Did Mobility Affect the Spread of COVID-19 during the First Pandemic Wave: An Investigation for Indian States Using Dynamic Regression

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. [ FROM AUTHOR] Copyright of Journal of Transportation. Part A: Systems is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 effects on urban driving, walking, and transit usage trends: Evidence from Indian metropolitan cities.

The outbreak of the COVID-19 pandemic disrupted all walks of life, including the transportation sector. Fear of the contagion coupled with government regulations to restrict mobility altered the travel behavior of the public. This study proposes integrating freely accessible aggregate mobility datasets published by tech giants Apple and Google, which opens a broader avenue for mobility research in the light of difficult data collection circumstances. A comparative analysis of the changes in usage of different mobility modes during the national lockdown and unlock policy periods across 6 Indian cities (Bangalore, Chennai, Delhi, Hyderabad, Mumbai, and Pune) explain the spatio-temporal differences in mode usages. The study shows a preference for individual travel modes (walking and driving) over public transit. Comparisons with pre-pandemic mode shares present evidence of inertia in the choice of travel modes. Association investigations through generalized linear mixed-effects models identify income, vehicle registrations, and employment rates at the city level to significantly impact the community mobility trends. The methods and interpretations from this study benefit government, planners, and researchers to boost informed policymaking and implementation during a future emergency demanding mobility regulations in the high-density urban conglomerations.