Model Of Malaria Transmission Research Articles

Analysis of a Malaria Transmission Model with Vaccination Proportion and Vaccine-Induced Immunity

This study presents a mathematical model to describe the transmission dynamics of malaria in a highly endemic region, with a focus on vaccination and vaccine-induced immunity as primary control measures. By determining the basic reproduction number (R0), we evaluate the impact of these interventions on malaria-free and malaria-persistent equilibria. Our analysis shows that the malaria-free equilibrium is locally asymptotically stable when R0<1 and unstable otherwise. Numerical simulations demonstrate that increasing vaccination coverage and improving vaccine-induced immunity significantly reduce R0. A sensitivity analysis using partial rank correlation coefficients highlights the influence of key parameters, such as the mosquito-to-human transmission rate and mosquito birth and death rates, on malaria transmission. These findings underscore the potential of integrated strategies, combining vaccination with other interventions, to manage malaria effectively in highly endemic regions.

Stability and backward bifurcation in a malaria-transmission model with sterile mosquitoes

In this paper, we establish and study a malaria-transmission dynamic model of releasing sterile mosquitoes, which focuses on the mosquito populations affected by the impact of limited resources. First, we formulate a dynamic model with mosquitoes where the releasing rate of sterile mosquitoes is constant, and analyze the existence and stability of the equilibrium. Using the stability theory and method of differential equations, the threshold value of releasing sterile mosquitoes is obtained. Furthermore, we establish a malaria-transmission dynamic model with sterile release, and derive a formula for the reproductive number of infection and investigate the existence of endemic equilibria. With the symmetry of mathematical expression, it is also shown that this model may undergo backward bifurcation, where the locally stable disease-free equilibrium coexists with an endemic equilibrium. Finally, numerical simulations to illustrate our findings and brief discussions are provided.

Contrasting vector competence of three main East African Anopheles malaria vector mosquitoes for Plasmodium falciparum

There are three Anopheles mosquito species in East Africa that are responsible for the majority of malaria transmission, posing a significant public health concern. Understanding the vector competence of different mosquito species is crucial for targeted and cost-effective malaria control strategies. This study investigated the vector competence of laboratory reared strains of East African An. gambiae sensu stricto, An. funestus s.s., and An. arabiensis mosquitoes towards local isolates of Plasmodium falciparum infection. Mosquito feeding assays using gametocytaemic blood from local donors revealed significant differences in both prevalence and intensity of oocyst and sporozoite infections among the three vectors. An. funestus mosquitoes presented the highest sporozoite prevalence 23.5% (95% confidence interval (CI) 17.5–29.6) and intensity of infection 6-58138 sporozoites. Relative to An. funestus, the odds ratio for sporozoites prevalence were 0.46 (95% CI 0.25–0.85) in An. gambiae and 0.19 (95% CI 0.07–0.51) in An. arabiensis, while the incidence rate ratio for sporozoite intensity was 0.31 (95% CI 0.14–0.69) in An. gambiae and 0.66 (95% CI 0.16–2.60) in An. arabiensis. Our findings indicate that all three malaria vector species may contribute to malaria transmission in East Africa, with An. funestus demonstrating superior vector competence. In conclusion, there is a need for comprehensive malaria control strategies targeting major malaria vector species, an update of malaria transmission models to consider vector competence and evaluation of malaria transmission blocking interventions in assays that include An. funestus mosquitoes.

A systematic review of age-structured malaria transmission models (2019–2024)

Malaria remains a serious and potentially fatal vector-borne disease, consistently ranking among the world’s deadliest infections. This study presents a systematic review of age-structured malaria transmission models. Articles were sourced from PubMed, Google Scholar, and the Research Gate Library, resulting in the identification and inclusion of eleven papers in the review. The findings highlight that children under the age of five are more susceptible to malaria than adults, due to their still-developing immune systems. The highest rates of morbidity and mortality are seen in youngsters, pregnant women, and people with impaired immune systems, making age structure a critical factor in the spread of malaria within populations. Personal protection and vector control are key strategies in reducing the transmission of malaria in communities. The study also suggests that the use of fractional operators in modeling could offer new insights into the dynamics of malaria transmission and potential control strategies.

A Non-Standard Finite Difference Discretization Scheme Applied to a Malaria Model

In this research work, a dynamically consistent non-standard finite difference (NSFD) scheme is developed to solve a continuous-time model of malaria transmission with herbal medicine as control strategy. We compared results from NSFD scheme with the standard finite difference methods (4th order Runge-kutta and forward Euler methods). The numerical investigation showed that the proposed NSFD method remains consistent, preserves the positivity of solutions and converges to true equilibrium points of the continuous model independent of the step size h.

