A predictive model and country risk assessment for COVID-19: An application of the Limited Failure Population concept

Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA

The objective of this study is to compare the different methods which are effective in predicting data of the short-term effect of COVID-19 confirmed cases and DJI closed stock market in the US. Data for confirmed cases of COVID-19 has been obtained from Worldometer, the database of Johns Hopkins University and the US stock market data (DJI) was obtained from Yahoo Finance. The data starts from 20 January 2020 (first confirmed COVID-19 case the US) to 06 December 2020 and DJI data covers 21 January 2019 to 04 December 2020. COVID-19 data was tested for the period 30 November to 06 December and DJI from 25 November 2020 to 04 December. From the result, we find that the method SutteARIMA was found more suitable to calculate the daily forecasts of COVID-29 confirmed cases and DJI in the US and this method has been used in this study. For the evaluation of the prediction methods, the accuracy measure means absolute percentage error (MAPE) has been used. The MAPE value with the SutteARIMA of 0.56 and 0.60 for COVID-19 and DJI stock respectively was found to be smaller than the MAPE value with ARIMA method.

Regional infectious risk prediction of COVID-19 based on geo-spatial data

After the first confirmed case of the novel coronavirus disease (COVID-19) was found, it is of considerable significance to divide the risk levels of various provinces or provincial municipalities in Mainland China and predict the spatial distribution characteristics of infectious diseases. In this paper, we predict the epidemic risk of each province based on geographical proximity information, spatial inverse distance information, economic distance and Baidu migration index. A simulation study revealed that the information based on geographical economy matrix and migration index could well predict the spatial spread of the epidemic. The results reveal that the accuracy rate of the prediction is over 87.10% with a rank difference of 3.1. The results based on prior information will guide government agencies and medical and health institutions to implement responses to major public health emergencies when facing the epidemic situation.