Multi-scale mathematical modelling of pathogen, drug and vaccine interactions: optimising public health and disease elimination strategies

Quantitative sciences and mathematical modelling are increasingly important to understand existing and emerging diseases, and to plan the interventions to tackle them. Models that capture detailed biological, epidemiological and health systems factors, and the complex interactions between, can provide insight into the factors which drive disease dynamics. We have shown that mathematical models can be used to predict the impact of disease interventions; to define the desired characteristics of a new intervention; and to identify optimal deployment strategies. As we move towards elimination of malaria and other diseases, mitigating resistance to current interventions becomes crucial. Since the dynamics of resistance differ between high- and low-prevalence settings, mitigation strategies are likely to change as we get closer to elimination. Defining and quantifying these strategies for different settings and understanding how they differ as pathogens evolve is therefore essential.In the current global pandemic, we are at a crucial moment to define strategies for vaccine rollout. SARS-CoV-2 has shown a high propensity for mutation, posing the danger that new vaccine-resistant variants may evolve. The proposed project directly examines the dynamics of resistance evolution, and will define strategies for vaccine deployment that mitigate vaccine resistance. Our work will bring theoretical modelling and disease evolution understandings directly to bear on public health policy and vaccine deployment strategy in Switzerland.Proposing disease models with sufficient detail to evaluate disease interventions presents significant computational challenges for model simulation and sensitivity analysis on one hand, and methodological challenges for model calibration on the other. In phase 1 of the grant we developed machine learning based approaches to solve both these issues. In this prolongation we will adopt our methods to build detailed within-host models of malaria parasite dynamics, and adapt existing individual based models of malaria and COVID-19 to explore evolution of vaccine and intervention resistance.In Objective 1 we will apply our novel approaches to build within-host models designed to be sufficiently expressive to tackle resistance, and designed with parameterisation to sparse data in mind.In Objective 2 we will use global sensitivity analysis and the new models to determine key implementation and disease factors driving emergence and spread of malaria genotypes resistant to vaccines, monoclonal antibody therapies, and the spread of mosquitos resistant to insecticide-based interventions.In Objective. 3 we will adapt our methods from Obj. 2 and our existing individual-based COVID-19 model to determine the key implementation and disease factors driving the emergence of new vaccine-resistant variants of SARS-CoV-2.We will bring these results together to guide strategies for interventions, for disease control and elimination, especially in the context of resistance. We will promote integration of our model-based research findings into decision making. The proposal builds on the applicant's previous research and links basic science innovation through to application via new mathematical models, and thus will deliver significant advances for epidemiological modelling and understanding of resistance and disease evolution. While immediately relevant to malaria and SARS-CoV-2, the project will also inform efforts to control other diseases, while sustaining the position of the applicant and Switzerland as global leaders in infectious disease modelling research.