TY - JOUR
T1 - Real-time spatiotemporal analysis of microepidemics of influenza and covid-19 based on hospital network data
T2 - Colocalization of neighborhood-level hotspots
AU - Mylona, Evangelia K.
AU - Shehadeh, Fadi
AU - Kalligeros, Markos
AU - Benitez, Gregorio
AU - Chan, Philip A.
AU - Mylonakis, Eleftherios
N1 - Funding Information:
The study was supported by the Brown University COVID-19 Research Seed Fund to P. A. Chan and E. Mylonakis.
Publisher Copyright:
© 2020 American Public Health Association Inc.. All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - Objectives. To identify spatiotemporal patterns of epidemic spread at the community level. Methods. 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. Results. 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. Conclusions. The use of readily available hospital data allows the real-time identification of spatiotemporal trends and hotspots of microepidemics. Public Health Implications. 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.
AB - Objectives. To identify spatiotemporal patterns of epidemic spread at the community level. Methods. 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. Results. 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. Conclusions. The use of readily available hospital data allows the real-time identification of spatiotemporal trends and hotspots of microepidemics. Public Health Implications. 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.
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U2 - 10.2105/AJPH.2020.305911
DO - 10.2105/AJPH.2020.305911
M3 - Article
C2 - 33058702
AN - SCOPUS:85096151843
VL - 110
SP - 1817
EP - 1824
JO - American Journal of Public Health
JF - American Journal of Public Health
SN - 0090-0036
IS - 12
ER -