Knowledge Chinhamu

Biographic Data

ID7750744
NAMEKnowledge Chinhamu
GIVEN NAMESKnowledge
FAMILY NAMEChinhamu
SIGNATURECHINHAMU K
AFFILIATIONSUniversity of KwaZulu-Natal
ORCID0000-0003-4296-7677
VERIFIEDYes
TOTAL WORKS5
TOTAL CITATIONS0
WORKS AS AUTHOR5
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2022
LATEST PUBLICATION YEAR2026
H-INDEX0

  • Determinants of CD4 count recovery among severely immunosuppressed HIV patients initiated on antiretroviral therapy

    Open AccessChiedza Elvina Mashiri, Retius Chifurira et al.ARTICLEFrontiers in Public Health2026

    Background For people living with HIV (PLWH), CD4 count serves as an effective indicator of response to antiretroviral therapy (ART) and a predictor of morbidity and mortality before and after ART initiation. In PLWH on ART, changes in CD4 count reflect immunological response to treatment. CD4 count recovery after ART initiation is defined as achieving a CD4 cell count of ≥500 cells/mm 3 . This study utilised survival analysis techniques to deter…

  • Syndemic mapping of HIV and other STIs in KwaZulu-Natal

    Open AccessExaverio Chireshe, Retius Chifurira et al.ARTICLEFrontiers in Public Health2025

    Syndemics involving Human immunodeficiency virus (HIV) and other sexually transmitted infections (STIs) remain a major public health challenge in sub-Saharan Africa, and understanding their spatial and temporal dynamics is critical for effective interventions. Using data from two consecutive, population-based cross-sectional surveys conducted in 2014 and 2015 under the HIV Incidence Provincial Surveillance System (HIPSS) in KwaZulu-Natal, South A…

  • Spatial Analysis of HIV Determinants Among Females Aged 15–34 in KwaZulu Natal, South Africa

    Open AccessExcellent Chireshe, Exaverio Chireshe et al.ARTICLEInternational Journal of…2025

    HIV remains a major public health challenge in sub-Saharan Africa, with South Africa bearing the highest burden. This study confirms that KwaZulu-Natal (KZN) is a hotspot, with a high HIV prevalence of 47.4% (95% CI: 45.7-49.1) among females aged 15-34. We investigated the spatial distribution and key socio-demographic, behavioural, and economic factors associated with HIV prevalence in this group using a Bayesian spatial logistic regression mode…

  • A Multistate Continuous Time-Inhomogeneous Markov Model for Assessing the CD4 Count Dynamics of HIV/Aids Patients Undergoing Antiretroviral Therapy in KwaZulu-Natal, South Africa

    Open AccessChiedza Elvina Mashiri, Jesca Mercy Batidzirai et al.ARTICLEInternational Journal of…2025

    Monitoring CD4 count levels is essential for tracking the progression of HIV in patients. This study aimed to identify the key factors influencing HIV progression by incorporating time-varying factors and transition probabilities. The data for this study were obtained from the Centre for the AIDS Programme of Research in South Africa (CAPRISA), which enrolled 3325 patients aged 14 to 76 who initiated antiretroviral therapy (ART) and were followed…

  • Estimating the risk of Sars-CoV-2 deaths using a Markov switching-volatility model combined with heavy-tailed distributions for South Africa

    Open AccessNobuhle Mthethwa, Retius Chifurira et al.ARTICLEBMC Public Health2022

    The MS(3)-GARCH(1,1)-PIVD model provides a reliable and accurate method for predicting the minimum number of death due to Covid-19. The accuracy of the proposed model will assist policymakers, academics and health practitioners in forecasting the volatility of future health-related deaths in which the predictability of volatility plays an integral role in health risk management