A Markov model of COVID-19 susceptibilities, infections, recoveries and fatalities: evidence from Nigeria

Abstract

Purpose The purpose of this study is to determine the proportion of the population that will be susceptible to the COVID-19 pandemic, as well as the proportions of infections, recoveries and fatalities from the COVID-19 pandemic. Design/methodology/approach The design was a longitudinal survey of COVID-19 infections, recoveries and fatalities in Nigeria using the data on the daily updates of the Nigeria Centre for Disease Control for the period 1 May to 23 August 2020. Markov chain analysis was performed on the data. Findings The results showed that in the long run, 8.4% of the population will be susceptible to COVID-19 infections, 26.4% of them will be infected, 61.2% of the infected will recover and 4% will become fatal. Thus, if this pattern of infections and recoveries continue, the majority of the infected people in Nigeria will recover whilst a very small proportion of the infected people will die. Research limitations/implications A dearth of the extant literature on the problem, especially from the management science perspective. Practical implications Results of the study will facilitate policymakers’ response to the curtailment of the pandemic in Nigeria. Social implications Curtailing the pandemic through the results of this study will assist in easing the social consequences of the pandemic. Originality/value The proposed adjustment to the susceptibilities, infections and recoveries model through the introduction of a fourth state (fatality) to get the susceptibilities, infections, recoveries and fatalities model, signalling a point of departure from previous studies.

MCMC algorithm based on Markov random field in image segmentation

“…By obtaining MC samples from the joint PD, the histogram of sample parameters can be utilized as an approximate PD of observation data. Due to the PD requiring integration of high-dimensional functions, a large amount of computation limits Bayesian inference [ 26 ]. The research uses MCMC algorithm to express the integral as the distribution expectation, and estimates the complex integral in image segmentation processing by sampling the sample points that obey the distribution.…”

A Time Series Forecast of COVID-19 Infections, Recoveries and Fatalities in Nigeria

“…Owing to its topicality and perceived importance, the COVID-19 pandemic attracted research interest from various fields such as health, agriculture, and management, as well as education, and finance among others, owing to the implication of the pandemic on different sectors of the economy. Some of the studies include those conducted by Rahman & Bahar (2020) [3], Qian, et al, (2020) [4], Inegbedion (2020) [5], Inegbedion (2021), [6]), Ayinde et al, (2020) [7], and Oyinlola et al, (2020) [1]. Despite the numerous studies on the research problem within a short time only the studies of Ayinda et al, (2020) [7] and Oyinlola et al, (2020) [1] deliberately adopted a forecasting approach in Nigeria at the time, neither did the studies that employed forecasting technique examine the forecasting errors to determine the accuracy of such forecasts.…”