Establishment of the approach to quantitatively define the framework guiding malaria product development

We developed a disease model and machine learning approach to quantitatively define malaria interventions. Our approach consists of three components: “Disease modelling”, “Machine learning”, and “Target product profiles” (Fig. 2). The “Disease modelling” component of our approach included the results of iterative consultations with product development experts to build sensibly informed TPP simulation scenarios, i.e., to define the breadth, range, and intervention profiles to simulate with OpenMalaria for the five malaria interventions considered, as well as a public health goal to optimize (see “Methods” and Additional file 1: Sect. 1 for a description of the iterative stakeholder engagement process). Following the expert discussions, the public health goal chosen in this study was to reduce the prevalence of Plasmodium falciparum malaria (denoted as PfPR0–99 when evaluated for all ages and PfPR2–10 when evaluated for 2‒10-year-old) between years one and three following deployment (Fig. 3A). Table 1 summarizes the results of the stakeholder discussions to set-up the OpenMalaria simulation scenarios and presents a comprehensive description of all subsequent intervention characteristics explored in this study, as well as the simulated malaria transmission settings.

Fig. 3

Training predictive Gaussian process emulators of simulated intervention impact with OpenMalaria. Examples are shown for attractive targeted sugar baits (ATSBs); results for other interventions are shown in Additional file 1: Figs. S3.1 and S4.2‒S4.7 and Table S4.1. A Simulated malaria PfPR0–99 time series at EIR = 10 where ATSBs were deployed at a coverage of 70% and had an efficacy of 70%. Results are shown for three intervention half-life levels. The dotted lines indicate when interventions were applied (beginning of June). The effect of the interventions was assessed by evaluating the yearly average PfPR0–99 reduction in all ages relative to the year prior to deployment (first grey block). Two outcomes were assessed, depending on whether the average prevalence was calculated over the year following deployment (immediate follow-up), or over the third year following deployment (late follow-up). B Correlation between simulated true (horizontal axis) and predicted (vertical axis) PfPR0–99 reduction with a GP emulator trained to predict the immediate impact of ATSBs. The GP emulator was trained in a cross-validation scheme (distribution of the Pearson correlation coefficient r2 shown in the boxplot) and validated on an out-of-sample test set (r2 left upper corner and grey diamond lower right corner of the boxplot). C Relationship between each normalized input parameter and the resulting PfPR0–99 reduction predicted with the trained GP emulator. Each parameter was in turn varied within its defined ranges (Table 1) while other parameters were set to their average values. D Estimated CPU execution time for varying sizes of input parameter sets evaluated with OpenMalaria (black) and with the trained GP emulator (grey)

We used a previously published calibration of the OpenMalaria model (Figs. 1 and 3A, and Additional file 1: Sect. 1 and Tables S1.1‒S1.3), which reflects demographics, epidemiology, entomology, health system access, and seasonality (Additional file 1: Fig. S2.1) for a catchment area in Tanzania [55]. Intervention impact was assessed through predicted reduction in PfPR0–99, corresponding to true infection prevalence and not patent [detected with a diagnostic such as rapid diagnostic test (RDT) or polymerase chain reaction (PCR), Fig. 3A and Additional file 1: Figs. S2.3 and S3.1–S3.4]. The simulated settings covered a broad spectrum of transmission and mosquito biting behavior archetypes relevant for attaining general guiding principles in the early development phase of new malaria interventions. A comprehensive set of simulated scenarios was built by uniformly sampling the parameter space defined by intervention and transmission setting characteristics (defined in Fig. 2A and detailed in Table 1). These scenarios were simulated with OpenMalaria, yielding an extensive database of disease outcomes for the defined scenarios.

