The unprecedented growth of the novel coronavirus (SARS-CoV-2) as a severe acute respiratory syndrome escalated to the coronavirus disease 2019 (COVID-19) pandemic. It has created an unanticipated global public health crisis that is spreading rapidly in India as well, posing a serious threat to 1350 million persons. Among the factors, population density is foremost in posing a challenge in controlling the COVID-19 contagion. In such extraordinary times, evidence-based knowledge is the prime requisite for pacifying the effect. In this piece, we have studied the district wise transmissions of the novel coronavirus in five south Indian states until 20th July 2020 and its relationship with their respective population density. The five states are purposefully selected for their records in better healthcare infrastructure vis-à-vis other states in India. The study uses Pearson’s correlation coefficient to account for the direct impact of population density on COVID-19 transmission rate. Response surface methodology approach is used to validate the correlation between density and transmission rate and spatiotemporal dynamics is highlighted using Thiessen polygon method. The analysis has found that COVID-19 transmission in four states (Kerala, Tamil Nadu, Karnataka and Telangana) strongly hinges upon the spatial distribution of population density. In addition, the results indicate that the long-term impacts of the COVID-19 crisis are likely to differ with demographic density. In conclusion, those at the helm of affairs must take cognizance of the vulnerability clusters together across districts.

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This study exclusively focuses on spatial and temporal change of temperature and precipitation before and after COVID-19 lockdown and also examines the extent of their variation and the spatial relationship between them. Our main objective is to analyze the spatiotemporal changes of two climatic variables in Indian subcontinent for the period of 2015-2020. Monthly precipitation and temperature data are collected from NOAA and NASA for January to May month across the four zones (northeast, northwest, central, and peninsular zone) of India. To conduct a zone-wise statistical analysis, we have adopted statistical process control (SPC) methods like exponentially weighted moving average (EWMA) control charts, individual charts (I-Chart) to detect the shift in temperature and precipitation over the study period and Pearson correlation coefficient applied to measure the spatial association between the two variables. The findings revealed that temperature parameter has experienced a lot of positive and negative trends in the span of 6 years and detected a weak to moderate negative correlation in many parts of the country in April 2020 after 2016. This study also identified a weak negative correlation mainly in NE zone in 2020 after 2017. This research provides vital scientific contribution to the effects of monthly temperature and precipitation before and after COVID-19 pandemic lockdown.

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