MODCOVID - Using model-based evidence to optimise medical intervention profiles and disease management strategies for COVID-19 control

Background: Country governments have imposed strict spatial distancing and lockdown measures to reduce infection incidence and associated disease burden of coronavirus infectious disease (COVID- 19). As of April 7th, there is no proven medical or pharmaceutical tool to prevent or treat the disease. Guidance is needed to design optimal profiles of medical interventions and deployment schedules of control strategies to suppress transmission and avert mortality, in the long term and in the short term while exiting from the current lockdown. Mathematical models of COVID-19 transmission dynamics can efficiently estimate the quantitative impact of interventions from available evidence on disease progression, transmission, host immunity, and health system interactions.

Key Objectives: In this project, we will develop and operationalize a model-based decision framework to inform the optimal properties of therapeutic and delivery strategies of new tools to achieve prevention and control of COVID-19 at the population and individual level. Specifically, we will (i) Quantitatively investigate and optimize diagnostics and testing/response strategies as well as new treatments including small molecule therapeutics, biologics, vaccines, and immune-enhancement technologies; (ii) Estimate and compare individual and population health consequences of alternative diagnostics, and pharmaceutical strategies.

Methods: Our approach combines mathematical models and machine learning with product development decision processes. Based on current scientific evidence and discussion with shareholders, we will adapt existing disease models and develop a comprehensive individual-based model of COVID-19 transmission as well as appropriate stand-alone within-host models. These models will be used for in-silico population studies and in-silico clinical trials in synthetic populations to estimate the impact of medical interventions. To determine optimal properties and deployment strategies of novel medical interventions and diagnostic tools for COVID-19, we will incorporate the developed models into a machine learning and optimisation approach developed at Swiss TPH.

Translation: Given the present situation, there is pressing need to inform the next strategies of government health policy that will necessarily include digital responses, virological and serological testing, as well as new medical tools. This project will provide model-based translational evidence to optimise new COVID-19 strategies, with respect to balancing between target health goals, operational considerations for delivery, and, therapeutic product properties that can realistically be optimised.

Public Health
Medical Interventions


Melissa Penny

Melissa Penny, SNF Professor, PhD, PD
Deputy Head of Unit, Group Leader

+41 61 284 8701

Project Facts