Real-Time Spatiotemporal Analysis of Microepidemics of Influenza and COVID-19 Based on Hospital Network Data: Colocalization of Neighborhood-Level Hotspots.

Abstract

<i>Objectives.</i> To identify spatiotemporal patterns of epidemic spread at the community level.<i>Methods.</i> We extracted influenza cases reported between 2016 and 2019 and COVID-19 cases reported in March and April 2020 from a hospital network in Rhode Island. We performed a spatiotemporal hotspot analysis to simulate a real-time surveillance scenario.<i>Results.</i> We analyzed 6527 laboratory-confirmed influenza cases and identified microepidemics in more than 1100 neighborhoods, and more than half of the neighborhoods that had hotspots in a season became hotspots in the next season. We used data from 731 COVID-19 cases, and we found that a neighborhood was 1.90 times more likely to become a COVID-19 hotspot if it had been an influenza hotspot in 2018 to 2019.<i>Conclusions.</i> The use of readily available hospital data allows the real-time identification of spatiotemporal trends and hotspots of microepidemics.<i>Public Health Implications.</i> As local governments move to reopen the economy and ease physical distancing, the use of historic influenza hotspots could guide early prevention interventions, while the real-time identification of hotspots would enable the implementation of interventions that focus on small-area containment and mitigation.