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Hierarchical clustering of 774 LGAs into 22 epidemiological archetypes using monthly rainfall, monthly temperature suitability index, relative abundance of vector species, 2000–2010 PfPR, and 2008–2010 ITN use rates. PfPR and ITN coverage estimates for the initial classification were obtained from the Malaria Atlas Project
Source publication
Background
For their 2021–2025 National Malaria Strategic Plan (NMSP), Nigeria’s National Malaria Elimination Programme (NMEP), in partnership with the World Health Organization (WHO), developed a targeted approach to intervention deployment at the local government area (LGA) level as part of the High Burden to High Impact response. Mathematical mo...
Citations
... At the national level, intervention policies are set by local decision-makers under the leadership of national malaria programs. Modeling can be used to predict the impact of combinations of possible interventions and to understand the impact of past interventions [6]. However, data gaps regarding each country's malaria context, and the exclusion of social factors by the models [7], mean that model results must have substantial uncertainty, even if the core model captures the natural history of malaria well. ...
When models are used to inform decision-making, both their strengths and limitations must be considered. Using malaria as an example, we explain how and why models are limited and offer guidance for ensuring a model is well-suited for its intended purpose.
... Widespread deployment of insecticide treated nets (ITNs) has prevented more malaria cases than any other intervention, with population weighted malaria prevalence in children halving from 33% (95% credible interval [CrI] [31][32][33][34][35] in 2000 to 16% (14)(15)(16)(17)(18)(19) in 2015. 1 Since 2019, these advances have stalled and there remains an intolerable disease burden, particularly in Africa. Multiple reasons contribute to this plateauing of cases, 2 although the increase in mosquitoes resistant to insecticides is likely a key contributing factor. ...
... Accessible interfaces can be developed that present model outputs representative of generic sites with defined characteristics, or specific locations. 18 The first interface to contain widely used vector control options is ...
... Tools such as MINT aim to help decision makers choose the best combination of interventions, although these projections should not be overly interpreted because results are very dependent on the inputted information and model assumptions. Users are encouraged to interpret results qualitatively and carefully consider the geographical scale over which decisions are made given the available data (appendix 1 pp [18][19][20][21][22]. When inputted information is uncertain decision makers could explore a range of plausible scenarios and investigate whether this changes the most efficient intervention mix. ...
Background
Insecticide treated nets (ITNs) are the most important malaria prevention tool in Africa but the rise of pyrethroid resistance in mosquitoes is likely impeding control. WHO has recommended a novel pyrethroid–pyrrole ITN following evidence of epidemiological benefit in two cluster-randomised, controlled trials (CRTs). It remains unclear how effective more costly pyrethroid–pyrrole ITNs are compared with other tools, or whether they should be deployed when budgets are limited. We aimed to compare the epidemiological impact and cost-effectiveness of the mass distribution of pyrethroid–pyrrole ITNs relative to other ITNs over 3 years in different African settings.
Methods
In this individual-based malaria transmission dynamics modelling study we characterise the entomological impact of ITNs using data from a systematic review of experimental hut trials from across Africa. This African entomological data was used to inform an individual-based malaria transmission dynamics model, which was validated against CRT results from Benin and Tanzania. The full impact of new ITNs was quantified for trial sites and simulation was used to project impact in different settings which were included within an accessible interface (the Malaria Intervention Tool) to support National Malaria Programmes to explore how vector control tools and budgets could be allocated across regions to avert the most cases.
Findings
The model projects that distributing pyrethroid–pyrrole ITNs averted 65% (95% credible interval 48–74) of cases over 3 years in Tanzania, and 75% (28–93) in Benin. The model indicates that trials might have underestimated the benefits of switching ITNs by 12–16% over 3 years because participants stopped using trial-allocated nets. In moderate endemicity non-trial settings, pyrethroid–pyrrole ITNs are projected to reduce malaria prevalence by 25–60% and switching from pyrethroid-only ITNs is probably highly cost-effective in most locations given current prices, averting an additional 10–30% of cases.