In the machine learning part of the approach (illustrated in the “Machine learning” panel of Fig. 2A), the database of simulated scenarios and corresponding outcomes (PfPR0–99 reductions following intervention deployment) was used to train predictive models for the OpenMalaria simulation results (for an example of simulated PfPR0–99 time series with OpenMalaria see Fig. 3A and Additional file 1: Fig. S3.1). A Heteroskedastic Gaussian process (GP) model was trained for each intervention and transmission setting (see detailed training procedure in Additional file 1: Sect. 2.3). Trained GP models accurately captured the dependencies between the disease model input parameters and the output intervention impact (Fig. 3B and C) and were able to reliably predict the reduction in PfPR0–99 attributable to any input intervention characteristics in a given malaria transmission setting. Precisely, the correlation between true and predicted PfPR0–99 reduction on out-of-sample test sets exceeded 95% while the absolute mean error was below 3% for all trained GP models (Fig. 3B and Additional file 1: Figs. S4.1‒S4.3 and Table S4.1). As a result, the trained predictive GP models acted as emulators of the detailed modelled parameter dynamics and non-linear relationships within the individual-based mathematical model of malaria transmission (Fig. 3C and Additional file 1: Figs. S4.5‒S4.6) and could predict the disease outcome for the given health goal and for any set of input parameters.

Due to the significantly less intensive computational requirements of our emulator-based approach compared with OpenMalaria, we could reduce the analysis execution time by several orders of magnitude. This allowed us to conduct global sensitivity and optimization analyses, which required a large number of parameter set evaluations and would otherwise not have been possible (Fig. 3D and Additional file 1: Fig. S4.7). Thus, the trained GP emulators could be efficiently and promptly used in downstream analyses to explore the multi-dimensional space of intervention properties to design TPPs of new malaria interventions (panel “Target product profiles” in Fig. 2A), i.e., to identify the drivers of their impact and their quantitative properties in meeting the health goals previously defined (Fig. 2B and C). Specifically, through global sensitivity analysis, we identified the key determinants of intervention impact (Fig. 2B). In addition, we performed a constrained search for intervention and delivery profiles (TPPs) that maximized impact under a particular health goal, given concrete, expert-informed, operational constraints such as possible deployment coverage, or feasible intervention properties such as efficacy or duration of protection (Fig. 2C). Results of these analyses are detailed in the following sections and illustrated for seasonal transmission settings with high indoor mosquito biting. Results for the other simulated transmission settings (perennial settings and for other mosquito biting patterns) are provided in the supplement (additional sensitivity analysis results presented in Additional file 1: Figs. S5.1‒S5.2, additional optimization results presented in Additional file 1: Figs. S6.1‒S6.6 and S7.1‒S7.5) and summarized in Table 2.

Table 2 Key findings of our quantitative approach guiding target product profiles of new malaria interventions

Impact of malaria interventions and the importance of their characteristics

Following simulation with OpenMalaria of deployment of each of the studied interventions through mass administration campaigns over several years (see “Methods”), we analyzed the predicted distributions of reduction in true PfPR0–99. We found that, in general, when aiming for substantial, prompt reductions in prevalence for this particular health target, vector control was by far the most impactful intervention across all settings. Conversely, monoclonal antibodies, anti-infective and transmission-blocking vaccines had a more pronounced impact in low-transmission settings compared to endemic settings (Fig. 4A and Additional file 1: Figs. S3.1–S3.4 and Table 2). Figure 4A displays the reductions of PfPR0–99 for all the five interventions in a seasonal malaria transmission setting, with high indoor mosquito biting, for both immediate follow-up (during the year after intervention deployment) and late follow-up (during the third year after intervention deployment). Accordingly, at immediate follow-up, attractive targeted sugar baits achieved a median PfPR0–99 reduction of 96% in low transmission settings (PfPR2–10 ≈ 7%) decreasing to a median PfPR0–99 reduction of 49% in high transmission settings (PfPR2–10 ≈ 59%). Similarly, eave tubes reduced PfPR0–99 by 98% (median value) in low transmission settings, decreasing to 43% reduction (median value) in high transmission settings. Monoclonal antibodies yielded the smallest impact compared to all the five tested interventions, reducing PfPR0–99 by 54% (median value) in low transmission settings to only 4.5% (median value) in high transmission settings. Anti-infective vaccines reduced PfPR0–99 by 70% (median value) in low transmission settings and by 17% (median value) in high transmission settings. Transmission-blocking vaccines reduced PfPR0–99 by 86.5% (median value) in low transmission settings and by 8% (median value) in high transmission settings. At long follow-up, long-lasting eave tubes maintained the high reductions in prevalence, ranging from 99% (median value) in low transmission settings to 23% (median value) in high transmission settings. Attractive targeted sugar baits, anti-infective and transmission-blocking vaccines led to similar reductions (e.g., 79.5% median reduction for anti-infective vaccines in low transmission settings and 9% in high transmission settings), while monoclonal antibodies only achieved a maximum of 41.5% median PfPR0–99 reduction in the low transmission settings, decreasing below 1% reduction in high transmission settings.

Fig. 4
figure 4

Effects of novel malaria interventions on PfPR0–99 and their key drivers of impact. A Distribution of obtained reduction in PfPR0–99 across the simulated scenarios with OpenMalaria following deployment of various malaria interventions under development (shown with different colors) for a range of simulated transmission settings (specified by median true PfPR2–10 rounded values, x-axis). Each boxplot displays the interquartile range (box), the median value (horizontal line), the largest and smallest values within 1.5 times the interquartile range (whiskers), and the remaining outside values (points) of the PfPR0–99 reduction values obtained across all the simulations for each given setting. The remaining panels present the results of global sensitivity analysis showing, across the same simulated PfPR2–10 settings, the contribution of intervention characteristics to the resulting PfPR0–99 reduction for anti-infective monoclonal antibodies (B), anti-infective vaccines (C), transmission-blocking vaccines (D), attractive targeted sugar baits (E), and eave tubes (F)

Sensitivity analysis indicated that the impact of these interventions on malaria prevalence was driven by different characteristics of their efficacy profiles, deployment strategies, or access to care for treatment of clinical cases, for short- and long-impact follow-up. Across a large proportion of the simulated scenarios, for all parasite and vector targets and interventions, deployed intervention coverage was overwhelmingly the primary driver of impact, especially in low-transmission settings (Fig. 4B–F and Additional file 1: Figs. S5.1–S5.2). For immunological interventions, the impact of short-term passive immunizations such as monoclonal antibodies relied on their deployment coverage and the health system (Fig. 4B and Additional file 1: Fig. S5.1). In contrast, for long-acting interventions such as vaccines, impact was driven by deployment coverage and efficacy (Fig. 4C and D and Additional file 1: Fig. S5.1). Highly efficient vector control interventions such as attractive targeted sugar baits had a strong effect on prevalence (Fig. 4A), and their duration of effect was the most important determinant (Fig. 4E and Additional file 1: Fig. S5.2). The immediate impact of long-term vector control interventions such as eave tubes was driven by deployment coverage, while their half-life was a key determinant for preventing resurgence (Fig. 4F and Additional file 1: Fig. S5.2). Determinants of impact were identified for both immediate and late follow-up when interventions were applied once per year for three years. A detailed description of the determinants of impact affecting the effectiveness of each intervention is provided in Table 2 (rows labelled “Key determinants of impact”).

