Project description
Enhancing epidemic monitoring and response with physics-informed neural networks
Physics-informed neural networks (PINNs) seamlessly integrate data and mathematical physics models, enabling prediction of a solution far from the experimental data points even in complex, uncertain and partially understood scenarios. This makes them uniquely suited to predicting an epidemic’s evolution on various time and space scales, facilitating effective policymaking. With the support of the Marie Skłodowska-Curie Actions programme, the REACT project will harness PINNs to integrate past epidemiological data with the physics model of epidemic spread. In combination with system-theoretic tools, the approach will enable closed-loop epidemic monitoring that effectively copes with uncertainties, validating the estimation algorithm in real time by predicting the future data and adjusting the epidemic model accordingly.
Objective
The current COVID-19 crisis has highlighted the failure of existing epidemic monitoring techniques in timely predicting the epidemic situation and facilitating efficient policy recommendations. Because of being open-loop or linearization-based, these techniques cannot handle model and data uncertainties effectively. Designing a feedback mechanism to enable reliable, closed-loop epidemic monitoring is crucial but challenging because of the nonlinearity and heterogeneities of the epidemic spread process. The control mechanisms for epidemic mitigation are well-known, such as testing, lockdown, social distancing, etc. However, when, where, and to what extent should the health authority implement these policies depends on the accurate estimation and forecasting of the epidemic situation, which is very difficult with the classic observer design techniques. To alleviate the difficulties posed by these observers, an interdisciplinary approach of physics-informed neural network (PINN) in combination with system-theoretic tools is proposed in this project for closed-loop epidemic monitoring that can effectively cope with uncertainties. The task of PINN is to estimate the unknown nonlinearity (i.e. disease transmission rate) and epidemic parameters by using both the physics of epidemic spread (i.e. model) and the past epidemiological data. The closed-loop structure copes with the uncertainties and validates the estimation algorithm in real-time by predicting the future data and adjusting the epidemic model accordingly. The information received by the PINN-based observer will be utilized by the optimal controller to devise optimal policy recommendations under socio-economic constraints for epidemic mitigation. The closed-loop epidemic monitoring and control technique will be integrated to understand the geographic and demographic heterogeneities during epidemic outbreaks, which will significantly enhance the effectiveness of optimal policies.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2021-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
100 44 STOCKHOLM
Sweden
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