Community vulnerability to epidemics in Nepal: A high-resolution spatial assessment amidst COVID-19 pandemic

Abstract

<jats:p>The coronavirus disease 19 (COVID-19), the biggest health problem at present, does not have uniform transmission and severity among the countries and communities therein. Knowledge of community vulnerability to the disease would facilitate interventions aimed at transmission control by the efficient deployment of available limited resources. Therefore, we assessed spatial variations and heterogeneity of disease vulnerability among the population in 753 municipal units of Nepal. We collected geospatial indicators representing the domain of socioeconomic inequalities, population dynamics, heterogeneity in the accessibility and the information related to an underlying health condition which potentially affect the severity of COVID-19 transmission. Those indicators were assembled to create three vulnerability indices using Geographic Information System (GIS); Social Vulnerability Index (SVI), Epidemiological Vulnerability Index (EVI) and a composite of the two- Social and Epidemiological Vulnerability Index (SEVI). The indicators were scaled to a common measurement scale and spatially overlaid via equally weighted arithmetic mean. The indices were classified into five level of risk and the municipal units and the population within vulnerabilities classes were quantified and visualized in the map. The index output indicated high vulnerability to epidemics in megacities like Kathmandu, Pokhara, Bharatpur, etc.; developing cities especially in the Province No 2; and, municipal units of Karnali and Sudoorpashchim provinces. Additionally, some other municipalities such as Dhulikhel, Beshishahar, Tansen etc. which have a higher prevalence of pulmonary and cardiovascular disorders are highly vulnerable. The SVI indicated that 174 municipal units and 41.5% population is highly vulnerable. The EVI identified 55 municipal units and 40.7% of the total population of the country highly vulnerable to COVID-19. The SEVI accounted that disease vulnerability is high in 105 municipal units and 40% population of Nepal. The vulnerability indices created are means for different tiers of the existing government in federal system of Nepal for prioritization and improved planning for disease intervention especially in highly vulnerable municipal units where the COVID-19 transmission could have high severity.</jats:p>

Execution of intervention matters more than strategy: A lesson from the spatiotemporal assessment of COVID-19 clusters in Nepal

“…Indian cities were severely affected of the COVID-19 since early April (Ray et al, 2020; Tomar and Gupta 2020) and inflow of infected but asymptomatic people from those areas without testing increased the cases in particular areas of Nepal. Additionally, those districts of elevated incidences are also characterized by higher population density, lower literacy rates, higher poverty, and in turn preeminent vulnerability to the epidemics (Khanal et al, 2020). Population density is one of the important factors in shaping the spatial pattern of the epidemics as the crowded cities worldwide could experience more prolonged epidemics (Rader et al, 2020).…”

“…The most likely cluster, the C1, emerged on 12 June that included 11 districts from Karnali and Sudoorpaschim provinces. These districts have higher poverty and majority of the households have one or more members of the family working as low-skilled manpower in Indian cities like Mumbai, Delhi, and others (Khanal et al, 2020). The first cluster of COVID-19 observed was the consequence of infected returnees from India.…”

“…The geographical distribution of the COVID-19 cases within the Nepal territory is not uniform. Communities associated with the poverty in densely populated growing cities are estimated more vulnerable to the infection (Khanal et al, 2020). Additionally, disease intervention efforts are not equal among the local administrative units of Nepal and so is the pattern of spread of the COVID-19.…”

“…The level of vulnerability to the COVID-19 differed among the communities based on the demographic, socioeconomic, accessibility to the health facilities, prevalence of the pulmonary and cardiac disorders, etc. (Khanal et al, 2020). The complex spatial and temporal epidemiology of COVID-19 due to rapid changes in human population dynamics and its demographic and environmental drivers challenges in its efficient control (Alkhamis et al, 2020).…”

Early Detection of COVID-19 Waves From Cases in a Neighboring Country With an Open Border

“…In fact, risk due to population outflow is higher than any other factors like geographical proximity and similarity in economic conditions. The open border and lack of documentation make it even harder to trace (12) and constrain the outbreak within one country. Thus, it is essential not only to know the strength of the relationship of COVID-19 incidence between these two countries but also to know the lead or the lag in the days for maximum similarity of the new cases.…”