Minimal requirements of novel malaria interventions to achieve a defined health goal

For the five aforementioned malaria interventions, we explored their optimal properties for a broad set of PfPR0–99 reduction targets, creating landscapes of intervention profiles according to their minimal characteristics across various transmission settings (Fig. 5 and 6 and Additional file 1: Figs. S6.1‒S6.6 and S7.1‒S7.5). These landscapes provide a comprehensive overview of the intervention potential capabilities and limitations in achieving a desired health goal. As opposed to an anti-infective monoclonal antibody which required high efficacy and duration to achieve large PfPR0–99 reductions in only a limited number of settings (Fig. 5A and B and Additional file 1: Figs. S6.1 and S6.2), attractive targeted sugar baits that kill mosquitoes also achieved a wider range of target PfPR0–99 reductions in high-transmission settings (Fig. 6A and B and Additional file 1: Fig. S6.5). Similarly, while anti-infective and transmission-blocking vaccines had comparable requirements in achieving similar PfPR0–99 reduction targets in settings with lower transmission (PfPR2–10 < 30%), anti-infective vaccines showed a higher potential and reached additional targets in high-transmission, endemic settings (Fig. 5C–F).

Fig. 5
figure 5

Estimated optimal intervention TPPs for immunological interventions. The heatmaps in panels (A), (C) and (E) display, for each intervention property (coverage, efficacy, or half-life), the landscape of minimum required values to achieve various target PfPR0–99 reductions (y-axis) across different simulated transmission settings (true PfPR2–10 rounded values, x-axis). Each row of the heatmap corresponds to a target of PfPR0–99 reduction and constitutes the minimum required profile of the considered intervention. For a health goal of 70% PfPR0–99 reduction (dotted line on each heatmap), panels (B), (D), and (F) present in detail how the minimum profile changes with transmission intensity. Each intervention characteristic was minimized in turn, while keeping other characteristics fixed (values marked on each panel where c = coverage, e = efficacy, and h = half-life). The simulated access to treatment, corresponding to a probability of seeking care within 5 days, was 25%. TPP = Target Product Profiles, mos = months

Fig. 6
figure 6

Estimated optimal intervention TPPs for vector control interventions. The heatmaps in panels (A) and (C) display, for each intervention property (coverage, efficacy, or half-life), the landscape of minimum required values to achieve various target PfPR0–99 reductions (y-axis) across different simulated transmission settings (true PfPR2–10 rounded values, x-axis). Each row of the heatmap corresponds to a target of PfPR0–99 reduction and constitutes the minimum required profile of the considered intervention. For a selected health goal of 60% PfPR0–99 reduction (dotted line on each heatmap), panels (B) and (D) present in detail how the minimum profile changes with transmission intensity. Each intervention characteristic was minimized in turn, while keeping the other characteristics fixed (values marked on each panel where c = coverage, e = efficacy, and h = half-life). The simulated access to treatment, corresponding to a probability of seeking care within 5 days, was 25%. TPP = Target Product Profiles

For a detailed overview of landscapes of intervention profiles for all simulated settings and interventions see Additional file 1: Figs. S6.1‒S6.6. These landscapes together with results of the sensitivity analysis offer an evidence-based prioritization of resources during product development. We found that while both efficacy and half-life were important for immediate prevalence reductions with monoclonal antibodies, their effect was limited in preventing resurgence and was only supported by high case-management levels (Figs. 4 and 5 and Additional file 1: Figs. S5.1 and S6.1‒S6.2). Conversely, the efficacy of anti-infective vaccines determined their immediate impact, whereas half-life of effect had greater importance for achieving and maintaining PfPR0–99 reductions (Figs. 4 and 5 and Additional file 1: Figs. S5.1, S6.3, and S6.4).

Our analysis showed that coverage was a primary driver of impact (Fig. 4B‒F and Additional File 1: Figs. S5.1 and S5.2). This has important implications for interventions requiring multiple applications to achieve high efficacy, indicating that it is of crucial importance to target both vulnerable populations and the proportion of the population missed by the intervention. While, for some interventions, high coverage deployment might be difficult or impossible to achieve, our analysis showed that this can be alleviated by increasing the deployment frequency or through deploying combinations of interventions, which may also have cost implications (Figs. 5B, D, F, 6B and Additional file 1: Figs. S6.1‒S6.5 and S7.1‒S7.4).

