Malaria Transmission Dynamics Research Articles

Malaria Epidemiology in Rask City and Surrounding Areas (2020 - 2023), Southeast Iran

Background: Malaria, a disease caused by Plasmodium parasites and transmitted by Anopheles mosquitoes, remains a major global health challenge, causing widespread illness and mortality. Objectives: This study provides an in-depth analysis of malaria trends in Rask, Southeast Iran, from 2018 to 2023, assessing malaria prevalence, transmission factors, and impacts on the local population and healthcare system. Methods: Blood samples were collected from individuals suspected of having malaria at health centers in Rask. For each positive case, demographic information was recorded in a questionnaire. Data analysis was performed using SPSS 26, with t-tests applied to compare variables. Results: Findings indicate a peak in malaria cases in 2022, with an annual incidence rate of 2.49%. The most common species identified was Plasmodium vivax, affecting primarily males over the age of 12, with the highest number of cases reported in September. Conclusions: The results underscore the urgent need to enhance malaria prevention and control measures. The study emphasizes the value of targeted interventions, including improvements to prevention and treatment programs, reinforcement of healthcare systems, and advancement of health infrastructure. These strategies should consider climatic patterns and rainfall rates to effectively address malaria transmission dynamics.

Impact of Climate Variability and Interventions on Malaria Incidence and Forecasting in Burkina Faso

Background: Malaria remains a climate-driven public health issue in Burkina Faso, yet the interactions between climatic factors and malaria interventions across different zones are not well understood. This study estimates time delays in the effects of climatic factors on malaria incidence, develops forecasting models, and assesses their short-term forecasting performance across three distinct climatic zones: the Sahelian zone (hot/arid), the Sudano-Sahelian zone (moderate temperatures/rainfall); and the Sudanian zone (cooler/wet). Methods: Monthly confirmed malaria cases of children under five during the period 2015–2021 were analyzed using Bayesian generalized autoregressive moving average negative binomial models. The predictors included land surface temperature (LST), rainfall, the coverage of insecticide-treated net (ITN) use, and the coverage of artemisinin-based combination therapies (ACTs). Bayesian variable selection was used to identify the time delays between climatic suitability and malaria incidence. Wavelet analysis was conducted to understand better how fluctuations in climatic factors across different time scales and climatic zones affect malaria transmission dynamics. Results: Malaria incidence averaged 9.92 cases per 1000 persons per month from 2015 to 2021, with peak incidences in July and October in the cooler/wet zone and October in the other zones. Periodicities at 6-month and 12-month intervals were identified in malaria incidence and LST and at 12 months for rainfall from 2015 to 2021 in all climatic zones. Varying lag times in the effects of climatic factors were identified across the zones. The highest predictive power was observed at lead times of 3 months in the cooler/wet zone, followed by 2 months in the hot/arid and moderate zones. Forecasting accuracy, measured by the mean absolute percentage error (MAPE), varied across the zones: 28% in the cooler/wet zone, 53% in the moderate zone, and 45% in the hot/arid zone. ITNs were not statistically important in the hot/arid zone, while ACTs were not in the cooler/wet and moderate zones. Conclusions: The interaction between climatic factors and interventions varied across zones, with the best forecasting performance in the cooler/wet zone. Zone-specific intervention planning and model development adjustments are essential for more efficient early-warning systems.

Mathematical Exploration of Malaria Transmission Dynamics: Insights from Fractional Models and Numerical Simulation

AbstractThis study presents an innovative mathematical model denoted as the fractional SIP(H)–SI(M) model, which aims to analyze and understand the dynamics of malaria transmission and spread. This model is distinguished by incorporating memory effects through fractional differential equations, allowing for a more accurate and realistic analysis of disease spread compared to traditional models. The proposed model is applied to Algeria by estimating its parameters using recent health data (from 2000). The results revealed that the disease‐free equilibrium is stable only when the basic reproduction number is less than one, indicating that controlling the spread of malaria and possibly eradicating it can be achieved by implementing appropriate preventive measures. Simulations also demonstrated a direct correlation between the rate of infection transmission and an increase in the number of infected individuals, highlighting the need for swift action when signs of an outbreak emerge. Based on these findings, a set of preventive measures is recommended, including insecticide spraying programs, widespread distribution of insecticide‐treated bed nets, and implementation of effective treatment protocols for infected individuals. This study also emphasizes the importance of continuous monitoring of health data and updating model parameters to ensure the effectiveness and sustainability of preventive measures.

