ABSTRACT
In the present global scenario, SARS-Cov-19 has brought many unprecedented challenges to health, public, and private sectors. Due to this dynamic infectious disease, we have seen drastic changes in the quality of life (QOL) of human beings. Then, how can policymakers and public health professionals draw rational decisions about when to open or close affected sites or when to allow, prohibit, or adopt certain mitigations for health decisions at the population level? In the context of the government policy domain, this study attempts to demonstrate the statistical models to address the various risk factors that affect human health during the SARS-Cov-19 pandemic crisis. A decision augmented model is constructed to explore the various methodological processes of clinical and epidemiological attributes that enable to test the statistical significance of surge of the third wave in its risk factors. According to research interventions, we classify the risk factors into different domains such as health, behavioral, policy, and economic risk factors pertaining to the global pandemic. World Health Organization’s objective risk stratification (ORS) tool is used to extrapolate the research interventions in selected respondents. Data are collected using tested and pretested questionnaires. Selected factors are grouped based on the total score obtained from each individual sub-sachet of questionnaires. An augmented model is employed to segregate the determined percentage variations on various risk attributes. Furthermore, the transformed score is substituted in major risk factors such as health, behavioral, economic, and policy. Exploratory factor analysis (EFA) is applied for the extraction of factor loading, communalities, and eigenvalues 6 to know the structural correlation between different components of risk factors. As per the resultant findings, this study finds that the sanitizer usage [coefficient −2.95, SE 0.42; t value 2.09; p < 0.01], vaccinated population [coefficient −0.09, SE 0.00; t value 3.46; p < 0.01], compulsory wearing of masks [coefficient −2.55, SE 0.21; t value 4.35; p = 0.0087] reduce the transmission and prevent the risk of the third wave in children [coefficient −1.21, SE 0.02; t value 2.59; p < 0.01]. All the above parameters show insignificant correlation for the third wave, and low augmentation is seen (<1% augmentation). However, besides all risk indicators, health risk is more prevalent (40%, p = 0.001; odds 6.33), followed by economic (25%, p = 0.001; odds 4.22), behavior (20%, p = 0.0157; odds 3.08), exposure (10%, p = 0.087; odds 2.58), and policy risk factors (5%, p = 0.0947; odds 1.89). All risk domains are significantly correlated with SARS-Cov-19 pandemic crisis in India. The results conclude that risk assessment is a powerful tool that provides a rational framework for designing and managing infectious and non-infectious diseases (e.g., SARS-Cov-19) at the early stage or phase.
