Person: Brownstein, John
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Brownstein
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Brownstein, John
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Publication Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data(Public Library of Science, 2017) McGough, Sarah; Brownstein, John; Hawkins, Jared; Santillana, MauricioBackground: 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.Publication Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study(Elsevier Science ;, The Lancet Pub. Group, 2017) Kraemer, Moritz U G; Faria, Nuno R; Reiner, Robert C; Golding, Nick; Nikolay, Birgit; Stasse, Stephanie; Johansson, Michael A; Salje, Henrik; Faye, Ousmane; Wint, G R William; Niedrig, Matthias; Shearer, Freya M; Hill, Sarah C; Thompson, Robin N; Bisanzio, Donal; Taveira, Nuno; Nax, Heinrich H; Pradelski, Bary S R; Nsoesie, Elaine O; Murphy, Nicholas R; Bogoch, Isaac I; Khan, Kamran; Brownstein, John; Tatem, Andrew J; de Oliveira, Tulio; Smith, David L; Sall, Amadou A; Pybus, Oliver G; Hay, Simon I; Cauchemez, SimonSummary Background: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock. Methods: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region. Findings: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5–7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34–0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52–0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13–0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92–0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. Interpretation Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. Funding Wellcome Trust.Publication Policy implications of big data in the health sector(World Health Organization, 2018) Vayena, Effy; Dzenowagis, Joan; Brownstein, John; Sheikh, AzizPublication Evaluating the Implementation of a Twitter-Based Foodborne Illness Reporting Tool in the City of St. Louis Department of Health(MDPI, 2018) Harris, Jenine K.; Hinyard, Leslie; Beatty, Kate; Hawkins, Jared; Nsoesie, Elaine O.; Mansour, Raed; Brownstein, JohnFoodborne illness is a serious and preventable public health problem affecting 1 in 6 Americans with cost estimates over $50 billion annually. Local health departments license and inspect restaurants to ensure food safety and respond to reports of suspected foodborne illness. The City of St. Louis Department of Health adopted the HealthMap Foodborne Dashboard (Dashboard), a tool that monitors Twitter for tweets about food poisoning in a geographic area and allows the health department to respond. We evaluated the implementation by interviewing employees of the City of St. Louis Department of Health involved in food safety. We interviewed epidemiologists, environmental health specialists, health services specialists, food inspectors, and public information officers. Participants viewed engaging innovation participants and executing the innovation as challenges while they felt the Dashboard had relative advantage over existing reporting methods and was not complex once in place. This study is the first to examine practitioner perceptions of the implementation of a new technology in a local health department. Similar implementation projects should focus more on process by developing clear and comprehensive plans to educate and involve stakeholders prior to implementation.Publication Evaluation of the EpiCore outbreak verification system(World Health Organization, 2018) Lorthe, Taryn Silver; Pollack, Marjorie P; Lassmann, Britta; Brownstein, John; Cohn, Emily; Divi, Nomita; Herrera-Guibert, Dionisio Jose; Olsen, Jennifer; Smolinski, Mark S; Madoff, Lawrence CAbstract 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.Publication Relationship between community prevalence of obesity and associated behavioral factors and community rates of influenza‐related hospitalizations in the United States(Blackwell Publishing Ltd, 2012) Charland, Katia M.; Buckeridge, David L.; Hoen, Anne G.; Berry, Jay; Elixhauser, Anne; Melton, Forrest; Brownstein, JohnPlease cite this paper as: Charland et al.