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Reliable Epidemic monitoring And Control under geographic and demographic heTero...

Reliable Epidemic monitoring And Control under geographic and demographic heTerogeneities 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 linear... ver más

31/08/2025

KTH

306K€

Presupuesto del proyecto: 306K€

Líder del proyecto

KUNGLIGA TEKNISKA HOEGSKOLAN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-07-18

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021 ExpectedOutcome:

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No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

KTH

305.93K€ | Lider

MASSACHUSETTS INSTITUTE OF T...

MIT

N.D. | Participante

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Duración del proyecto: 37 meses Fecha Inicio: 2022-07-18
Fecha Fin: 2025-08-31

Líder del proyecto

KUNGLIGA TEKNISKA HOEGSKOLAN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 306K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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