key: cord-0883272-qjngf6o3 authors: Nesteruk, I. title: The COVID-19 pandemic storm in India subsides, but the calm is still far away date: 2021-06-03 journal: nan DOI: 10.1101/2021.06.01.21258143 sha: b7a447fa2b5bc26b9ff46632ea70893bffed5251 doc_id: 883272 cord_uid: qjngf6o3 In May 2021, the number of new COVID-19 patients in India began to decline, as predicted by the generalized SIR-model (susceptible-infected-removed). The calculations of the final size of this pandemic wave and its duration probably were too pessimistic. New SIR simulations with the use of fresher datasets are necessary in order to update the predictions and to calculate the difference between the registered (laboratory-confirmed) and real number of cases. At the end of May 2021, the daily number of new laboratory-confirmed COVID-19 cases in India was already half the maximum of 400,000 observed in April, [1] . Such a rapid decline was predicted by the results of preliminary modeling of the epidemic dynamics, published in [2] in early May 2021. Here we will compare the latest data reported from Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) [1] with the forecasts based on the generalized SIR-model [2] and will analyze their accuracy. We will use the data sets regarding the accumulated numbers of confirmed COVID-19 cases in India (V j ) from JHU) [1] . The values V j and corresponding time moments t j in May 2021 are shown in Table 1 (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 3, 2021. ; In [2] the generalized SIR-model and the exact solution of the set of non-linear differential equations relating the number of susceptible S, infectious I and removed persons R was used (see, e.g., [3, 4] ). The exact solution depends on five unknown parameters. The values V j , corresponding to the moments of time t j from the period April 10-23, 2021 have been used in [2] to find the optimal values of these parameters corresponding to the new epidemic wave in India. The details of the optimization procedure can be found in [5] . (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 3, 2021. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 3, 2021. ; https://doi.org/10.1101/2021.06.01.21258143 doi: medRxiv preprint We can also compare the theoretical curve (1) with the average daily number of new cases which can be calculated with the use of smoothing registered V j values [3, 4]: and its derivative: The blue "crosses" represent the results of calculations of the derivative (3) Probably new simulation with the use of fresher datasets could fix this problem and provide new more optimistic predictions for the final size and the pandemic duration in India. The second reason for discrepancies may be the large number of unregistered cases observed in other countries [6] [7] [8] [9] . Estimates for Ukraine and Qatar made in [10, 11] showed that the real number of cases is probably 4-5 times higher than registered and reflected in official statistics. Similar estimates have to be done for the case of India. COVID-19 Data Repository by the Center for Systems Science and Engineering Waves of COVID-19 pandemic. Detection and SIR simulations Procedures of Parameter Identification for the Waves of Epidemics Estimating the early death toll of COVID-19 in the United States Visible and real sizes of new COVID-19 pandemic waves in Ukraine Innov Biosyst Bioeng Impact of vaccination and undetected cases on the COVID-19 pandemic dynamics in Qatar in 2021 The author is grateful to Oleksii Rodionov for his help in collecting and processing data.