(2012) Relationship between community prevalence of obesity and associated behavioral factors and community rates of influenza‐related hospitalizations in the United States. Influenza and Other Respiratory Viruses DOI: 10.1111/irv.12019. Background Findings from studies examining the association between obesity and acute respiratory infection are inconsistent. Few studies have assessed the relationship between obesity‐related behavioral factors, such as diet and exercise, and risk of acute respiratory infection. Objective To determine whether community prevalence of obesity, low fruit/vegetable consumption, and physical inactivity are associated with influenza‐related hospitalization rates. Methods Using data from 274 US counties, from 2002 to 2008, we regressed county influenza‐related hospitalization rates on county prevalence of obesity (BMI ≥ 30), low fruit/vegetable consumption (<5 servings/day), and physical inactivity (<30 minutes/month recreational exercise), while adjusting for community‐level confounders such as insurance coverage and the number of primary care physicians per 100 000 population. Results A 5% increase in obesity prevalence was associated with a 12% increase in influenza‐related hospitalization rates [adjusted rate ratio (ARR) 1·12, 95% confidence interval (CI) 1·07, 1·17]. Similarly, a 5% increase in the prevalence of low fruit/vegetable consumption and physical inactivity was associated with an increase of 12% (ARR 1·12, 95% CI 1·08, 1·17) and 11% (ARR 1·11, 95% CI 1·07, 1·16), respectively. When all three variables were included in the same model, a 5% increase in prevalence of obesity, low fruit/vegetable consumption, and physical inactivity was associated with 6%, 8%, and 7% increases in influenza‐related hospitalization rates, respectively. Conclusions Communities with a greater prevalence of obesity were more likely to have high influenza‐related hospitalization rates. Similarly, less physically active populations, with lower fruit/vegetable consumption, tended to have higher influenza‐related hospitalization rates, even after accounting for obesity.Publication Using Twitter to Identify and Respond to Food Poisoning: The Food Safety STL Project(Wolters Kluwer Health, Inc., 2017) Harris, Jenine K.; Hawkins, Jared; Nguyen, Leila; Nsoesie, Elaine O.; Tuli, Gaurav; Mansour, Raed; Brownstein, JohnContext: Foodborne illness affects 1 in 4 US residents each year. Few of those sickened seek medical care or report the illness to public health authorities, complicating prevention efforts. Citizens who report illness identify food establishments with more serious and critical violations than found by regular inspections. New media sources, including online restaurant reviews and social media postings, have the potential to improve reporting. Objective: We implemented a Web-based Dashboard (HealthMap Foodborne Dashboard) to identify and respond to tweets about food poisoning from St Louis City residents. Design and Setting: This report examines the performance of the Dashboard in its first 7 months after implementation in the City of St Louis Department of Health. Main Outcome Measures: We examined the number of relevant tweets captured and replied to, the number of foodborne illness reports received as a result of the new process, and the results of restaurant inspections following each report. Results: In its first 7 months (October 2015-May 2016), the Dashboard captured 193 relevant tweets. Our replies to relevant tweets resulted in more filed reports than several previously existing foodborne illness reporting mechanisms in St Louis during the same time frame. The proportion of restaurants with food safety violations was not statistically different (P = .60) in restaurants inspected after reports from the Dashboard compared with those inspected following reports through other mechanisms. Conclusion: The Dashboard differs from other citizen engagement mechanisms in its use of current data, allowing direct interaction with constituents on issues when relevant to the constituent to provide time-sensitive education and mobilizing information. In doing so, the Dashboard technology has potential for improving foodborne illness reporting and can be implemented in other areas to improve response to public health issues such as suicidality, spread of Zika virus infection, and hospital quality.Publication Advances in using Internet searches to track dengue(Public Library of Science, 2017) Yang, Shihao; Kou, Samuel C.; Lu, Fred; Brownstein, John; Brooke, Nicholas; Santillana, MauricioDengue 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.Publication Digital Drug Safety Surveillance: Monitoring Pharmaceutical Products in Twitter(Springer International Publishing, 2014) Freifeld, Clark C.; Brownstein, John; Menone, Christopher M.; Bao, Wenjie; Filice, Ross; Kass-Hout, Taha; Dasgupta, NabarunBackground: Traditional adverse event (AE) reporting systems have been slow in adapting to online AE reporting from patients, relying instead on gatekeepers, such as