ABSTRACT
Introduction: The outbreak of COVID-19 has differentially affected countries in the world, with health infrastructure and other related vulnerability indicators playing a role in determining the extent of the COVID-19 spread. Vulnerability of a geographical region/country to COVID-19 has been a topic of interest, particularly in low- and middle-income countries like India to assess the multi-factorial impact of COVID-19 on the incidence, prevalence or mortality data. Datasets and Methods Based on publicly reported socio-economic, demographic, health-based and epidemiological data from national surveys in India, we compute contextual, COVID-19 Vulnerability Indices (cVIs) across multiple thematic resolutions for different geographical and spatial administrative regions. These multi-resolution cVIs were used in regression models to assess their impact on indicators of the spread of COVID-19 such as the average time-varying instantaneous reproduction number. Results Our observational study was focused on 30 districts of the eastern Indian state of Odisha. It is an agrarian state, prone to natural disasters and one of the largest contributors of an unprotected migrant workforce. Our analyses identified housing and hygiene conditions, availability of health care and COVID preparedness as important spatial indicators. Conclusion Odisha has demonstrated success in containing the COVID-19 infection to a reasonable level with proactive measures to contain the spread of the virus during the first wave. However, with the onset of the second wave of COVID, the virus has been making inroads into the hinterlands and peripheral districts of the state, burdening the already deficient public health system in these areas. The vulnerability index presented in this paper identified vulnerable districts in Odisha. While some of them may not have a large number of COVID-19 cases at a given point of time, they could experience repercussions of the pandemic. Improved understanding of the factors driving COVID-19 vulnerability will help policy makers prioritise resources and regions leading to more effective mitigation strategies for the COVID-19 pandemic and beyond.
Subject(s)
COVID-19 , DyssomniasABSTRACT
Understanding dynamics of an outbreak like that of COVID-19 is important in designing effective control measures. This study aims to develop an agent based model that compares changes in infection progression by manipulating different parameters in a synthetic population. Model input includes population characteristics like age, sex, working status etc. of each individual and other factors influencing disease dynamics. Depending on number of epicentres of infection, location of primary cases, sensitivity, proportion of asymptomatic and frequency or duration of lockdown, our simulator tracks every individual and hence infection progression through community over time. In a closed community of 10000 people, it is seen that without any lockdown, number of cases peak around 6th week and wanes off around 15th week. If primary case is located inside dense population cluster like slums, cases peak early and wane off slowly. With introduction of lockdown, cases peak at slower rate. If sensitivity of identifying infection decreases, cases and deaths increase. Number of cases declines with increase in proportion of asymptomatic cases. The model is robust and provides reproducible estimates with realistic parameter values. It also guides in identifying measures to control outbreak in a community. It is flexible in accommodating different parameters like infectivity period, yield of testing, socio-economic strata, daily travel, awareness level, population density, social distancing, lockdown etc. and can be tailored to study other infections with similar transmission pattern.