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Distinguishing recrudescence from new infections is crucial for the assessment of antimalarial drug efficacy againstPlasmodium falciparum (P. falciparum). Different genotyping methods are used and may impede the comparison of drug efficacy estimates in space and time, particularly in patients from high transmission settings with polyclonal infections.We compared five different genotyping methods currently used to assess their sensitivity in detecting minority clones in polyclonal infections, their robustness, and the genetic diversity of the markers used. Our study utilized four well-characterizedP. falciparumlaboratory strains mixed in varying ratios, and 20 paired patient samples collected from a clinical trial.We found that high-resolution capillary electrophoresis (H-CE) using length-polymorphic markers, as well as targeted amplicon deep sequencing (TADS) using single nucleotide polymorphism (SNP)-rich markers, revealed the highest sensitivity in detecting minority clones, while also exhibiting robustness, and high genetic diversity in the used markers. Moreover, markers used by TADS gave more consistent results. We observed that microsatellites had a lower genetic diversity compared to markers such as msp1,msp2, glurpand SNP-rich markers, with some genotypes having allelic frequencies of > 30 %.The replacement ofglurpby microsatellites did not result in a change in the genotyping outcome, probably due to the lower genetic diversity of microsatellites used in comparison toglurp. More studies with large sample sizes are necessary to identify the most suitable microsatellites that could replaceglurpas per the latest recommendations from the World Health Organization (WHO) on genotyping to distinguish recrudescence from new infections in high transmission settings. Our study indicates that TADS should be considered the gold standard for genotyping to differentiate recrudescence from new infection and should be used to validate other techniques.

1.AbstractSeasonal malaria chemoprevention (SMC) has proven highly efficacious in reducing malaria incidence. However, the continued success of SMC is threatened by the spread of resistance against one of its main preventive ingredients, Sulfadoxine-Pyrimethamine(SP), operational challenges in delivery, and incomplete adherence to the regimens. Via a simulation study with an individual-based model of malaria dynamics, we provide quantitative evidence to assess long-acting injectables (LAIs) as potential alternatives to SMC. We explored the predicted impact of a range of novel preventive LAIs as a seasonal prevention tool in children aged three months to five years old during late-stage clinical trials and at implementation. LAIs were co-administered with a blood-stage clearing drug once at the beginning of the transmission season. We found the establishment of non-inferiority of LAIs to standard 3 or 4 rounds of SMC with SP-amodiaquine was challenging in clinical trial stages due to high intervention deployment coverage. However, our analysis of implementation settings where the achievable SMC coverage was much lower, LAIs with fewer visits per season are potential suitable replacements to SMC. Suitability as a replacement with higher impact is possible if the duration of protection of LAIs covered the duration of the transmission season. Furthermore, optimizing LAIs coverage and protective efficacy half-life via simulation analysis in settings with an SMC coverage of 60% revealed important trade-offs between protective efficacy decay and deployment coverage. Our analysis additionally highlights that for seasonal deployment for LAIs, it will be necessary to investigate the protective efficacy decay as early as possible during clinical development to ensure a well-informed candidate selection process.

Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose a novel approach to calibrate disease transmission models via a Bayesian optimization framework employing machine learning emulator functions to guide a global search over a multi-objective landscape. We demonstrate our approach by application to an established individual-based model of malaria, optimizing over a high-dimensional parameter space with respect to a portfolio of multiple fitting objectives built from datasets capturing the natural history of malaria transmission and disease progression. Outperforming other calibration methodologies, the new approach quickly reaches an improved final goodness of fit. Per-objective parameter importance and sensitivity diagnostics provided by our approach offer epidemiological insights and enhance trust in predictions through greater interpretability. One Sentence Summary We propose a novel, fast, machine learning-based approach to calibrate disease transmission models that outperforms other methodologies

BackgroundSubstantial research is underway to develop next-generation interventions that address current malaria control challenges. As there is limited testing in their early development, it is difficult to predefine intervention properties such as efficacy that achieve target health goals, and therefore challenging to prioritize selection of novel candidate interventions. Here, we present a quantitative approach to guide intervention development using mathematical models of malaria dynamics coupled with machine learning. Our analysis identifies requirements of efficacy, coverage, and duration of effect for five novel malaria interventions to achieve targeted reductions in malaria prevalence. This study highlights the role of mathematical models to support intervention development.MethodsA mathematical model of malaria transmission dynamics is used to simulate deployment and predict potential impact of new malaria interventions by considering operational, health-system, population, and disease characteristics. Our method relies on consultation with product development stakeholders to define the putative space of novel intervention specifications. We couple the disease model with machine learning to search this multi-dimensional space and efficiently identify optimal intervention properties that achieve specified health goals. We demonstrate the power of our approach by application to five malaria interventions in development.ResultsAiming for malaria prevalence reduction, we identify and quantify key determinants of intervention impact along with their minimal properties required to achieve the desired health goals. While coverage is generally identified as the largest driver of impact, higher efficacy, longer protection duration or multiple deployments per year are needed to increase prevalence reduction. We show that the efficacy and duration needs depend on the biological action of the interventions. Interventions on multiple parasite or vector targets, as well as combinations the new interventions with drug treatment, lead to significant burden reductions and lower efficacy or duration requirements.ConclusionsOur approach uses disease dynamic models and machine learning to support decision-making and resource investment, facilitating development of new malaria interventions. By evaluating the intervention capabilities in relation to the targeted health goal, our analysis allows prioritization of interventions and of their specifications from an early stage in development, and subsequent investments to be channeled cost-effectively towards impact maximization. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions.