Browsing by Author "Masanja, Verdiana"

Now showing 1 - 16 of 16

An in-depth analysis of female students enrollment dynamics in Tanzanian mathematics Master’s programs using mixed-methods
(Elsevier Ltd, 2025-09-09) Karunde, Safina; Mduma, Neema; Masanja, Verdiana
The under representation of female students in mathematics Master’s programs is a global challenge, including in Tanzania. Despite recognition, gaps persist in understanding historical enrollment patterns and contextual factors influencing female enrollment in Tanzanian mathematics Master’s programs. This research addresses these gaps through a mixed-methods approach. Quantitative analysis using an Auto regressive Integrated Moving Average (ARIMA) model for enrollment forecasting reveals nuanced dynamics in female enrollment, enhancing precision in predictions. Specifically, the ARIMA(0,1,1) model predicts the constant change of future values beyond the available data, while the prediction interval indicates enrollment fluctuations, gradually decreasing within the confidence intervals, and considering the simulated features. Qualitative document analysis provides contextual insights into socioeconomic and policy-related factors, such as cultural attitudes, societal norms, educational policies, availability of female role models, level of support, and gender biases. A Support Vector Machine (SVM) model comprehensively analyzes the impact of these (contextual) factors on the probability of female enrollment. Essentially, the prioritization of efforts should commence with addressing educational policies, promoting female role models, increasing support mechanisms, challenging cultural attitudes and societal norms, and addressing gender biases with nuanced strategies. Eventually, this study offers a cohesive under- standing of female enrollment dynamics, providing a foundation for targeted interventions and policies to foster gender diversity in Tanzanian mathematics Master’s programs.
CFD Analysis of Flow Characteristics and Diagnostics of Leaks in Water Pipelines
(ETASR, 2024-09) Mushumbusi, Philbert; Leo, Judith; Chaudhari, Ashvinkumar; Masanja, Verdiana
This study utilizes Computational Fluid Dynamics (CFD) to generate pressure and flow rate values for the analysis of flow characteristics and the diagnosis of leaks in inclined pipelines. The Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) solver in OpenFOAM software was modified to incorporate the effect of pipe orientation angle. Subsequently, the SIMPLE solver was employed to simulate the flow of water through the pipe. Leakage rates were observed to vary in magnitude with respect to leak position and pipe orientation angle, except that leaks close to the flow inlet and pipes with a greater inclination were associated with higher leakage rates. A mathematical leak model is proposed based on non- dimensional flow variables and pipe orientation angle. To generate sufficient pressure values and leakage rates, the CFD simulation was performed 70 times. These values were then incorporated into the mathematical model for the leak location to be predicted. The proposed method is applicable to the detection of leakages of varying sizes in pipelines with different orientations. Therefore, knowing the pipe orientation angle and measurements of inlet flow rate, outlet flow rate, and pressure drop, the model can be used to precisely locate leaks in a pipeline.
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 comprehensive survey on linear programming and energy optimization methods for maximizing lifetime of wireless sensor network
(Springer Link, 2024-07-17) Machiwa, Erick; Masanja, Verdiana; Kisangiri, Michael; Matiko, Joseph
The wireless sensor network (WSN) is considered as a network, encompassing small-embedded devices named sensors that are wirelessly connected to one another for data forwarding within the network. These sensor nodes (SNs) follow an ad-hoc configuration and are connected with the Base Station (BS) through the internet for data sharing. When more amounts of data are shared from several SNs, traffic arises within the network, and controlling and balancing the traffic loads (TLs) are significant. The TLs are the amount of data shared by the network in a given time. Balancing these loads will extend the network’s lifetime and reduce the energy consumption (EC) rate of SNs. Thus, the Load Balancing (LB) within the network is very efficient for the network’s energy optimization (EO). However, this EO is the major challenging part of WSN. Several existing research concentrated and worked on energy-efficient LB optimization to prolong the lifetime of the WSN. Therefore, this review collectively presents a detailed survey of the linear programming (LP)-based optimization models and alternative optimization models for energy-efficient LB in WSN. LP is a technique used to maximize or minimize the linear function, which is subjected to linear constraints. The LP methods are utilized for modeling the features, deploying, and locating the sensors in WSN. The analysis proved the efficacy of the developed model based on its fault tolerance rate, latency, topological changes, and EC rates. Thus, this survey briefly explained the pros and cons of the developed load-balancing schemes for EO in WSN.
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.
Developing a Stochastic Model for Studying and Simulating Sediment Transport in Ports and Harbors
(International Journal of Advances in Scientific Research and Engineering (ijasre), 2019-09) Msamba, Oscar C.; Masanja, Verdiana; Mahera, Wilson
A particle model to describe and predict sediment transport in shallow water is developed with the use of random walk models. The model is developed by showing consistency between the Fokker-Plank equation and the Advection diffusion equations. Erosion and deposition process in the model are developed probabilistically where the erosion term is considered to be a constant and deposition term is taken as a function by relating sediment settling velocity and diffusion coefficient. Eventually, we simulated the particle model by considering three environment tests. In each environment test the simulations show the distribution of particle and the position of each particle at any given time t. The simulations also show the particles that will finally remain in suspension state and the particles that will be deposited during the transport process following the deployment of 10,000 particles. It was also established that there is uniform distribution of particles in test environment I and III and a linear dependence between the number of particles in different grid cell and the water depth in test environment II
Ecological modeling for the dynamics of potato tuber moth in Irish potatoes biomass
(Elsevier, 2025-09) Sanga, Luta; Masanja, Verdiana; Mbalawata, Isambi
The Potato Tuber Moth (Phthorimaea operculella) threatens food security and livelihoods in developing nations, especially for smallholder farmers dependent on Irish potatoes (Solanum tuberosum L.). This study has developed a non-linear deterministic ecological model to analyze the moth's dynamics in Irish potatoes biomass with deployment of predators and farming campaigns aiming to minimize Moth population growth and enhance Irish potato harvest. The model analysis demonstrates that the coexistence equilibrium point is globally asymptotically stable, as evidenced by phase portraits. To validate the model, parameter estimation and model fitting were conducted using non-linear least squares. Furthermore, a global sensitivity analysis was carried out using Latin Hypercube Sampling to determine the most influential parameters affecting the system's behavior. Numerical simulations reveals that Integrated Pest Management (IPM) strategies, combining predators and farming campaigns, effectively reduced pest populations while maximizing potato yields to approximately 100 tubers per square meter, compared to sub-optimal yields of 33 tubers per square meter with predators alone. This study recommends IPM strategies as a good approach to minimize Potato Tuber Moth (PTM) and optimize Irish potatoes yields.
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.
Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania
(Hindawi, 2024-01-30) Kyojo, Erick; Mirau, Silas; Osima, Sarah; Masanja, Verdiana
This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Gen- eralized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Tree estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. Te Bayesian MCMC approach demonstrated superior per- formance by incorporating noninformative priors to ensure that the prior information had minimal infuence on the analysis, allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various future periods were estimated, providing guidance for food protection measures and infrastructure design. Trend analysis using p value, Kendall’s tau, and Sen’s slope indicated no statistically signifcant trends in rainfall patterns, although a weak positive trend in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best ft dis- tribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region.
Heat and mass transfer investigation of unsteady magnetohydro dynamic nanofluid flow in a porous pipe in the presence of chemical reactions
(Journal of Engineering and Technology Research, 2023-08-30) Zahor, Feda; Ali, Ahmada; Jain, Reema; Masanja, Verdiana
This article presents a numerical investigation of mass and heat transfer effects on an unsteady Magnetohydrodynamic (MHD) nanofluid flow in a permeable pipe. The influences of the chemical reaction and magnetic flux are considered. With the central finite-difference technique, the fundamental equations are discretized. The resulting equations are solved numerically using methods of lines, bvp4, and shooting methods. The influences of material factors on the solution are investigated and displayed through tabular and graphical illustrations. The study revealed that the Sherwood number, skin friction, and rate of heat transfer are all decreased by an increase in the magnetic field. Additionally, the rate of rise of the chemical reaction and Brownian motion is observed to reduce the concentration of the nanofluid. Furthermore, the study finds that the Soret number, porosity medium resistance parameter, and thermophoresis parameter all cause the concentration profile to to decline.
