the recognition of risk factors associated with disease occurrence continue to be important components of any comprehensive surveillance system for monitoring disease activity across populations. The LMS method, with its minimal resource commitment, could be one tool used to address the information gaps seen in global 'hot spot' regions.

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]

Realistic and modifiable infection models are required to study the pathogenesis of amphibian chytridiomycosis. Understanding the mechanism by which Batrachochytrium dendrobatidis (Bd) can infect and kill diverse amphibians is key to mitigating this pathogen and preventing further loss of biodiversity. In vitro studies of Bd typically rely on a tryptone based growth media, whereas the recent development of a kidney cell-line infection model has provided a more realistic alternative, without the need for live animals. Here we use expression of a fluorescent reporter to enhance the in vitro cell-line based growth assay, and show that transformed Bd cells are able to invade and grow in an amphibian kidney epithelial cell line (A6) as well as in a new system using a lung fibroblast cell line (DWJ). Both Bd and host cells were modified to express reporter fluorescent proteins, enabling immediate and continuous observation of the infection process without the need for destructive sampling for fixation and staining. Plasmid DNA conferring hygromycin resistance and TdTomato (RFP) expression was delivered to Bd zoospores via electroporation, and continuous antibiotic selection after recovery produced stable fluorescent Bd transformants. Host cells (A6 and DWJ) were transfected before each assay using lipofection to deliver plasmid DNA conferring green fluorescent protein (GFP) and containing an empty shRNA expression cassette. Bd RFP expression allowed easy localisation of fungal cells and identification of endobiotic growth was assisted by host GFP expression, by allowing visualization of the space in the host cell occupied by the invading fungal body. In addition to enabling enhanced live imaging, these methods will facilitate future genetic modification and characterisation of specific genes and their effect on Bd virulence. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic has raised concerns about the global capacity for timely outbreak reporting. However, gaps remain in our understanding of barriers and enablers to outbreak reporting, particularly at the local level. Field epidemiology training program (FETP) fellows often participate in the outbreak reporting process as part of both their training and the public health roles they assume after graduating; they therefore represent a potentially valuable source of information for better understanding these barriers and enablers. This study will investigate the barriers and enablers to outbreak reporting through a mixed methods approach that will encompass a review of the existing literature as well as surveying and interviewing FETP trainees and graduates from the Asia-Pacific region. Methods: This study will begin with a scoping review of the literature to identify existing evidence of barriers and enablers to outbreak reporting. Based on our findings from the scoping review, we will administer a survey to FETP trainees and graduates from the World Health Organization Western Pacific and South-East Asian Regions and conduct interviews with a subset of survey respondents to investigate the survey findings in more detail. We will summarise and compare the survey results according to various country-level economic and political indicators, and we will employ thematic analysis to evaluate the interview responses. Based on the findings from the scoping review, survey, and interviews, we will construct a model to comprehensively describe the various barriers and enablers to outbreak reporting. Conclusion: This study will contribute to our understanding of the determinants of outbreak reporting across several geographic, political, and economic contexts by eliciting the viewpoints and experiences of persons involved with outbreak reporting, particularly at the local level. This information will help improve the outbreak reporting process, allowing for more timely reporting and helping prevent future outbreaks from becoming pandemics. [ABSTRACT FROM AUTHOR]

Big data collected from the Internet possess great potential to reveal the ever-changing trends in society. In particular, accurate infectious disease tracking with Internet data has grown in popularity, providing invaluable information for public health decision makers and the general public. However, much of the complex connectivity among the Internet search data is not effectively addressed among existing disease tracking frameworks. To this end, we propose ARGO-C (Augmented Regression with Clustered GOogle data), an integrative, statistically principled approach that incorporates the clustering structure of Internet search data to enhance the accuracy and interpretability of disease tracking. Focusing on multi-resolution %ILI (influenza-like illness) tracking, we demonstrate the improved performance and robustness of ARGO-C over benchmark methods at various geographical resolutions. We also highlight the adaptability of ARGO-C to track various diseases in addition to influenza, and to track other social or economic trends. [ABSTRACT FROM AUTHOR]

