A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance

Abstract

The outbreak of the COVID-19 disease was first reported in Wuhan, China, in December 2019. Cases in the United States began appearing in late January. On March 11, the World Health Organization (WHO) declared a pandemic. By mid-March COVID-19 cases were spreading across the US with several hotspots appearing by April. Health officials point to the importance of surveillance of COVID-19 to better inform decision makers at various levels and efficiently manage distribution of human and technical resources to areas of need. The prospective space-time scan statistic has been used to help identify emerging COVID-19 disease clusters, but results from this approach can encounter strategic limitations imposed by constraints of the scanning window. This paper presents a different approach to COVID-19 surveillance based on a spatiotemporal event sequence (STES) similarity. In this STES based approach, adapted for this pandemic context we compute the similarity of evolving daily COVID-19 incidence rates by county and then cluster these sequences to identify counties with similarly trending COVID-19 case loads. We analyze four study periods and compare the sequence similarity-based clusters to prospective space-time scan statistic-based clusters. The sequence similarity-based clusters provide an alternate surveillance perspective by identifying locations that may not be spatially proximate but share a similar disease progression pattern. Results of the two approaches taken together can aid in tracking the progression of the pandemic to aid local or regional public health responses and policy actions taken to control or moderate the disease spread.

A Web-based Spatial Decision Support System of Wastewater Surveillance for COVID-19 Monitoring: A Case Study of a University Campus

“…Spatiotemporal analysis and modeling can be of great help in discovering interesting patterns in these spatiotemporal data, represented by the clusters of positive samples or residence halls detected using space-time scan approach and similarity analysis of space-time series in this study. The combination of the spatiotemporal analysis approaches has been suggested in the literature (see Xu & Beard, 2021).…”

Evaluating Space Time Cluster and Co-occurrence of Malaria Vectors of West Bengal in India

“…Univariate space-time statistics have been used to identify the outbreaks and space time clusters of diseases, such as malaria (5,18,19), Dengue and Chikungunya (20), COVID 19 (21), Lyme disease (22), Chikungunya (23) and other public health problems like crime (24), deaths of despairs (25) etc. Space time scan statistics were also used to identify the cluster pattern of P. vivax and Falciparum individually in Ahmedabad City (26), Karnataka (27), Bhutan (5) etc.…”

Analysis of distribution characteristics of COVID-19 in America based on space-time scan statistic

“…Hohl et al ( 14 ) used the daily new coronavirus case data provided by the John Hopkins University at the county level, and applied SaTScan to conduct a prospective space-time analysis, and detected the active clusters in various provinces and cities in the US. To avoid using prospective space-time scan statistic to identify emergence of COVID-19 disease groups, Beard et al ( 15 ) proposed the COVID-19 monitoring method, which was based on spatiotemporal event sequence similarity. Hohl et al ( 16 ) used prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at successive county levels in 48 states and Washington DC, which was helpful to facilitate decision-making and public health resource allocation.…”