Fractional-order analysis of temperature- and rainfall-dependent mathematical model for malaria transmission dynamics

Malaria remains a substantial public health challenge and economic burden globally. Currently, malaria has been declared as endemic in 85 countries. In this study, we developed and analyzed a fractional-order mathematical model for malaria transmission dynamics that incorporates variability of temperature and rainfall using Caputo-type AB operators. The existence and uniqueness of the model's solutions were established using the Banach fixed-point theorem. The model system's equilibria (both disease-free and endemic) were identified, and lemmas and theorems were developed to prove their stability. Furthermore, we used different temperature ranges and rainfall data, validating them against existing literature. Numerical simulations using the Toufik-Atangana schemes with various fractional-order alpha values revealed that as the value of alpha approaches 1, the behavior of the fractional-order model converges to that of the classical model. The numerical results are promising and are expected to be valuable for future research related to fractional-order models.

A deterministic Analysis of an Age-Gender structured model for malaria transmission dynamics

Objectives: Children below five years and women especially pregnant women are at high risk of malaria infection and death due to their weak immunity and exposition to mosquitoes. Different studies have been undertaken considering only the age structured model and other factors but did not take into account the gender. The objective of this work is to develop and analyze the malaria transmission model including this structure, to contribute on the existing measures and mechanisms to eradicate malaria in Rwanda.Methods: The dynamic malaria transmission model considering age and gender 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 infection rate for vectors and Infection rate for female of 5 years and above. The most negative sensitive parameters are the per capita death rate for vectors and humans.Conclusion: To control the spread of malaria in Rwanda, the biting and infection rate should be decreased.Therefore, women must comply with government measures against malaria and educate children about it.

Influence of Altitudinal Gradient on Human Malaria Prevalence in Plateau State, Central Nigeria: A Longitudinal Study

Study’s Excerpt The relationship between altitude and malaria transmission dynamics in Plateau State, North-Central Nigeria, is assessed. The highest malaria prevalence was observed at lower altitudes and vice-versa. Environmental factors (altitude) have an influence on malaria epidemiology in the region. Full Abstract This longitudinal study assessed the prevalence of human malaria infection across different altitudinal zones in Plateau State, North-Central Nigeria, aiming to identify the impact of altitude on malaria transmission dynamics. Malaria is a potentially lethal disease caused by protozoan parasites called Plasmodium. Abiotic factors are known to be the main factors influencing the epidemiology of the disease. In 2018 and 2019, blood samples were taken and examined for the presence of Plasmodium falciparum malaria using the SD-PAN Ag mRDT. After that, blood film slides were created in order to calculate the parasite density for each of the four seasons' worth of positive samples. The prevalence rates of malaria in the three locations vary significantly, with the highest rates observed at lower altitudes (Pangwasa 211m) above sea level and the lowest at the highest altitude (Vwang 1330m) above sea level. Vwang has the lowest malaria prevalence of 16.25%, Jing (663m) has a higher prevalence of 40.0%, and Pangwasa has the highest prevalence at 46.9.

A cross-sectional study investigating malaria prevalence and associated predictors of infection among migrants to a newly established gold mining settlement in the Gambella Region of Ethiopia.

