Assistant Professor of Biostatistics
Dr. Rumi Chunara researches and develops ways to use unstructured data in real-world applications and understand population health. As a computer engineers and scientist, she has revolutionized how medical and public health researchers collect health information through the Internet and mobile technology.
Driven to understand how and why diseases spread in populations, she has developed cutting-edge research models at HealthMap and the Children's Hospital Informatics Program at Harvard Medical School. Through the GoViral study, Dr. Chunara works closely with students on campus to collect crowd sourced data of influenza in real-time. GoViral uses the collected data and modeling methods to better understand viral spread, uncover geographical variation in spread and epidemiology, and predict and recommend behaviors that limit disease spread. At NYU, Dr. Chunara also leads the Chunara Lab, which develops computational and statistical methods across data mining, natural language processing, spatio-temporal analyses and machine learning, to study population health.
BS, Electrical Engineering (Honors), CaltechMS, Electrical Engineering and Computer Science, MITPhD, Medical and Electrical Engineering, MIT (Harvard-MIT Division of Health Sciences and Technology)
MIT Technology Review 35 (2014)MIT Presidential Fellow (2004)
Data miningPersonally-generated dataSpatio-temporal statistics
Quantitative methods for measuring neighborhood characteristics in neighborhood health researchDuncan, D., Goedel, W. C., & Chunara, R.
Reports of the workshops held at the 2018 international AAAI conference on web and social mediaAn, J., Chunara, R., Crandall, D. J., Frajberg, D., French, M., Jansen, B. J., Kulshrestha, J., Mejova, Y., Romero, D. M., Salminen, J., Sharma, A., Sheth, A., Tan, C., Taylor, S. H., & Wijeratne, S.
Journal titleAI Magazine
Socio-spatial self-organizing maps: Using social media to assess relevant geographies for exposure to social processesRelia, K., Akbari, M., Duncan, D., & Chunara, R.
Journal titleProceedings of the ACM on Human-Computer Interaction
Volume2Social media offers a unique window into attitudes like racism and homophobia, exposure to which are important, hard to measure and understudied social determinants of health. However, individual geo-located observations from social media are noisy and geographically inconsistent. Existing areas by which exposures are measured, like Zip codes, average over irrelevant administratively-defined boundaries. Hence, in order to enable studies of online social environmental measures like attitudes on social media and their possible relationship to health outcomes, first there is a need for a method to define the collective, underlying degree of social media attitudes by region. To address this, we create the Socio-spatial-Self organizing map, “SS-SOM” pipeline to best identify regions by their latent social attitude from Twitter posts. SS-SOMs use neural embedding for text-classification, and augment traditional SOMs to generate a controlled number of non-overlapping, topologically-constrained and topically-similar clusters. We find that not only are SS-SOMs robust to missing data, the exposure of a cohort of men who are susceptible to multiple racism and homophobia-linked health outcomes, changes by up to 42% using SS-SOM measures as compared to using Zip code-based measures.
Etiology of respiratory tract infections in the community and clinic in Ilorin, NigeriaKolawole, O., Oguntoye, M., Dam, T., & Chunara, R.
Journal titleBMC Research Notes
Issue1OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria.RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
High-resolution temporal representations of alcohol and tobacco behaviors from social media dataHuang, T., Elghafari, A., Relia, K., & Chunara, R.
Journal titleProceedings of the ACM on Human-Computer Interaction
Volume1Understanding tobacco- and alcohol-related behavioral patterns is critical for uncovering risk factors and potentially designing targeted social computing intervention systems. Given that we make choices multiple times per day, hourly and daily patterns are critical for better understanding behaviors. Here, we combine natural language processing, machine learning and time series analyses to assess Twitter activity specifically related to alcohol and tobacco consumption and their sub-daily, daily and weekly cycles. Twitter self-reports of alcohol and tobacco use are compared to other data streams available at similar temporal resolution. We assess if discussion of drinking by inferred underage versus legal age people or discussion of use of different types of tobacco products can be differentiated using these temporal patterns. We find that time and frequency domain representations of behaviors on social media can provide meaningful and unique insights, and we discuss the types of behaviors for which the approach may be most useful.
Denominator Issues for Personally Generated Data in Population Health MonitoringChunara, R., Wisk, L. E., & Weitzman, E. R.
