Model Predictive Pandemic Control project granted by ZonMW
Research at the interface of control systems and epidemiology.
TU/e researchers Mauro Salazar and Dinesh Krishnamoorthy got their ‘Model Predictive Pandemic Control’ project granted by ZonMW. The aim of this research is to create a computer model that allows governments to smartly limit outbreaks with measures that minimally burden mental health and society. Considering the impact of Covid-19 had, this project will probably have a major societal impact. Salazar and Krishnamoorthy collaborate with experts at the MMC and RIVM to make sure the project becomes successful and really reaches its potential to support policy makers in making decisions about measures we will all have to face.
Since 2020, we have been facing the COVID-19 pandemic. Governments have been struggling to decide which policies (face masks, lockdown measures, etc.) to implement to limit the outbreak, whilst minimizing collateral damage. Arguably, every policy is linked to different benefits and costs, so that, to be effective whilst not overly increasing the burden on society, non-pharmaceutical interventions (NPIs) need to be carefully selected and timely implemented. This interdisciplinary project aims at providing quantitative arguments to policy makers when choosing which NPIs to implement by combining epidemiological models with optimal control techniques.
The tool strives to systematically evaluate different strategies and scenarios, including their uncertainty, to pro-actively adapt NPI strategies, whilst also providing recourse strategies that anticipate potential outcomes that could deviate from the planned or expected results, and have a backup plan in place to address them. In this context, the challenge lies in identifying models that are accurate enough to reproduce and predict the evolution of the pandemic, as well as simple enough to be used within numerical optimization frameworks (algorithmic tools used to choose the ‘best’ strategies). This is how the project name, Model Predictive Pandemic Control (MPPC), was generated.
The envisioned framework will provide policymakers with insightful information about the various strategies available when planning NPIs during a pandemic outbreak, and a clear benchmark to assess the efficacy of implementable policies that are comprehensible and acceptable by the population. This project will build on data from the recent COVID-19 pandemic to develop a decision-support framework to respond to future pandemic outbreaks in a systematic and effective fashion.
The Covid-19 pandemic motivated the researchers to make their contribution to society: “we recognized that as control engineers we have the knowledge and the skill-set/expertise to help choose the ‘best’ NPIs accounting for the dynamic evolution of the pandemic, as we have been studying similar problems in mobility and other health applications”. Moreover, Salazar had carried out a preliminary project with Maxima Medisch Centrum (MMC) in 2021, aimed at optimizing the allocation of 2-shot vaccines [https://arxiv.org/pdf/2112.11908.pdf]. When a ZonMW funding opportunity arose, he teamed up with his colleague Krishnamoorthy, and set up an interdisciplinary group with Edwin van den Heuvel from M&CS, Paul de Klaver from MMC and Jacco Wallinga from the Infectious Disease Modelling group at RIVM. Now they are very excited about the interdisciplinary journey ahead with this great team of experts.
Are you interested in the project and looking for a Postdoctoral scholarship, then please have a look at: jobs.tue.nl/en/vacancy/2-postdocs-model-predictive-pandemic-control-mppc-1010401.html
ZorgOnderzoek Nederland (Care Research Netherlands, Dutch abbreviation: ZON) was established by law in 1998 (ZON Act). Since 2001, there has been a partnership between ZON and the Medical Sciences (Dutch abbreviation: MW) domain of the Dutch Research Council (NWO). This partnership is called ZonMw and constitutes an independent self-governing organization.