Xiao, Yunyu2021-04-152021-04-152020Xiao, Y. (2020). Predicting spatial and temporal responses to non-pharmaceutical interventions on COVID-19 growth rates across 58 counties in New York State: A prospective event-based modeling study on county-level sociological predictors. JMIR public health and surveillance. https://doi.org/10.2196/22578https://hdl.handle.net/1805/25650Background: Non-pharmaceutical interventions (NPIs) have been implemented in the New York State since the COVID-19 outbreak on March 1, 2020 to control the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Projecting the growth rate of incidence as a response to key NPIs is crucial to guide future policy making. Few studies, however, considered spatial variations of incidence growth rate across different time points of NPIs. Objective: This study quantifies county-level predictors of the time evolution of COVID-19 incidence growth rate following key NPIs in New York State. Methods: County-level COVID-19 incidence data were retrieved from the Coronavirus Case Data from Social Explorer Website between March and June 2020. 5-day moving average growth rates of COVID-19 were calculated for 16 selected time points on the dates of eight NPIs and their respective 14-day-lag-behind time points. A total of 36 county-level predictors were extracted from multiple public datasets. Geospatial mapping was used to display the spatial heterogeneity of county-level COVID-19 outbreak. Generalized mixed effect least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant county-level predictors related to the change of county-level COVID-19 growth rate over time. Results: Since March 1, the growth rate of COVID-19 infection increased and peaked by the end of March, followed by a decrease. Over time, the region with the highest growth rates shifted from New York metropolitan area towards Western and Northern areas. Proportions of population aged 45 years and above (β=3.25 [0.17–6.32]), living alone at residential houses (β=3.31 [0.39–-6.22]), and proportion of crowd residential houses (β=6.15 [2.15–10.14]) were positively associated with the growth rate of COVID-19 infection. In contrast, living alone at rental houses (β=-2.47 [-4.83–-0.12]) and rate of mental health providers (β=-1.11 [-1.95–-0.28]) were negatively associated with COVID-19 growth rate across all 16 time points. Conclusions: Tailored interventions and policies are required to effectively control the epidemic for different counties. Attention towards economic, racial/ethnic, and healthcare resource disparities are needed to narrow the unequal health impact on vulnerable populations.enAttribution 4.0 InternationalCOVID-19non-pharmaceutical interventionsincidencePreprint