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce. This study introduces an innovative methodological approach that allows us to extract valuable insights directly from patients' voices. By analyzing written texts shared on social media platforms, we aim to collect information on the psychological aspects of people who report experiencing Long Covid. In particular, we collect texts of patients they wrote BEFORE they were afflicted with Long Covid. We examined the differences in communication style, sentiment, language complexity, and psychological factors of natural language use among the profiles of 6.107 Reddit users, distinguishing between those who claim they have never contracted Covid -19, those who claim to have had it, and those who claim to have experienced Long Covid symptoms. Our findings reveal that people in the Long Covid group frequently discussed health-related topics before the pandemic, indicating a greater focus on health-related concerns. Furthermore, they exhibited a more limited network of connections, lower linguistic complexity, and a greater propensity to employ emotionally charged expressions than the other groups. Using social media data, we can provide a unique opportunity to explore potential risk factors associated with Long Covid, starting from the patient's perspective. [ABSTRACT FROM AUTHOR]

Health communicators are faced with the challenge that people can hesitate vaccines for different reasons. Our aim was to identify and describe the qualities of distinct COVID-19 and influenza vaccine-hesitancy subgroups to facilitate the development of tailored vaccine-hesitancy communication. In two studies, we used agglomerative hierarchical cluster analysis to identify COVID-19 (N = 554) and influenza (N = 539) vaccine-hesitancy subgroups in the general population based on nine vaccine hesitancy-related variables (intent to get vaccinated, perceived vaccine safety, perceived vaccine efficacy, perceived disease threat, perceived vaccination responsibility, perceived vaccination convenience, distrust in authorities, conspiracy mentality, and reliance on anecdotal testimonies). We identified and described six distinct COVID-19 vaccine-hesitancy subgroups (the Vaccination Positive, the Ambivalent, the Fearing Skeptic, the Unconvinced, the Constrained Skeptic, and the Vaccination Opponent), and three influenza vaccine-hesitancy subgroups (the Vaccination Positive, the Complacent, and the Vaccination Opponent), with different levels of hesitancy. We discuss the implications of the results for health communicators. Our results shed light on the (dis)similarities between people who hesitate COVID-19 and influenza vaccines and suggest that there is greater variety in hesitancy concerning COVID-19 vaccinations than influenza vaccinations. These findings can be used to design and test tailored vaccination messages. [ABSTRACT FROM AUTHOR]

Antibiotic exposure is associated with resistant bacterial colonization, but this relationship can be obscured in community settings owing to horizontal bacterial transmission and broad distributions. Locality-level exposure estimates considering inhabitants' length of stay, exposure history, and exposure conditions of areas nearby could clarify these relationships. We used prescription data filled during 2010–2015 for 23 antibiotic types for members of georeferenced households in a population-based infectious disease surveillance platform. For each antibiotic and locality, we generated exposure estimates, expressed in defined daily doses (DDD) per 1000 inhabitant days of observation (IDO). We also estimated relevant environmental parameters, such as the distance of each locality to water, sanitation, and other amenities. We used data on ampicillin, ceftazidime, and trimethoprim-and-sulfamethoxazole resistant Escherichia coli colonization from stool cultures of asymptomatic individuals in randomly selected households. We tested exposure-colonization associations using permutation analysis of variance and logistic generalized linear mixed-effect models. Overall, exposure was highest for trimethoprim-sulfamethoxazole (1.8 DDD per 1000 IDO), followed by amoxicillin (0.7 DDD per 1000 IDO). Of 1,386 unique household samples from 195 locations tested between September 2015 and January 2016, 90%, 85% and 4% were colonized with E. coli resistant to trimethoprim and sulfamethoxazole, ampicillin, and ceftazidime, respectively. Ceftazidime-resistant E. coli colonization was common in areas with increased trimethoprim-sulfamethoxazole, cloxacillin, and erythromycin exposure. No association with any of the physical environmental variables was observed. We did not detect relationships between distribution patterns of ampicillin or trimethoprim-and-sulfamethoxazole resistant E. coli colonization and the risk factors assessed. Appropriate temporal and spatial scaling of raw antibiotic exposure data to account for evolution and ecological contexts of antibiotic resistance could clarify exposure-colonization relationships in community settings and inform community stewardship program. [ABSTRACT FROM AUTHOR]

