Person: Chunara, Rumi
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Chunara
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Rumi
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Chunara, Rumi
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Publication Flu Near You: An Online Self-reported Influenza Surveillance System in the USA(University of Illinois at Chicago Library, 2013) Chunara, Rumi; Aman, Susan; Smolinski, Mark; Brownstein, JohnObjective: To develop a participatory system for monitoring the activity of influenza-like-illness among the United States general population. Introduction: The emergence of new influenza strains including H1N1, H5N1, H3N2v as well as other respiratory pathogens such as SARS, along with generally weak information about household and community transmission of influenza, enforce the need for augmented influenza surveillance. At the same time, Internet penetration and access has grown, with 82% of American adults using the Internet [1], enabling transfer and communication of information that can be collected and aggregated in near real-time. Surveillance targeted towards influenza in other countries, and towards malaria in India, has previously been executed with good user engagement [2,3]. In this study, we created an online participatory influenza surveillance tool in the United States, called Flu Near You. Methods: Volunteer users were primarily solicited via collaboration with the American Public Health Association and their members’ networks starting Oct. 24, 2011. Upon registration, each user is sent a weekly email, taking them to the Flu Near You website. On the website they fill in a short survey asking if they had any of 10 symptoms: fever, cough, sore throat, shortness of breath, chills/night sweats, fatigue, nausea or vomiting, diarrhea, body aches and headache, in the last week. Users can also enroll their household members and enter information in for them weekly. A map of influenza activity is made available to users, and anyone accessing the website [Figure 1]. On the map, the number of individuals reporting with no symptoms, some symptoms, or Influenza-like illness are visualized, aggregated to the zip code level. Users can also compare the contributed data with other surveillance systems: the Centers for Disease Control and Prevention, and Google Flu Trends for the same time period [Figure 1]. We also obtained user feedback through a survey in early July 2012. Results: As of August 21, 2012, there are over 9300 Flu Near You users, from all 50 states. 94.0% of users are between 20 and 70 years, although 37.2% of household members are < 20 years old. Overall 62.0% of members (users and household) were female. We found that 46.4% of users answered 3 or more surveys. Qualitatively from survey responses, we learned that simple feedback and an emphasis on public health education are important in this type of system. Conclusions: We found that it is possible to engage users in a symptom self-reporting system and augment information about influenza for the nation. Increased uptake would increase the value of the system for the public and public health professionals. Flu Near You is expanding in its second season, working to increase user participation. Other connected projects are also examining the expansion into other diseases with high prevalence and in need of augmented surveillance. With a larger user base and through a longer period of execution, systems like Flu Near You will help to improve our understanding of influenza epidemiology as well as guide implementation of relevant and timely public health interventions, for example in estimating vaccination rates and efficacy among different demographic groups. Information reported by individuals can augment traditional public health surveillance methods for more timely detection of disease outbreaks, monitoring disease activity and increasing the public’s engagement in their own and population’s health.Publication Monitoring Influenza Epidemics in China with Search Query from Baidu(Public Library of Science, 2013) Yuan, Qingyu; Nsoesie, Elaine O.; Lv, Benfu; Peng, Geng; Chunara, Rumi; Brownstein, JohnSeveral 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.Publication Public health for the people: participatory infectious disease surveillance in the digital age(BioMed Central, 2014) Wójcik, Oktawia P; Brownstein, John; Chunara, Rumi; Johansson, Michael AThe 21st century has seen the rise of Internet-based participatory surveillance systems for infectious diseases. These systems capture voluntarily submitted symptom data from the general public and can aggregate and communicate that data in near real-time. We reviewed participatory surveillance systems currently running in 13 different countries. These systems have a growing evidence base showing a high degree of accuracy and increased sensitivity and timeliness relative to traditional healthcare-based systems. They have also proven useful for assessing risk factors, vaccine effectiveness, and patterns of healthcare utilization while being less expensive, more flexible, and more scalable than traditional systems. Nonetheless, they present important challenges including biases associated with the population that chooses to participate, difficulty in adjusting for confounders, and limited specificity because of reliance only on syndromic definitions of disease limits. Overall, participatory disease surveillance data provides unique disease information that is not available through traditional surveillance sources.Publication Using search queries for malaria surveillance, Thailand(BioMed Central, 2013) Ocampo, Alex; Chunara, Rumi; Brownstein, JohnBackground: Internet search query trends have been shown to correlate with incidence trends for select infectious diseases and countries. Herein, the first use of Google search queries for malaria surveillance is investigated. The research focuses on Thailand where real-time malaria surveillance is crucial as malaria is re-emerging and developing resistance to pharmaceuticals in the region. Methods: Official Thai malaria case data was acquired