PROPHET MODEL'S EFFICIENCY IN SHORT-TERM COVID-19 CUMULATIVE CASE PROJECTIONS: G7 COUNTRIES

Abstract

The Covid-19 pandemic has had a significant impact on the health and well-being of people across
the globe, as well as the global economy at large. It has become essential to predict the spread of infectious
diseases like Covid-19 to understand its impact on public health and the economy. This study analyzes shortterm predictions of Covid-19 cases in G7 countries using the Prophet model. The model uses a trend function,
a seasonality function, and a holiday function to generate accurate short-term predictions. The study compares
the predictions for G7 countries and finds that Canada and Germany had the lowest root mean square error
(RMSE) values. The economic and financial impacts of the pandemic on global supply chains, job losses, and
business closures are also analyzed. The study highlights the significant increase in public debt due to largescale fiscal stimulus packages implemented by governments to mitigate the economic impact of the pandemic.
The study emphasizes that accurate predictions of the spread mechanism are crucial for managing the
pandemic effectively and mitigating its impact. The literature review of various models indicates the
importance of accurate predictions and the difficulties in creating them. The study recommends the use of
machine learning models like the Prophet model to generate accurate short-term predictions to combat the
Covid-19 pandemic's spread