clinicians and drug safety groups, to verify each potential event. In the meantime, increasing numbers of patients have turned to social media to share their experiences with drugs, medical devices, and vaccines. Objective: The aim of the study was to evaluate the level of concordance between Twitter posts mentioning AE-like reactions and spontaneous reports received by a regulatory agency. Methods: We collected public English-language Twitter posts mentioning 23 medical products from 1 November 2012 through 31 May 2013. Data were filtered using a semi-automated process to identify posts with resemblance to AEs (Proto-AEs). A dictionary was developed to translate Internet vernacular to a standardized regulatory ontology for analysis (MedDRA®). Aggregated frequency of identified product-event pairs was then compared with data from the public FDA Adverse Event Reporting System (FAERS) by System Organ Class (SOC). Results: Of the 6.9 million Twitter posts collected, 4,401 Proto-AEs were identified out of 60,000 examined. Automated, dictionary-based symptom classification had 72 % recall and 86 % precision. Similar overall distribution profiles were observed, with Spearman rank correlation rho of 0.75 (p < 0.0001) between Proto-AEs reported in Twitter and FAERS by SOC. Conclusion: Patients reporting AEs on Twitter showed a range of sophistication when describing their experience. Despite the public availability of these data, their appropriate role in pharmacovigilance has not been established. Additional work is needed to improve data acquisition and automation.Publication Accuracy of epidemiological inferences based on publicly available information: retrospective comparative analysis of line lists of human cases infected with influenza A(H7N9) in China(BioMed Central, 2014) Lau, Eric HY; Zheng, Jiandong; Tsang, Tim K; Liao, Qiaohong; Lewis, Bryan; Brownstein, John; Sanders, Sharon; Wong, Jessica Y; Mekaru, Sumiko R; Rivers, Caitlin; Wu, Peng; Jiang, Hui; Li, Yu; Yu, Jianxing; Zhang, Qian; Chang, Zhaorui; Liu, Fengfeng; Peng, Zhibin; Leung, Gabriel M; Feng, Luzhao; Cowling, Benjamin J; Yu, HongjieBackground: Appropriate public health responses to infectious disease threats should be based on best-available evidence, which requires timely reliable data for appropriate analysis. During the early stages of epidemics, analysis of ‘line lists’ with detailed information on laboratory-confirmed cases can provide important insights into the epidemiology of a specific disease. The objective of the present study was to investigate the extent to which reliable epidemiologic inferences could be made from publicly-available epidemiologic data of human infection with influenza A(H7N9) virus. Methods: We collated and compared six different line lists of laboratory-confirmed human cases of influenza A(H7N9) virus infection in the 2013 outbreak in China, including the official line list constructed by the Chinese Center for Disease Control and Prevention plus five other line lists by HealthMap, Virginia Tech, Bloomberg News, the University of Hong Kong and FluTrackers, based on publicly-available information. We characterized clinical severity and transmissibility of the outbreak, using line lists available at specific dates to estimate epidemiologic parameters, to replicate real-time inferences on the hospitalization fatality risk, and the impact of live poultry market closure. Results: Demographic information was mostly complete (less than 10% missing for all variables) in different line lists, but there were more missing data on dates of hospitalization, discharge and health status (more than 10% missing for each variable). The estimated onset to hospitalization distributions were similar (median ranged from 4.6 to 5.6 days) for all line lists. Hospital fatality risk was consistently around 20% in the early phase of the epidemic for all line lists and approached the final estimate of 35% afterwards for the official line list only. Most of the line lists estimated >90% reduction in incidence rates after live poultry market closures in Shanghai, Nanjing and Hangzhou. Conclusions: We demonstrated that analysis of publicly-available data on H7N9 permitted reliable assessment of transmissibility and geographical dispersion, while assessment of clinical severity was less straightforward. Our results highlight the potential value in constructing a minimum dataset with standardized format and definition, and regular updates of patient status. Such an approach could be particularly useful for diseases that spread across multiple countries.