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

English title Multi-scale mathematical modelling of pathogen, drug and vaccine interactions: optimising public health and disease elimination strategies
Applicant Penny Melissa
Number 203450
Funding scheme SNSF Professorships
Research institution Department of Epidemiology and Public Health Swiss Tropical and Public Health Institute Universität Basel
Institution of higher education University of Basel - BS
Main discipline Infectious Diseases
Start/End 01.09.2021 - 31.08.2023
Approved amount 800'000.00
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Keywords (8)

Malaria; drug resistance; Resistance; SARS-CoV-2; Mathematical Modelling; Vaccines; Parasite Dynamics; COVID-19

Lay Summary (German)

Lead
Mathematische Modellierung zur Untersuchung von Krankheiten, Bekämpfungsmaßnahmen, Wechselwirkungen zwischen Impfstoffen und Resistenzen als Grundlage für Strategien der öffentlichen Gesundheit und der Krankheitsbekämfpung.
Lay summary
Mathematische Modelle werden immer wichtiger, wenn es darum geht, neue und bestehende Krankheiten zu verstehen und zu planen, wie wir diese Krankheiten bekämpfen können. Modelle, die hinreichend detailliert beschreiben, wie eine Krankheit mit dem Körper interagiert, wie sich Medikamente und andere Maßnahmen auf die Krankheit auswirken und wie die Gesundheitssysteme darauf reagieren, können uns helfen, die besten Ansätze zur Bekämpfung von Krankheiten zu bewerten.

In diesem Projekt werden wir die Auswirkungen der Resistenz von Krankheitserregern auf Krankheitsbekämpfungsmaßnahmen untersuchen. Wir befassen uns mit zwei wichtigen Krankheitserregern: Malaria und SARS-Cov-2. Je näher wir der Ausrottung von Malaria und anderen Krankheiten kommen, desto wichtiger wird es, zu verhindern, dass die Erreger eine Resistenz gegen die Maßnahmen entwickeln. Da die Merkmale der Resistenz je nach Grad der Erkrankung unterschiedlich sind, werden sich die Strategien der öffentlichen Gesundheit wahrscheinlich ändern, je näher wir der Eliminierung kommen. Bei der derzeitigen weltweiten SARS-CoV-2-Pandemie befinden wir uns in einem entscheidenden Moment, um Strategien für den Einsatz von Impfstoffen festzulegen. Da das Virus, das COVID-19 verursacht, wahrscheinlich weiter mutieren wird, könnten neue gefährliche impfstoffresistente Varianten entstehen.

Wir werden untersuchen, wie Resistenzen gegen Impfstoffe und andere Behandlungsmaßnahmen entstehen, und wir werden Strategien für die Einführung von Massnahmen festlegen, um Resistenzen gegen verschiedene Krankheitserreger bestmöglich zu vermeiden. Unsere Arbeit wird mathematische Modelle und unser Wissen über die Entwicklung von Krankheiten verknüpfen, um die bestmöglichen gesundheitspolitischen Massnahmen und Pläne für die Einführung von Impfstoffen in den kommenden Jahren zu entwickeln. Wir werden detaillierte Parasitenmodelle und Modelle für die Moskito-zu-Mensch Übertragung Übertragung erstellen und kalibrieren und Modelle auf der Ebene des Individuums für Malaria anpassen. Wir werden auch Modelle für SARS-CoV-2 anpassen, um das Auftreten von impfstoffresistenten COVID-Varianten zu untersuchen.

Diese neuen Modelle werden dazu beitragen, die wichtigsten Krankheitsmerkmale zu identifizieren, die die Entwicklung von Resistenzen und die Verbreitung resistenter Erreger vorantreiben. Wir werden die Resistenz gegen Impfstoffe, Medikamente und Immuntherapien untersuchen; im Falle von Malaria werden wir auch die Verbreitung von Moskitos untersuchen, die gegen Insektizide resistent sind.

Unsere Arbeit wird Anhaltspunkte für die Auswahl von Strategien zur Krankheitsbekämpfung und -eliminierung sowie für die Entscheidungsfindung liefern, insbesondere im Hinblick auf Resistenzen. Sie sind zwar von unmittelbarer Bedeutung für Malaria und COVID-19, werden aber auch bei der Bekämpfung anderer Krankheiten von Nutzen sein.

Direct link to Lay Summary Last update: 13.09.2021

Lay Summary (English)

Lead
Mathematical modelling to examine disease, control measures, vaccine interactions and resistance to guide public health and disease elimination strategies
Lay summary
Mathematical models are increasingly important to understand new and existing diseases and for planning how we tackle these diseases. Models with enough detail of how a disease interacts with the body, how drugs and other interventions affect the disease, and how health systems respond can help us evaluate the best approaches for addressing diseases.

In this project, we will examine the impact of pathogen resistance to disease interventions. We address two important pathogens: malaria and SARS-Cov-2. As we get closer to eliminating malaria and other diseases, preventing diseases from evolving resistance to response measures becomes ever more critical. Since the characteristics of resistance differ depending on the level of disease, public health strategies are likely to change as we near elimination. In the current global SARS-CoV-2 pandemic, we are at a crucial moment to define strategies for deploying vaccines. Since the virus that causes COVID-19 is likely to mutate further, new vaccine-resistant variants may emerge.

We will examine how resistance to vaccines and other treatment measures comes about, and will define intervention roll-out strategies to best avoid resistance for different pathogens. Our work will bring together mathematical models and what we know about how diseases evolve, to come up with the best possible health policies and vaccine roll-out plans over the coming years. We will build and calibrate detailed parasite models, mosquito-to-human transmission models and adapt individual-level models for malaria. We will also adapt models of SARS-CoV-2 to examine the emergence of vaccine-resistant COVID variants.

These new models will help identify key disease characteristics that drive the evolution of resistance and the spread of resistant pathogens. We will examine resistance to vaccines, drugs and immune response therapies; in the case of malaria, we will also examine the spread of mosquitoes resistant to insecticides.

Our work will provide evidence to support the selection of disease control and elimination strategies and inform decision-making, especially surrounding resistance. Of immediate relevance to malaria and COVID-19, it will also inform efforts to control other diseases.

Direct link to Lay Summary Last update: 13.09.2021

Responsible applicant and co-applicants

Employees

Associated projects

Number Title Start Funding scheme
170702 Multi-scale mathematical modelling of parasite, drug and vaccine interactions: optimising public health and disease elimination strategies 01.07.2017 SNSF Professorships

Abstract

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.
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