The study tries to explain the impact of high temperature on the number of confirmed cases by excluding all measures that have been taken by specific governments in certain global hotspots like New York City, Lombardy, Madrid (Spain) and Maharashtra. Some statistical analysis like the F-statistical test, ANOVA and t-test has been performed to know how much variance is there among the regions for the parameters 1) proportion of cumulative confirmed cases to the whole population and 2) mean temperature. The analysis includes graphs to have a clear visualization at the first glance for all four zones that have been taken for the case study and ends with all statistical results, discussion and conclusion of the tests that have been performed. Keywords: f-statistical test, ANOVA (Analysis of Variance), t-test, temperature, Coronavirus, Pearson’s Correlation Introduction The outbreak of this novel SARS-CoV-2 overwhelmed the global health system making an outrageous mark on each and every aspect of life be it social, economic and psychological. All around the world researchers and scientists are trying to solve the mystery or questions behind the spread of this virus which is highly contagious looking at all those parameters which can help in curbing the spread as, till now it has affected almost 4,553,432 people globally. In this particular paper, we are looking at those parts of the world which have been highly impacted by this pandemic and trying to relate & analyse how much temperature as a parameter can help in controlling the spread. The data has been looked from 10th March 2020 onwards till 23rd April 2020, New York has been the worst hit by this pandemic till 23rd April 2020 having 1,45,855 number of confirmed cases, on second Lombardy with the number of confirmed cases 70,165, third is Madrid with 61,588 and lastly Maharashtra with 5652.
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