The SARS-CoV-2 pandemic has rapidly saturated healthcare resources across the globe and has led to a restricted screening process, hindering efforts at comprehensive case detection. This has not only facilitated community spread but has also resulted in an underestimation of the true incidence of disease, a statistic which is useful for policy making aimed at controlling the current pandemic and in preparing for future outbreaks. In this perspective, we present a crowdsourced platform developed by us for the true estimation of all SARS-CoV-2 infections in the community, through active self-reporting and layering other authentic datasets. The granularity of data captured by this system could prove to be useful in assisting governments to identify SARS-CoV-2 hotspots in the community facilitating lifting of restrictions in a controlled fashion.
【저자키워드】 COVID-19, Surveillance, Digital health, Pandemic response, post code map, crowdsource, 【초록키워드】 SARS-CoV-2, pandemic, SARS-COV-2 infection, SARS-CoV-2 pandemic, Spread, Outbreaks, healthcare, Community, incidence, resource, disease, SARS-CoV-2 infections, platform, incidence of disease, Government, Perspective, this system, effort, datasets, screening process, hotspot, lifting, statistic, granularity, identify, globe, facilitated, saturated, 【제목키워드】 response, System, Hyperlocal,