Interpretation
The benefit of pyrethroid–pyrrole ITNs varies by setting but is generally the most cost-effective indoor vector control intervention in Africa. National Malaria Programmes can strategise deployment to maximise impact. Entomological data could broadly predict epidemiological impact, although there are some inconsistencies, illustrating the challenge in capturing the dynamics across diverse settings.
Funding
Unitaid, Bill & Melinda Gates Foundation, the UK Medical Research Council, Wellcome Trust, and the UK Foreign, Commonwealth & Development Office.
... By utilizing mathematical analysis, it is possible to identify the main aspects of the transmission process associated with various infections [8,9]. In the literature, numerous researchers have developed models for the management and prevention of malaria, each with different assumptions [10][11][12]. Researchers looked at how drug resistance and treatment interventions affected malaria dynamics in endemic areas [13]. ...
Vector-borne infections pose serious public health challenges due to the complex interplay of biological, environmental, and social factors. Therefore, comprehensive approaches are essential to mitigate the burden of vector-borne infections and minimize their impact on public health. In this research, an epidemic model for the vector-borne disease malaria is structured with a saturated incidence rate via fractional calculus and preventive measures. The essential results and concepts are introduced to examine the proposed model. The solution of the system is examined for some necessary results, and the threshold parameter of the model, indicated by R 0 , is calculated. In this paper, the proposed malaria model is analyzed both quantitatively and qualitatively. The fixed-point theorems of Banach and Schaefer are utilized to examine the uniqueness and existence of the solution dynamics. Furthermore, the necessary conditions for the stability of the model have been determined. A numerical approach is offered to visualize the solution pathways of the system and identify its key factors. Through the results, the most influential factors for the control and management of the disease are highlighted.
... This limitation makes the reproducibility of the model across similar or neighbouring countries challenging and to some extent inaccessible. It is important to note that there are known current modelling efforts for informing allocation of malaria interventions in collaboration with country NMCPs [30,39,40]. These efforts on the use of non-optimization modelling techniques have gotten stakeholders involved in discussions surrounding the application of these models within countries, and in the development of policy engagement tools such as open access applications to facilitate the translation of these models [39][40][41][42][43]. ...
... It is important to note that there are known current modelling efforts for informing allocation of malaria interventions in collaboration with country NMCPs [30,39,40]. These efforts on the use of non-optimization modelling techniques have gotten stakeholders involved in discussions surrounding the application of these models within countries, and in the development of policy engagement tools such as open access applications to facilitate the translation of these models [39][40][41][42][43]. ...
Background
Despite advances made in curbing the global malaria burden since the 2000s, progress has stalled, in part due to a plateauing of the financing available to implement needed interventions. In 2020, approximately 3.3 billion USD was invested globally for malaria interventions, falling short of the targeted 6.8 billion USD set by the GTS, increasing the financial gap between desirable and actual investment. Models for malaria control optimization are used to disentangle the most efficient interventions or packages of interventions for inherently constrained budgets. This systematic review aimed to identify and characterise models for malaria control optimization for resource allocation in limited resource settings and assess their strengths and limitations.
Methods
Following the Prospective Register of Systematic Reviews and Preferred reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search across PubMed and Embase databases was performed of peer-reviewed literature published from inception until June 2024. The following keywords were used: optimization model; malaria; control interventions; elimination interventions. Editorials, commentaries, opinion papers, conference abstracts, media reports, letters, bulletins, pre-prints, grey literature, non-English language studies, systematic reviews and meta-analyses were excluded from the search.
