Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base.

Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.

Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread.

Data from digital disease surveillance tools such as ProMED and HealthMap can complement the field surveillance during ongoing outbreaks. Our aim was to investigate the use of data collected through ProMED and HealthMap in real-time outbreak analysis. We developed a flexible statistical model to quantify spatial heterogeneity in the risk of spread of an outbreak and to forecast short term incidence trends. The model was applied retrospectively to data collected by ProMED and HealthMap during the 2013-2016 West African Ebola epidemic and for comparison, to WHO data. Using ProMED and HealthMap data, the model was able to robustly quantify the risk of disease spread 1-4 weeks in advance and for countries at risk of case importations, quantify where this risk comes from. Our study highlights that ProMED and HealthMap data could be used in real-time to quantify the spatial heterogeneity in risk of spread of an outbreak.

Underrepresentation of Phenotypic Variability of 16p13.11 Microduplication Syndrome Assessed With an Online Self-Phenotyping Tool (Phenotypr): Cohort Study.

BACKGROUND: 16p13.11 microduplication syndrome has a variable presentation and is characterized primarily by neurodevelopmental and physical phenotypes resulting from copy number variation at chromosome 16p13.11. Given its variability, there may be features that have not yet been reported. The goal of this study was to use a patient "self-phenotyping" survey to collect data directly from patients to further characterize the phenotypes of 16p13.11 microduplication syndrome. OBJECTIVE: This study aimed to (1) discover self-identified phenotypes in 16p13.11 microduplication syndrome that have been underrepresented in the scientific literature and (2) demonstrate that self-phenotyping tools are valuable sources of data for the medical and scientific communities. METHODS: As part of a large study to compare and evaluate patient self-phenotyping surveys, an online survey tool, Phenotypr, was developed for patients with rare disorders to self-report phenotypes. Participants with 16p13.11 microduplication syndrome were recruited through the Boston Children's Hospital 16p13.11 Registry. Either the caregiver, parent, or legal guardian of an affected child or the affected person (if aged 18 years or above) completed the survey. Results were securely transferred to a Research Electronic Data Capture database and aggregated for analysis. RESULTS: A total of 19 participants enrolled in the study. Notably, among the 19 participants, aggression and anxiety were mentioned by 3 (16%) and 4 (21%) participants, respectively, which is an increase over the numbers in previously published literature. Additionally, among the 19 participants, 3 (16%) had asthma and 2 (11%) had other immunological disorders, both of which have not been previously described in the syndrome. CONCLUSIONS: Several phenotypes might be underrepresented in the previous 16p13.11 microduplication literature, and new possible phenotypes have been identified. Whenever possible, patients should continue to be referenced as a source of complete phenotyping data on their condition. Self-phenotyping may lead to a better understanding of the prevalence of phenotypes in genetic disorders and may identify previously unreported phenotypes.

Mapping global variation in human mobility.

The geographic variation of human movement is largely unknown, mainly due to a lack of accurate and scalable data. Here we describe global human mobility patterns, aggregated from over 300 million smartphone users. The data cover nearly all countries and 65% of Earth's populated surface, including cross-border movements and international migration. This scale and coverage enable us to develop a globally comprehensive human movement typology. We quantify how human movement patterns vary across sociodemographic and environmental contexts and present international movement patterns across national borders. Fitting statistical models, we validate our data and find that human movement laws apply at 10 times shorter distances and movement declines 40% more rapidly in low-income settings. These results and data are made available to further understanding of the role of human movement in response to rapid demographic, economic and environmental changes.

Tuberculosis and foreign-born populations in the United States: A mixed methods pilot study of media reporting and political identification.

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.

Epidemiological data from the COVID-19 outbreak, real-time case information.

Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.

Real-time Epidemic Forecasting: Challenges and Opportunities.

Infectious disease outbreaks play an important role in global morbidity and mortality. Real-time epidemic forecasting provides an opportunity to predict geographic disease spread as well as case counts to better inform public health interventions when outbreaks occur. Challenges and recent advances in predictive modeling are discussed here. We identified data needs in the areas of epidemic surveillance, mobility, host and environmental susceptibility, pathogen transmissibility, population density, and healthcare capacity. Constraints in standardized case definitions and timely data sharing can limit the precision of predictive models. Resource-limited settings present particular challenges for accurate epidemic forecasting due to the lack of granular data available. Incorporating novel data streams into modeling efforts is an important consideration for the future as technology penetration continues to improve on a global level. Recent advances in machine-learning, increased collaboration between modelers, the use of stochastic semi-mechanistic models, real-time digital disease surveillance data, and open data sharing provide opportunities for refining forecasts for future epidemics. Epidemic forecasting using predictive modeling is an important tool for outbreak preparedness and response efforts. Despite the presence of some data gaps at present, opportunities and advancements in innovative data streams provide additional support for modeling future epidemics.

