Comparison of forecasting of the risk of coronavirus (COVID 19) in high quality and low quality healthcare systems, using ANN models

TL;DR

This paper compares the effectiveness of ANN models and regression models in forecasting COVID-19 risks within high and low quality healthcare systems, using data from a hospital in a case study.

Contribution

It introduces a comparative analysis of ANN and regression models for COVID-19 risk forecasting in different healthcare quality contexts.

Findings

ANN models provided more accurate forecasts than regression models.

Forecasting parameters included age, gender, infection rates, comorbidities, and mortality.

The study offers recommendations for managing COVID-19 based on model predictions.

Abstract

COVID 19 is a disease that has abnormal over 170 nations worldwide. The number of infected people (either sick or dead) has been growing at a worrying ratio in virtually all the affected countries. Forecasting procedures can be instructed so helping in scheming well plans and in captivating creative conclusions. These procedures measure the conditions of the previous thus allowing well forecasts around the state to arise in the future. These predictions strength helps to make contradiction of likely pressures and significances. Forecasting procedures production a very main character in elastic precise predictions. In this case study used two models in order to diagnose optimal approach by compared the outputs. This study was introduced forecasting procedures into Artificial Neural Network models compared with regression model. Data collected from Al Kindy Teaching Hospital from the period of 28/5/2019 to 28/7/2019 show an energetic part in forecasting. Forecasting of a disease can be done founded on several parameters such as the age, gender, number of daily infections, number of patient with other disease and number of death. Though, forecasting procedures arise with their private data of tests. This study chats these tests and also offers a set of commendations for the persons who are presently hostile the global COVID 19 disease.