Malaria is a major disease burden in Ethiopia. Migration can influence malaria transmission dynamics, with individuals relocating from malaria-free highland regions to malarious lowlands potentially facing elevated risks of contracting malaria. Migrants may find it difficult to protect themselves against malaria and have limited access to diagnosis or treatment. Settlers in gold mining sites are one type of migrant and are often neglected in malaria research yet may have particularly high malaria risk. This study was a malaria prevalence survey among settlers in a new gold mining settlement in the highly malarious Gambella Region, Ethiopia. n = 590 people were surveyed for demographic information and their knowledge and practices of malaria. Participants were tested for malaria using rapid diagnostic tests and microscopy. Using logistic regressions, the influence of demographic characteristics on malaria infections and bed net access were analysed. A sub-sample of participants was interviewed to comprehend settlement living conditions and healthcare accessibility. The overall prevalence of Plasmodium falciparum was 37.5% (CI 32.4-42.3%). Young children were most likely to have malaria, with individuals aged 15-24 having 67% lower odds (aOR: 0.33; CI 0.13-0.86) of infection compared to those aged 1-4years old. Meanwhile, those age 25-plus had 75% decreased odds of malaria infection (aOR 0.25; CI 0.10-0.65). Individuals with bed nets had ~50% decreased odds of testing positive for falciparum malaria than those reporting having no bed net (aOR: 0.47; CI 0.22-0.97). Individuals who relocated from low elevation with high malaria test positivity rate areas were more prone to testing positive for malaria, as were those residing in densely populated households with multiple malaria cases. Conversely, individuals from higher elevations with low malaria test positivity rates, and those living in households with 5-10 occupants and < 2 malaria infections, were more likely to possess bed nets. This gold mining settlement provides an example of an oft-neglected atypical community where malaria is a significant, but under-addressed, health problem. Within this community, future interventions focused on distributing bed nets, particularly to larger households and those with children, have great potential to alleviate the malaria burden. Efforts should also be made to provide affordable, and accessible, early diagnosis and treatment.

Risk of Neglected Zoonotic Malaria in Western Ghats: How the Ecosystem Favors Transmission of an Impending Public Health Threat.

The threat of zoonotic malaria remains largely overlooked in India, particularly in regions such as the Western Ghats (WG), a biodiversity hotspot. The WG has diverse species of non-human primates that serve as reservoir hosts for simian malaria parasites. The presence of the Leucosphyrus group of mosquitoes and other vectors of human malaria makes WG vulnerable to the risk of parasite spillover. Natural and anthropogenic factors have contributed to rampant changes in the WG landscape, leading to increased interaction with the sylvatic hosts and vectors. The simian host-human-vector-environment interactions govern the transmission dynamics of zoonotic malaria; however, our knowledge of these interlinkages, encompassing the effect of anthropogenic disruptions in the WG is limited. The impending threat of zoonotic malaria in India could decelerate progress toward malaria elimination, warranting a comprehensive and systematic investigation of disease dynamics in the WG.

Comparing newly developed SNP barcode panels with microsatellites to explore population genetics of malaria parasites in the Peruvian Amazon.

Malaria molecular surveillance (MMS) can provide insights into transmission dynamics, guiding national control/elimination programs. Considering the genetic differences among parasites from different areas in the Peruvian Amazon, we previously designed SNP barcode panels for Plasmodium vivax (Pv) and P. falciparum (Pf), integrated into AmpliSeq assays, to provide population genetics estimates of malaria parasites. These AmpliSeq assays are ideal for MMS: multiplexing different traits of interest, applicable to many use cases, and high throughput for large numbers of samples. The present study compares the genetic resolution of the SNP barcode panels in the AmpliSeq assays with widely used microsatellite (MS) panels to investigate Amazonian malaria parasites. Malaria samples collected in remote areas of the Peruvian Amazon (51 Pv & 80 Pf samples) were characterized using the Ampliseq assays and MS. Population genetics estimates (complexity of infection, genetic diversity and differentiation, and population structure) were compared using the SNP barcodes (Pv: 40 SNPs & Pf: 28 SNPs) and MS panels (Pv: 16 MS & Pf: 7 MS). The genetic diversity of Pv (expected heterozygosity, He ) was similar across the subpopulations for both makers: He MS = 0.68 - 0.78 (p = 0.23) and He SNP = 0.36 - 0.38 (p = 0.80). Pairwise genetic differentiation (fixation index, F ST ) was also comparable: F ST-MS = 0.04 - 0.14 and F ST-SNP = 0.03 - 0.12 (p = 0.34 - 0.85). No geographic clustering was observed with any panel. In addition, Pf genetic diversity trends ( He MS = 0 - 0.48 p = 0.03 - 1; He SNP = 0 - 0.09, p = 0.03 - 1) and pairwise F ST comparisons (F ST-MS = 0.14 - 0.65, F ST-SNP = 0.19 - 0.61, p = 0.24 - 0.83) were concordant between the panels. Similar population structure clustering was observed with both SNP and MS, highlighting one Pf subpopulation in an indigenous community. The SNP barcodes in the Pv AmpliSeq v2 Peru and Pf AmpliSeq v1 Peru assays offer comparable results to MS panels when investigating population genetics in Pv and Pv populations. Therefore, the AmpliSeq assays can efficiently characterize malaria transmission dynamics and population structure and support malaria elimination efforts in Peru.