Journal titleAmerican Journal of Preventive Medicine
Characterizing sleep issues using TwitterMcIver, D. J., Hawkins, J. B., Chunara, R., Chatterjee, A. K., Bhandari, A., Fitzgerald, T. P., Jain, S. H., & Brownstein, J. S.
Journal titleJournal of Medical Internet Research
Page(s)e140Background: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. Objective: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. Methods: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, "can't sleep", Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. Results: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues. Conclusions: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered.
Estimating influenza attack rates in the United States using a participatory cohortChunara, R., Goldstein, E., Patterson-Lomba, O., & Brownstein, J. S.
Journal titleScientific Reports
Volume5We considered how participatory syndromic surveillance data can be used to estimate influenza attack rates during the 2012-2013 and 2013-2014 seasons in the United States. Our inference is based on assessing the difference in the rates of self-reported influenza-like illness (ILI, defined as presence of fever and cough/sore throat) among the survey participants during periods of active vs. low influenza circulation as well as estimating the probability of self-reported ILI for influenza cases. Here, we combined Flu Near You data with additional sources (Hong Kong household studies of symptoms of influenza cases and the U.S. Centers for Disease Control and Prevention estimates of vaccine coverage and effectiveness) to estimate influenza attack rates. The estimated influenza attack rate for the early vaccinated Flu Near You members (vaccination reported by week 45) aged 20-64 between calendar weeks 47-12 was 14.7%(95% CI(5.9%,24.1%)) for the 2012-2013 season and 3.6%(â '3.3%,10.3%) for the 2013-2014 season. The corresponding rates for the US population aged 20-64 were 30.5% (4.4%, 49.3%) in 2012-2013 and 7.1%(-5.1%, 32.5%) in 2013-2014. The attack rates in women and men were similar each season. Our findings demonstrate that participatory syndromic surveillance data can be used to gauge influenza attack rates during future influenza seasons.
Flu near you: Crowdsourced symptom reporting spanning 2 influenza seasonsSmolinski, M. S., Crawley, A. W., Baltrusaitis, K., Chunara, R., Olsen, J. M., Wójcik, O., Santillana, M., Nguyen, A., & Brownstein, J. S.
Journal titleAmerican Journal of Public Health
Page(s)2124-2130Objectives. We summarized Flu Near You (FNY) data from the 2012?2013 and 2013?2014 influenza seasons in the United States. Methods. FNY collects limited demographic characteristic information upon registration, and prompts users each Monday to report symptoms of influenzalike illness (ILI) experienced during the previous week. We calculated the descriptive statistics and rates of ILI for the 2012?2013 and 2013?2014 seasons. We compared raw and noise-filtered ILI rates with ILI rates from the Centers for Disease Control and Prevention ILINet surveillance system. Results. More than 61 000 participants submitted at least 1 report during the 2012?2013 season, totaling 327 773 reports. Nearly 40 000 participants submitted at least 1 report during the 2013?2014 season, totaling 336 933 reports. Rates of ILI as reported by FNY tracked closely with ILINet in both timing and magnitude. Conclusions. With increased participation, FNY has the potential to serve as a viable complement to existing outpatient, hospital-based, and laboratory surveillance systems. Although many established systems have the benefits of specificity and credibility, participatory systems offer advantages in the areas of speed, sensitivity, and scalability.
Surveillance of acute respiratory infections using community-submitted symptoms and specimens for molecular diagnostic testingGoff, J., Rowe, A., Brownstein, J. S., & Chunara, R.
Journal titlePLoS Currents
Volume7Participatory 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.
A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectivesNagar, R., Yuan, Q., Freifeld, C. C., Santillana, M., Nojima, A., Chunara, R., & Brownstein, J. S.
Journal titleJournal of Medical Internet Research
Page(s)e236Background: Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches. Objective: The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases. Methods: From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords "flu", "influenza", "gripe", and "high fever". The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis. Results: Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal. Conclusions: While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable.
Averting the perfect storm: addressing youth substance use risk from social media useSalimian, P. K., Chunara, R., & Weitzman, E. R.