A deterministic analysis of an age-sex-structured model for malaria transmission dynamics

Objectives: Children younger than 5 years and women, especially pregnant women, are at high risk of malaria and death because of their weak immunity and exposure to mosquitoes. Several studies have considered only the age-structured model and other factors but have not considered sex. The objective of this work is to develop and analyze the malaria transmission model including this structure, to contribute to existing measures and mechanisms to eradicate malaria in Rwanda. Methods: A dynamic malaria transmission model considering age and sex structure was developed and analyzed. To study the dynamics of disease, the basic reproduction number was analytically computed and numerically estimated, and the normalized forward sensitivity index was used to highlight its sensitive parameters. Results: The most positive sensitive parameters in the model are the force of infection and the infection rates for vectors and female humans aged 5 years or older. The most negative sensitive parameters are the per capita death rates for vectors and humans. Conclusion: To control the spread of malaria in Rwanda, the biting and infection rates should be decreased. Therefore, women must comply with government measures against malaria and educate children about it.

Stability and bifurcation of a delayed Aron–May model for malaria transmission

In this paper, the dynamical behavior in a delayed Aron–May model for malaria transmission is investigated. The basic reproduction number is defined. The global stability of the malaria-free equilibrium (MFE) is established. By using the Bendixson theorem, a sufficient condition for the global stability of the delay-free equilibrium (DFE) is also established. Furthermore, to deal with the local stability of endemic equilibrium (EE), by means of the stability switches analysis method proposed in [E. Beretta and Y. Kuang, Geometric stability switch criteria in delay differential systems with delay dependent parameters, SIAM J. Math. Anal. 33(5) (2002) 1144–1165.] the related characteristic equation at EE is investigated, and the occurrence of Hopf bifurcation is discussed by using the incubation period in mosquito as a bifurcation parameter. Last, the simulation analysis is also performed to verify the dynamical behavior of the model.

AnophelesModel: An R package to interface mosquito bionomics, human exposure and intervention effects with models of malaria intervention impact.

In recent decades, field and semi-field studies of malaria transmission have gathered geographic-specific information about mosquito ecology, behaviour and their sensitivity to interventions. Mathematical models of malaria transmission can incorporate such data to infer the likely impact of vector control interventions and hence guide malaria control strategies in various geographies. To facilitate this process and make model predictions of intervention impact available for different geographical regions, we developed AnophelesModel. AnophelesModel is an online, open-access R package that quantifies the impact of vector control interventions depending on mosquito species and location-specific characteristics. In addition, it includes a previously published, comprehensive, curated database of field entomological data from over 50 Anopheles species, field data on mosquito and human behaviour, and estimates of vector control effectiveness. Using the input data, the package parameterizes a discrete-time, state transition model of the mosquito oviposition cycle and infers species-specific impacts of various interventions on vectorial capacity. In addition, it offers formatted outputs ready to use in downstream analyses and by other models of malaria transmission for accurate representation of the vector-specific components. Using AnophelesModel, we show how the key implications for intervention impact change for various vectors and locations. The package facilitates quantitative comparisons of likely intervention impacts in different geographical settings varying in vector compositions, and can thus guide towards more robust and efficient malaria control recommendations. The AnophelesModel R package is available under a GPL-3.0 license at https://github.com/SwissTPH/AnophelesModel.

Correction to: A mathematical model of malaria transmission with media-awareness and treatment interventions

Correction to: A mathematical model of malaria transmission with media-awareness and treatment interventions

Seasonal malaria chemoprevention and the spread of Plasmodium falciparum quintuple-mutant parasites resistant to sulfadoxine–pyrimethamine: a modelling study

Seasonal malaria chemoprevention (SMC) with sulfadoxine-pyrimethamine plus amodiaquine prevents millions of clinical malaria cases in children younger than 5years in Africa's Sahel region. However, Plasmodium falciparum parasites partially resistant to sulfadoxine-pyrimethamine (with quintuple mutations) potentially threaten the protective effectiveness of SMC. We evaluated the spread of quintuple-mutant parasites and the clinical consequences. We used an individual-based malaria transmission model with explicit parasite dynamics and drug pharmacological models to identify and quantify the influence of factors driving quintuple-mutant spread and predict the time needed for the mutant to spread from 1% to 50% of inoculations for several SMC deployment strategies. We estimated the impact of this spread on SMC effectiveness against clinical malaria. Higher transmission intensity, SMC coverage, and expanded age range of chemoprevention promoted mutant spread. When SMC was implemented in a high-transmission setting (40% parasite prevalence in children aged 2-10years) with four monthly cycles to children aged 3months to 5years (with 95% initial coverage declining each cycle), the quintuple mutant required 53·1years (95% CI 50·5-56·0) to spread from 1% to 50% of inoculations. This time increased in lower-transmission settings and reduced by half when SMC was extended to children aged 3months to 10years, or reduced by 10-13years when an additional monthly cycle of SMC was deployed. For the same setting, the effective reduction in clinical cases in children receiving SMC was 79·0% (95%CI 77·8-80·8) and 60·4% (58·6-62·3) during the months of SMC implementation when the quintuple mutant was absent or fixed in the population, respectively. SMC with sulfadoxine-pyrimethamine plus amodiaquine leads to a relatively slow spread of sulfadoxine-pyrimethamine-resistant quintuple mutants and remains effective at preventing clinical malaria despite the mutant spread. SMC with sulfadoxine-pyrimethamine plus amodiaquine should be considered in seasonal settings where this mutant is already prevalent. Swiss National Science Foundation and Marie Curie Individual Fellowship.

Seasonal variability and stochastic branching process in malaria outbreak probability

Background: Malaria is the world’s most fatal and challenging parasitic disease, caused by the Plasmodium parasite, which is transmitted to humans by the bites of infected female mosquitoes. Bangladesh is the most vulnerable region to spread malaria because of its geographic position. In this paper, we have considered the dynamics of vector-host models and observed the stochastic behavior. This study elaborates on the seasonal variability and calculates the probability of disease outbreaks. Methods: We present a model for malaria disease transmission and develop its corresponding continuous-time Markov chain (CTMC) representation. The proposed vector-host models illustrate the malaria transmission model along with sensitivity analysis. The deterministic model with CTMC curves is depicted to show the randomness in real scenarios. Sequentially, we expand these studies to a time-varying stochastic vector-host model that incorporates seasonal variability. Phase plane analysis is conducted to explore the characteristics of the disease, examine interactions among various compartments, and evaluate the impact of key parameters. The branching process approximation is developed for the corresponding vector-host model to calculate the probability outbreak. Numerous numerical results are accomplished to observe the analytical investigation. Results: Seasonality and contact patterns affect the dynamics of disease outbreaks. The numerical illustration provides that the probability of a disease outbreak depends on the infected host or vector. Additionally, periodic transmission rates have a great influence on the probability outbreak. The basic reproduction number (R0) is derived, which is the main justification for studying the dynamical behavior of epidemic models. Conclusions: Seasonal variability significantly impacts malaria transmission, and the probability of disease outbreaks is influenced by time and the initial number of infected individuals. Moreover, the branching process approximation is applicable when the population size is large enough and the basic reproduction number is less than 1. In the future, such analysis can help decision-makers understand the impact of various parameters and their stochastic behavior in the vector-host model to prevent such types of disease outbreaks.

The Analysis of a Co-Dynamic Ebola and Malaria Transmission Model Using the Laplace Adomian Decomposition Method with Caputo Fractional-Order

The Analysis of a Co-Dynamic Ebola and Malaria Transmission Model Using the Laplace Adomian Decomposition Method with Caputo Fractional-Order

Mathematical Assessment of the Role of Intervention Programs for Malaria Control

Malaria remains a global health problem despite the many attempts to control and eradicate it. There is an urgent need to understand the current transmission dynamics of malaria and to determine the interventions necessary to control malaria. In this paper, we seek to develop a fit-for-purpose mathematical model to assess the interventions needed to control malaria in an endemic setting. To achieve this, we formulate a malaria transmission model to analyse the spread of malaria in the presence of interventions. A sensitivity analysis of the model is performed to determine the relative impact of the model parameters on disease transmission. We explore how existing variations in the recruitment and management of intervention strategies affect malaria transmission. Results obtained from the study imply that the discontinuation of existing interventions has a significant effect on malaria prevalence. Thus, the maintenance of interventions is imperative for malaria elimination and eradication. In a scenario study aimed at assessing the impact of long-lasting insecticidal nets (LLINs), indoor residual spraying (IRS), and localized individual measures, our findings indicate that increased LLINs utilization and extended IRS coverage (with longer-lasting insecticides) cause a more pronounced reduction in symptomatic malaria prevalence compared to a reduced LLINs utilization and shorter IRS coverage. Additionally, our study demonstrates the impact of localized preventive measures in mitigating the spread of malaria when compared to the absence of interventions.

A mathematical model of malaria transmission with media-awareness and treatment interventions

A mathematical model of malaria transmission with media-awareness and treatment interventions

MGDrivE 3: A decoupled vector-human framework for epidemiological simulation of mosquito genetic control tools and their surveillance.