The Role of Climate and Environmental Factors in Malaria Transmission in Nigeria: Challenges and Prospects

Malaria remained a major public health challenge in Nigeria, contributing to high rates of morbidity and mortality, particularly among vulnerable groups like children and pregnant women. The transmission of malaria is heavily influenced by climate and environmental factors such as temperature, rainfall, humidity, land use, and ecological changes, which affect the lifecycle of the malaria parasite (Plasmodium spp.) and its mosquito vector (Anopheles species). This review explored how these climatic and environmental factors impacted malaria transmission dynamics across Nigeria’s diverse regions. It also examined the challenges posed by climate variability, inadequate infrastructure, and the emergence of resistance to insecticides and antimalarials, while highlighting the potential of integrated vector management, climate-smart strategies, and community engagement in mitigating these impacts. The methodology used involved a comprehensive review and synthesis of relevant literature and data sources, including peer-reviewed journals, government reports, and global health organization publications. Understanding the intricate relationships between these factors and malaria transmission was crucial for developing adaptive strategies to reduce the burden of malaria in Nigeria, particularly in the face of ongoing environmental and climatic changes. Keywords: Malaria Transmission, Climate Factors, Environmental Factors, Nigeria, Vector Control.

Mosquitoes: The Long-term Effects of Malaria Eradication in West Africa

Malaria has been a critical public health issue in West Africa for centuries, resulting in widespread morbidity and mortality, especially among vulnerable groups like children and pregnant women. Recent eradication efforts, bolstered by insecticide-treated bed nets (ITNs), indoor residual spraying (IRS), and antimalarial drugs, have significantly reduced the disease burden. This review examines the long-term socio-economic impacts of malaria eradication in West Africa, including improved public health, enhanced productivity, economic growth, and better access to healthcare. The reduction of malaria-related healthcare costs has led to increased investments in education, infrastructure, and other essential services. However, challenges persist, such as insecticide resistance among mosquitoes, financial constraints, and emerging public health concerns. Ecological effects of mosquito eradication, including impacts on biodiversity and the evolution of insecticide-resistant mosquitoes, raise important considerations for future control strategies. The role of climate change in shaping mosquito ecology and malaria transmission dynamics is also explored. This review underscores the need for sustained political and financial commitment, regional collaboration, and innovation to ensure the continued success of malaria eradication in West Africa. Integrating these efforts with broader Sustainable Development Goals (SDGs) and addressing ecological sustainability are essential for achieving long-term health and socio-economic benefits. Keywords: Malaria eradication, West Africa, public health, insecticide resistance, socio-economic impact, mosquito ecology.

Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection

BackgroundThe government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space–time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022.MethodsSpatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level.ResultsMalaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010–2011, and with P. vivax incidence in the north and central regions during 2012–2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south.ConclusionsMalaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

Unravelling heterogeneous malaria transmission dynamics in the Peruvian Amazon: insights from a cross-sectional survey

