Spatiotemporal patterns and trends of community transmission of the pandemic COVID-19 in South Asia: Bangladesh as a case study

Abstract

South Asian (SA) countries have been fighting with the pandemic novel coronavirus disease 2019 (COVID-19) since January 2020. Earlier, the country-specific descriptive study has been done. Nevertheless, as transboundary infection, the border sharing, shared cultural and behavioral practice, effects on the temporal and spatial distribution of COVID-19 in SA is still unveiled. Therefore, this study has been revealed the spatial hotspot along with descriptive output on different parameters of COVID-19 infection. We extracted data from the WHO and the worldometer database from the onset of the outbreak up to 15 May, 2020. Europe has the highest case fatality rate (CFR, 9.22%), whereas Oceania has the highest (91.15%) recovery rate from COVID-19. Among SA countries, India has the highest number of cases (85,790), followed by Pakistan (38,799) and Bangladesh (20,065). However, the number of tests conducted was minimum in this region in comparison with other areas. The highest CFR was recorded in India (3.21%) among SA countries, whereas Nepal and Bhutan had no death record due to COVID-19 so far. The recovery rate varies from 4.75% in the Maldives to 51.02% in Sri Lanka. In Bangladesh, community transmission has been recorded, and the highest number of cases were detected in Dhaka, followed by Narayanganj and Chattogram. We detected Dhaka and its surrounding six districts, namely Gazipur, Narsingdi, Narayanganj, Munshiganj, Manikganj, and Shariatpur, as the 99% confidence-based hotspot where Faridpur and Madaripur district as the 95% confidence-based spatial hotspots of COVID-19 in Bangladesh. However, we did not find any cold spots in Bangladesh. We identified three hotspots and three cold spots at different confidence levels in India. Findings from this study suggested the "Test, Trace, and Isolation" approach for earlier detection of infection to prevent further community transmission of COVID-19.

Keywords: Bangladesh; Case fatality rate; Community transmission; Dhaka; Risk; South Asia.

Fig. 1

Case fatality rate and recovery rate of COVID-19 patients in different regions of the globe.

Fig. 2

Tested population per 1 million for COVID-19 in different region across the globe.

Fig. 3

Linear (at the top) versus log-linear (at the bottom) distribution of COVID-19 cases from 15 February to 15 May (total = 91 days) in different SA countries. (A) Left pole: Afghanistan, India, Pakistan, and Bangladesh (scale in thousand); (B) Right pole: Sri Lanka, Nepal, Bhutan, and Maldives (scale in hundred).

Fig. 4

Geospatial distribution of Active, recovered, and death cases of COVID-19 in South Asian countries. The colour gradient of the base map is the illustration of a number of cases that have been reported yet as of 15 May, 2020). The pie chart of the map depicted the number of active cases (yellow coloured), recovered case (green coloured), and the death count (red coloured) in different areas.

Fig. 5

Case fatality rate and recovery rates in South Asian countries (the values are in percentage).

Fig. 6

Spatial distribution of COVID-19 cases in different districts of Bangladesh. The graduated colour of the base map is the illustration of population density, where different sized red coloured circle depicted the case count in each district.

Fig. 7

Confirmed COVID-19 cases and death among different age categories in Bangladesh as of May 15, 2020; blue bar denotes the percentage of cases, and the orange bar indicates the rate of death.

Fig. 8

Spatial hotspots of COVID-19 in India as of May 15, 2020. The poinsettia red coloured area is indicating the Hot Spot region for COVID-19 at 99% confidence. The grey shaded area depicted the cold spot region at 90% confidence. The yucca yellow shaded area is an indication of a non-significant part.

Fig. 9

Spatial hotspots of COVID-19 in Bangladesh as of May 15, 2020. The poinsettia red coloured area is the indication of Hot Spot at 99% confidence. The cantaloupe coloured area is the illustration of the 90% confidence based Hot Spot area of Bangladesh. The yucca yellow coloured area is the indication of a non-significant region. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)