ABSTRACT
New York Citys Health Department developed a SARS-CoV-2 percent test positivity cluster detection system using census tract resolution and the SaTScan prospective space-time scan statistic. One cluster led to identifying a gathering with inadequate social distancing where viral transmission likely occurred, and another cluster prompted targeted community testing and outreach.
ABSTRACT
BackgroundCOVID-19 is a new coronavirus that has spread from person to person throughout the world. Geographical disease surveillance is a powerful tool to monitor the spread of epidemics and pandemic, providing important information on the location of new hot-spots, assisting public health agencies to implement targeted approaches to minimize mortality. MethodsCounty level data from January 22-April 28 was downloaded from USAfacts.org to create heat maps with ArcMap for diagnosed COVID-19 cases and mortality. The data was analyzed using spatial and space-time scan statistics and the SaTScan software, to detect geographical cluster with high incidence and mortality, adjusting for multiple testing. Analyses were adjusted for age. While the spatial clusters represent counties with unusually high counts of COVID-19 when averaged over the time period January 22-April 20, the space-time clusters allow us to identify groups of counties in which there exists a significant change over time. ResultsThere were several statistically significant COVID-19 clusters for both incidence and mortality. Top clusters with high rates included the areas in and around New York City, New Orleans and Chicago, but there were also several small rural clusters. Top clusters for a recent surge in incidence and mortality included large parts of the Midwest, the Mid-Atlantic Region, and several smaller areas in and around New York and New England. ConclusionsSpatial and space-time surveillance of COVID-19 can be useful for public health departments in their efforts to minimize mortality from the disease. It can also be applied to smaller regions with more granular data.
ABSTRACT
<p><b>OBJECTIVE</b>To analyze the spatial distribution of hemorrhagic fever with renal syndrome (HFRS) in Haidian district, Beijing and to explore the geographical characteristics of HFRS in highly endemic areas.</p><p><b>METHODS</b>Administration boundary layer was established under the background of 1:100,000 map in the ArcInfo 8.1 software. The HFRS cases from 1997 to 2002 were positioned on the map. Highly endemic areas were identified by spatial cluster analysis using SaTScan 3.0 software. Distribution of HFRS cases was shown in different colors and contours by spatial analysis of geographic information system (GIS).</p><p><b>RESULTS</b>Spatial Cluster Analysis of confirmed cases of HFRS identified in 1997 - 2002 in Haidian district showed that HFRS patients were not randomly distributed. The highly endemic areas were founded in Sujiatuo township, Yongfeng township, Shangzhuang township, Wenquan town and Bei'anhe township (relative risk = 4.43, P = 0.001). A thematic map of HFRS in haidian district was set up.</p><p><b>CONCLUSION</b>HFRS infections were not randomly distributed, since the distribution was related to geographic-environmental factors.</p>