Novel mosquito genetic control tools, such as CRISPR-based gene drives, hold great promise in reducing the global burden of vector-borne diseases. As these technologies advance through the research and development pipeline, there is a growing need for modeling frameworks incorporating increasing levels of entomological and epidemiological detail in order to address questions regarding logistics and biosafety. Epidemiological predictions are becoming increasingly relevant to the development of target product profiles and the design of field trials and interventions, while entomological surveillance is becoming increasingly important to regulation and biosafety. We present MGDrivE 3 (Mosquito Gene Drive Explorer 3), a new version of a previously-developed framework, MGDrivE 2, that investigates the spatial population dynamics of mosquito genetic control systems and their epidemiological implications. The new framework incorporates three major developments: i) a decoupled sampling algorithm allowing the vector portion of the MGDrivE framework to be paired with a more detailed epidemiological framework, ii) a version of the Imperial College London malaria transmission model, which incorporates age structure, various forms of immunity, and human and vector interventions, and iii) a surveillance module that tracks mosquitoes captured by traps throughout the simulation. Example MGDrivE 3 simulations are presented demonstrating the application of the framework to a CRISPR-based homing gene drive linked to dual disease-refractory genes and their potential to interrupt local malaria transmission. Simulations are also presented demonstrating surveillance of such a system by a network of mosquito traps. MGDrivE 3 is freely available as an open-source R package on CRAN (https://cran.r-project.org/package=MGDrivE2) (version 2.1.0), and extensive examples and vignettes are provided. We intend the software to aid in understanding of human health impacts and biosafety of mosquito genetic control tools, and continue to iterate per feedback from the genetic control community.

Analysis of a delayed malaria transmission model including vaccination with waning immunity and reinfection

Analysis of a delayed malaria transmission model including vaccination with waning immunity and reinfection

Design and selection of drug properties to increase the public health impact of next-generation seasonal malaria chemoprevention: a modelling study.

Seasonal malaria chemoprevention (SMC) is recommended for disease control in settings with moderate to high Plasmodium falciparum transmission and currently depends on the administration of sulfadoxine-pyrimethamine plus amodiaquine. However, poor regimen adherence and the increased frequency of parasite mutations conferring sulfadoxine-pyrimethamine resistance might threaten the effectiveness of SMC. Guidance is needed to de-risk the development of drug compounds for malaria prevention. We aimed to provide guidance for the early prioritisation of new and alternative SMC drugs and their target product profiles. In this modelling study, we combined an individual-based malaria transmission model that has explicit parasite growth with drug pharmacokinetic and pharmacodynamic models. We modelled SMC drug attributes for several possible modes of action, linked to their potential public health impact. Global sensitivity analyses identified trade-offs between drug elimination half-life, maximum parasite killing effect, and SMC coverage, and optimisation identified minimum requirements to maximise malaria burden reductions. Model predictions show that preventing infection for the entire period between SMC cycles is more important than drug curative efficacy for clinical disease effectiveness outcomes, but similarly important for impact on prevalence. When children younger than 5 years receive four SMC cycles with high levels of coverage (ie, 69% of children receiving all cycles), drug candidates require a duration of protection half-life higher than 23 days (elimination half-life >10 days) to achieve reductions higher than 75% in clinical incidence and severe disease (measured over the intervention period in the target population, compared with no intervention across a range of modelled scenarios). High coverage is crucial to achieve these targets, requiring more than 60% of children to receive all SMC cycles and more than 90% of children to receive at least one cycle regardless of the protection duration of the drug. Although efficacy is crucial for malaria prevalence reductions, chemoprevention development should select drug candidates for their duration of protection to maximise burden reductions, with the duration half-life determining cycle timing. Explicitly designing or selecting drug properties to increase community uptake is paramount. Bill & Melinda Gates Foundation and the Swiss National Science Foundation.

Global Stability Analysis of the Mathematical Model for Malaria Transmission between Vector and Host Population

Global Stability Analysis of the Mathematical Model for Malaria Transmission between Vector and Host Population

Mathematical analysis and numerical simulations of the piecewise dynamics model of Malaria transmission: A case study in Yemen

<abstract><p>This study presents a mathematical model capturing Malaria transmission dynamics in Yemen, incorporating a social hierarchy structure. Piecewise Caputo-Fabrizio derivatives are utilized to effectively capture intricate dynamics, discontinuities, and different behaviors. Statistical data from 2000 to 2021 is collected and analyzed, providing predictions for Malaria cases in Yemen from 2022 to 2024 using Eviews and Autoregressive Integrated Moving Average models. The model investigates the crossover effect by dividing the study interval into two subintervals, establishing existence, uniqueness, positivity, and boundedness of solutions through fixed-point techniques and fractional-order properties of the Laplace transformation. The basic reproduction number is computed using a next-generation technique, and numerical solutions are obtained using the Adams-Bashforth method. The results are comprehensively discussed through graphs. The obtained results can help us to better control and predict the spread of the disease.</p></abstract>

Optimal Control of a Malaria Transmission Model with Saturated Incidence

Optimal Control of a Malaria Transmission Model with Saturated Incidence