Filamentous cell growth is a vital property of fungal pathogens. The mechanisms of filamentation in the emerging multidrug-resistant fungal pathogen Candida auris are poorly understood. Here, we show that exposure of C. auris to glycerol triggers a rod-like filamentation-competent (RL-FC) phenotype, which forms elongated filamentous cells after a prolonged culture period. Whole-genome sequencing analysis reveals that all RL-FC isolates harbor a mutation in the C2H2 zinc finger transcription factor-encoding gene GFC1 (Gfc1 variants). Deletion of GFC1 leads to an RL-FC phenotype similar to that observed in Gfc1 variants. We further demonstrate that GFC1 mutation causes enhanced fatty acid β-oxidation metabolism and thereby promotes RL-FC/filamentous growth. This regulation is achieved through a Multiple Carbon source Utilizer (Mcu1)-dependent mechanism. Interestingly, both the evolved RL-FC isolates and the gfc1Δ mutant exhibit an enhanced ability to colonize the skin. Our results reveal that glycerol-mediated GFC1 mutations are beneficial during C. auris skin colonization and infection. Author summary: Candida auris is an emerging multidrug-resistant fungal pathogen that has rapidly spread worldwide in the past decade. This fungus can manifest filamentous morphology under certain conditions, such as passage through a mammalian host. Here, we demonstrate that exposure of C. auris to glycerol triggers a rod-like filamentation-competent (RL-FC) phenotype, resulting in elongated filamentous cells post a prolonged cultivation. Whole-genome sequencing analysis indicates that mutation of a C2H2 zinc finger transcription factor-encoding gene GFC1 is responsible for the generation of RL-FC/filamentous phenotype and enhances ability of skin colonization. We further substantiate that Multiple Carbon source Utilizer (Mcu1)-mediated fatty acid β-oxidation metabolism contributes to Gfc1-suppressed filamentous growth. Our findings link a single gene mutation to metabolic adaptation and acquisition of filamentous competence in C. auris, shedding light on how adaptive traits emerge in pathogens to enhance colonization and infection of the host. [ABSTRACT FROM AUTHOR]

Background: For the first time, an outbreak of chikungunya has been reported in the Americas. Locally acquired infections have been confirmed in fourteen Caribbean countries and dependent territories, Guyana and French Guiana, in which a large number of North American travelers vacation. Should some travelers become infected with chikungunya virus, they could potentially introduce it into the United States, where there are competent Aedes mosquito vectors, with the possibility of local transmission., Methods: We analyzed historical data on airline travelers departing areas of the Caribbean and South America, where locally acquired cases of chikungunya have been confirmed as of May 12th, 2014. The final destinations of travelers departing these areas between May and July 2012 were determined and overlaid on maps of the reported distribution of Aedes aeygpti and albopictus mosquitoes in the United States, to identify potential areas at risk of autochthonous transmission., Results: The United States alone accounted for 52.1% of the final destinations of all international travelers departing chikungunya indigenous areas of the Caribbean between May and July 2012. Cities in the United States with the highest volume of air travelers were New York City, Miami and San Juan (Puerto Rico). Miami and San Juan were high travel-volume cities where Aedes aeygpti or albopictus are reported and where climatic conditions could be suitable for autochthonous transmission., Conclusion: The rapidly evolving outbreak of chikungunya in the Caribbean poses a growing risk to countries and areas linked by air travel, including the United States where competent Aedes mosquitoes exist. The risk of chikungunya importation into the United States may be elevated following key travel periods in the spring, when large numbers of North American travelers typically vacation in the Caribbean.

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.

Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012-2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools.

Background: Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives., Method and Findings: Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems' performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p=0001) whereas other indicators were similar (C-DR: p=020; C-Se, p=013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables., Conclusion: Although differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p=00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance.

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: Hurricane Isaac made landfall in southeastern Louisiana in late August 2012, resulting in extensive storm surge and inland flooding. As the lead federal agency responsible for medical and public health response and recovery coordination, the Department of Health and Human Services (HHS) must have situational awareness to prepare for and address state and local requests for assistance following hurricanes. Both traditional and non-traditional data have been used to improve situational awareness in fields like disease surveillance and seismology. This study investigated whether non-traditional data (i.e., tweets and news reports) fill a void in traditional data reporting during hurricane response, as well as whether non-traditional data improve the timeliness for reporting identified HHS Essential Elements of Information (EEI)., Methods: HHS EEIs provided the information collection guidance, and when the information indicated there was a potential public health threat, an event was identified and categorized within the larger scope of overall Hurricane Issac situational awareness. Tweets, news reports, press releases, and federal situation reports during Hurricane Isaac response were analyzed for information about EEIs. Data that pertained to the same EEI were linked together and given a unique event identification number to enable more detailed analysis of source content. Reports of sixteen unique events were examined for types of data sources reporting on the event and timeliness of the reports., Results: Of these sixteen unique events identified, six were reported by only a single data source, four were reported by two data sources, four were reported by three data sources, and two were reported by four or more data sources. For five of the events where news tweets were one of multiple sources of information about an event, the tweet occurred prior to the news report, press release, local government\emergency management tweet, and federal situation report. In all circumstances where citizens were reporting along with other sources, the citizen tweet was the earliest notification of the event., Conclusion: Critical information is being shared by citizens, news organizations, and local government representatives. To have situational awareness for providing timely, life-saving public health and medical response following a hurricane, this study shows that non-traditional data sources should augment traditional data sources and can fill some of the gaps in traditional reporting. During a hurricane response where early event detection can save lives and reduce morbidity, tweets can provide a source of information for early warning. In times of limited budgets, investing technical and personnel resources to efficiently and effectively gather, curate, and analyze non-traditional data for improved situational awareness can yield a high return on investment.

