Relation between mobility, extreme weather events, and health: A cases study of COVID-19 outbreaks

Abstract:

This paper aims to understand whether outbreaks of Covid-19 cases may increase after major storms due to the displacement and movement of a large number of people affected by fallen trees, power lines, and resultant power outages. After Tropical Storm Isais caused widespread power outages in Connecticut (CT), we compiled hourly power outage data, and daily COVID-19 cases for 21 days before and after the storm. First, we identified towns with statistically significant increasing trends of daily COVID-19 cases before and after the storm using time series functions. Using a retrospective space-time scan statistic, we further identified COVID-19 space-time clusters that were active (i.e., more observed than expected cases) before and after the storm. Second, we measured the movement of people from cell phone data aggregated by Safegraph. Third, we measured statistical associations between power outages and movement of people with the increase in COVID-19 cases. Results showed positive associations between power outages and COVID- 19 cases increase. It may be possible to predict the location of future outbreaks after major storms using power outage information. We can estimate times to power restoration by locations using outage predictions and then direct appropriate public health interventions such as increased testing and contact tracing when people return to their homes and businesses to prevent future disease outbreaks. This paper has the potential for advancing the understanding of coupled human-environment systems through an investigation of the relationships between extreme weather events, environmental management, and human health.

Keywords: COVID-19, storms, power outages, mobility, environmental management, spatial analysis

Authors: