Jean Marie Ntaganda

A mathematical model of the human cardiovascular-respiratory system is proposed in several MATLAB graphical user interface (GUI) designs. However, it is observed that the latter are not web-based designed, making it hard for users to use them anywhere. This article proposes a web-based GUI design that integrates MATLAB server as a computational tool, worldwide accessible by users, upon subscription. The proposed web-based GUI design has been tested using a small sample of customers, males and females having different attributes relative to the model. After testing across normal and abnormal medical conditions in Rwanda, our findings demonstrate that the proposed web-based GUI design is highly efficient. This suggests robust usability, indicating that users worldwide can securely utilize it to assess pressures on the human cardiovascular-respiratory system.

This study presents a novel, context‐specific mathematical modeling framework for predicting and monitoring COVID‐19 transmission in Rwanda, addressing the increasing demand for integrated and data‐driven public health decision‐support tools. The model incorporates localized epidemiological dynamics, transmission pathways, and major intervention measures, including lockdowns, vaccination, quarantine, and home‐based care, which is considered a critical factor influencing disease transmission. Using a system of differential equations, the framework models transition among infected, home‐based care, hospitalized, recovered, and deceased populations. The model was calibrated and validated using Rwanda's national COVID‐19 surveillance data collected between March 2020 and December 2022, covering multiple epidemic waves and intervention phases. Calibration utilized time‐series data on confirmed cases, active infections, hospitalizations, intensive care unit admissions, recoveries, and mortality. Detailed home‐based care records improved the estimation of transmission and recovery parameters. Parameter optimization was performed through least‐squares fitting and cross‐validation techniques. Model performance was assessed using the root mean square error (RMSE) and the coefficient of determination ( R ² ) metrics. Validation through out‐of‐sample predictions demonstrated strong accuracy in capturing infection trends and healthcare burden. An interactive dashboard complements the framework by enabling real‐time analytics, forecasting, and evidence‐based public health decision‐making.

Chikungunya is a re‐emerging mosquito‐borne disease whose transmission in the Sahel is strongly modulated by seasonal rainfall. In 2020, an outbreak affected the eastern provinces of Chad, yet no model to date has combined the climatic driver of the epidemic with a quantitative assessment of the available control measures. In this paper, a developed SEITR‐SEI compartmental model is proposed, in which the mosquito recruitment rate is an explicit function of rainfall and a treatment class is introduced into the human population. The model is calibrated with epidemiological and entomological data from the 2020 outbreak, giving a basic reproduction number . We establish the positivity and boundedness of the solutions, the existence of a biologically feasible invariant region, and the local stability of the disease‐free equilibrium. An optimal control problem is then formulated with three time‐dependent interventions vector control, personal protection, and treatment and solved numerically using the forward–backward sweep method derived from Pontryagin′s maximum principle. The results show that rainfall governs mosquito recruitment and therefore the size of the vector population, and that the three controls differ markedly in effectiveness: Personal protection alone reduces the infectious human population by 99.8% and brings the control reproduction number below unity (), whereas vector control and treatment applied in isolation slow but do not reverse the epidemic ( and 1.74, respectively). The combined strategy achieves near‐complete containment (). These findings identify personal protection as the cornerstone measure and highlight the rainy season as the critical window for intervention, providing evidence‐based guidance for the control of chikungunya in Chad, and comparable climate‐sensitive settings.