Background: A novel coronavirus (MERS-CoV) causing severe, life-threatening respiratory disease has emerged in the Middle East at a time when two international mass gatherings in Saudi Arabia are imminent. While MERS-CoV has already spread to and within other countries, these mass gatherings could further amplify and/or accelerate its international dissemination, especially since the origins and geographic source of the virus remain poorly understood., Methods: We analyzed 2012 worldwide flight itinerary data and historic Hajj pilgrim data to predict population movements out of Saudi Arabia and the broader Middle East to help cities and countries assess their potential for MERS-CoV importation. We compared the magnitude of travel to countries with their World Bank economic status and per capita healthcare expenditures as surrogate markers of their capacity for timely detection of imported MERS-CoV and their ability to mount an effective public health response., Results: 16.8 million travelers flew on commercial flights out of Saudi Arabia, Jordan, Qatar, and the United Arab Emirates between June and November 2012, of which 51.6% were destined for India (16.3%), Egypt (10.4%), Pakistan (7.8%), the United Kingdom (4.3%), Kuwait (3.6%), Bangladesh (3.1%), Iran (3.1%) and Bahrain (2.9%). Among the 1.74 million foreign pilgrims who performed the Hajj last year, an estimated 65.1% originated from low and lower-middle income countries., Conclusion: MERS-CoV is an emerging pathogen with pandemic potential with its apparent epicenter in Saudi Arabia, where millions of pilgrims will imminently congregate for two international mass gatherings. Understanding global population movements out of the Middle East through the end of this year's Hajj could help direct anticipatory MERS-CoV surveillance and public health preparedness to mitigate its potential global health and economic impacts.

Immediately following the Boston Marathon attacks, individuals near the scene posted a deluge of data to social media sites. Previous work has shown that these data can be leveraged to provide rapid insight during natural disasters, disease outbreaks and ongoing conflicts that can assist in the public health and medical response. Here, we examine and discuss the social media messages posted immediately after and around the Boston Marathon bombings, and find that specific keywords appear frequently prior to official public safety and news media reports. Individuals immediately adjacent to the explosions posted messages within minutes via Twitter which identify the location and specifics of events, demonstrating a role for social media in the early recognition and characterization of emergency events. *Christopher Cassa and Rumi Chunara contributed equally to this work.

Several approaches have been proposed for near real-time detection and prediction of the spread of influenza. These include search query data for influenza-related terms, which has been explored as a tool for augmenting traditional surveillance methods. In this paper, we present a method that uses Internet search query data from Baidu to model and monitor influenza activity in China. The objectives of the study are to present a comprehensive technique for: (i) keyword selection, (ii) keyword filtering, (iii) index composition and (iv) modeling and detection of influenza activity in China. Sequential time-series for the selected composite keyword index is significantly correlated with Chinese influenza case data. In addition, one-month ahead prediction of influenza cases for the first eight months of 2012 has a mean absolute percent error less than 11%. To our knowledge, this is the first study on the use of search query data from Baidu in conjunction with this approach for estimation of influenza activity in China.

Background: Understanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data., Purpose: This article explores the relationship between online social environment via web-based social networks and population obesity prevalence., Methods: We performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook., Results: Higher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set., Conclusions: Activity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions.

Simon Hay and colleagues discuss the potential and challenges of producing continually updated infectious disease risk maps using diverse and large volume data sources such as social media.

The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7-13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.

