Geospatial modelling on the spread and dynamics of 154 day outbreak of the novel coronavirus (COVID-19) pandemic in Bangladesh towards vulnerability zoning and management approaches

Abstract

The novel COVID-19 is a worldwide transmitted pandemic and has received global attention. Since there is no effective medication yet, to minimize and control the transmission of the COVID-19, non-pharmaceutical interventions (NPIs) are followed globally. However, for the implementation of needful NPIs through effective management strategies and planning, space-time-based information on the nature, magnitude, pattern of transmission, hotspots, the potential risk factors, vulnerability, and risk level of the pandemic are important. Hence, this study was an attempt to in-depth assess and analyze the COVID-19 outbreak and transmission dynamics through space and time in Bangladesh using 154 day real-time epidemiological data series. District-level data were analyzed for the geospatial analysis and modelling using GIS. Getis-Ord Gi* statistics was applied for the hotspot analysis, and on the other hand, the analytical hierarchy process-based weighted sum method (AHP-WSM) was used for the modelling of vulnerability zoning of COVID-19. In Bangladesh, the status of the pandemic COVID-19 still is in exposure level. Disease transmitted at a high rate (20.37%), and doubling time of the cases were 11 days (latest week of the study period). The fatality rate was comparatively low (1.3%), and the recovery rate was about 57.50%. Geospatial analysis exhibits the disease propagates from the central parts, and Dhaka was the most exposed district followed by Chattogram, Narayanganj, Cumilla, and Bogra. A single strong clustering pattern in the central part, which spread out mainly to the southeastern part, was identified as a prime hotspot in both the cases and deaths distributions. Additionally, potential linkages between the transmission of disease and the selected factors that gear up the spreading of the disease were identified. The central, eastern, and southeastern parts were recognized as high vulnerable zone, and conversely, the western, southwestern , northwestern , and northeastern parts as medium vulnerable zone. The vulnerable zoning exercise made it possible to identify vulnerable areas with the different magnitude that require urgent intervention through proper management and action plan, and accordingly, comprehensive management strategies were anticipated. Thus, this study will be a useful guide towards understanding the space-time-based investigations and vulnerable area delineation of the COVID-19 and assist to formulate an effective management action plan to reduce and control the disease propagation and impacts. By appropriate adjustment of some factors with local relevance, COVID-19 vulnerability zoning derived here can be applied to other regions, and generally can be used for any other infectious disease. This method was applied at a regional scale, but the availability of larger scale data of the determining factors could be applied in small areas too, and accordingly, management strategies can be formulated.

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

“…The left column describes the main categories in this group of articles. Subcategory Country and references Effects on spreading Argentina, Bolivia, Brazil, Chile, Colombia, Paraguay, Peru, and Uruguay [309] ; Australia [310] Bangladesh [311] ; China [31] , [32] , [33] , [312] , [313] , [314] , [315] ; China, Germany, India, Italy, Singapore, South Korea, Spain, UK, USA [316] ; Italy [317] , [318] ; New Zealand [319] ; Puerto Rico [320] ; South Korea [321] ; Taiwan [322] ; USA [323] , [324] , [325] , [326] , [327] , [328] ; 44 countries [329] ; 53 countries [330] ; 56 countries [331] ; 142 countries [332] ; 149 countries [333] Effects on behaviors China [334] ; China, Italy [335] ; Denmark, Sweden [336] ; Germany [337] USA [338] , [339] , [340] , [341] ; 20 countries [342] ; 67 countries [343] Effects on medical practice Italy [344] ; Netherlands [345] ; UK [346] …”

“…These articles study the impact on the unfolding of the disease focusing on the measures adopted in a specific country [31] , [32] , [33] , [309] , [310] , [311] , [312] , [313] , [315] , [317] , [318] , [319] , [320] , [322] , [323] , [324] , [325] , [326] , [327] , proposing multi country analyses [316] , [329] , [330] , [331] , [332] , [333] , and characterizing the effects of NPIs on other diseases [314] , [321] , [328] .…”

A Spatio‐temporal analysis of COVID‐19 outbreak in Italy

“…According to previous studies (Meng, Wang, Liu, Wu, & Zhong, 2005; Wang, Christakos, Han, & Meng, 2008), researchers have focused on the existence of spatial effects in the diffusion through Moran's I statistic for COVID‐19 and other pathogens in China (Kang, Choi, Kim, & Choi, 2020; Li, Calder, & Cressie, 2007; Zhang, Rao, Wu, Huang, & Dai, 2020). The spatial dynamics of COVID‐19 outbreak have been also tested using global Moran's I statistic for other countries (Bag, Ghosh, Biswas, & Chatterjee, 2020; Rahman, Islam, & Islam, 2020; Shariati, Mesgari, Kasraee, & Jahangiri‐rad, 2020).…”

Impact of population density on Covid-19 infected and mortality rate in India

“…In the absence of any precise medicine for the treatment of Covid-19 or any effective vaccine to prevent it, several efforts are made, based on the available pandemic data, in modeling the Covid-19 cases to understand the dynamics of infections (Chen et al 2020a, b;Roy et al 2020;Rahman et al 2020) and subsequently forecasting about the future course of the pandemic for scheming strategies to quickly contain the spreading of the infections by other means like physical distancing, lockdown, etc.…”