REACT
Financiado
Reliable Epidemic monitoring And Control under geographic and demographic heTero...
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...
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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Duración del proyecto: 37 meses
Fecha Inicio: 2022-07-18
Fecha Fin: 2025-08-31
Fecha Fin: 2025-08-31
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-07-18
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
HORIZON-MSCA-2021-PF-01-01:
MSCA Postdoctoral Fellowships 2021
Cerrada
hace 3 años
Presupuesto
El presupuesto total del proyecto asciende a
306K€
Líder del proyecto
KUNGLIGA TEKNISKA HOEGSKOLAN
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5