Results
The search yielded 2950 records, of which 15 met the inclusion criteria. The studies were carried out mainly in countries in Africa (53.3%), such as Ghana, Nigeria, Tanzania, Uganda, and countries in Asia (26.7%), such as Thailand and Myanmar. The most used interventions for analyses were insecticide-treated bed nets (93.3%), IRS (80.0%), Seasonal Malaria Chemoprevention (33.3%) and Case management (33.3%). The methods used for estimating health benefits were compartmental models (40.0%), individual-based models (40.0%), static models (13.0%) and linear regression model (7%). Data used in the analysis were validated country-specific data (60.0%) or non-country-specific data (40.0%) and were analysed at national only (40.0%), national and subnational levels (46.7%), or subnational only levels (13.3%).
Conclusion
This review identified available optimization models for malaria resource allocation. The findings highlighted the need for country-specific analysis for malaria control optimization, the use of country-specific epidemiological and cost data in performing modelling analyses, performing cost sensitivity analyses and defining the perspective for the analysis, with an emphasis on subnational tailoring for data collection and analysis for more accurate and good quality results. It is critical that the future modelling efforts account for fairness and target at risk malaria populations that are hard-to-reach to maximize impact.
Trial registration: PROSPERO Registration number: CRD42023436966
... The significance of these models has grown because while statistical models have been valuable in revealing relationships between environmental variables and transmission intensity, process-based mathematical models offer a more explanatory insight into the balance between internal factors (resulting from biological processes) and external factors (such as changes in environmental variables) that drive transmission. Dynamical models are essential because they account for the biological processes driving malaria transmission within an environment that changes dynamically over different time scales [20,[24][25][26][27]. Furthermore, a dynamic model is crucial for capturing invasion dynamics and effectively forecasting the emergence of new outbreaks. ...
... + 2) 3 + 0.14(T + 2) 2 − 3(T + 2) + 22 (25) κ p (T ) = −0.0018(T + 2) 3 + 0.12(T + 2) 2 − 2.7(T + 2) + 20 (26) This last approach is presented in Abiodun et al. [2], where the environmental carrying capacity of mosquito eggs K e (R), and the survival probabilities of eggs p e (R), larvae p l (R, T ), and pupae p p (R), depend on rainfall. The rainfall threshold for flushing out of aquatic mosquitoes due to heavy rainfall is denoted by R l . ...
Background
Malaria transmission is primarily limited to tropical regions where environmental conditions are conducive for the development of Plasmodium parasites and Anopheles mosquitoes. Adequate rainfall provides breeding sites, while suitable temperatures facilitate mosquito life-cycles and parasite development. Evaluating the efficacy of vector control interventions, such as insecticide treated nets and indoor residual spraying, is crucial to determine their effectiveness in reducing malaria transmission. In this context, mathemati-cal modeling offers a valuable framework for understanding the impacts of these meteorological factors on malaria transmission and evaluating the efficacy of vector control interventions.
Methods
We develop a vector-host compartmental mathematical model to compare three published approaches to incorporating weather influences on malaria transmission. The first approach examines mosquito biting behavior and mortality rates in larval and adult stages. The second focuses on temperature effects on mosquito life-cycle characteristics during aquatic stages. The third considers how temperature and rainfall influence adult mosquito behavior, environmental carrying capacity, and survival during aquatic stages. The model is simulated with varying intervention efficacy for vector control to identify differences in predicted malaria incidence, prevalence, cases averted, and transmission dynamics.
Results
Simulation results for the same initial conditions and no vector control, indicate that prevalence stabilizes around 500 cases per 1000 for all modelling approaches. Increasing vector control efficacy significantly reduces prevalence for all approaches, with the first approach showing the most considerable reduction and the longest delay to the start of the transmission season. While malaria incidence peaks are highest for the second approach, more cases are averted when the first approach is adopted, followed by the second, then the third.
Conclusion
Adopting an approach that accounts for how rainfall influences mosquito environmental capacity and the temperature regulation of parasite development, but excludes aquatic stage development, limits the number of mosquitoes available to transmit the disease. Investigating temperature regulation of mosquito development and survival provides a detailed and reliable description of mosquito population dynamics but projects higher peaks in malaria incidence. In contrast, the approach that examines how temperature influences the biting rates, larval mortality, and adult mosquito mortality projects lower peaks but also demonstrates significant reductions in incidence and prevalence as vector control efficacy improves. While this approach offers a simplified model of the dynamics, they may underestimate actual mosquito population trends, thereby impacting the effectiveness of modeled interventions.
