In This Article
- Accurate predictive models are vital to inform public health recommendations on reopening society post-COVID-19
- Existing COVID-19 models rely on incomplete data that is not collected uniformly, creating a need for more accurate surveillance tools
- Participatory syndromic-surveillance tools can provide faster signals of infectious disease transmission to help rapidly address critical near-term needs
As COVID-19 cases gradually decrease, federal and local governmental bodies must decide how to begin easing restrictions and reopening aspects of society. While these decisions are generally based on the intersection of political, economic and public health factors, it is evident that there is a great need for more accurate estimates of the virus's impact on communities.
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In a New England Journal of Medicine perspective piece, co-author Andrew Chan, MD, MPH, director for Cancer Epidemiology in the Mass General Cancer Center, chief of the Clinical and Translational Epidemiology Unit (CTEU) in the Department of Medicine and vice chief for clinical research in the Division of Gastroenterology, explains the effectiveness of mobile symptom-surveillance tools in predictive modeling.
Most COVID-19 models to date use numbers from testing, hospitalizations and deaths. These projections are highly limited for a multitude of reasons—the data is not collected uniformly on a national scale, testing availability has been very limited to date, asymptomatic cases are not accounted for and antibody testing is still not fully understood.
New participatory syndromic-surveillance tools, like the COVID Symptom Study app developed by Mass General and Boston Children's Hospital's COVIDNearYou tool, are cost-effective tools that can rapidly address critical near-term needs. These tools work by capturing user-input data on reported symptoms through phone apps or internet-based questionnaires, providing faster signals of infectious disease transmission. Syndromic surveillance data can be used to determine real-time emerging hotspots and risk factors, recruit volunteers for clinical trials, estimate the risk of reinfection and many other important insights that can be used to inform public health recommendations.
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