Research Article: Participatory Epidemiology: Use of Mobile Phones for Community-Based Health Reporting

Date Published: December 7, 2010

Publisher: Public Library of Science

Author(s): Clark C. Freifeld, Rumi Chunara, Sumiko R. Mekaru, Emily H. Chan, Taha Kass-Hout, Anahi Ayala Iacucci, John S. Brownstein

Abstract: Clark Freifeld and colleagues discuss mobile applications, including their own smartphone application, that show promise for health monitoring and information sharing.

Partial Text: In traditional clinical and public health structures, information flows through a hierarchy of providers and local or national authorities, who then communicate with the public via periodic announcements [1]. Meanwhile, broad adoption of the Internet around the world has enabled a new class of participatory systems that allow people to contribute and share information and work together in real time [2]. Wikipedia is perhaps the best-known such project. In the field of public health, online patient communities provide a forum for patients to share their experiences, collect information, and inform biomedical researchers [3]–[5]. Participatory systems in which data and intelligence are gathered from the population, traditionally through discussion or surveys, have also been used to gain an understanding of disease transmission, especially for zoonotic diseases [6]. However, new internet community-based systems represent a departure from the careful control, verification, and data-informed actions of traditional structures, but can provide advantages in scalability, coverage, timeliness, and transparency. Furthermore, engaging the public transforms users from passive recipients of information to active participants in a collaborative community, helping to improve their own health as well as the health of those around them.

The use of mobile systems for health is a growing field with several participatory systems for public health. Selected systems are introduced here; the applications and geographies covered are outlined in Table 1.

Because of its impact across borders and social strata, the 2009 H1N1 influenza pandemic both created broad public awareness of infectious disease threats and presented new challenges for disease detection and response systems. Part of a new generation of online real-time disease outbreak monitoring systems, HealthMap has demonstrated the effectiveness of collecting and filtering news media sources, outside of formal public health channels, for improved situational awareness [18]. However, limitations in coverage, timeliness of reporting, availability of human reviewers, and effectiveness of automated algorithms remain. To address some of these limitations, we created Outbreaks Near Me, where we ask users from the general public to contribute reports from their own knowledge and experiences through a mobile application. We released the Outbreaks Near Me application for iPhone and Android in Fall 2009, during the second wave of pandemic H1N1 infection in the northern hemisphere.

Despite the potential for participatory epidemiology, many challenges remain. Perhaps the most significant concern is the question of how to corroborate or verify submitted information. Public health officials may rightfully have reservations about this type of data: their obligation to respond to individual reports could represent an added burden to their surveillance responsibilities. However, one preliminary way of analyzing the crowd-sourced data is through cross-validation with other sources, as demonstrated in Table 2. In addition, these systems are by nature venues for two-way information exchange. Rather than simply supplying the end-users with reports, many of the projects we highlighted make use of crowds for evaluating the quality of information as well. By publishing submitted information, they allow users to review and assess the data. This idea is being tested via Ushahidi’s Swift River project, amongst others. Swift River further makes use of automated algorithms for scoring and filtering information based on the credibility of sources. Collecting contact information from the person reporting enables system owners to contact the submitter to request additional details if a report raises particular interest. With an effective review and filtering process, we can help avoid information overload.

Although the data as yet support only preliminary conclusions, we have already seen concrete benefits of community participation in a range of public health settings from pandemics to natural disasters. Promoting technology adoption, verifying reported information, and aligning user incentives remain important challenges for all the systems.



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