... Several mathematical models have been deployed to model the spread of malaria across the globe [19,20,21,22,23,24,25] among others. For example, [19] formulated a malaria transmission model with variables based on the climate. ...
... In order to simulate malaria morbidity and mortality across Nigeria's 774 LGAs under four potential intervention strategies from 2020 to 2030, an agent-based model of Plasmodium falciparum transmission was utilized by [20]. The scenarios included the currently implemented plan (business as usual), the NMSP with a coverage level of 80% or above, and two plans that were prioritized based on the resources available to Nigeria. ...
Background: With the highest burden in northern Nigeria, malaria is a vector-borne disease that causes serious illness. Nigeria contributed 27% (61.8 million) of malaria burden worldwide and 23% (94 million) of malaria deaths globally in 2019. Despite the fact that Nigeria has made a significant step in malaria elimination, the process has remained stagnant in recent years. The global technical strategy targets of reducing malaria death to less than 50 per 1000 population at risk was unachievable for the past 5 years.
As part of the national malaria strategic plan of 2021-2025 to roll back malaria, it is imperative to provide a framework that will aid in understanding the effective reproduction number R_e and the time dependent-contact rates C(t) of malaria in Nigeria which is quite missing in the literature.
Methods: The data of the reported malaria cases between January 2014 and December 2017 and demography of all the northern states are used to estimate C(t) and R_e using Bayesian statistical inference.
We formulated a compartmental model with seasonal-forcing term in order to
account for seasonal variation of the malaria cases. In order to limit the infectiousness of the asymptomatic individuals, super-infection was also incorporated into the model.
Results: The posterior mean obtained shows that Adamawa state has the highest mean R_e of 5.92 (95% CrI : 1.60-10.59) while Bauchi has the lowest 3.72 (95% CrI : 1.11-7.08). Niger state has the highest mean contact rate C(t) 0.40 (95% CrI : 0.08-0.77) and the lowest was Gombe 0.26 (95% CrI: 0.04-0.55 ). The results also confirm that there is a mosquito abundance and high reproduction number during the rainy season compared to the dry season. The results further show that over 60% of the reported cases are from the asymptomatic individuals.
Conclusion: This research continues to add to our understanding of the epidemiology of malaria in Nigeria. It is strongly advised that a complete grasp of the malaria reproduction number and the contact rate between human and mosquitoes are necessary in order to develop more effective prevention and control strategies. It will support the public health practitioners strategy and effective planning for malaria eradication.
... The Nigerian Malaria Elimination Programme (NMEP) heeded the call for a more targeted response to malaria control and, in the 2021-2025 National Malaria Strategic Plan, interventions were tailored for each Local Government Area (LGA) [11]. Mathematical models supported the selection of optimal intervention mixes for each LGA [11,12]. Similarly, cities need intervention tailoring, particularly targeting informal settlements, slums and neighbourhoods situated close to farms, areas where residents may be at higher malaria risk as compared to planned settlements with high-quality housing and improved environmental conditions. ...
... This calibration aims to capture transmission intensity, considering seasonal variations in vectoral larval habitats in both the Ibadan and Kano metropolis. This study will follow a comparable approach to the one outlined by Ozodiegwu and colleagues [12], while upholding the current general parameters. The model will integrate data on intervention effect sizes, coverages, and distribution schedules. ...
Background:
Rapid urbanization in Nigerian cities may lead to localized variations in malaria transmission, particularly with a higher burden in informal settlements and slums. However, there is a lack of available data to quantify the variations in transmission risk at the city level and inform the selection of appropriate interventions. To bridge this gap, field studies will be undertaken in Ibadan and Kano, two major Nigerian cities. These studies will involve a blend of cross-sectional and longitudinal epidemiological research, coupled with longitudinal entomological studies. The primary objective is to gain insights into the variation of malaria risk at the smallest administrative units, known as wards, within these cities.