We found that combining several interventions targeting different stages in the transmission cycle can strongly affect the minimum requirements of a putative new intervention, potentially increasing the impact of an otherwise weaker intervention. For an anti-infective monoclonal antibody with an initial half-life of 4 months that is deployed at a coverage of 60% reflecting completion of multiple doses, achieving 80% prevalence reduction was impossible when deployed once yearly for three years (Fig. 5A, and Additional file 1: Fig. S6.1). Furthermore, achieving the aforementioned health goal required an efficacy of over 80% when the intervention was deployed twice per year for three years (Additional file 1: Fig. S6.2). However, when monoclonal antibody deployment was coupled with a short half-life blood-stage parasite treatment such as dihydroartemisinin-piperaquine or artemether-lumefantrine, its minimum required efficacy was considerably reduced for both delivery frequencies (Fig. 5B and Additional file 1: Figs. S6.1, S6.2, and S7.1). Conversely, if an initial efficacy of 85% for the monoclonal antibody was assumed, its minimal required half-life could be reduced if this intervention was deployed in combination with a blood-stage parasite-clearing drug (Fig. 5B and Additional file 1: Figs. S6.1, S6.2, and S7.1). These results partly motivated the current development of anti-infective monoclonal antibodies; use-cases will likely include deployment with existing or new antimalarial treatment.

When coupled with a short half-life blood-stage parasite treatment, requirements of coverage, efficacy and half-life were also reduced for anti-infective and transmission blocking vaccines to achieve targeted reductions of PfPR0–99 (Fig. 5C–F and Additional file 1: Figs. S6.3, S6.4, S7.2, and S7.3). In particular, for high-transmission settings (PfPR2–10 > 30%), given an RTS,S-like half-life of seven months, both anti-infective and transmission-blocking vaccines could not achieve a defined prevalence reduction goal of 70% if deployed singly (Figs. 5D and F). This was the case for any deployment coverage given an initial efficacy of 85%, as well as for any efficacy given a 60% deployment coverage. Combining vaccine deployment with a blood-stage drug not only significantly expanded the achievable health targets in high-transmission settings, but also reduced vaccine properties requirements. Our analysis revealed that anti-infective vaccines had a higher potential than transmission-blocking vaccines, requiring less performance and achieving higher prevalence reductions targets in higher transmission settings. When combined with blood-stage parasite treatment, the coverage, efficacy, and half-life requirements of anti-infective vaccines were lower compared with those of transmission-blocking vaccines for the same prevalence reduction targets (Fig. 5 and Additional file 1: Figs. S6.3, S6.4, S7.2, and S7.3).

We also showed that a modified deployment schedule could reduce requirements for properties of some interventions. For highly efficacious attractive targeted sugar baits, higher coverage and half-life were required when implemented once per year for three years compared with accelerated delivery of twice per year for three years (Fig. 6A and B). Except for high-transmission settings (PfPR2–10 > 41%), a required efficacy of 70% was sufficient to attain the desired health goal for the majority of settings, for both delivery schedules (Fig. 6A and B, and Additional file 1: Figs. S6.5 and S7.4). This result was also reflected in the sensitivity analysis (Fig. 4E). Accordingly, the variation in intervention efficacy, across its investigated ranges, had little importance in driving the intervention impact. This suggests that, once a vector control intervention, such as attractive targeted sugar baits, has achieved a high killing efficacy (here ≥ 70%), a next step of optimizing other intervention characteristics, such as deployment coverage or duration, would lead to higher impact.

Our comprehensive analysis was applied to explore determinants of impact and required profiles of interventions across two seasonal settings (seasonal and perennial) and three types of indoor mosquito biting patterns (low, medium, and high). A detailed overview of impact determinants and optimal intervention profiles is presented in Additional file 1: Figs. S6.1‒S6.6 and S7.1‒S7.5, with additional key results summarized in Table 2.

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