Abstract
COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020.
Highlights
The SARS-CoV-2, i.e. severe acute respiratory syndrome coronavirus disease 2 (COVID-19) has to date (27 September 2020) infected more than 33 Million people worldwide, caused almost one million mortalities, and forced more than 10000 Million people to stay within their homes [1], [2]
We demonstrate the performance and limitations of the Holt-Winters method as means of providing short-term COVID-19 forecasts in India based on observed records of infections
This paper demonstrates the performance of short-term statistical forecasts using Holt-Winters method and suggests that this method could be suitable for providing operational COVID-19 forecasts in India aimed at different administrative levels
Summary
The SARS-CoV-2, i.e. severe acute respiratory syndrome coronavirus disease 2 (COVID-19) has to date (27 September 2020) infected more than 33 Million people worldwide, caused almost one million mortalities, and forced more than 10000 Million people to stay within their homes [1], [2]. We demonstrate the performance and limitations of the Holt-Winters method as means of providing short-term COVID-19 forecasts in India based on observed records of infections. This approach is fully data-driven [29] and thereby adaptable for regional and local usage. These assumptions proved to have very little influence on the COVID-19 projections and in the following we highlight only the third scenario According to these different assumptions regarding the size of the susceptible population, transmission and recovery rates, we use the basic form of the SIR model to estimate the temporal development of COVID-19 in India for the three compartments (Figure 3) since 22 January, 2020
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