A dearth of information obscures the true scale of the global illegal trade in wildlife. Herein, we introduce an automated web crawling surveillance system developed to monitor reports on illegally traded wildlife. A resource for enforcement officials as well as the general public, the freely available website, http://www.healthmap.org/wildlifetrade, provides a customizable visualization of worldwide reports on interceptions of illegally traded wildlife and wildlife products. From August 1, 2010 to July 31, 2011, publicly available English language illegal wildlife trade reports from official and unofficial sources were collected and categorized by location and species involved. During this interval, 858 illegal wildlife trade reports were collected from 89 countries. Countries with the highest number of reports included India (n = 146, 15.6%), the United States (n = 143, 15.3%), South Africa (n = 75, 8.0%), China (n = 41, 4.4%), and Vietnam (n = 37, 4.0%). Species reported as traded or poached included elephants (n = 107, 12.5%), rhinoceros (n = 103, 12.0%), tigers (n = 68, 7.9%), leopards (n = 54, 6.3%), and pangolins (n = 45, 5.2%). The use of unofficial data sources, such as online news sites and social networks, to collect information on international wildlife trade augments traditional approaches drawing on official reporting and presents a novel source of intelligence with which to monitor and collect news in support of enforcement against this threat to wildlife conservation worldwide.

Tiffany Bogich and colleagues find that breakdown or absence of public health infrastructure is most often the driver in pandemic outbreaks, whose prevention requires mainstream development funding rather than emergency funding.

Mobile, social, real-time: the ongoing revolution in the way people communicate has given rise to a new kind of epidemiology. Digital data sources, when harnessed appropriately, can provide local and timely information about disease and health dynamics in populations around the world. The rapid, unprecedented increase in the availability of relevant data from various digital sources creates considerable technical and computational challenges.

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.

Smokers can use the web to continue or quit their habit. Online vendors sell reduced or tax-free cigarettes lowering smoking costs, while health advocates use the web to promote cessation. We examined how smokers' tax avoidance and smoking cessation Internet search queries were motivated by the United States' (US) 2009 State Children's Health Insurance Program (SCHIP) federal cigarette excise tax increase and two other state specific tax increases. Google keyword searches among residents in a taxed geography (US or US state) were compared to an untaxed geography (Canada) for two years around each tax increase. Search data were normalized to a relative search volume (RSV) scale, where the highest search proportion was labeled 100 with lesser proportions scaled by how they relatively compared to the highest proportion. Changes in RSV were estimated by comparing means during and after the tax increase to means before the tax increase, across taxed and untaxed geographies. The SCHIP tax was associated with an 11.8% (95% confidence interval [95%CI], 5.7 to 17.9; p<.001) immediate increase in cessation searches; however, searches quickly abated and approximated differences from pre-tax levels in Canada during the months after the tax. Tax avoidance searches increased 27.9% (95%CI, 15.9 to 39.9; p<.001) and 5.3% (95%CI, 3.6 to 7.1; p<.001) during and in the months after the tax compared to Canada, respectively, suggesting avoidance is the more pronounced and durable response. Trends were similar for state-specific tax increases but suggest strong interactive processes across taxes. When the SCHIP tax followed Florida's tax, versus not, it promoted more cessation and avoidance searches. Efforts to combat tax avoidance and increase cessation may be enhanced by using interventions targeted and tailored to smokers' searches. Search query surveillance is a valuable real-time, free and public method, that may be generalized to other behavioral, biological, informational or psychological outcomes manifested online.

Background: There is little empirical evidence in support of a relationship between rates of influenza infection and level of material deprivation (i.e., lack of access to goods and services) and social deprivation (i.e. lack of social cohesion and support)., Method: Using validated population-level indices of material and social deprivation and medical billing claims for outpatient clinic and emergency department visits for influenza from 1996 to 2006, we assessed the relationship between neighbourhood rates of influenza and neighbourhood levels of deprivation using Bayesian ecological regression models. Then, by pooling data from neighbourhoods in the top decile (i.e., most deprived) and the bottom decile, we compared rates in the most deprived populations to the least deprived populations using age- and sex-standardized rate ratios., Results: Deprivation scores ranged from one to five with five representing the highest level of deprivation. We found a 21% reduction in rates for every 1 unit increase in social deprivation score (rate ratio [RR] 0.79, 95% Credible Interval [CrI] 0.66, 0.97). There was little evidence of a meaningful linear relationship with material deprivation (RR 1.06, 95% CrI 0.93, 1.24). However, relative to neighbourhoods with deprivation scores in the bottom decile, those in the top decile (i.e., most materially deprived) had substantially higher rates (RR 2.02, 95% Confidence Interval 1.99, 2.05)., Conclusion: Though it is hypothesized that social and material deprivation increase risk of acute respiratory infection, we found decreasing healthcare utilization rates for influenza with increasing social deprivation. This finding may be explained by the fewer social contacts and, thus, fewer influenza exposure opportunities of the socially deprived. Though there was no evidence of a linear relationship with material deprivation, when comparing the least to the most materially deprived populations, we observed higher rates in the most materially deprived populations.