Effectiveness of Government Policies in Controlling COVID-19 in India

Abstract

The purpose of this study is to find the demographic factors associated with the spread of COVID-19 and to suggest a measure for identifying the effectiveness of government policies in controlling COVID-19. The study hypothesizes that the cumulative number of confirmed COVID-19 patients depends on the urban population, rural population, number of persons older than 50, population density, and poverty rate. A log-linear model is used to test the stated hypothesis, with the cumulative number of confirmed COVID-19 patients up to period [Formula: see text] as a dependent variable and demographic factors as an independent variable. The policy effectiveness indicator is calculated by taking the difference of the COVID rank of the [Formula: see text]th state based on the predicted model and the actual COVID rank of the [Formula: see text]th state[Formula: see text]Our study finds that the urban population significantly impacts the spread of COVID-19. On the other hand, demographic factors such as rural population, density, and age structure do not impact the spread of COVID-19 significantly. Thus, people residing in urban areas face a significant threat of COVID-19 as compared to people in rural areas.

Predictores de salud y pobreza en contagios de covid-19 en municipios rurales

“…En un estudio similar de contagio de COVID-19 que fue elaborado por una regresión múltiple por el método de mínimos cuadrados ordinarios se presentó un coeficiente de 0.25 para la tasa de pobreza, sin embargo, el estudio no fue realizado por género, en tanto que en esta investigación se tiene un coeficiente de 4.98 para el contagio en mujeres en situación de pobreza extrema y de carencias sociales se tiene 1.23 y 2.49 para hombres y mujeres respectivamente. Se mencionó carencias sociales, debido a que esta variable representa los indicadores de pobreza, de tal forma que se aprecia que las mujeres con mayores carencias en las comunidades rurales son las de mayor riesgo de contagio por COVID-19 (Pandey, & Saxena, 2020).…”

“…En la India se realizó un estudio y se encontró que el número acumulado de pacientes con COVID-19 confirmados, depende de la población urbana, la población rural, el número de personas mayores de 50 años, la densidad de población y la tasa de pobreza, pues se encontró a través de un modelo por mínimos cuadrados ordinarios que el contagio de Covid-19 presentó un coeficiente de 0.25 para la tasa de pobreza, así como un coeficiente de 1.61 y de -0.33 para zonas urbanas y rurales respectivamente, y para la edad presentó un coeficiente de -0.03 (Pandey & Saxena, 2020).…”

COVID-19 preparedness and response in rural and remote areas: A scoping review

“…In some LMIC countries with large rural populations, national policies identified through the review appeared not to differentiate between rural and urban areas [17,18]. Examples of challenges were described in reviews of India's national policies in which authors identified overall health policy and system inadequacies that could hinder an effective COVID-19 response, but also highlighted the capacity for successful responses in states with large rural populations such as Uttar Pradesh and Rajasthan [19,20].…”

Similar yet so different: The analysis of factors explaining the variance in COVID-19 deaths across the Baltic and Nordic states

“…There are two theoretical assumptions which are usually mentioned in most articles: age structure of society and population density. The former frequently refers to elderly citizens who belong to the high-risk group and are more vulnerable to the severe symptoms and illnesses related to or provoked by the coronavirus 24 . The latter is more selfexplanatory and refers to the social distance, a guarantee of which could be hindered by the high density of population, leading to an increased number of infections and deaths 25 .…”