BackgroundMalaria remains a global health challenge, particularly in Peru's Loreto region. Despite ongoing efforts, high infection rates and asymptomatic cases perpetuate transmission. The Peruvian Ministry of Health’s “Zero Malaria Plan” targets elimination. This novel study combines microscopic, molecular, and serological techniques to assess transmission intensity, identify epidemiological risk factors, and characterize species-specific patterns across villages. The findings aim to inform targeted interventions and support broader malaria elimination efforts in line with the Zero Malaria Plan initiative.MethodsA cross-sectional malaria survey was conducted in the Zungarococha community, comprising the villages Llanchama (LL), Ninarumi (NI), Puerto Almendra (PA), and Zungarococha (ZG), using microscopic, molecular, and serological techniques to evaluate malaria transmission intensity. Statistical analysis, including multivariate-adjusted analysis, seroprevalence curves, and spatial clustering analysis, were performed to assess malaria prevalence, exposure, and risk factors.ResultsThe survey revealed a high prevalence of asymptomatic infections (6% by microscopy and 18% by PCR), indicating that molecular methods are more sensitive for detecting asymptomatic infections. Seroprevalence varied significantly between villages, reflecting the heterogeneous malaria transmission dynamics. Multivariate analysis identified age, village, and limited bed net use as significant risk factors for malaria infection and species-specific exposure. Seroprevalence curves demonstrated community-specific patterns, with Llanchama and Puerto Almendra showing the highest seroconversion rates for both Plasmodium species.ConclusionsThe study highlights the diverse nature of malaria transmission in the Loreto region, particularly nothing the pronounced heterogeneity as transmission rates decline, especially in residual malaria scenarios. The use of molecular and serological techniques enhances the detection of current infections and past exposure, aiding in the identification of epidemiological risk factors. These findings underscore the importance of using molecular and serological tools to characterize malaria transmission patterns in low-endemic areas, which is crucial for planning and implementing targeted interventions and elimination strategies. This is particularly relevant for initiatives like the Zero Malaria Plan in the Peruvian Amazon.

Malaria Severity in the Elimination Continuum: A Retrospective Cohort Study between Beitbridge and Lupane Districts in Zimbabwe, 2021-2023.

Malaria has created a resurgence crisis in Zimbabwe's elimination continuum, diverging from global commitment to malaria elimination by 2030. This retrospective cohort study aimed to determine the risk factors associated with severe malaria in the Beitbridge and Lupane districts. Multistage sampling was used to recruit 2414 individuals recorded in the District Health Information Software2 Tracker database. The study used IBM SPSS 29.0.2.0(20) for data analysis, and odds ratios (ORs) to estimate the relative risk (RR; 95% C.I; p < 0.05). The study revealed significant relative risks (p-value < 0.05) for individuals who had no Long-Lasting Insecticidal Nets (Beitbridge 47.4; Lupane 12.3), those who owned but used the LLINs (Beitbridge 24.9; Lupane 7.83), those who slept outdoors during the night (Beitbridge 84.4; Lupane 1.93), and adults (Beitbridge 0.18; Lupane 0.22) compared to the corresponding reference groups. Other factors showed varying RR: sex (Beitbridge 126.1), prompt treatment (Beitbridge 6.78), hosting visitor(s) (Lupane 6.19), and residence (Lupane 1.94) compared to the corresponding reference groups. Risk factor management needs to focus on increasing local awareness of malaria, universal LLINs coverage of indoor and outdoor sleeping spaces, community-based programs on proper and consistent LLIN usage, screening of visitors from malaria-endemic areas, comprehensive entomological activities, mixed malaria interventions in rural hotspots, and future research on local malaria transmission dynamics. While Zimbabwe has the potential to meet the global goal of malaria elimination, success depends on overcoming the risk factors to sustain the gains already made among malaria elimination districts.

Fine-scale maps of malaria incidence to inform risk stratification in Laos

BackgroundMalaria risk maps are crucial for controlling and eliminating malaria by identifying areas of varying transmission risk. In the Greater Mekong Subregion, these maps guide interventions and resource allocation. This article focuses on analysing changes in malaria transmission and developing fine-scale risk maps using five years of routine surveillance data in Laos (2017–2021). The study employed data from 1160 geolocated health facilities in Laos, along with high-resolution environmental data.MethodsA Bayesian geostatistical framework incorporating population data and treatment-seeking propensity was developed. The models incorporated static and dynamic factors and accounted for spatial heterogeneity.ResultsResults showed a significant decline in malaria cases in Laos over the five-year period and a shift in transmission patterns. While the north became malaria-free, the south experienced ongoing transmission with sporadic outbreaks.ConclusionThe risk maps provided insights into changing transmission patterns and supported risk stratification. These risk maps are valuable tools for malaria control in Laos, aiding resource allocation, identifying intervention gaps, and raising public awareness. The study enhances understanding of malaria transmission dynamics and facilitates evidence-based decision-making for targeted interventions in high-risk areas.

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.