Estimation of Pneumonic Plague Transmission in Madagascar, August-November 2017.

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.

Evaluation of the EpiCore outbreak verification system.

OBJECTIVE: To describe a crowdsourced disease surveillance project (EpiCore) and evaluate its usefulness in obtaining information regarding potential disease outbreaks. METHODS: Volunteer human, animal and environmental health professionals from around the world were recruited to EpiCore and trained to provide early verification of health threat alerts in their geographical region via a secure, easy-to-use, online platform. Experts in the area of emerging infectious diseases sent requests for information on unverified health threats to these volunteers, who used local knowledge and expertise to respond to requests. Experts reviewed and summarized the responses and rapidly disseminated important information to the global health community through the existing event-based disease surveillance network, ProMED. FINDINGS: From March 2016 to September 2017, 2068 EpiCore volunteers from 142 countries were trained in methods of informal disease surveillance and use of the EpiCore online platform. These volunteers provided 790 individual responses to 759 requests for information addressing unverified health threats in 112 countries; 361 (45%) responses were considered to be useful. Most responses were received within hours of the requests. The responses led to 194 ProMED posts, of which 99 (51%) supported verification of an outbreak, were published on ProMED and sent to over 87 000 subscribers. CONCLUSION: There is widespread willingness among health professionals around the world to voluntarily assist efforts to verify and provide supporting information on unconfirmed health threats in their region. By linking this member network of health experts through a secure online reporting platform, EpiCore enables faster global outbreak detection and reporting.

Predicting social response to infectious disease outbreaks from internet-based news streams.

Infectious disease outbreaks often have consequences beyond human health, including concern among the population, economic instability, and sometimes violence. A warning system capable of anticipating social disruptions resulting from disease outbreaks is urgently needed to help decision makers prepare appropriately. We designed a system that operates in near real-time to identify and predict social response. Over 150,000 Internet-based news articles related to outbreaks of 16 diseases in 72 countries and territories were provided by HealthMap. These articles were automatically tagged with indicators of the disease activity and population reaction. An anomaly detection algorithm was implemented on the population reaction indicators to identify periods of unusually severe social response. Then a model was developed to predict the probability of these periods of unusually severe social response occurring in the coming week, 2 and 3 weeks. This model exhibited remarkably strong performance for diseases with substantial media coverage. For country-disease pairs with a median of 20 or more articles per year, the onset of social response in the next week was correctly predicted over 60% of the time, and 87% of weeks were correctly predicted. Performance was weaker for diseases with little media coverage, and, for these diseases, the main utility of our system is in identifying social response when it occurs, rather than predicting when it will happen in the future. Overall, the developed near real-time prediction approach is a promising step toward developing predictive models to inform responders of the likely social consequences of disease spread.

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.

Internet-based media coverage on dengue in Sri Lanka between 2007 and 2015.

BACKGROUND: Internet-based media coverage to explore the extent of awareness of a disease and perceived severity of an outbreak at a national level can be used for early outbreak detection. Dengue has emerged as a major public health problem in Sri Lanka since 2009. OBJECTIVE: To compare Internet references to dengue in Sri Lana with references to other diseases (malaria and influenza) in Sri Lanka and to compare Internet references to dengue in Sri Lanka with notified cases of dengue in Sri Lanka. DESIGN: We examined Internet-based news media articles on dengue queried from HealthMap for Sri Lanka, for the period January 2007 to November 2015. For comparative purposes, we compared hits on dengue with hits on influenza and malaria. RESULTS: There were 565 hits on dengue between 2007 and 2015, with a rapid rise in 2009 and followed by a rising trend ever since. These hits were highly correlated with the national epidemiological trend of dengue. The volume of digital media coverage of dengue was much higher than of influenza and malaria. CONCLUSIONS: Dengue in Sri Lanka is receiving increasing media attention. Our findings underpin previous claims that digital media reports reflect national epidemiological trends, both in annual trends and inter-annual seasonal variation, thus acting as proxy biosurveillance to provide early warning and situation awareness of emerging infectious diseases.

Mapping global environmental suitability for Zika virus.

Zika virus was discovered in Uganda in 1947 and is transmitted by Aedes mosquitoes, which also act as vectors for dengue and chikungunya viruses throughout much of the tropical world. In 2007, an outbreak in the Federated States of Micronesia sparked public health concern. In 2013, the virus began to spread across other parts of Oceania and in 2015, a large outbreak in Latin America began in Brazil. Possible associations with microcephaly and Guillain-Barré syndrome observed in this outbreak have raised concerns about continued global spread of Zika virus, prompting its declaration as a Public Health Emergency of International Concern by the World Health Organization. We conducted species distribution modelling to map environmental suitability for Zika. We show a large portion of tropical and sub-tropical regions globally have suitable environmental conditions with over 2.17 billion people inhabiting these areas.