from the World Health Organization (WHO) from 2005 to 2009. Using Google correlate, an openly available online tool, and by surveying Thai physicians, search queries potentially related to malaria prevalence were identified. Four linear regression models were built from different sub-sets of malaria-related queries to be used in future predictions. The models’ accuracies were evaluated by their ability to predict the malaria outbreak in 2009, their correlation with the entire available malaria case data, and by Akaike information criterion (AIC). Results: Each model captured the bulk of the variability in officially reported malaria incidence. Correlation in the validation set ranged from 0.75 to 0.92 and AIC values ranged from 808 to 586 for the models. While models using malaria-related and general health terms were successful, one model using only microscopy-related terms obtained equally high correlations to malaria case data trends. The model built strictly of queries provided by Thai physicians was the only one that consistently captured the well-documented second seasonal malaria peak in Thailand. Conclusions: Models built from Google search queries were able to adequately estimate malaria activity trends in Thailand, from 2005–2010, according to official malaria case counts reported by WHO. While presenting their own limitations, these search queries may be valid real-time indicators of malaria incidence in the population, as correlations were on par with those of related studies for other infectious diseases. Additionally, this methodology provides a cost-effective description of malaria prevalence that can act as a complement to traditional public health surveillance. This and future studies will continue to identify ways to leverage web-based data to improve public health.Publication Assessing the Online Social Environment for Surveillance of Obesity Prevalence(Public Library of Science, 2013) Chunara, Rumi; Bouton, Lindsay Legault; Ayers, John W.; Brownstein, JohnBackground: 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.Publication Online Reporting for Malaria Surveillance Using Micro-Monetary Incentives, in Urban India 2010-2011(BioMed Central, 2012) Chunara, Rumi; Chhaya, Vina; Bane, Sunetra; Mekaru, Sumiko R; Chan, Emily H; Freifeld, Clark C; Brownstein, JohnBackground: The objective of this study was to investigate the use of novel surveillance tools in a malaria endemic region where prevalence information is limited. Specifically, online reporting for participatory epidemiology was used to gather information about malaria spread directly from the public. Individuals in India were incentivized to self-report their recent experience with malaria by micro-monetary payments. Methods: Self-reports about malaria diagnosis status and related information were solicited online via Amazon's Mechanical Turk. Responders were paid $0.02 to answer survey questions regarding their recent experience with malaria. Timing of the peak volume of weekly self-reported malaria diagnosis in 2010 was compared to other available metrics such as the volume over time of and information about the epidemic from media sources. Distribution of Plasmodium species reports were compared with values from the literature. The study was conducted in summer 2010 during a malaria outbreak in Mumbai and expanded to other cities during summer 2011, and prevalence from self-reports in 2010 and 2011 was contrasted. Results: Distribution of Plasmodium species diagnosis through self-report in 2010 revealed 59% for Plasmodium vivax, which is comparable to literature reports of the burden of P. vivax in India (between 50 and 69%). Self-reported Plasmodium falciparum diagnosis was 19% and during the 2010 outbreak and the estimated burden was between 10 and 15%. Prevalence between 2010 and 2011 via self-reports decreased significantly from 36.9% to 19.54% in Mumbai (p = 0.001), and official reports also confirmed a prevalence decrease in 2011. Conclusions: With careful study design, micro-monetary incentives and online reporting are a rapid way to solicit malaria, and potentially other public health information. This methodology provides a cost-effective way of executing a field study that can act as a complement to traditional public health surveillance methods, offering an opportunity to obtain information about malaria activity, temporal progression, demographics affected or Plasmodium-specific diagnosis at a finer resolution than official reports can provide. The recent adoption of technologies, such as the Internet supports self-reporting mediums, and self-reporting should continue to be studied as it can foster preventative health behaviours.Publication Preventing Pandemics via International Development: A Systems Approach(Public Library of Science, 2012) Bogich, Tiffany L.; Chunara, Rumi; Scales, David; Chan, Emily; Pinheiro, Laura C.; Chmura, Aleksei A.; Carroll, Dennis; Daszak, Peter; Brownstein, JohnPublication Participatory Epidemiology: Use of Mobile Phones for Community-Based Health Reporting(Public Library of Science, 2010) Freifeld, Clark C.; Chunara, Rumi; Mekaru, Sumiko R.; Chan, Emily H.; Kass-Hout, Taha; Ayala Iacucci, Anahi; Brownstein, John* Traditional health systems serve a key role in protecting populations, but are typically hierarchical, and information often travels slowly. * Novel Internet-based collaborative systems can have an important role in gathering information quickly and improving coverage and accessibility. * Mobile Internet usage is growing rapidly worldwide, making real-time information tools more readily available to both clinicians and the general public. * We present a brief summary of some promising mobile applications for health monitoring and information sharing, together with preliminary results from a study of our deployment of a smartphone application which enabled the general public to report infectious disease events. * These early efforts at tapping the power of mobile software tools illustrate potentially important steps in improving health systems as well as engaging the public as participants in the public health process.