Modelling population-level impact to inform target product profiles for childhood malaria vaccines

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

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

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-022-00981-1.

Target Product Profiles for Mosquito Gene Drives: Incorporating Insights From Mathematical Models

Mosquito-borne diseases such as malaria continue to pose a major global health burden, and the impact of currently-available interventions is stagnating. Consequently, there is interest in novel tools to control these diseases, including gene drive-modified mosquitoes. As these tools continue to be refined, decisions on whether to implement them in the field depend on their alignment with target product profiles (TPPs) that define product characteristics required to achieve desired entomological and epidemiological outcomes. TPPs are increasingly being used for malaria and vector control interventions, such as attractive targeted sugar baits and long-acting injectable drugs, as they progress through the development pipeline. For mosquito gene drive products, reliable predictions from mathematical models are an essential part of these analyses, as field releases could potentially be irreversible. Here, we review the prior use of mathematical models in developing TPPs for malaria and vector control tools and discuss lessons from these analyses that may apply to mosquito gene drives. We recommend that, as gene drive technology gets closer to field release, discussions regarding target outcomes engage a wide range of stakeholders and account for settings of interest and vector species present. Given the relatively large number of parameters that describe gene drive products, machine learning approaches may be useful to explore parameter space, and an emphasis on conservative fitness estimates is advisable, given the difficulty of accurately measuring these parameters prior to field studies. Modeling may also help to inform the risk, remediation and cost dimensions of mosquito gene drive TPPs.

Insights from modelling malaria vaccines for policy decisions: the focus on RTS,S

Mathematical models are increasingly used to inform decisions throughout product development pathways from pre-clinical studies to country implementation of novel health interventions. This review illustrates the utility of simulation approaches by reviewing the literature on malaria vaccine modelling, with a focus on its link to the development of policy guidance for the first licensed product, RTS,S/AS01. The main contributions of modelling studies have been in inferring the mechanism of action and efficacy profile of RTS,S; to predicting the public health impact; and economic modelling mainly comprising cost-effectiveness analysis. The value of both product-specific and generic modelling of vaccines is highlighted.

Meeting report: WHO consultation on malaria vaccine development, Geneva, 15-16 July 2019

Considerable progress has been made in malaria control in the last two decades, but progress has stalled in the last few years. New tools are needed to achieve public health goals in malaria control and elimination. A first generation vaccine, RTS,S/AS01, is currently being evaluated as it undergoes pilot implementation through routine health systems in parts of three African countries. The development of this vaccine took over 30 years and has been full of uncertainties. Even now, important unknowns remain as to its future role in public health. Lessons need to be learnt for second generation and future vaccines, including how to facilitate early planning of investments, streamlining of development, regulatory and policy pathways. A number of candidate vaccines populate the current development pipeline, some of which have the potential to contribute to burden reduction if efficacy is confirmed in conditions of natural exposure, and if they are amenable to affordable supply and programmatic implementation. New, innovative technologies will be needed if future malaria vaccines are to overcome important scientific hurdles and induce durable, high level protection. WHO convened a stakeholder consultation on the status of malaria vaccine research and development to inform the recently reconstituted Malaria Vaccine Advisory Committee (MALVAC) which will assist WHO in updating its current guidance and recommendations about priorities and product preferences for malaria vaccines.

Predicting the public health impact of a malaria transmission-blocking vaccine

Transmission-blocking vaccines that interrupt malaria transmission from humans to mosquitoes are being tested in early clinical trials. The activity of such a vaccine is commonly evaluated using membrane-feeding assays. Understanding the field efficacy of such a vaccine requires knowledge of how heavily infected wild, naturally blood-fed mosquitoes are, as this indicates how difficult it will be to block transmission. Here we use data on naturally infected mosquitoes collected in Burkina Faso to translate the laboratory-estimated activity into an estimated activity in the field. A transmission dynamics model is then utilised to predict a transmission-blocking vaccine's public health impact alongside existing interventions. The model suggests that school-aged children are an attractive population to target for vaccination. Benefits of vaccination are distributed across the population, averting the greatest number of cases in younger children. Utilising a transmission-blocking vaccine alongside existing interventions could have a substantial impact against malaria.

Modelling the roles of antibody titre and avidity in protection from Plasmodium falciparum malaria infection following RTS,S/AS01 vaccination

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Models capturing key malaria life-cycle stages can help us evaluate vaccine candidates.

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Model fitting revealed antibody avidity to be an important determinant of RTS,S vaccine efficacy.

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High avidity and titre were associated with increased levels of vaccine efficacy.

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Did not identify any thresholds of protection for either immune marker.

Anti-circumsporozoite antibody titres have been established as an essential indicator for evaluating the immunogenicity and protective capacity of the RTS,S/AS01 malaria vaccine. However, a new delayed-fractional dose regime of the vaccine was recently shown to increase vaccine efficacy, from 62.5% (95% CI 29.4–80.1%) under the original dosing schedule to 86.7% (95% CI, 66.8–94.6%) without a corresponding increase in antibody titres. Here we reanalyse the antibody data from this challenge trial to determine whether IgG avidity may help to explain efficacy better than IgG titre alone by adapting a within-host mathematical model of sporozoite inoculation. We demonstrate that a model incorporating titre and avidity provides a substantially better fit to the data than titre alone. These results also suggest that in individuals with a high antibody titre response that also show high avidity (both metrics in the top tercile of observed values) delayed-fractional vaccination provided near perfect protection upon first challenge (98.2% [95% Credible Interval 91.6–99.7%]). This finding suggests that the quality of the vaccine induced antibody response is likely to be an important determinant in the development of highly efficacious pre-erythrocytic vaccines against malaria.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Challenges and strategies for developing efficacious and long-lasting malaria vaccines

Although there has been major recent progress in malaria vaccine development, substantial challenges remain for achieving highly efficacious and durable vaccines against Plasmodium falciparum and Plasmodium vivax malaria. Greater knowledge of mechanisms and key targets of immunity are needed to accomplish this goal, together with new strategies for generating potent, long-lasting, functional immunity against multiple antigens. Implementation considerations in endemic areas will ultimately affect vaccine effectiveness, so innovations to simplify and enhance delivery are also needed. Whereas challenges remain, recent exciting progress and emerging knowledge promise hope for the future of malaria vaccines.