Background Stunting is a serious public health concern in Rwanda, affecting around 33.3% of children under five in 2020. The researchers have employed machine learning algorithms to predict stunting in Rwanda; however, few studies used ANNs, despite their strong capacity to predict stunting. The purpose of this study was to predict stunting in Rwanda using ANNs and the most recent DHS data from 2020. Methods DHS 2020 dataset was used to train and test an ANN model for predicting stunting in children. The dataset, which included various child, parental, and socio-demographic characteristics, was split into 80% training data and 20% testing and validation data. The model utilised a multilayer perceptron (MLP). Model performance was assessed using accuracy, precision, recall, and AUC-ROC. Feature importances were determined and highlighted the most critical predictors of stunting. Results An overall accuracy of 72.0% on the test set was observed, with an AUC-ROC of 0.84, indicating the model’s good performance. Factors appear to contribute to stunting among the negative value aspects. First and foremost, the mother’s height is important, as a lower height suggests an increased risk of stunting in children. Positive value characteristics, on the other hand, emphasise elements that reduce the likelihood of stunting. The timing of the initiation of breastfeeding stands out as a crucial factor, showing that early breastfeeding initiation has been linked with a decreased risk of stunting. Conclusions These findings suggest that ANNs can be a useful tool for predicting stunting in Rwanda and identifying the most important associated factors for stunting. These insights can inform targeted interventions to reduce the burden of stunting in Rwanda and other low- and middle-income countries. Potential targeted interventions include nutritional support programs for pregnant and lactating mothers, and providing educational programs for parents on nutrition and hygiene.

Background Stunting is a serious public health concern in Rwanda, affecting around 33.3% of children under the age of five in 2020. Several examples of research have employed machine learning algorithms to predict stunting in Rwanda; however, no study used artificial neural networks (ANNs), despite their strong capacity to predict stunting. The purpose of this study was to predict stunting in Rwanda using ANNs and the most recent Demographic and Health Survey (DHS) data from 2020. Methods We used a multilayer perceptron (MLP) architecture to train and test the ANN model on a subset of the DHS dataset. The input variables for the model included child, parental and socio-demographic’s characteristics. The output variable was a binary indicator of stunting status (stunted vs. not stunted). Results An overall accuracy of 72.0% on the test set was observed, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.84, indicating the model’s good performance. Several factors appear as important contributors to the probability of stunting among the negative value aspects. First and foremost, the mother’s height is important, as a lower height suggests an increased risk of stunting in children. Positive value characteristics, on the other hand, emphasie elements that reduce the likelihood of stunting. The timing of the initiation of breastfeeding stands out as a crucial factor, showing that early breastfeeding initiation has been linked with a decreased risk of stunting. Conclusions Our findings suggest that ANNs can be a useful tool for predicting stunting in Rwanda and identifying the most important associated factors for stunting. These insights can inform targeted interventions to reduce the burden of stunting in Rwanda and other low- and middle-income countries.

This study uses the Hamiltonian Monte Carlo (HMC) and Gibbs methods to predict of Hypertension, Tobacco use, Overweight and Obesity. The methods were applied on data from World Health Organization report 2015. Corresponding stochastic matrices were generated and utilized in the prediction and comparison of the two sampling methods. Using Python and the NumPy library, the state vectors for these conditions were predicted, with the results showing slight variations between the two methods. In the long term, both methods converged to stable probability distributions, indicating steady-state values for each condition. HMC predicted long-term prevalence of 17.48% for hypertension, 17.5% for tobacco use, 17.56% of Overweight, 14.36% of Obesity and 33.1% for "Other subjects, while Gibbs predicted 17.82%, 26.27%, 17.13%, 4.80% and 33.98%, respectively. Additionally, Gibbs was found to outperform HMC in terms of error metrics, with lower values for Mean Squared Error (MSE), Mean Absolute Error (MAE), indicating better prediction accuracy. This analysis aids practitioners in selecting the most suitable method based on specific problem characteristics and dataset complexities.

On March 12, 2020, the WHO declared the COVID-19 pandemic, in response to widespread infections and thousands of fatalities resulting from the outbreak. Researchers worldwide have paid close attention to this pandemic. This situation has been aided by mathematical models that guide public policy. In Rwanda, this problem involves uncertainty and incomplete information, and a stochastic approach is appropriate for capturing a broad range of possible outcomes. However, many epidemiological models do not account for preventive measures using a stochastic approach to analyze COVID-19 in Rwanda. Therefore, the aim of the paper is to build and analyze a COVID-19 pandemic stochastic model with preventive measures implemented in Rwanda. A deterministic model is formulated and transformed into stochastic equations. The numerical solutions of both models were computed and compared. The stochastic model is considered to be the best to use in the Rwanda context because of the model’s fluctuations, which capture even the residuals and uncertainties. The results indicate that the pandemic could spread faster if awareness-raising strategies are not effectively implemented, and preventive actions for the pandemic are considered crucial tools for effective control and mitigation.

