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?:abstract
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Coronavirus Disease 2019 (COVID‐19) pandemic poses extreme threat to public health and economy, particularly to the nations with higher population density. The disease first reported in Wuhan, China; later, it spreads elsewhere, and currently, India emerged as COVID‐19 hotspot. In India, we selected 20 densely populated cities having infection counts higher than 500 (by 15 May) as COVID‐19 epicenters. Daily COVID‐19 count has strong covariability with local temperature, which accounts approximately 65–85% of the explained variance; i.e., its spread depends strongly on local temperature rise prior to community transmission phase. The COVID‐19 cases are clustered at temperature and humidity ranging within 27–32°C and 25–45%, respectively. We introduce a combined temperature and humidity profile, which favors rapid COVID‐19 growth at the initial phase. The results are highly significant for predicting future COVID‐19 outbreaks and modeling cities based on environmental conditions. On the other hand, CO(2) emission is alarmingly high in South Asia (India) and entails high risk of climate change and extreme hot summer. Zoonotic viruses are sensitive to warming induced climate change; COVID‐19 epicenters are collocated on CO(2) emission hotspots. The COVID‐19 count distribution peaks at 31.0°C, which is 1.0°C higher than current (2020) and historical (1961–1990) mean, value. Approximately, 72% of the COVID‐19 cases are clustered at severe to record‐breaking hot extremes of historical temperature distribution spectrum. Therefore, extreme climate change has important role in the spread of COVID‐19 pandemic. Hence, a strenuous mitigation measure to abate greenhouse gas (GHG) emission is essential to avoid such pandemics in future.
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