- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.

In this paper, we analyse the COVID-19 data of the number of confirmed positive infectious COVID-19 cases (I) in Maharashtra, the second most populous state of India, having a population of nearly 123 million – more than the population of most of the European countries. For this analysis, we use COVID-19 data for the period from April 1, 2020 to August 24, 2020 to find the flex in the I - t curve, where the second derivative of the curve becomes negative. i.e. the date from which the rate of growth of the number of infections starts decreasing – or a peak occurs in the daily new COVID – 19 positive cases. Here I is the total number of cumulative COVID-19 positive cases and t is the time (in days). The observed data are fitted by employing the Gauss error function formula

a + b erf(cx - d),

(with four adjustable arbitrary parameters a, b, c, and d) following the prescriptions adopted by Ciufolini and Paolozzi [1] for the analysis of the COVID-19 data from Italy and China. The date of flex is found using data from April 1 to August 11, then the data from April 1 to August 12, and so on, till the data from April 1 to August 24. There is a variation in the dates of flex for these 14 sets of data; however, the date of flex converges to a definite date towards the later sets. We also calculate the standard deviation of these values to calculate the uncertainty in the expected date of flex. Using these parameter values, we also calculate the expected values of COVID–19 positive cases for future. From this data, we estimate the date(s) at which there is sufficient reduction in the number of new daily positive cases and the number of such cases are likely to increase by say, 1000.

The data for the number of fatalities in the city is also fitted to a Gauss error function with four parameters of the above type and we estimate in this case also, the date of flex as well as the dates at which the number of fatalities reduces in proportion to sufficient reduction in the number of new COVID-19 cases, which in this case is approximately 34 (for 1000 new infections). The data for the above analysis has been taken from the website covid19India.org and Aarogya Setu App [2] of the Government of India.

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Posted 01 Sep, 2020

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Posted 01 Sep, 2020

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- The author has confirmed that a statement listing potential conflicts of interest or lack thereof is included in the text.

In this paper, we analyse the COVID-19 data of the number of confirmed positive infectious COVID-19 cases (I) in Maharashtra, the second most populous state of India, having a population of nearly 123 million – more than the population of most of the European countries. For this analysis, we use COVID-19 data for the period from April 1, 2020 to August 24, 2020 to find the flex in the I - t curve, where the second derivative of the curve becomes negative. i.e. the date from which the rate of growth of the number of infections starts decreasing – or a peak occurs in the daily new COVID – 19 positive cases. Here I is the total number of cumulative COVID-19 positive cases and t is the time (in days). The observed data are fitted by employing the Gauss error function formula

a + b erf(cx - d),

(with four adjustable arbitrary parameters a, b, c, and d) following the prescriptions adopted by Ciufolini and Paolozzi [1] for the analysis of the COVID-19 data from Italy and China. The date of flex is found using data from April 1 to August 11, then the data from April 1 to August 12, and so on, till the data from April 1 to August 24. There is a variation in the dates of flex for these 14 sets of data; however, the date of flex converges to a definite date towards the later sets. We also calculate the standard deviation of these values to calculate the uncertainty in the expected date of flex. Using these parameter values, we also calculate the expected values of COVID–19 positive cases for future. From this data, we estimate the date(s) at which there is sufficient reduction in the number of new daily positive cases and the number of such cases are likely to increase by say, 1000.

The data for the number of fatalities in the city is also fitted to a Gauss error function with four parameters of the above type and we estimate in this case also, the date of flex as well as the dates at which the number of fatalities reduces in proportion to sufficient reduction in the number of new COVID-19 cases, which in this case is approximately 34 (for 1000 new infections). The data for the above analysis has been taken from the website covid19India.org and Aarogya Setu App [2] of the Government of India.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

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