Monica Golumbeanu

Therapeutic efficacy studies (TESs) are the standard to evaluate antimalarial drug efficacy and guide malaria treatment policy. TESs are particularly relevant now, with resistance to first-line regimens emerging in sub-Saharan Africa. For TESs, a range of parasite genotyping and data analyses are available for genotype correction, a process to distinguish whether recurrent parasitemia after therapy is due to recrudescence of initially infecting parasites (treatment failure) or a new infection. The choice of methods for laboratory genotyping and data analyses can have a large effect on how outcomes are classified, and thereby on trial results. The currently recommended and most widely used laboratory and analytical methods for TES genotyping do not incorporate recent methodological advances and can produce biased results. As such current TES results can be difficult to interpret, especially in areas with high malaria transmission, such as much of sub-Saharan Africa. Thus, improving the accuracy and reliability of TES genotyping and data analysis are a major priority. To that end, we present target product profiles that outline key specifications for genetic data generation, processing, and data analysis, with the goal of creating rigorous and consistent community standards. Primary recommended specifications for laboratory methods include high sensitivity, specificity, and reproducibility, and guidance on the number and genetic diversity of targets; criteria which are best and likely only met by amplicon sequencing. Primary recommendations for data analysis methods include high classification accuracy, accounting for errors in genotyping, and accounting for alleles matching by chance. All laboratory and data analysis methods used should be systematically validated and publicly documented so that TES results, which have major policy implications, can be relied upon for sound programmatic decision making.

Background: The malaria transmission potential and the vulnerability of Anopheles mosquitoes to different vector control methods depend, among other factors, on the endophily, endophagy, anthropophagy and survival of each species. Local information on these bionomic parameters is generally unavailable. Methods: To address this, we estimated species-specific values of these parameters using an augmented version of the global database of bionomics data by Massey et al. (2016). We applied inclusion and exclusion criteria to select eligible studies with relevant experimental designs that minimise bias from collection methods for parous, sac, endophagy, and endophily rates as well as for the resting duration. For the human blood index (HBI), we separated data from indoor and outdoor collections. We fitted hierarchical Bayesian models with levels based on Anopheles taxonomy to estimate these quantities. Based on the estimated bionomics, we quantified the expected vectorial capacity reduction after the introduction of a pyrethroid-pyrrole insecticide-treated net (ITN) for 57 Anopheles species. Results: We identified 26 eligible studies for endophagy and 61 for the parous rate, leading to a Bayesian posterior average for the Anopheles genus of 42% (95% credible interval: 18-70) and 55% (32-77) respectively. HBI values widely varied depending on the location of collection, except for some species showing strong anthropophilic behaviours. Resting duration was estimated to be 2.1 days (1.2 – 4.8) at the genus level. Few studies were available to estimate the sac and endophily rates, which prevented us from deriving precise estimates for the whole Anopheles genus. Our estimates of the vectorial capacity reduction following the introduction of a pyrrole-pyrethroid ITN ranged between 48% and 76% across species, highlighting the important differences among mosquito species in vulnerability to vector control interventions. Conclusion: This work demonstrates how data from both Anopheles species complexes and individual species can be leveraged to generate species-specific estimates of bionomic parameters, capturing the local characteristics and behaviour of malaria vectors. The dataset is readily updatable as new data become available. However, more frequent and standardised field surveys are still needed to accurately characterise local vector behaviour.

As insecticide resistance spreads in Africa, next-generation insecticide-treated nets (ITNs) are increasingly being deployed to protect vulnerable populations against malaria. While these nets provide greater entomological efficacy against resistant mosquitoes, their effectiveness against malaria transmission also depends on other factors, such as durability, access, usage, and activity patterns of hosts and vectors. Here, we quantify the impact of two next-generation ITNs, namely Interceptor®G2 (chlorfenapyr-pyrethroid) and Olyset® Plus (piperonyl butoxide-pyrethroid), in a cascade from entomological efficacy to population-level effectiveness. We use a mathematical model that we parameterize with entomological data and validate against results from randomized controlled trials. We found that, beyond entomological factors, operational factors including functional survival, ITN use and in-bed exposure critically impact ITN effectiveness overall and per ITN types. Our results obtained for Tanzania can be extended to other contexts in a dashboard allowing users to explore product selection based on setting-specific factors that influence ITN effectiveness.

