An Analysis Comparing the Predictions of Predictive Models with the Actual Number of New Cases during the COVID-19 Pandemic
Keywords:
Predictive Models, COVID-19, Confirmed Cases, Python, AutoTS, Automatic Time Series ForecastingAbstract
On January 3, 2020, Chinese health officials discovered a pneumonia outbreak in the Chinese city of Wuhan. The virus swiftly spread to most countries, infecting a substantial portion of the population. On September 28, 2020, about a million deaths were reported worldwide. The virus is typically transmitted through coughing or sneezing on others. Because there is no effective vaccine, the majority of governments have enforced lockdowns to slow the virus's spread. Other preventive measures, such as travel bans, social isolation, hand hygiene, and the use of face masks, were critical in restricting the virus's spread. However, it proved difficult to contain the virus, which had spread to over 200 nations with a population of over 7 billion people. Large data sets were acquired on a daily basis, and data analytics became critical for uncovering trends and establishing how the infection spread. Several studies have been conducted to develop a mathematical forecast for the pandemic since the disease's first cases in India. First examined with reference to India, these models differed significantly in their scope, underlying assumptions, and numerical forecasts. The objective of this research is to evaluate the predictive models' efficacy by comparing their forecasts with the actual number of new cases reported every day in India during the COVID-19 outbreak.
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