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Crawley Adam

Because the dynamics and severity of influenza in the US vary each season, yearly estimates of disease burden in the population are essential to evaluate interventions and allocate resources. The CDC uses data from a national health-care based surveillance system and mathematical models to estimate the overall burden of disease in the general population. Over the past decade, crowd-sourced syndromic surveillance systems have emerged as a digital data source that collects health-related information in near real-time. These systems complement traditional surveillance systems by capturing individuals who do not seek medical care and allowing for a longitudinal view of illness burden. However, because not all participants report every week and participants are more likely to report when ill, the number of weekly reports is temporally and spatially inconsistent and the estimates of disease burden and incidence may be biased. In this study, we use data from Flu Near You (FNY), a participatory surveillance system based in the US and Canada1, to estimate and compare Influenza-like Illness (ILI) ARs using different approaches to adjust for reporting biases in participatory surveillance data.

Objective:

To estimate and compare influenza attack rates (AR) in the United States (US) using different approaches to adjust for reporting biases in participatory syndromic surveillance data.

Submitted by elamb on

Zika, chikungunya, and dengue have surged in the Americas over the past several years and pose serious health threats in regions of the U.S. where Ae. aegypti and Ae. albopictus mosquito vectors occur. Ae. aegypti have been detected up to 6 months of the year or longer in parts of Arizona, Florida, and Texas where mosquito surveillance is regularly conducted. However, many areas in the U.S. lack basic data on vector presence or absence. The Zika, dengue, and chikungunya viruses range in pathogenicity, but all include asymptomatic or mild presentations for which individuals may not seek care. Traditional passive surveillance systems rely on confirmatory laboratory testing and may not detect emergent disease until there is high morbidity in a community or severe disease presentation. Participatory surveillance is an approach to disease detection that allows the public to directly report symptoms electronically and provides rapid visualization of aggregated data to the user and public health agencies. Several such systems have been shown to be sensitive, accurate, and timelier than traditional surveillance. We developed Kidenga, a mobile phone app and participatory surveillance system, to address some of the challenges in early detection of day-biting mosquitoes and Aedes-borne arboviruses and to enhance dissemination of information to at-risk communities. 

Objective

(1) Early detection of Aedes-borne arboviral disease;

(2) improved data on Ae. aegypti and Ae. albopictus distribution in the United States (U.S.); and

(3) education of clinicians and the public. 

 

Submitted by Magou on

Citizen engagement in public health is being transformed through systems that enable users to directly report on symptoms of disease via email and smartphone technology. Participatory systems encourage routine submission of syndromic data by the general public. Participant data can be aggregated and shared in near real-time with users and health authorities.

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