Early detection of seasonality and second-wave prediction in the COVID-19 pandemic
Watanabe, M.
Show abstract
Seasonality plays an essential role in the dynamics of many infectious diseases. In this study, we use statistical methods to show how to detect the presence of seasonality in a pandemic at the beginning of the seasonal period and that seasonality strongly affects SARS-coV-2 transmission. We measure the expected seasonality effect in the mean transmission rate of SARS-coV-2 and use available data to predict when a second wave of the Covid-19 will happen. In addition, we measure the average global effect of social distancing measures. The seasonal force of transmission of Covid-19 increases in September in the Northern hemisphere and in April in the Southern hemisphere. These predictions provide critical information for public health officials to plan their actions to combat the new coronavirus disease and to identify and measure seasonal effects in a future pandemic.
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