Introduction: Accurate estimation of antimalarial drug efficacy against P. falciparum requires PCR correction to distinguish recrudescence from new infection in recurrent infections. Genotyping length polymorphic or SNP-rich markers, and various decision algorithms based on match counting or probabilistic approaches may be used for this purpose. In this study, we compared several markers and decision algorithms using samples collected in a therapeutic efficacy study conducted in Rwanda to identify the most suitable and robust approaches for PCR correction. Methods: We optimized nested PCR assays to genotype four microsatellites and assessed their sensitivity in detecting minority clones with laboratory parasite strain mixtures. We analyzed patient samples by capillary electrophoresis and amplicon deep sequencing and assessed the diversity and allelic frequency of msp1 , msp2 , glurp , microsatellites ( Poly-α, PfPK2, TA40, TA81 ), and SNP-rich markers ( ama1-D2, ama1-D3, cpmp, cpp, csp ). We then classified the samples into recrudescence or new infection based on different marker combinations ( msp1/msp2/glurp, msp1/msp2/ microsatellite, and SNP-rich markers) and three decision algorithms (WHO algorithm, 2/3 algorithm, and a Bayesian approach), and compared our results with previously published findings. Results: Among microsatellites, TA40 and PfPK2 had the highest sensitivity in detecting minority clones; however, their intra-assay reproducibility was found to be limited. The 3D7 allelic family of the msp2 gene, glurp and SNP-rich markers had the highest genetic diversity. In distinguishing recrudescence from new infection, the WHO algorithm identified the fewest recrudescences across all marker combinations. Conversely, the 2/3 algorithm identified the highest number of recrudescences, and the algorithm based on Bayesian statistics yielded intermediate results. We observed the least variability in classification results among the different algorithms for the SNP-rich markers ama1-D2/ama1-D3/cpmp combination. In our study, replacing glurp with one of the four analyzed microsatellites or using SNP-rich markers did not significantly alter the final number of recrudescent infections. Interpretation: Our findings emphasize the importance of using assays with high sensitivity in detecting minority clones, and markers with high diversity and low allelic frequency for accurate PCR correction. The SNP-rich markers method yielded the most consistent results in detecting recrudescences, regardless of the decision algorithm used. Therefore, it holds great potential for performing reliable PCR correction. Further evaluation of decision algorithms based on probabilistic approaches, compared to match-counting methods, is essential to ensure the accuracy and consistency of PCR-corrected drug efficacy estimations.

As insecticide resistance spreads in Africa, new-generation insecticide-treated nets (ITNs) are increasingly being deployed to protect vulnerable populations against malaria. While these nets provide greater entomological efficacy against resistant mosquitoes, their effectiveness against malaria transmission also depends on other factors, such as durability, access, usage, and activity patterns of hosts and vectors. Here, we quantify the impact of two new-generation ITNs, namely Interceptor®G2 (chlorfenapyr-pyrethroid) and Olyset®Plus (piperonyl butoxide-pyrethroid), in a cascade from entomological efficacy to population-level effectiveness. We use a mathematical model that we parameterize with entomological data and validate against results from randomized controlled trials. We found that, beyond entomological factors, operational factors including functional survival, ITN use and in-bed exposure critically impact ITN effectiveness overall and per ITN types. Our results obtained for Tanzania can be extended to other contexts in a dashboard ( https://aimswisstph.shinyapps.io/ITNcascadesdashboard ) allowing users to explore product selection based on setting-specific factors that influence ITN effectiveness.

: An increasing number of molecular and genomic assays are available to study malaria parasite populations. However, so far they have played a marginal role in informing policy and programmatic decision-making. Currently, molecular data are mainly used for monitoring drug efficacy against Plasmodium falciparum; assessing molecular markers of drug and insecticide resistance; and assessing P. falciparum histidine-rich protein 2 and 3 genes (Pfhrp2/3) deletion. We argue that additional use cases for molecular routine surveillance could be implemented in the near future, especially in transmission settings approaching elimination. These would include using quantitative polymerase chain reaction to monitor the prevalence of sub-patent infections in asymptomatic carriers, monitoring parasite genetic diversity as transmission intensity is changing, using genomic data to determine the origin of imported infections and characterize transmission chains in settings with very low malaria transmission, and using serology to monitor recent and past exposures in low-transmission settings. Molecular surveillance could inform control programs on adapting novel strategies, such as reactive case detection or focal mass drug administration, and help evaluate the impact of interventions currently in place. To better integrate molecular and genomic data into control program decision-making, engagement of national malaria control experts is crucial. Local laboratory capacity needs to be strengthened, shortening the time from sample collection to data availability. Here, we discuss opportunities and challenges of the use of molecular and genomic data for supporting malaria control and elimination efforts, as well as the avenues to link molecular and genomic data with gold standard epidemiological measurements through mathematical modeling.

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.

The availability of nutrients from mosquito blood meals accelerates the development of Plasmodium falciparum laboratory strains in artificially infected Anopheles gambiae mosquitoes. The impact of multiple blood meals on the number of P. falciparum genotypes developing from polyclonal natural human malaria infections (field-isolates) remains unexplored. Here, we experimentally infect An. gambiae with P. falciparum field-isolates and measure the impact of an additional non-infectious blood meal on parasite development. We also assess parasite genetic diversity at the blood stage level of the parasite in the human host and of the sporozoites in the mosquito. Additional blood meals increase the sporozoite infection prevalence and intensity, but do not substantially affect the genetic diversity of sporozoites in the mosquito. The most abundant parasite genotypes in the human blood were transmitted to mosquitoes, suggesting that there was no preferential selection of specific genotypes. This study underlines the importance of additional mosquito blood meals for the development of parasite field-isolates in the mosquito host.

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 directly allows incorporating generated entomological data for adjustment of models to assess intervention scenarios according to 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 on 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 .