Browsing by Author "Tchuenche, Jean"

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Bayesian prediction of under-five mortality rates for Tanzania
(Elsevier Inc., 2025-01-26) Mwanga, Mohamed; Mirau, Silas; Tchuenche, Jean; Mbalawata, Isambi
Under-five mortality is a burden on health and economic systems in developing countries. This study used under-five mortality rate (U5MR) data for Tanzania from 1960 to 2020 to predict trends of under-five mortality over the period of 2021 to 2051. Using a Bayesian state space model, it is found that the model is stable in forecasting. Results show that under-five mortality will continue to decline from 48.9 in 2020 to 32.9 in 2030, a decrease of 32.7%. But despite this decrease, Tanzania will likely not meet the Sustainable Development Goal (SDG) for under-five mortality by 2030. Additional efforts by the government through evidence-based interventions should be undertaken to improve child survival by expanding access to health care, especially in rural areas, taking into account local context.
A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model
(Elsevier, 2025-01-20) Renald, Edwiga; Amadi, Miracle; Haario, Heikki; Buza, Joram; Tchuenche, Jean; Masanja, Verdiana
The livestock industry has been economically affected by the emergence and reemergence of infectious diseases such as Lumpy Skin Disease (LSD). This has driven the interest to research efficient mitigating measures towards controlling the transmission of LSD. Mathematical models of real-life systems inherit loss of information, and consequently, accuracy of their results is often complicated by the presence of uncertainties in data used to estimate parameter values. There is a need for models with knowledge about the confidence of their long-term predictions. This study has introduced a novel yet simple technique for analyzing data uncertainties in compartmental models which is then used to examine the reliability of a deterministic model of the transmission dynamics of LSD in cattle which involves investigating scenarios related to data quality for which the model parameters can be well identified. The assessment of the uncertainties is determined with the help of Adaptive Metropolis Hastings algorithm, a Markov Chain Monte Carlo (MCMC) standard statistical method. Simulation results with synthetic cases show that the model parameters are identifiable with a reasonable amount of synthetic noise, and enough data points spanning through the model classes. MCMC outcomes derived from synthetic data, generated to mimic the characteristics of the real dataset, significantly surpassed those obtained from actual data in terms of uncertainties in identifying parameters and making predictions. This approach could serve as a guide for obtaining informative real data, and adapted to target key interventions when using routinely collected data to investigate long-term transmission dynamic of a disease.
A deterministic mathematical model with non-linear least squares method for investigating the transmission dynamics of lumpy skin disease
(Elsevier, 2024-05-18) Renald, Edwiga; Masanja, Verdiana; Tchuenche, Jean; Buza, Joram
Lumpy skin disease (LSD) is an economically significant viral disease of cattle caused by the lumpy disease virus (LSDV) which is primarily spread mechanically by blood feeding vectors such as particular species in flies, mosquitoes and ticks. Despite efforts to control its spread, LSD has been expanding geographically, posing challenges for effective control measures. This study develops a Susceptible–Exposed–Infectious–Recovered–Susceptible (SEIRS) model that incorporates cattle and vector populations to investigate LSD transmission dynamics. The model considers the waning rate of natural active immunity in recovered cattle, disease-induced mortality, and the biting rate. Using a standard dynamical system approach, we conducted a qualitative analysis of the model, defining the invariant region, establishing conditions for solution positivity, computing the basic reproduction number, and examining the stability of disease-free and endemic equilibria. We employ a non-linear least squares method for model calibration, fitting it to a synthetic dataset. We subsequently test it with actual infectious cases data. Results from the calibration and testing phases demonstrate the model’s validity and reliability for diverse settings. Local and global sensitivity analyses were conducted to determine the model’s robustness to parameter values. The biting rate emerged as the most significant parameter, followed by the probabilities of infection from either population and the recovery rate. Additionally, the waning rate of LSD infection-induced immunity gained positive significance in LSD prevalence from the beginning of the infectious period onward. Simulation results suggest reducing the biting rate as the most effective LSD control measure, which can be achieved by applying vector repellents in cattle farms/herds, thereby mitigating the disease’s prevalence in both cattle and vector populations and reducing the chances of infection from either population. Furthermore, measures aiming to boost LSD infection-induced immunity upon recovery are recommended to preserve the immune systems of the cattle population.
Extinction and persistence of lumpy skin disease: a deep learning framework for parameter estimation and model simulation
(Springer Nature Link, 2024-12-31) Renald, Edwiga; Tchuenche, Jean; Buza, Joram; Masanja, Verdiana
Lumpy Skin Disease (LSD) of cattle, an infectious and fatal viral ailment, poses a significant challenge to the farming sector due to its economic impact. A deterministic Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model, is utilized in developing a Physics-Informed Neural Network—a deep learning framework for parameter estimation and simulation of LSD dynamics. The deep learning structure is presented alongside an illustration of its application using synthetic data on infectious cattle counts. To accommodate inherent variability in the model, the deterministic version is extended to a stochastic model by introducing environmental noise, assuming that biting rate is the primary source of randomness. Lyapunov second method is used to prove the existence of a unique global positive solution for the stochastic model under specified initial conditions. Subsequently, the stochastic model is employed to establish conditions for both extinction and persistence. Results of the stochastic model simulation indicate potential eradication of the disease when the environmental noise decreases. On the other hand the designed Physics-Informed Neural Network for LSD demonstrates high efficiency in model prediction and parameter estimation especially when few data is available. Analytical results underscore the importance of implementing strategies to reduce biting such as biological control methods as a means to mitigate the transmission of LSD.
Posterior Distribution of the Unknown Parameter of Poisson Distribution Under Different Priors: An Application to Under-Fivemortality Data
(SSRN, 2023-03-30) Mwanga, Mohamed; Tchuenche, Jean; Mirau, Silas; Mbalawata, Isambi
Under-five mortality rate is one of the most essential indicator of acountry’s socio-economic well-being and public health status. Poissondistribution under different priors such as conjugate/Gamma prior,uniform and Jeffrey’s prior is used to obtain posterior distribution ofthe unknown parameter with an application to under-five mortalitydata in six East Africa countries from 1960 to 2020. The estimatesare examined through a Bayesian analysis while all the calculationsare carried out the R-statistical software and MS Excel. Among allpriors used in this study, conjugate prior was found to be compatiblefor the unknown parameters of the Poisson distribution. The casesof under-five mortality are found to reduce over time. East Africacountries through East Africa Community (EAC) should build strongand resilient health systems, identify and prioritize interventions tomitigate under-five mortality among Member States.
Posterior Distribution of the Unknown Parameter of Poisson Distribution Under Different Priors: An Application to Under-Fivemortality Data
(Elsevier Inc, 2023-04-23) Mwanga, Mohamed; Tchuenche, Jean; Mirau, Silas; Mbalawata, Isambi
Under-five mortality rate is one of the most essential indicator of acountry’s socio-economic well-being and public health status. Poissondistribution under different priors such as conjugate/Gamma prior,uniform and Jeffrey’s prior is used to obtain posterior distribution ofthe unknown parameter with an application to under-five mortalitydata in six East Africa countries from 1960 to 2020. The estimatesare examined through a Bayesian analysis while all the calculationsare carried out the R-statistical software and MS Excel. Among allpriors used in this study, conjugate prior was found to be compatiblefor the unknown parameters of the Poisson distribution. The casesof under-five mortality are found to reduce over time. East Africacountries through East Africa Community (EAC) should build strongand resilient health systems, identify and prioritize interventions tomitigate under-five mortality among Member States.
The role of modeling in the epidemiology and control of lumpy skin disease: a systematic review
(Springer Berlin Heidelberg, 2023-09-15) Renald, Edwiga; Buza, Joram; Tchuenche, Jean; Masanja, Verdiana
Background Lumpy skin disease (LSD) is an economically important viral disease of cattle caused by lumpy disease virus (LSDV) and transmitted by blood-feeding insects, such as certain species of flies and mosquitoes, or ticks. Direct transmission can occur but at low rate and efficiency. Vaccination has been used as the major disease control method in cooperation with other methods, yet outbreaks recur and the disease still persists and is subsequently spreading into new territories. LSD has of late been spreading at an alarming rate to many countries in the world including Africa where it originated, Middle East, Asia and some member countries of the European Union except the Western Hemisphere, New Zealand and Australia. In order to take control of the disease, various research endeavors are going on different fronts including epidemiology, virology, social economics and modeling, just to mention a few. This systematic review aims at exploring models that have been formulated and/or adopted to study the disease, estimate the advancement in knowledge accrued from these studies and highlight more areas that can be further advanced using this important tool. Main body of the abstract Electronic databases of PubMed, Scopus and EMBASE were searched for published records on modeling of LSD in a period of ten (10) years from 2013 to 2022 written in English language only. Extracted information was the title, objectives of the study, type of formulated or adopted models and study findings. A total of 31 publications met the inclusion criteria in the systematic review. Most studies were conducted in Europe reflecting the concern for LSD outbreaks in Eastern Europe and also availability of research funding. Majority of modeling publications were focused on LSD transmission behavior, and the kernel-based modeling was more popular. The role of modeling was organized into four categories, namely risk factors, transmission behaviors, diagnosis and forecasting, and intervention strategies. The results on modeling outbreaks data identified various factors including breed type, weather, vegetation, topography, animal density, herd size, proximity to infected farms or countries and importation of animals and animal products. Using these modeling techniques, it should be possible to come up with LSD risk maps in many regions or countries particularly in Africa to advise cattle herders to avoid high risk areas. Indirect transmission by insect vectors was the major transmission route with Stomoxys calcitrans being more effective, indicating need to include insect control mechanisms in reducing the spread of LSD. However, as the disease spread further into cold climates of Russia, data show new emerging trends; in that transmission was still occurring at temperatures that preclude insect activities, probably by direct contact, and furthermore, some outbreaks were not caused by field viruses, instead, by vaccine-like viruses due to recombination of vaccine strains with field viruses. Machine learning methods have become a useful tool for diagnosing LSD, especially in resource limited countries such as in Africa. Modeling has also forecasted LSD outbreaks and trends in the foreseeable future indicating more outbreaks in Africa and stability in Europe and Asia. This brings African countries into attention to develop long-term plans to deal with LSD. Intervention methods represented by culling and vaccination are showing promising results in limiting the spread of LSD. However, culling was more successful when close to 100% of infected animals are removed. But this is complicated, firstly because the cost of its implementation is massive and secondly it needed application of diagnostic techniques in order to be able to rapidly identify the infected and/or asymptomatic animals. Vaccination was more successful when an effective vaccine, such as the homologous LSD vaccine, was used and complemented by a high coverage of above 90%. This is hard to achieve in resource-poor countries due to the high costs involved. Short conclusion Modeling has made a significant contribution in addressing challenges associated with the epidemiology and control of LSD, especially in the areas of risk factors, disease transmission, diagnosis and forecasting as well as intervention strategies. However, more studies are needed in all these areas to address the existing gaps in knowledge.