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During the progress of the COVID-19, many countries have observed that their active cases tend to rise again after falling for some time. This may cause some mathematical models like the one discussed in [2] tend to make errors in the future prediction. We discuss a simple method to better the future prediction in such cases. This method is applied on the active and total cases data for the countries USA and Canada. In the case of Canada, the method succeeded in predicting the date when the active cases began to decrease. In the case of USA, a major improvement in prediction was observed when the method was applied: the predicted active and total cases are 1465602 and 2729015 for June 30; whereas the actual values are 1455400 and 2728856. We also give the active and total cases prediction for Canada and the USA for the first week of July 2020.

Its spreading speed together with the risk of fatality might be the main characteristic that separates COVID-19 from other infectious diseases in our recent history. In this scenario, mathematical modeling for predicting the spread of the disease could have great value in containing the disease. Several very recent papers have contributed to this purpose. In this study we propose a birth-and-death model for predicting the number of COVID-19 active cases. It relation to the Susceptible-Infected-Recovered (SIR) model has been discussed. An explicit expression for the expected number of active cases helps us to identify a stationary point on the infection curve, where the infection ceases increasing. Parameters of the model are estimated by fitting the expressions for active and total reported cases simultaneously. We analyzed the movement of the stationary point and the basic reproduction number during the infection period up to the 20th of April 2020. These provide information about the disease progression path and therefore could be really useful in designing containment strategies.

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