Methods/results:
The findings will contribute to the tailoring of interventions as part of Nigeria's National Malaria Strategic Plan. The study design incorporates a combination of model-based clustering and on-site visits for ground-truthing, enabling the identification of environmental archetypes at the ward-level to establish the study's framework. Furthermore, community participatory approaches will be utilized to refine study instruments and sampling strategies. The data gathered through cross-sectional and longitudinal studies will contribute to an enhanced understanding of malaria risk in the metropolises of Kano and Ibadan.
Conclusions:
This paper outlines pioneering field study methods aimed at collecting data to inform the tailoring of malaria interventions in urban settings. The integration of multiple study types will provide valuable data for mapping malaria risk and comprehending the underlying determinants. Given the importance of location-specific data for microstratification, this study presents a systematic process and provides adaptable tools that can be employed in cities with limited data availability.
In the context of high malaria burden and insufficient resources, several national malaria programs (NMPs) used subnational tailoring (SNT) as a tool for evidence-informed decision-making on their national malaria strategic plans and funding requests. The SNT process included the formation of an SNT team, determination of criteria for targeting interventions, data assembly and review, stratification, application of targeting criteria to determine preliminary plans, mathematical modeling, finalization of intervention plans, and monitoring and evaluation of the eventual implemented plan, all under the leadership of the NMP. Analysis steps of SNT were supported by the World Health Organization (WHO) and other partners. As SNT was a new approach, this study used semi-structured interviews to understand the perspectives and experiences of personnel from five NMPs (Burkina Faso, Ghana, Guinea, Nigeria, and Togo) that undertook SNT between 2019 and 2023. Participants reported that SNT outputs were used to inform national strategic plans and prioritized plans, that the process incentivized improvements in data collection and data quality, and that NMPs were strongly motivated to grow their capacity to conduct more steps of the SNT analysis process internally. Major challenges included the lack of resources available to implement the full strategic plans as well as challenges with data quality and alignment of stakeholders. Participants reported a moderate to strong sense of ownership over the process and were eager to extend, adapt, and reuse the SNT process in the future. Among countries supported by WHO, SNT was well-accepted and allowed NMPs to successfully use evidence to inform their decision-making, advocate for themselves, and mobilize resources.
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.
Understanding and forecasting infectious disease spread is pivotal for effective public health management. Traditional dynamic disease modeling is an essential tool for characterization and prediction, but often requires extensive expertise and specialized software, which may not be readily available in low-resource environments. To address these challenges, we introduce an AI-powered modeling assistant that utilizes advanced capabilities from OpenAI’s latest models and functionality. This tool enhances the accessibility and usability of infectious disease models and simulation frameworks by allowing users to generate or modify model configurations through intuitive natural language inputs or by importing explicit model descriptions. Our prototype integrates with an established open-source disease simulation framework called the Compartmental Modeling Software (CMS) to provide a seamless modeling experience from setup to analysis. The AI assistant efficiently interprets disease model parameters, constructs accurate model files, executes simulations in a controlled environment, and assists in result interpretation using advanced analytics tools. It encapsulates expert knowledge and adheres to best practices to support users ranging from novices to expert modelers. Furthermore, we discuss the limitations of this AI assistant, particularly its performance in complex scenarios where it might generate inaccurate specifications. By enhancing the ease of disease modeling and supporting ongoing capacity-building initiatives, we believe that AI assistants like this one could significantly contribute to global health efforts by empowering researchers, especially in regions with limited resources, to develop and refine their disease models independently. This innovative approach has the potential to democratize disease modeling in global health, offering a scalable solution that adapts to diverse needs across a wide-range of geographies, languages, and populations.