Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria

Reiker, Theresa; Golumbeanu, Monica; Shattock, Andrew J.; Burgert, Lydia; Smith, T.; Filippi, Sarah; Cameron, Ewan; Penny, Melissa A.

doi:10.1038/s41467-021-27486-z

Cited by 31 publications

(44 citation statements)

References 49 publications

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“…Third, the emulator was employed to perform sensitivity analysis and optimisation of tool properties with respect to health outcomes. We employed the use of emulators as it would be computational infeasible to simulate over the entire parameter-space, as well as to perform global-sensitivity analysis and utilize iterative optimization algorithms [25]. This analysis allowed us to define the optimal product characteristics of a LAI that maximises the chance of achieving a desired health goal.…”

Section: Methodsmentioning

confidence: 99%

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Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention

Burgert

Reiker

Golumbeanu

et al. 2022

PLOS Glob Public Health

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Seasonal 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, show 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, optimising 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.

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Section: Methodsmentioning

confidence: 99%

“…All simulations were performed using OpenMalaria version 38. The source code and comprehensive documentation for Open-Malaria, including a detailed model of demography, transmission dynamics and interventions is available online [41] or in a recent publication [25].…”

Section: Simulated Disease Scenariosmentioning

confidence: 99%

Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention

Burgert

Reiker

Golumbeanu

et al. 2022

PLOS Glob Public Health

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“…Various health outcomes are monitored over time, including Plasmodium falciparum prevalence of infections ( Pf PR), uncomplicated clinical or severe disease, hospitalization, and malaria mortality. Model assumptions have been described and validated with field data in previous studies [ 39 , 53 ].…”

Section: Methodsmentioning

confidence: 99%

Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions

et al. 2022

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Background Substantial 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. Methods A 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. Results We apply our approach to five malaria interventions under development. Aiming 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 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. Conclusions Our 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. This study highlights the role of mathematical models to support intervention development. Although we focus on five malaria interventions, the analysis is generalizable to other new malaria interventions. Graphical abstract

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“…Our individual-based model, OpenMalaria, simulates the dynamics of P. falciparum in humans and links it to a periodically forced deterministic model of P. falciparum in mosquitoes [83–85]. The model structure and fitting are described in detail elsewhere [84, 85], including open-access code (https://github.com/SwissTPH/openmalaria) and documentation (https://github.com/SwissTPH/openmalaria/wiki), and a recently published manuscript provides a new calibration [86]. Here, we have summarised the main components of OpenMalaria and its latest developments in version 40.1, which enabled us to model the establishment and spread of drug-resistant parasites.…”

Section: Methodsmentioning

confidence: 99%

“…Due to the computational requirements for a large number of simulations of OpenMalaria, and the number of factors investigated, it was not feasible to simulate either a full-factorial set of simulations to perform a multi-way sensitivity analysis, or to perform a global sensitivity analysis. Therefore, we trained a Heteroskedastic Gaussian Process (HGP) [100] on a set of OpenMalaria simulations and performed global sensitivity analyses using this emulator (Figure 1C), adapting a similar approach to [101] and [86]. Our approach involved: (i) randomly sampling combinations of parameters; (ii) simulating and estimating the rate of spread of the resistant genotype for each parameter combination in OpenMalaria; (iii) training an HGP to learn the relationship between the input (for the different drivers) and output (the rate of spread) with iterative improvements to fitting through adaptive sampling, and (iv) performing a global sensitivity analysis based on the Sobol variance decomposition [70].…”

Section: Approach To Identify the Key Drivers Of The Spread Of Drug-r...mentioning

confidence: 99%

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

Masserey

Lee

Golumbeanu

et al. 2022

Preprint

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The effectiveness of artemisinin-based combination therapies (ACTs) to treat Plasmodium falciparum malaria is threatened by resistance. The complex interplay between sources of selective pressure - treatment properties, biological factors, transmission intensity, and access to treatment - obscures understanding how, when, and why resistance establishes and spreads across different locations. We developed a disease modelling approach with emulator-based global sensitivity analysis to systematically quantify which of these factors drive establishment and spread of drug resistance. Drug resistance was more likely to evolve in low transmission settings due to the lower levels of (i) immunity and (ii) within-host competition between genotypes. Spread of parasites resistant to artemisinin partner drugs depended on the period of low drug concentration (known as the selection window). Spread of partial artemisinin resistance was slowed with prolonged parasite exposure to artemisinin derivatives and accelerated when the parasite was also resistant to the partner drug. Thus, to slow the spread of partial artemisinin resistance, molecular surveillance should be supported to detect resistance to partner drugs and to change ACTs accordingly. Furthermore, implementing more sustainable artemisinin-based therapies will require extending parasite exposure to artemisinin derivatives, and mitigating the selection windows of partner drugs, which could be achieved by including an additional long-acting drug.

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Emulator-based Bayesian optimization for efficient multi-objective calibration of an individual-based model of malaria

Cited by 31 publications

References 49 publications

Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention

Model-informed target product profiles of long-acting-injectables for use as seasonal malaria prevention

Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions

The influence of biological, epidemiological, and treatment factors on the establishment and spread of drug-resistant Plasmodium falciparum

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