Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters

Abstract

severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a pandemic with an estimated death rate between 1% and 5%; and an estimated R 0 between 2.2 and 6.7 according to various sources. As of March 28th, 2020, there were over 649,000 confirmed cases and 30,249 total deaths, globally. In the United States, there were over 115,500 cases and 1891 deaths and this number is likely to increase rapidly. It is critical to detect clusters of COVID-19 to better allocate resources and improve decision-making as the outbreaks continue to grow. Using daily case data at the county level provided by Johns Hopkins University, we conducted a prospective spatial-temporal analysis with SaTScan. We detect statistically significant space-time clusters of COVID-19 at the county level in the U.S. between January 22nd-March 9th, 2020, and January 22nd-March 27th, 2020. The space-time prospective scan statistic detected "active" and emerging clusters that are present at the end of our study periodsnotably, 18 more clusters were detected when adding the updated case data. These timely results can inform public health officials and decision makers about where to improve the allocation of resources, testing sites; also, where to implement stricter quarantines and travel bans. As

Risk clusters of COVID-19 transmission in northeastern Brazil: prospective space–time modelling

“…We built the cluster analysis model with the following conditions: minimum aggregation time of 2 days, minimum of five cases, without overlapping of clusters, circular clusters, the maximum size of the spatial cluster of 10% of the population at risk and maximum size of the temporal cluster of 50% of the study period [6]. The primary cluster and secondary clusters were detected using the log-likelihood ratio test and represented on maps [11].…”

“…Particularly, those that perform temporal and spatial analyses of COVID-19 have demonstrated the impact of morbidity, mortality and global geographical dissemination of the disease in the world. The use of aggregate spatial data allows to map the patterns of the rapid progression of the disease and to support decision-making in the allocation of resources for the prevention and control of COVID-19 in priority areas [3][4][5][6].…”

Business Closures, Stay-at-Home Restrictions, and COVID-19 Testing Outcomes in New York City

“…Effective on March 22, 2020, the state government issued a "New York State on PAUSE" executive order that closed all nonessential businesses, prohibited nonessential gatherings of individuals outside their homes, and limited outdoor recreational activities (6). The business closures affected different neighborhoods differently, as the location of stores and workplaces is not randomly distributed across New York City.…”

Forecasting spatial, socioeconomic and demographic variation in COVID-19 health care demand in England and Wales

“…Using the pre-crisis availability of hospital and critical care beds as a starting point and calibrating this to local demographic population composition and socioeconomic deprivation, we identify potential health care pressure points in England and Wales where expected hospitalization rates are disproportionately high and the per capita availability of hospital beds is relatively low. These spatial variations in underlying risk are key to inform disease monitoring in the coming months [11]. The early outbreak of COVID-19 in the UK was concentrated in densely populated urban areas with larger groups of ethnic minorities such as London.…”

“…As lockdown measures start to relax and until a vaccine becomes available, there is an urgent need to anticipate hospital bed demand in new ways and in particular from a geographic perspective. Understanding spatial variations will be key to effective strategic allocation of limited health care resources [10] as well as crucial in informing effective disease monitoring and prevention [11]. The aim of this study is to offer flexible estimates at more fine-grained local and regional levels that take into account multiple socioeconomic and demographic sources of variation in COVID-19-related health care demand and permit a flexible real-time adjustment of the assumed infection rate as local hotspots of infection may arise.…”