tetano
Editor, Senior Moderator
Transbound Emerg Dis
. 2021 Jan 1.
doi: 10.1111/tbed.13973. Online ahead of print.
Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh
Ariful Islam[SUP] 1 2 [/SUP], Md Abu Sayeed[SUP] 2 3 [/SUP], Md Kaisar Rahman[SUP] 2 4 [/SUP], Jinnat Ferdous[SUP] 2 4 [/SUP], Shariful Islam[SUP] 2 5 [/SUP], Mohammad Mahmudul Hassan[SUP] 4 [/SUP]
Affiliations
Abstract
The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic Information System (GIS) software. We calculated the epidemiological measures including disease incidence, case fatality rate (CFR), and spatiotemporal pattern of COVID-19. We used Inverse Distance Weighting (IDW), Geographically Weighted Regression (GWR), Moran's I, and Getis-Ord Gi* statistics for prediction, spatial autocorrelation, and hotspot. We used retrospective space-time scan statistic to analyze clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50 % of infected cases were reported among young adults (21-40 year age group). The incidence varies from 0.03-0.95 at the end of March to 15.59-308.62 per 100000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among the Dhaka, Gazipur, and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; P≤0.01). Dhaka and its surrounding six districts were identified as the significant spatial hotspot where Chattogram is an extended diseased area. The outcomes acquired from the spatiotemporal investigation of COVID-19 could offer significant data and measurements to help the government checking and powerful arrangement creation of related organizations in medical, social, monetary, and environmental viewpoints.
Keywords: Bangladesh; COVID-19; Clustering; Hotspot; Moran’s I; Scan statistics.
. 2021 Jan 1.
doi: 10.1111/tbed.13973. Online ahead of print.
Geospatial dynamics of COVID-19 clusters and hotspots in Bangladesh
Ariful Islam[SUP] 1 2 [/SUP], Md Abu Sayeed[SUP] 2 3 [/SUP], Md Kaisar Rahman[SUP] 2 4 [/SUP], Jinnat Ferdous[SUP] 2 4 [/SUP], Shariful Islam[SUP] 2 5 [/SUP], Mohammad Mahmudul Hassan[SUP] 4 [/SUP]
Affiliations
- PMID: 33386654
- DOI: 10.1111/tbed.13973
Abstract
The coronavirus disease 2019 (COVID-19) is an emerging and rapidly evolving profound pandemic, which causes severe acute respiratory syndrome and results in significant case fatality around the world including Bangladesh. We conducted this study to assess how COVID-19 cases clustered across districts in Bangladesh and whether the pattern and duration of clusters changed following the country's containment strategy using Geographic Information System (GIS) software. We calculated the epidemiological measures including disease incidence, case fatality rate (CFR), and spatiotemporal pattern of COVID-19. We used Inverse Distance Weighting (IDW), Geographically Weighted Regression (GWR), Moran's I, and Getis-Ord Gi* statistics for prediction, spatial autocorrelation, and hotspot. We used retrospective space-time scan statistic to analyze clusters of COVID-19 cases. COVID-19 has a CFR of 1.4%. Over 50 % of infected cases were reported among young adults (21-40 year age group). The incidence varies from 0.03-0.95 at the end of March to 15.59-308.62 per 100000, at the end of July. Global Moran's Index indicates a robust spatial autocorrelation of COVID-19 cases. Local Moran's I analysis stated a distinct High-High (HH) clustering of COVID-19 cases among the Dhaka, Gazipur, and Narayanganj districts. Twelve statistically significant high rated clusters were identified by space-time scan statistics using a discrete Poisson model. IDW predicted the cases at the undetermined area, and GWR showed a strong relationship between population density and case frequency, which was further established with Moran's I (0.734; P≤0.01). Dhaka and its surrounding six districts were identified as the significant spatial hotspot where Chattogram is an extended diseased area. The outcomes acquired from the spatiotemporal investigation of COVID-19 could offer significant data and measurements to help the government checking and powerful arrangement creation of related organizations in medical, social, monetary, and environmental viewpoints.
Keywords: Bangladesh; COVID-19; Clustering; Hotspot; Moran’s I; Scan statistics.