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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., (© 2024. The Author(s).)

Background: Many public health interventions lead to disruption or decrease of transmission, providing a beneficial effect for people in the population regardless of whether or not they individually participate in the intervention. This protective benefit has been referred to as a herd or community effect and is dependent on sufficient population participation. In practice, public health interventions are implemented at different spatial scales (i.e., at the village, district, or provincial level). Populations, however defined (i.e., neighbourhoods, villages, districts) are frequently connected to other populations through human movement or travel, and this connectedness can influence potential herd effects., Methods: The impact of a public health intervention (mass drug administration for malaria) was modelled, for different levels of connectedness between populations that have similar disease epidemiology (e.g., two nearby villages which have similar baseline malaria incidences and similar malaria intervention measures), or between populations of varying disease epidemiology (e.g., two nearby villages which have different baseline malaria incidences and/or malaria intervention measures)., Results: The overall impact of the interventions deployed could be influenced either positively (adding value to the intervention) or negatively (reducing the impact of the intervention) by how much the intervention units are connected with each other (e.g., how frequent people go to the other village or town) and how different the disease intensity between them are. This phenomenon is termed the "assembly effect", and it is a meta-population version of the more commonly understood "herd effect"., Conclusions: The connectedness of intervention units or populations is an important factor to be considered to achieve success in public health interventions that could provide herd effects. Appreciating the assembly effect can improve the cost-effective strategies for global disease elimination projects.

Background: Malaria programmes in countries with low transmission levels require evidence to optimize deployment of current and new tools to reach elimination with limited resources. Recent pilots of elimination strategies in Ethiopia, Senegal, and Zambia produced evidence of their epidemiological impacts and costs. There is a need to generalize these findings to different epidemiological and health systems contexts., Methods: Drawing on experience of implementing partners, operational documents and costing studies from these pilots, reference scenarios were defined for rapid reporting (RR), reactive case detection (RACD), mass drug administration (MDA), and in-door residual spraying (IRS). These generalized interventions from their trial implementation to one typical of programmatic delivery. In doing so, resource use due to interventions was isolated from research activities and was related to the pilot setting. Costing models developed around this reference implementation, standardized the scope of resources costed, the valuation of resource use, and the setting in which interventions were evaluated. Sensitivity analyses were used to inform generalizability of the estimates and model assumptions., Results: Populated with local prices and resource use from the pilots, the models yielded an average annual economic cost per capita of $0.18 for RR, $0.75 for RACD, $4.28 for MDA (two rounds), and $1.79 for IRS (one round, 50% households). Intervention design and resource use at service delivery were key drivers of variation in costs of RR, MDA, and RACD. Scale was the most important parameter for IRS. Overall price level was a minor contributor, except for MDA where drugs accounted for 70% of the cost. The analyses showed that at implementation scales comparable to health facility catchment area, systematic correlations between model inputs characterizing implementation and setting produce large gradients in costs., Conclusions: Prospective costing models are powerful tools to explore resource and cost implications of policy alternatives. By formalizing translation of operational data into an estimate of intervention cost, these models provide the methodological infrastructure to strengthen capacity gap for economic evaluation in endemic countries. The value of this approach for decision-making is enhanced when primary cost data collection is designed to enable analysis of the efficiency of operational inputs in relation to features of the trial or the setting, thus facilitating transferability.

Malaria remains at the forefront of scientific research and global political and funding agendas. Malaria models have consistently oversimplified how mass interventions are implemented. Here, we present an individual based, spatially explicit model of P. falciparum malaria transmission that includes all the programmatic implementation details of mass drug administration (MDA) campaigns. We uncover how the impact of MDA campaigns is determined by the interaction between implementation logistics, patterns of human mobility and how transmission risk is distributed over space. Our results indicate that malaria elimination is only realistically achievable in settings with very low prevalence and can be hindered by spatial heterogeneities in risk. In highly mobile populations, accelerating MDA implementation increases likelihood of elimination; if populations are more static, deploying less teams would be cost optimal. We conclude that mass drug interventions can be an invaluable tool towards malaria elimination in low endemicity areas, specifically when paired with effective vector control., Competing Interests: BG, SS, YL, LW, AD, RA No competing interests declared, (© 2020, Gao et al.)

Assessing the importance of targeting the chronic Plasmodium falciparum malaria reservoir is pivotal as the world moves toward malaria eradication. Through the lens of a mathematical model, we show how, for a given malaria prevalence, the relative infectivity of chronic individuals determines what intervention tools are predicted be the most effective. Crucially, in a large part of the parameter space where elimination is theoretically possible, it can be achieved solely through improved case management. However, there are a significant number of settings where malaria elimination requires not only good vector control but also a mass drug administration campaign. Quantifying the relative infectiousness of chronic malaria across a range of epidemiological settings would provide essential information for the design of effective malaria elimination strategies. Given the difficulties obtaining this information, we also provide a set of epidemiological metrics that can be used to guide policy in the absence of such data.

