| description abstract | The unprecedented Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2), or COVID-19, pandemic adversely affected all walks of life, causing loss of lives and livelihood. The disruptions caused to the economy, social well-being, and transportation systems are almost unfathomable. The scenario in India was grave during the first wave, where high-density urban conglomerations affected the most. Transmissions of the contagion due to human-to-human interactions forced the government to employ strict lockdown policies as an immediate measure to curb the spread. However, gradual relaxations on lockdowns during the initial stages in India demonstrated similar trends between the rise in mobility and COVID-19 positive cases. This study leverages publicly available activity-based mobility datasets to model and predict the number of virus-positive cases during the first pandemic wave in Indian states. Dynamic regression models, which consider the ripple effects of the response and explanatory variables as feedbacks to the response variable, are utilized to analyze the panel data. In addition to the mobility data, the cumulative number of COVID-19 cases is also related to the regional demographics and other information concerning the infection spread and testing data. The proposed model produces good short-term forecasts for Indian states. Findings from the study concerning mobility point to the positive effects of curtailing travel for the effective control of pandemic diffusion through human interactions. Comprehending the effects of mobility and testing rates on the reported number of cases is essential to devising strategies best suited for a region during such an instance. The methodology and contextual knowledge from the study can aid planners, decision-makers, and researchers to bolster support systems in the future. | |