This paper aims at solving the optimal control problem of the dynamic of HBV infection under treatment using the perturbation iteration method. This method serves as a tool to determine the approximate solutions of nonlinear equations for which exact solutions cannot be obtained. To test the efficacy of this method, the authors propose to compare the numerical simulation results with those of the direct method and fuzzy logic strategy. The newly used method for solving the above optimal control problem is very important since the findings compared to those obtained from the two other methods are in good agreement with experimental data and they demonstrate the response drugs to the dynamics of uninfected hepatocytes, infected hepatocytes, and free virions for a patient suffering from HBV. Since the perturbation iteration method provides satisfactory results which are close to other used numerical methods, it is an important numerical tool to determine the solution of an optimal control problem. In particular, it provides optimal trajectories in medicine, biology, and other related scientific fields. For instance, the response of treatment as control of the human body ensures the health of patients.

In Rwanda, the prevalence of hypertension was 15.3% in 2015. At present, there are no accurate predictions of the prevalence of hypertension and its trend over time in Rwanda to assist decision makers in making plans for prevention and more effective interventions. This study used the Gibbs sampling method in combination with the Markov Chain Monte Carlo approach to predict the prevalence of hypertension and its associated risk factors in Rwanda over a period of ten years. The data were from World Health Organization (WHO) reports. The findings showed that the prevalence of hypertension is estimated to reach 17.82% in 2025, with tobacco use, being overweight or obese, and other risk factors having a respective prevalence of 26.26%, 17.13%, 4.80%, and 33.99%, which shows the increase and, therefore, measures for prevention to be taken. Therefore, to prevent and reduce the prevalence of this disease, the government of Rwanda should take appropriate measures to promote a balanced diet and physical exercise.

Background: Malnutrition is a major public health concern worldwide; a recent study found that 22% of children under the age of five were stunted worldwide in 2020. Stunting in Rwanda has decreased dramatically over the last 15 years, from 51% in 2005 to 33% by 2020. However, because few geospatial studies have been conducted, geographical survey data analysis is required to effectively focus stakeholders' efforts in response to successful stewardship of health programs in the eradication of all types of malnutrition. The study's goal is to map the prevalence distribution of stunted children under the age of five, make Projections, and provide exceedance probability maps for each stunting at the 30% threshold value, as well as identify risk factors associated with stunting in Rwanda. Methods: This study makes use of Rwandan Demographic and Health Surveys (R-DHS)2019/2020. To obtain the marginal posterior distribution of stunting prevalence at each location in Rwanda, the Bayesian model was developed using an integrated nested Laplace approximation technique. The risk factors for stunting were identified using multivariate logistic regression. Results: This research finds the prevalence of stunting in 500 clusters. Kigali city clusters had the lowest prevalence, ranging from 0% to 25%. The Western Province, specifically the Congo Nile Divide, has the highest rate where some clusters have more than 60%. The 30% probability threshold enables the identification of Rwandan areas and communities most vulnerable to stunting. In Rwanda, regional disparities in childhood stunting are significant. There is statistically significant child, maternal, and socio-demographic characteristics with p-values less than 5% in a 95% confidence interval (CI). Among the risk factors are the baby's birth weight (OR:2.18, CI: [1.613-2.95], intestinal parasites (OR:1.544, 95% CI: [1.253-1.903], and the baby's age (OR:1.095, 95% CI: [1.024-1.171]. Province and altitude have ORs of 1.088 and 1.575, respectively, in socio-demographic factors. Conclusions: Finally, geospatial studies should be conducted to identify locations that require more attention in disease and epidemic control and multivariate logistic regression to identify risk factors. Therefore, more attention of malnutrition eradication should be paid to Rwanda's Western and Northern Provinces. The findings of this study may be useful to program managers and decision makers working to reduce the burden of stunting.