The Disease Modelling unit develops and uses approaches in computational sciences, statistics and mathematical modelling to understand and address contemporary issues in infectious diseases and global health. We work in the areas of malaria and neglected tropical diseases, and recently SARS-CoV-2 to support efforts against the current COVID-19 pandemic.
Using Models to Understand Diseases
We are an interdisciplinary group of researchers that apply mathematical models to understand disease and to inform public health decision-making. We design data-, biology-, and epidemiologically-informed mathematical models covering all aspects of disease and treatment dynamics — within-host immune and infection; population transmission; parasite and vector life-cycles; health systems access and interventions; as well as detailed interactions with pharmaceutical and non-pharmaceutical interventions.
We use these models to understand disease progression, pathogenesis and disease, and to estimate impacts of health interventions in individual or at the population level, in the contexts of real-world health systems. We contribute epidemiological insights and evidence to inform public health decision making. We aim to increase the impact of interventions, to eliminate, prevent and treat infectious diseases.
Our work also helps estimate the potential of new innovations against infectious diseases, such as new drugs, immune therapies or ways of killing mosquitoes. We evaluate possible intervention strategies and resource allocation strategies to achieve effective and equitable impact on infectious diseases, including for emerging diseases. We work closely with other research institutions in Switzerland and abroad, as well as global health donors and public health policy stakeholders.
Despite progress to reduce malaria burden, malaria parasites are becoming resistant to antimalarial drugs; therefore, new interventions are needed to protect those most vulnerable. However, developing new medical interventions is resource-intensive, and it is often unclear until late in the development process the impact a new intervention will have. Additionally, selection among promising candidates for new antimalarial interventions and immunotherapies must occur early in development. To support this decision-making process, we use mathematical disease models and machine learning tools to define the key performance characteristics for an impactful intervention. We are collaborating with the Bill & Melinda Gates Foundation to bring together R&D, implementation, and global health specialists to define target product profiles for next-generation interventions. Read more
Core to our modelling work is the development and use of an individual-based model of malaria transmission dynamics known as OpenMalaria. This model is an open-source tool for simulating the dynamics of malaria transmission and epidemiology, and the impact of interventions on health and economic outcomes. The model can simulate malaria in village or district size human populations, and has been used to address questions on disease dynamics and the use of interventions. Developed and maintained by the Disease Modelling Unit of Swiss TPH, our OpenMalaria model is used by a wide range of modellers worldwide. A new collaboration with the Bill & Melinda Gates Foundation supports the improvement and maintenance of OpenMalaria as well as the development of new mathematical malaria models. This project will provide model-based evidence for the Foundation and key partners towards improved investment and decision-making for better malaria control and elimination. Read more
Mathematical models can help us understand disease, and help us plan how to fight disease. This is especially important in the face of diseases that develop resistance to existing drugs and vaccines. Beginning with malaria and COVID-19, our work will provide evidence to select disease control and elimination strategies, and we will extend this to other diseases. We combine data from clinical trials, pre-clinical drug development, information about the disease spreads through the population, about a country’s health system, and how the immune system fights a disease, to predict the optimal way to use drugs and vaccines to fight and eliminate disease. New models in this project also include the evolution of resistant disease strains — this will let us predict the best way to develop and use drugs, vaccines, and other interventions to avoid formation of resistance. Read more
In response to the ongoing COVID-19 pandemic, we developed a new individual-based mathematical model, OpenCOVID, to assess the impact of a range of prevention measures, vaccines, and medical interventions to improve the response and minimise cases, hospitalisations, and deaths in Switzerland and abroad. Our model OpenCOVID has been applied to support Swiss decision-making and predict the public health impact of new emerging SARS-Cov-2 variants of concern. Our model is being used to inform future strategies for ongoing vaccination and response efforts as the world strives to recover from the pandemic's devastating health and economic effects. Read more
There has been substantial progress in reducing malaria transmission in the Greater Mekong Subregion. However, progress has stalled with reports of increase in the number of cases, multidrug-resistant malaria, and pyrethroid-resistant mosquitoes. The risk of infection is greatest among mobile and migrant populations. Many of the dominant malaria vectors in this subregion feed and rest outdoors, yet there is limited available protection against mosquito biting outdoors for at-risk populations. To address these pressing challenges, the University of California, San Francisco, Malaria Elimination Initiative in partnership with the University of Notre Dame, IVCC, Swiss TPH, and a consortium of project partners propose to evaluate new vector control tools that aim to reduce human exposure to mosquito bites outdoors. Read more
RTS,S/AS01 (RTS,S) is the world’s first malaria vaccine shown to provide partial protection against malaria in young children. In October 2021, WHO recommended widespread use of RTS,S among children in regions with moderate to high P. falciparum malaria transmission. This recommendation is based on a Phase 3 trial completed in Africa in 2015 and the implementation programme in Ghana, Kenya, and Malawi that has reached over 830,000 children as of December 2021. In collaboration with Imperial College London and PATH, we provided modelling evidence to support this recommendation and continue to support the WHO’s malaria advisory groups on policy (MPAG) and immunization (SAGE) and Gavi, the Vaccine Alliance in this initiative. This collaboration also supported Gavi’s decision to approve investment to support RTS,S vaccine introduction in eligible countries in sub-Saharan Africa in 2022-2025, as announced in December 2021. We are now assessing the public health impact and cost-effectiveness of introducing the vaccine alongside existing malaria interventions. Read more
Selected ProjectsAll Projects
Latest PublicationsAll Publications
Ali A.M et al. Population pharmacokinetics of antimalarial naphthoquine in combination with artemisinin in Tanzanian children and adults: dose optimization. Antimicrob Agents Chemother. 2022;66(5):e0169621. DOI: 10.1128/aac.01696-21
Golumbeanu M et al. Leveraging mathematical models of disease dynamics and machine learning to improve development of novel malaria interventions. Infect Dis Poverty. 2022;11:61. DOI: 10.1186/s40249-022-00981-1
Kelly S.L, Le Rütte E.A, Richter M, Penny M.A, Shattock A.J. COVID-19 vaccine booster strategies in light of emerging viral variants: frequency, timing, and target groups. Infect Dis Ther. 2022(In Press). DOI: 10.1007/s40121-022-00683-z
Le Rutte E.A, Shattock A.J, Chitnis N, Kelly S.L, Penny M.A. Modelling the impact of Omicron and emerging variants on SARS-CoV-2 transmission and public health burden. Commun Med (Lond). 2022;2:93. DOI: 10.1038/s43856-022-00154-z
Lee T.E, Bonhoeffer S, Penny M.A. The competition dynamics of resistant and sensitive infections. Results Phys. 2022;34:105181. DOI: 10.1016/j.rinp.2022.105181