GIS-based spatial modeling to identify factors affecting COVID-19 incidence rates in Bangladesh

Abstract

The outbreak of the COVID-19 pandemic is an unprecedented shock throughout the world which leads to generate a massive social, human, and economic crisis. However, there is a lack of research on geographic modeling of COVID-19 as well as identification of contributory factors affecting the COVID-19 in the context of developing countries. To fulfill the gap, this study aimed to identify the potential factors affecting the COVID-19 incidence rates at the district-level in Bangladesh using spatial regression model (SRM). Therefore, data related to 32 demographic, economic, weather, built environment, health, and facilities related factors were collected and analyzed to explain the spatial variability of this disease incidence. Three global (Ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) SRMs were developed in this study. The results of the models showed that four factors significantly affected the COVID-19 incidence rates in Bangladesh. Those four factors are urban population percentage, monthly consumption, number of health workers, and distance from the capital. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh with a R square value of 78.6%. Findings from this research offer a better insight into the COVID-19 situation and would help to develop policies aimed to prevent the future epidemic crisis.

Data mining and socio-spatial patterns of COVID-19: geo-prevention keys for tackling the pandemic

“…GWR is widely used in spatial patterns for healthcare, which we intend to consider in further research. In any case, regression methods need to include geographic adaptation because, as demonstrated in our study with the Koenker Index (p<0.01), nonstationarity implies variable behaviour of COVID-19 with contextual variables depending on places, as found by other authors in health-related spatial studies (Mou et al, 2017;Rahman et al, 2020;Mansour et al, 2021). Here, we obtain another important result in relation to geoprevention keys.…”

“…This is an strategic contribution to help policy-makers to design future rules for coexisting with the virus, because the study reveals locations with a significant presence of cases, areas with an increasing trend in the last period (new and consecutive) and areas with recurrent presence of cases (sporadic pattern). Most prospective studies seek to model future trends using geographically weighted regression (GWR) to analyse the link between COVID-19 and space (Rahman et al, 2020). We, however, argue for 3D bins and emerging hotspots as a necessary first stage in modelling the pandemic because this method does not influence the result by selecting variables; it directly identifies problematic areas by combining space and time.…”

Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore

“…Thus, it is not startling that 93.8% of COVID-19 cases were reported from sub-zones defined as urban in this study. Associations of increased COVID-19 incidence in districts with high population density or commercial and economic activity spaces were previously observed in Wuhan, China 48 , 49 . In Tehran, Iran, spatial heterogeneity of COVID-19 incidence was observed across the urbanised and highly connected provinces 50 .…”

Exploring the Spatial and Temporal Patterns of Children and Adolescents with COVID-19 Infections in Slovakia during March 2020 to July 2022

“…The model outcomes indicated that urban population and proximity to the capital city were significant factors contributing to the COVID-19 incidence rates in Bangladesh. Moreover, the study revealed that urban areas exhibited a higher prevalence of COVID-19 infections compared to rural areas [25].…”