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Dec 2025 DOI 10.14302/issn.2643-2811.jmbr-25-5731
Background Typhoid fever remains a significant public health issue in Harare City, Zimbabwe, exacerbated by recurrent outbreaks between 2018 and 2020. Key challenges, including inadequate water supply and sanitation infrastructure, high population density, and limited healthcare access, have intensified the disease burden. Understanding the key transmission drivers and assessing the impact of various interventions are essential for informing policy and health strategies. Objectives This study aimed to: 1: To predict future trends in typhoid fever cases Harare City typhoid hot areas. 2: To develop a mathematical model to simulate the spread of typhoid fever incidence under different intervention scenarios and recommend evidence-based strategies for reducing the disease burden in Harare City. Methods A dynamic compartmental SIR-based model, adapted from the Pitzer Vaccine Effectiveness (VE) framework, was employed to simulate disease transmission. This model accounted for both short-cycle (human-to-human) and long-cycle (environmental) transmission pathways. Data from Harare City (2018–2020) were used for model calibration and forecasting, and sensitivity analysis was performed to assess the impact of different intervention levels. Findings The model identified inadequate sanitation, contaminated water sources, and low health- seeking behaviors as primary drivers of typhoid transmission. In the absence of interventions, the model projected a sustained high rate of transmission. However, treatment and WASH interventions could reduce the disease burden by 50–60%, while combined strategies incorporating vaccination and education led to an 80% reduction in cases. Sensitivity analysis indicated that treatment and WASH interventions were particularly impactful at moderate coverage levels. Conclusion Mathematical modeling effectively demonstrated the multifactorial drivers of typhoid fever transmission in Harare. Integrated interventions that combine WASH, vaccination, treatment, and education present the most promising approach for long-term control of the disease. The findings offer a solid, data-driven foundation for public health decision-making and resource allocation.
Sep 2020 DOI 10.14302/issn.2694-1198.jge-20-3515
The solution of a genetic-mathematical problem of interaction of the human population cells and virus population to a problem of pandemic COVID-19 is submitted. The mathematical model based on the Hardy - Weinberg law consisting of two interdependent differential equations is used. The equations reflect time dynamics of the human cells and virus populations during their interaction. Solutions of the differential equations are found and results of these solutions are analyzed. The estimation of duration pandemic is received at use of parameters of the human liver cells and a flu virus.
Jul 2019 DOI 10.14302/issn.2694-1198.jge-19-2756
Processes of genetic-mathematical modeling of a population development are considered. A basic distinction in the mathematical description of a family tree and a population is shown. In a family tree alternation of generations has discrete character. In a population there is a continuous alternation of generations. The method of the differential equations is applied for the description of a population. It is shown that mutational process in a population can be described with use of a Green’s function. For radiating influence on a population the universal evolutionary law is found.
Jan 2017 DOI 10.14302/issn.2572-3030.jcgb-16-1307
Interpreting variants of uncertain significance (VUS) for their effect on protein function, and therefore for the risk of developing cancer, has become a challenge in clinical practice for genetic counselling services. The present work combines structural bioinformatics and systems biology based mathematical modelling approaches with the aim of determining the pathogenicity of the mutation c.5434C->G (p.Pro1812Ala) in the BRCA1 gene (detected in a patient from a high risk family) and also to mechanistically understand the effect of this mutation in DNA damage response, a key process in cancer development. The results obtained showed that this mutation prevents the interaction of BRCA1 with key proteins of the cell cycle, subsequently impairing BRCA1-dependent induction of cell cycle arrest. The comparison of the molecular mechanisms associated with the native BRCA1 protein and the mutated variant function in DNA damage response showed that the latter undergoes a reduction in its ability to modulate pathways that are critical for DNA repair and cell cycle control. Therefore, this variant will not be able to exert its tumor suppressive action. Interestingly, these conclusions can be extrapolated to all mutations that, like c.5434C>G (p.Pro1812Ala) BRCA1, cause loss of BRCT domain activity.