Journal titlePediatric Annals
Page(s)411Adolescents are developmentally sensitive to pathways that influence alcohol and other drug (AOD) use. In the absence of guidance, their routine engagement with social media may add a further layer of risk. There are several potential mechanisms for social media use to influence AOD risk, including exposure to peer portrayals of AOD use, socially amplified advertising, misinformation, and predatory marketing against a backdrop of lax regulatory systems and privacy controls. Here the authors summarize the influences of the social media world and suggest how pediatricians in everyday practice can alert youth and their parents to these risks to foster conversation, awareness, and harm reduction.
Public health for the people: Participatory infectious disease surveillance in the digital ageWójcik, O. P., Brownstein, J. S., Chunara, R., & Johansson, M. A.
Journal titleEmerging Themes in Epidemiology
Issue1The 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.
Assessing the Online Social Environment for Surveillance of Obesity PrevalenceChunara, R., Bouton, L., Ayers, J. W., & Brownstein, J. S.
Journal titlePLoS One
Issue4Background: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.
Monitoring Influenza Epidemics in China with Search Query from BaiduYuan, Q., Nsoesie, E. O., Lv, B., Peng, G., Chunara, R., & Brownstein, J. S.
Journal titlePLoS One
Issue5Several 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.
Twitter as a Sentinel in Emergency Situations: Lessons from the Boston Marathon ExplosionsCassa, C. A., Chunara, R., Mandl, K., & Brownstein, J. S.
Journal titlePLoS CurrentsImmediately 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.
Using search queries for malaria surveillance, ThailandOcampo, A. J., Chunara, R., & Brownstein, J. S.
Journal titleMalaria Journal
Issue1Background: 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.
Why we need crowdsourced data in infectious disease surveillanceChunara, R., Smolinski, M. S., & Brownstein, J. S.
Journal titleCurrent Infectious Disease Reports
Page(s)316-319In infectious disease surveillance, public health data such as environmental, hospital, or census data have been extensively explored to create robust models of disease dynamics. However, this information is also subject to its own biases, including latency, high cost, contributor biases, and imprecise resolution. Simultaneously, new technologies including Internet and mobile phone based tools, now enable information to be garnered directly from individuals at the point of care. Here, we consider how these crowdsourced data offer the opportunity to fill gaps in and augment current epidemiological models. Challenges and methods for overcoming limitations of the data are also reviewed. As more new information sources become mature, incorporating these novel data into epidemiological frameworks will enable us to learn more about infectious disease dynamics.
New technologies for reporting real-time emergent infectionsChunara, R., Freifeld, C. C., & Brownstein, J. S.
Page(s)1843-1851Novel technologies have prompted a new paradigm in disease surveillance. Advances in computation, communications and materials enable new technologies such as mobile phones and microfluidic chips. In this paper we illustrate examples of new technologies that can augment disease detection. We describe technologies harnessing the internet, mobile phones, point of care diagnostic tools and methods that facilitate detection from passively collected unstructured data. We demonstrate how these can all assist in quicker detection, investigation and response to emerging infectious events. Novel technologies enable collection and dissemination of epidemic intelligence data to both public health practitioners and the general public, enabling finer temporal and spatial resolution of disease monitoring than through traditional public health processes.
Online reporting for malaria surveillance using micro-monetary incentives, in urban India 2010-2011Chunara, R., Chhaya, V., Bane, S., Mekaru, S. R., Chan, E. H., Freifeld, C. C., & Brownstein, J. S.
Journal titleMalaria Journal
Volume11Background: 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.
Preventing Pandemics Via International Development: A Systems ApproachBogich, T. L., Chunara, R., Scales, D., Chan, E., Pinheiro, L. C., Chmura, A. A., Carroll, D., Daszak, P., & Brownstein, J. S.
Journal titlePLoS Medicine
Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreakChunara, R., Andrews, J. R., & Brownstein, J. S.
Journal titleAmerican Journal of Tropical Medicine and Hygiene
Page(s)39-45During infectious disease outbreaks, data collected through health institutions and official reporting structures may not be available for weeks, hindering early epidemiologic assessment. By contrast, data from informal media are typically available in near real-time and could provide earlier estimates of epidemic dynamics. We assessed correlation of volume of cholera-related HealthMap news media reports, Twitter postings, and government cholera cases reported in the first 100 days of the 2010 Haitian cholera outbreak. Trends in volume of informal sources significantly correlated in time with official case data and was available up to 2 weeks earlier. Estimates of the reproductive number ranged from 1.54 to 6.89 (informal sources) and 1.27 to 3.72 (official sources) during the initial outbreak growth period, and 1.04 to 1.51 (informal) and 1.06 to 1.73 (official) when Hurricane Tomas afflicted Haiti. Informal data can be used complementarily with official data in an outbreak setting to get timely estimates of disease dynamics.
