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Short-Term Statistical Forecasts of COVID-19 Infections in India
Ram Kumar Singh1, Martin Drews2, Manuel De La Sen3
1Department of Natural ResourcesTERI School of Advanced Studies New Delhi 110070 India.
IEEE Access : Practical Innovations, Open Solutions
|November 23, 2021
Summary
Mathematical models forecast COVID-19 in India. A statistical model predicts one-third of the population may be infected, with recovery taking 450 days. The pandemic is expected to peak in early November 2020.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health
Background:
- COVID-19 cases surged in India, prompting a national lockdown.
- Mathematical-epidemiological models are crucial for assessing infection probability and preparing health systems.
- Scarcity of updated regional data hinders effective coping strategies.
Purpose of the Study:
- To demonstrate a transferable statistical model for COVID-19 forecasting in India.
- To provide forecasts at various administrative levels using daily infection data.
- To estimate future infection trends and recovery timelines.
Main Methods:
- Utilized the Holt-Winters statistical method for time series forecasting.
- Employed daily data on accumulated infections, active infections, and deaths.
- Integrated results with a complementary SIR (Susceptible-Infected-Recovered) model.
Main Results:
- Generated 48-day forecasts for COVID-19 cases from September 28 to November 15, 2020.
- Projected that one-third of India's population could eventually be infected.
- Estimated a full recovery from COVID-19 will take approximately 450 days from January 2020.
Conclusions:
- The Holt-Winters model provides effective COVID-19 forecasts for India at regional levels.
- The SIR model indicates a potential peak in the first week of November 2020.
- Sustained coping strategies are essential for managing the pandemic's long-term impact.
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