Challenges for Mathematical Modeling of Multidrug-resistant Tuberculosis in Sub-Saharan Africa

Abstract

Background: In sub-Saharan Africa, where there are inadequate diagnostic and reporting facilities, limited data availability hinders the accurate estimation of key parameters in mathematical models of multi-drug-resistant tuberculosis. Furthermore, gaps in knowledge about multi-drug-resistant tuberculosis (MDR-TB) dynamics add another layer of complexity to these modeling efforts. Methods: We analyzed databases such as google scholar, PubMed, scopus, Web of Science etc, using relevant keywords to identify relevant articles on challenges for mathematical modeling of multi-drug-resistant tuberculosis in sub–Saharan Africa covering the period from 2010 to the present. Results: This review highlights the epidemiology of multidrug resistant tuberculosis in sub–Saharan Africa and the limitations in mathematical modeling of multi-drug-resistant tuberculosis (MDR-TB) in the region. Conclusion: Accurate diagnosis and reliable data are crucial barriers to effective modeling. The review also underscores the potential of machine learning techniques to improve data quality and address issues related to incomplete data, suggesting that these methods could become essential components of future mathematical models.