Suspended microchannel resonators with piezoresistive sensorsLee, J., Chunara, R., Shen, W., Payer, K., Babcock, K., Burg, T. P., & Manalis, S. R.
Journal titleLab on a Chip - Miniaturisation for Chemistry and Biology
Page(s)645-651Precision frequency detection has enabled the suspended microchannel resonator (SMR) to weigh single living cells, single nanoparticles, and adsorbed protein layers in fluid. To date, the SMR resonance frequency has been determined optically, which requires the use of an external laser and photodiode and cannot be easily arrayed for multiplexed measurements. Here we demonstrate the first electronic detection of SMR resonance frequency by fabricating piezoresistive sensors using ion implantation into single crystal silicon resonators. To validate the piezoresistive SMR, buoyant mass histograms of budding yeast cells and a mixture of 1.6, 2.0, 2.5, and 3.0 m diameter polystyrene beads are measured. For SMRs designed to weigh micron-sized particles and cells, the mass resolution achieved with piezoresistive detection (∼3.4 fg in a 1 kHz bandwidth) is comparable to what can be achieved by the conventional optical-lever detector. Eliminating the need for expensive and delicate optical components will enable new uses for the SMR in both multiplexed and field deployable applications.
Participatory epidemiology: Use of mobile phones for community-based health reportingFreifeld, C. C., Chunara, R., Mekaru, S. R., Chan, E. H., Kass-Hout, T., Iacucci, A. A., & Brownstein, J. S.
Journal titlePLoS Medicine
Individualized formative assessment in online module improves learning of glomerular filtrationHenrickson, S., Chunara, R., Cajigas, I., Gunter, H., & Bonventre, J.
Journal titleASEE Annual Conference and Exposition, Conference ProceedingsWhile capillary filtration is a fundamental physiology topic, students report that this material is difficult to master. In addition, overall exam performance in related courses does not correlate with performance on questions regarding capillary filtration. A module that presents capillary filtration in the context of glomerular filtration has been developed and incorporated into the curriculum of the Harvard-MIT Division of Health Sciences and Technology renal pathophysiology course. Water is a major constituent of the human body. Exchange of fluids between different body systems, therefore, is an important process to understand. Capillary filtration plays a fundamental role in all physiology1. This is a complex topic, requiring understanding of foundations in fluid and vascular mechanics. These topics are important but have generally proven difficult for students in medical physiology classes to master. To relay the material in a renal physiology class we develop a module concerning glomerular filtration. The glomerulus is a capillary bed that acts as the main filtration unit of the kidney. The module is based on the principles outlined in How People Learr?. These include creating a learningcentered environment, focusing on core concepts and big ideas in the learning environment, being assessment centered to help students' thinking become more visible to both themselves and the instructors. Previously, it has been shown in our and others' work that an HPL-informed instruction strategy improves student learning of material3,4. The module replaces traditional instruction, which consisted of an in-class lecture followed by a problem set and assigned textbook reading. The module replaces both the problem set and textbook reading with two interactive online exercises that introduce core content and provide real time formative assessment to students. The first exercise is assigned before the lecture and presents basic concepts including hydrostatic and oncotic pressure. Student performance and feedback collected during this exercise allows the lecturer to tailor the lecture to the learners. A novel Java simulation of glomerular filtration that permits manipulation of independent variables while displaying the dependent variables is projected during the lecture. The second online exercise is assigned after the lecture and reviews and extends the concepts presented in the lecture. To improve student understanding, the newest version of the module includes adaptive feedback; described in more detail below. Also this year we analyzed the patterns of incorrect responses in the past iterations of both online exercises and developed specific feedback for common mistakes, to help students revise their specific misconceptions about the material, which the majority of students found to be helpful. Knowledge based outcomes demonstrate that students who used the module have improved mastery of the three learning objectives compared to those taught using traditional techniques. While the majority of all students prefer the new module to a traditional problem set, we have found that both student undergraduate major and student graduate program had an impact on their preference of learning tools. Future efforts will focus on dissemination to other programs as well as continued improvements to ensure that students from all backgrounds find the module useful.