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Covid-19 Cases and Preparedness of a Himalayan State to Fight Against the Pandemic: A Study of Arunachal Pradesh
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Abstract
This paper State experienced a community transmission like situation as many cases were detected from the people without travel history, forcing the State government to impose capital region lockdown. However, the State has recorded extremely low fatality rate of Covid-19 and a good recovery rate, despite having relatively poor health care facilities. The growing cases of Covid-19 in the State called for better preparedness to fight against the pandemic. The results from the field survey data collected from 44 respondents spread across 30 health care centres and quarantine facilities of the State revealed that though various measures were taken by the government to provide facilities and strengthen the capacity to face the challenges posed by the pandemic, yet there is a need to invest more in building up health care facilities to win the battle against the pandemic. The State needs to provide basic facilities and personal protection equipment in all the health care centres and quarantine facilities, provide adequate stock of essential medicines, train health workers to deal with Covid-19 patients and install emergency care units. It also needs to strictly implement the social/physical distancing norms in order check the spread of the pandemic.
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This paper uncovers the socioeconomic and health/lifestyle factors that can explain the differential impact of the coronavirus pandemic on different parts of the United States during the initial outbreak phase of the pandemic. Using a dynamic panel representation of an epidemiological model of disease spread, the paper develops a Vulnerability Index for US counties from the daily reported number of cases over a 20-day period of rapid disease growth. County-level economic, demographic, and health factors are used to explain the differences in the values of this index and thereby the transmission and concentration of the disease across the country. These factors are also used to examine the number of reported deaths. The paper finds that counties with high median income have a high incidence of cases but reported lower deaths. Income inequality, as measured by the Gini coefficient, is found to be associated with more deaths and more cases. The remarkable similarity in the distribution of cases across the country and the distribution of distance-weighted international passengers served by the top international airports is evidence of the spread of the virus by way of international travel. The distributions of age, race and health risk factors such as obesity and diabetes are found to be particularly significant factors in explaining the differences in mortality across counties. Counties with better access to health care, as measured by the number of primary care physicians per capita, have lower deaths, and so do places with more health awareness as measured by flu vaccination prevalence. Environmental health conditions such as the amount of air pollution are found to be associated with counties with higher deaths from the virus. It is hoped that research such as these will help policymakers to develop risk factors for each region of the country to better contain the spread of infectious diseases in the future.
In December 2019, SARS COV-2 which originated in the Chinese city of Wuhan achieved pandemic proportions and spread rapidly to countries through International air traffic causing acute respiratory infection and deaths. Presence of International airports, demography, health financing and human developments factors were assumed to influence COVID-19 cases burden and case fatality rate (CFR). So, this study was undertaken to find a association between these factors and COVID-19 cases and deaths. The study used 48 districts using purposive sampling as proxy for cities and used secondary data analysis. Data was obtained for various variables like demographic, Health Financing, Indices and Testing infrastructure, COVID cases burden and case fatality from trusted sources. Descriptive statistics correlational statistics using Pearsons coefficient students T was used to describe, correlate and find significant difference in the data. The analysis found a significant difference between COVID cases burden in districts with International Airports (p<0.039) and those without it. Positive correlation of population density (r=0.65) with COVID-19 case burden and negative correlation of case fatality rate with NITI Aayogs health index (r=-0.12), human development index (HDI) (r=-0.18), per-capita expenditure on health (r=-0.072) and a correlation of r=0.16 was observed for gross state domestic product. Decongestion of cities through perspective urban planning is the need of the hour. Stricter quarantine measures in those districts with international airports can help reduce the transmission. Negative correlation of HDI and NITI Aayogs health index with CFR emphasizes the importance of improvements in social determinants of health.
Amongst the most robust consensus related to the COVID-19 disease is that the elderly are by far the most vulnerable population group. Hence, public authorities target older people in order to convince them to comply with preventive measures. However, we still know little about older people’s attitudes and compliance toward these measures. In this research, I aim to improve our understanding of elderly people’s responses to the pandemic using data from 27 countries. Results are surprising and quite troubling. Elderly people’s response is substantially similar to their fellow citizens in their 50’s and 60’s. This research (i) provides the first thorough description of the most vulnerable population’s attitudes and compliance in a comparative perspective (ii) suggest that governments’ strategies toward elderly people are far from successful and (iii) shows that methodologically, we should be more cautious in treating age as having a linear effect on COVID-19 related outcomes.
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