Background: Preventing the emergence of anti-malarial drug resistance is critical for the success of current malaria elimination efforts. Prevention strategies have focused predominantly on qualitative factors, such as choice of drugs, use of combinations and deployment of multiple first-line treatments. The importance of anti-malarial treatment dosing has been underappreciated. Treatment recommendations are often for the lowest doses that produce "satisfactory" results., Methods: The probability of de-novo resistant malaria parasites surviving and transmitting depends on the relationship between their degree of resistance and the blood concentration profiles of the anti-malarial drug to which they are exposed. The conditions required for the in-vivo selection of de-novo emergent resistant malaria parasites were examined and relative probabilities assessed., Results: Recrudescence is essential for the transmission of de-novo resistance. For rapidly eliminated anti-malarials high-grade resistance can arise from a single drug exposure, but low-grade resistance can arise only from repeated inadequate treatments. Resistance to artemisinins is, therefore, unlikely to emerge with single drug exposures. Hyperparasitaemic patients are an important source of de-novo anti-malarial drug resistance. Their parasite populations are larger, their control of the infection insufficient, and their rates of recrudescence following anti-malarial treatment are high. As use of substandard drugs, poor adherence, unusual pharmacokinetics, and inadequate immune responses are host characteristics, likely to pertain to each recurrence of infection, a small subgroup of patients provides the particular circumstances conducive to de-novo resistance selection and transmission., Conclusion: Current dosing recommendations provide a resistance selection opportunity in those patients with low drug levels and high parasite burdens (often children or pregnant women). Patients with hyperparasitaemia who receive outpatient treatments provide the greatest risk of selecting de-novo resistant parasites. This emphasizes the importance of ensuring that only quality-assured anti-malarial combinations are used, that treatment doses are optimized on the basis of pharmacodynamic and pharmacokinetic assessments in the target populations, and that patients with heavy parasite burdens are identified and receive sufficient treatment to prevent recrudescence.

Background: Malaria has recently been identified as a candidate for global eradication. This process will take the form of a series of national eliminations. Key issues must be considered specifically for elimination strategy when compared to the control of disease. Namely the spread of drug resistance, data scarcity and the adverse effects of failed elimination attempts. Mathematical models of various levels of complexity have been produced to consider the control and elimination of malaria infection. If available, detailed data on malaria transmission (such as the vector life cycle and behaviour, human population behaviour, the acquisition and decay of immunity, heterogeneities in transmission intensity, age profiles of clinical and subclinical infection) can be used to populate complex transmission models that can then be used to design control strategy. However, in many malaria countries reliable data are not available and policy must be formed based on information like an estimate of the average parasite prevalence., Methods: A simple deterministic model, that requires data in the form of a single estimate of parasite prevalence as an input, is developed for the purpose of comparison with other more complex models. The model is designed to include key aspects of malaria transmission and integrated control., Results: The simple model is shown to have similar short-term dynamic behaviour to three complex models. The model is used to demonstrate the potential of alternative methods of delivery of controls. The adverse effects on clinical infection and spread of resistance are predicted for failed elimination attempts. Since elimination strategies present an increased risk of the spread of drug resistance, the model is used to demonstrate the population level protective effect of multiple controls against this very serious threat., Conclusion: A simple model structure for the elimination of malaria is suitable for situations where data are sparse yet strategy design requirements are urgent with the caveat that more complex models, populated with new data, would provide more information, especially in the long-term.

Background: Clinical management of malaria is a major health issue in sub-Saharan Africa. New strategies based on intermittent preventive treatment (IPT) can tackle disease burden by simultaneously reducing frequency of infections and life-threatening illness in infants (IPTi) and children (IPTc), while allowing for immunity to build up. However, concerns as to whether immunity develops efficiently in treated individuals, and whether there is a rebound effect after treatment is halted, have made it imperative to define the effects that IPTi and IPTc exert on the clinical malaria scenario., Methods and Findings: Here, we simulate several schemes of intervention under different transmission settings, while varying immunity build up assumptions. Our model predicts that infection risk and effectiveness of acquisition of clinical immunity under prophylactic effect are associated to intervention impact during treatment and follow-up periods. These effects vary across regions of different endemicity and are highly correlated with the interplay between the timing of interventions in age and the age dependent risk of acquiring an infection. However, even when significant rebound effects are predicted to occur, the overall intervention impact is positive., Conclusions: IPTi is predicted to have minimal impact on the acquisition of clinical immunity, since it does not interfere with the occurrence of mild infections, thus failing to reduce the underlying force of infection. On the contrary, IPTc has a significant potential to reduce transmission, specifically in areas where it is already low to moderate.