Optimal control and cost effectiveness analysis of a Zika–Malaria co-infection model

Malaria and Zika, are two significant public health concerns, particularly in tropical and subtropical regions. The co-infection of Malaria and Zika can occur due to overlapping geographical distributions of their respective vectors, namely Anopheles mosquitoes for Malaria and Aedes mosquitoes for Zika. These mosquitoes serve as intermediate hosts for the parasites and viruses, respectively. The proposed mathematical models capture the complex interactions between the human population and the mosquito population to provide insights into the transmission dynamics. The primary transmission route to humans is through the bite of infected mosquitoes for both diseases. Moreover, Zika virus can be also transmitted sexually and vertically. The models provide a valuable tool for evaluating the impact of different interventions on the transmission dynamics of Malaria and Zika co-infection such as prevention schemes against co-infection with Malaria and Zika, prompt Malaria treatment, and effective mosquito control measures. Using Pontryagin’s maximum principle, the model determines the most effective strategies for reducing Malaria and Zika co-infection over time. By combining modeling with cost-effectiveness analysis and targeted interventions, it is possible to develop evidence-based approaches that reduce disease transmission, protect vulnerable populations, and make efficient use of available resources.

Identifying and predicting climate change impact on vector-borne disease using machine learning: Case study of Plasmodium falciparum from Africa

Abstract. Vector-borne diseases pose a significant threat to human health, particularly in regions vulnerable to climate change. Among these diseases, malaria, caused by the parasite Plasmodium falciparum and transmitted through the Anopheles mosquito, remains a major global health concern, particularly in sub-Saharan Africa. This study explores the use of machine learning techniques to identify and predict the impact of climate change on the transmission dynamics of P. falciparum malaria in Africa.The research utilizes a combination of climate data, epidemiological records, and machine learning algorithms to analyze historical patterns and project future trends in malaria transmission. Key climate variables such as temperature, precipitation, humidity, and vegetation cover are integrated into predictive models to assess their influence on the abundance and distribution of mosquito vectors and the parasite's lifecycle. Through the application of machine learning models such as Maximum Entropy, this study aims to uncover complex relationships between climatic factors and malaria transmission dynamics. By training these models on historical data, they can accurately predict future scenarios under various climate change scenarios. The findings of this research will provide valuable insights into the potential impact of climate change on the spatial and temporal distribution of P. falciparum malaria in Africa. Such insights are crucial for designing targeted interventions and adaptation strategies to mitigate the anticipated rise in malaria cases and associated morbidity and mortality in the region. Moreover, the methodology developed in this study can serve as a framework for assessing and addressing the impact of climate change on other vector-borne diseases globally.

Mathematical modeling and simulation for malaria disease transmission using the CF fractional derivative

A major global health problem continues to be malaria, a disease that can be fatal brought on by Plasmodium parasites and spread by the bite of infected Anopheles mosquitoes. We provide a deterministic mathematical model in this study to simulate the dynamics of malaria transmission between humans and mosquitoes. We present a new compartment for hospitalized patients as well as fractional calculus. The next-generation matrix technique is used to obtain the fundamental reproduction number, R0, and stability conditions for the model’s equilibrium points are derived. Both locally and globally, the sensitivity analysis for the fundamental reproduction number R0 is satisfied. In MAPLE, the Runge–Kutta fourth-order approach is used to simulate the model.

Global Dynamics of a Social Hierarchy-Stratified Malaria Model: Insight from Fractional Calculus

In this study, a mathematical model for the transmission dynamics of malaria among different socioeconomic groups in the human population interacting with a susceptible-infectious vector population is presented and analysed using a fractional-order derivative of the Caputo type. The total human population is stratified into two distinguished classes of lower and higher income individuals, with each class further subdivided into susceptible, infectious, and recovered populations. The socio hierachy-structured fractional-order malaria model is analyzed through the application of different dynamical system tools. The theory of positivity and boundedness based on the generalized mean value theorem is employed to investigate the basic properties of solutions of the model, while the Banach fixed point theory approach is used to prove the existence and uniqueness of the solution. Furthermore, unlike the existing related studies, comprehensive global asymptotic dynamics of the fractional-order malaria model around both disease-free and endemic equilibria are explored by generalizing the usual classical methods for establishing global asymptotic stability of the steady states. The asymptotic behavior of the trajectories of the system are graphically illustrated at different values of the fractional (noninteger) order.