Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations

TL;DR

This paper compares day-level forecasting models for COVID-19 spread using time series and mathematical approaches, highlighting the importance of social distancing measures in controlling exponential growth.

Contribution

It introduces a comparative analysis of forecasting models for COVID-19 at the day level, emphasizing the impact of social distancing policies.

Findings

COVID-19 cases grow exponentially without restrictions

Forecasting models can predict spread patterns effectively

Social distancing measures significantly reduce case growth

Abstract

In mid of March 2020, Coronaviruses such as COVID-19 is declared as an international epidemic. More than 125000 confirmed cases and 4,607 death cases have been recorded around more than 118 countries. Unfortunately, a coronavirus vaccine is expected to take at least 18 months if it works at all. Moreover, COVID -19 epidemics can mutate into a more aggressive form. Day level information about the COVID -19 spread is crucial to measure the behavior of this new virus globally. Therefore, this study presents a comparison of day level forecasting models on COVID-19 affected cases using time series models and mathematical formulation. The forecasting models and data strongly suggest that the number of coronavirus cases grows exponentially in countries that do not mandate quarantines, restrictions on travel and public gatherings, and closing of schools, universities, and workplaces (Social Distancing).