Forecasting COVID-19 infections in the Arabian Gulf region

COVID-19 case-fatality variations with application to the Middle East countries

During a pandemic outbreak, it is important for health officials to know the proportions of deaths among infected individuals and to understand how these proportions change overtime, to accurately predict the impact of the pandemic and to implement effectively new intervention policies and health protocols and to adjust them accordingly. However, most studies where efforts have been made to estimate accurately the case fatality rates did not address the issue of measuring the dynamics of the pandemic deadliness during its course. Daily data on COVID-19 cases and deaths were collected from selected MENA countries. In this paper, two new measures of the pandemic fatality are developed based on the estimated time it takes hospitalized infected patients to eventually die from the disease. The first measure assigns COVID-19 deaths to its most significant lagged number of cases based on a fixed-effects panel data model. The second fatality measure relates pandemic deaths and cases based on their respective change points. The results find notable variations of the pandemic lethality between the Middle East countries, likely due to the difference in the quality of health care. Although crude case-fatality rate does not identify the pandemic lethality variations during the ongoing of the disease, this paper develops two novel measures for COVID-19 case fatality which can identify the dynamics and the variations of the pandemic deadliness.

A Spatiotemporal Analysis of the COVID-19 Pandemic in North Africa

Spatial panel-data models are estimated to identify the factors of the prevalence of the coronavirus outbreak in North Africa. Using daily data on the number of cases collected between March 2020 and December 2021, three types of general models are investigated, and they include spatial spillovers between the neighboring countries of the region. In one model the spatial dependence is accounted for by adding a spatial lag of the dependent variable (SAR model). In an alternative specification, spatially correlated error terms are considered in the model (SEM), and in the third model a spatial lag dependent variable and spatially correlated errors are both added (SAC). To deal with unobservable individual heterogeneity, random and fixed individual effects specification are investigated in each of these models. The results of the maximum likelihood and generalized method of moments' estimations show that the lift of travel restrictions had an important impact on the spike in the numbers of COVID-19 cases in North Africa and that the effects of endogenous interactions between the countries are strongly significant. It is found that spatial spillovers and a change in the travel policy are the main factors that can explain the mechanism of spread the coronavirus pandemic in North Africa. However, more data on socio-demographic and behavioral variables and on vaccination rates are needed to better understand what caused the recent surge in the number of infections in the region.