ARIMA model in predicting of COVID-19 epidemic for the Southern Africa region

Background: Coronavirus pandemic, a serious global public health threat, affects the Southern African countries more than any other country on the continent. The region has become the epicenter of the coronavirus with South Africa accounting for the most cases. To cap the deadly effect caused by the pandemic, we apply a statistical modelling approach to investigate and predict COVID-19 incidence. Methods: Using secondary data on the daily confirmed COVID-19 cases per million for Southern Africa Development Community (SADC) member states from March 5, 2020, to July 15, 2021, we model and forecast the spread of coronavirus in the region. We select the best ARIMA model based on the log-likelihood, AIC, and BIC of the fitted models. Results: The ARIMA (11,1,11) model for the complete data set was finally selected among ARIMA models based upon the parameter test and the Box­Ljung test. The ARIMA (11,1,9) was the best candidate for the training set. A 15-day forecast was also made from the model, which shows a perfect fit with the testing set. Conclusion: The number of new COVID-19 cases per million for the SADC shows a downward trend, but the trend is characterized by peaks from time to time. Tightening up of the preventive measures continuously needs to be adapted in order to eradicate the coronavirus epidemic from the population.

HIV Infection Progression Monitoring System Based on CD4 Counts and Wishart Distribution.

The human immunodeficiency virus (HIV) is a viral infection that destroys the human immune system, resulting in the acquired immunodeficiency syndrome (AIDS). The management and care of patients on antiretroviral therapy (ART) consumes a large portion of the health budget of many countries. ART improves the lives of the HIV patients. However, benefiting from the treatment remains to be low due to the nonadherence, adverse events, and treatment failure associated with the transmitted drug resistance mutations (TDRMs). Extra care is therefore required in prescribing switch of ART regimens for HIV-naive patients. We propose a disease monitoring system, which depends on how the HIV-naive patients respond to the ART regimen. We model cluster of differentiation 4 (CD4) counts data measured at every 3 months in a period of 48 weeks on a cohort of 87 HIV-naive patients on ART, from Zambia. We demonstrate how to apply the Bayesian Wishart distribution to model CD4 counts, leading to an informative HIV progression monitoring system. We found a steady increase in the average of the CD4 counts (from 219 to 315) for HIV-naive patients on the ART regimen. The average was still below the expected 500 CD4 counts for a normal person. The derived precision matrix shows an increase in probability of potency of the ART regimen, which ranges from 0.1261 to 0.8678. An early detection is crucial as it allows for timely switch of regimen from first to second line or to the third line. The proposed HIV disease progression monitoring system for HIV-naive patients on ART regimen that is based on CD4 counts could enable physicians make informed decisions on the management and care of the patients.

Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region.

Background: The coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other attributes of the series, thus providing crucial information to address the problem. Objective: To formulate an effective model that timely predicts the spread of COVID-19 in the SADC region. Methods: Using the Quantile regression approaches; linear quantile regression averaging (LQRA), monotone composite quantile regression neural network (MCQRNN), partial additive quantile regression averaging (PAQRA), among others, we combine point forecasts from four candidate models namely, the ARIMA (p, d, q) model, TBATS, Generalized additive model (GAM) and a Gradient Boosting machine (GBM). Results: Among the single forecast models, the GAM provides the best model for predicting the spread of COVID-19 in the SADC region. However, it did not perform well in some periods. Combined forecasts models performed significantly better with the MCQRNN being the best (Theil's U statistic=0.000000278). Conclusion: The findings present an insightful approach in monitoring the spread of COVID-19 in the SADC region. The spread of COVID-19 can best be predicted using combined forecasts models, particularly the MCQRNN approach.

The Estimation of Transmitted Drug Resistance Mutation Strains Probability in the Treatment of HIV Using the Beta-Binomial Model.

The human immunodeficiency virus (HIV) is a viral infection that destroys the human immune system resulting in acquired immunodeficiency syndrome (AIDS). The Zambia HIV prevalence rate (11.3%) remains among the highest in the sub-Saharan Africa. In the treatment of HIV-naive patients, a problem that relates to the transmitted drug resistance mutation strains (TDRMs) occurs in the administration of antiretroviral (ARV) drugs. To address this problem, we propose the use of transition probabilities when prescribing a switch from the first-line to the second-line or to the third-line regimen on the ARV drugs combination. We formulate a statistical technique to determine an optimal ARV drugs combination. To compute a transition probability matrix chart on ARV drugs combinations of the first-line and second-line regimens, we apply a beta-binomial hierarchical model on HIV data. The transition probability matrices corresponding to the ARV drugs combinations TDF+ETC+NVP, TDF+FTC+EFV, AZT+3TC+NVP, AZT+3TC+EFV, D4T+3TC+NVP, and D4T+3TC+EFV provide an upper triangular matrix of probabilities. We observe a higher probability of remaining in the same regimen state than moving to another state. A transition probability chart provides information on the most effective combination to prescribe to a patient in the presence of transmitted drug resistance mutation (TDRM) test results. The transmission probabilities play a major role in aiding the physicians make an informed decision to prescribe an optimal ARV drugs combination. We suggest a TDRM test to be carried out to all newly diagnosed HIV individuals before prescribing any of the ARV drugs combination.