New Models of Transmission of COVID-19 with Time under the Influence of Meteorological Determinants

Author(s): Atin Adhikari, Shilpi Ghosh, Moon M Sen and Rathin Adhikari

This work is aimed at modelling the progressions of COVID-19 cases in time in relation to meteorological determinants, in large cities of Brazil, Italy, Spain, and USA, and predicting the viability of SARS-CoV-2 virus in different weather conditions based on models. Our statistical analysis indicates that the spreading of infection does not vary exponentially with time and hence, does not have similarity with law of mass action in chemistry as considered in general for spreading of infectious diseases. New models are constructed showing the relationship of the I' (the number of infected individuals divided by the total population of a city) with the independent variables-time, temperature, relative humidity, and wind velocity. The regression models fitting in the data are statistically validated by : 1) plot of observed and predicted response; 2) standardized residual plots showing the characteristics of errors; 3) adjusted  value; 4) the p value for the parameters associated with the various independent variables; and 5) the predictive power of the model beyond data points. Models indicate that 1) the transmission of COVID-19 could be relatively high either for elevated temperatures with lower relative humidity or for lower temperatures with higher relative humidity conditions; 2) disease transmission is expected to be reduced more with higher wind velocity; 3) for meteorological factors remaining same, the rate of increase in the number of COVID-19 cases increases in one model with a constant rate and in the other two with varying rates in time. These transmission features seem to have connections with the structural components of the SARSCoV- 2 virus. Under suitable meteorological conditions, the partial natural disappearance of COVID-19, pandemic could be possible. New models for Ij may be con

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