A Mathematical Model for Transmission of Taeniasis and Neurocysticercosis
(Hindawi, 2024-03-11) Rwabona, Gideon; Masanja, Verdiana; Mfinanga, Sayoki; Degoot, Abdoelnaser; Mirau, Silas
In this study, we present a mathematical model for the codynamics of taeniasis and neurocysticercosis and rigorously analyze it. To understand the underlying dynamics of the proposed model, basic system properties such as the positivity and boundedness of solutions are investigated through the completing differential process. The basic reproduction number was calculated using the next-generation matrix method, and the analysis showed that when R0 < 1, the disease in the community eventually dies out, and when R0 > 1, the diseases persist. Local stability of the equilibria was analyzed using the Jacobian matrix, and Lyapunov function techniques were used to determine the global analysis, which showed that the endemic equilibrium point was globally stable when R0 > 1. On the other hand, the disease-free equilibrium was determined to be globally stable when R0 < 1. To identify the most influential parameters of the proposed model, partial correlation coefficient techniques were used. The numerical results depict that the model aligns well with the transmission dynamics, which goes through two populations: humans and pigs, whereby the model system stabilizes after some time, showing the validity of the proposed model. Furthermore, the simulations of the proposed model revealed that the shedding habit of infected humans with taeniasis and the bad cooking habit or eating of raw or undercooked pork products have a higher impact on the spread of neurocysticercosis and taeniasis in the community. Hence, this study proposes that in order to control taeniasis and neurocysticercosis, effective disease control measures should primarily prioritize hygienic behaviour and proper cooking of pork meat to the required temperature.
Mathematical model to assess the impact of contact rate and environment factor on transmission dynamics of rabies in humans and dogs
(Heliyon, 2024-06-15) Masanja, Verdiana; Charles, Mfano; Torres, Delfim; Mfinanga, Sayoki; Lyakurwa, GA
This paper presents a mathematical model to understand how rabies spreads among humans, free-range, and domestic dogs. By analyzing the model, we discovered that there are equilibrium points representing both disease-free and endemic states. We calculated the basic reproduction number, using the next generation matrix method. When , the disease-free equilibrium is globally stable, whereas when , the endemic equilibrium is globally stable. To identify the most influential parameters in disease transmission, we used the normalized forward sensitivity index. The simulations revealed that the contact rates between the infectious agent and humans, free-range dogs, and domestic dogs, have the most significant impact on rabies transmission. The study also examines how periodic changes in transmission rates affect the disease dynamics, emphasizing the importance of transmission frequency and amplitude on the patterns observed in rabies spread. To reduce disease sensitivity, one should prioritize effective disease control measures that focus on keeping both free-range and domestic dogs indoors. This is a crucial factor in preventing the spread of disease and should be implemented as a primary disease control measure.
Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania
(Elsevier, 2024-09) Masanja, Verdiana; Osima, Sarah; Mirau, Silas; Kyojo, Erick
The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall events exhibit a rising trend with increasing return period, indicating heightened intensity over longer time spans, whereas, a relative uncertainty analysis illustrate escalating uncertainty with increasing return periods, emphasizing challenges in long-term prediction.
Modelling the Transmission Dynamics of Banana Xanthomonas Wilt Disease with Contaminated Soil
(Journal of Mathematics and Informatics, 2019-10-31) Mapinda, John Joel; Mwanga, Gasper Godson; Masanja, Verdiana
Banana Xanthomonas Wilt disease (BXW) is a bacterial disease which highly threaten banana production in east and central Africa. It is caused by a bacteria known as Xanthomonas campestris pv. musacearum (Xcm). Mathematical modelling gives an insight on how to best understand the transmission dynamics and control of the disease. The existing mathematical models have not included contaminated soil in the dynamics of BXW. In this study we formulated a model which includes contaminated soil, calculated the basic reproduction number and carried out sensitivity analysis of some model parameters. We further conducted numerical simulation to validate the results. The simulations show that the infection rate by contaminated farming tools ( i b and e b ), the infection rate by contaminated soil ( 2 w ), vertical disease transmission rate (q ), and the shedding rate of Xcm bacteria in the soil (f ) are positively sensitive to the basic reproduction number. While, the most negative sensitive parameters are the clearance rate of Xcm bacteria from the soil ( h m ), removal of infected plants from the farm ( r ), harvesting ( p a ), and banana plants disease induced death rate ( d ).The result also shows that contaminated soil contributes to the transmission and persistence of BXW disease. Therefore, we recommend that, along with the existing control measures scientist and technologist should carry out studies to find a way to reduce or avoid vertical disease transmission and increase the Xcm clearance rate in the soil. Furthermore, technology for early detection of infected plants should be brought down to the local farmers at affordable costs. This will help stakeholders to detect and remove the infected plants from the farm in time and hence reduce the number of secondary infections.
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.
The role of modeling in the epidemiology and control of lumpy skin disease: a systematic review
(Springer Berlin Heidelb, 2023-09-15) Renald, Edwiga; Buz, Joram; Tchuench, Jean; Masanja, Verdiana
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.