Uncertainty Quantification for Epidemic Risk Management: Case of SARS-CoV-2 in Morocco

Lamia Hammadi (), Hajar Raillani, Babacar Mbaye Ndiaye, Badria Aggoug, Abdessamad El Ballouti, Said Jidane, Lahcen Belyamani and Eduardo Souza de Cursi
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Lamia Hammadi: Laboratory of Engineering Sciences for Energy, National School of Applied Sciences ENSAJ, UCD, El Jadida 24000, Morocco
Hajar Raillani: Laboratory of Engineering Sciences for Energy, National School of Applied Sciences ENSAJ, UCD, El Jadida 24000, Morocco
Babacar Mbaye Ndiaye: Laboratory of Mathematics of Decision and Numerical Analysis, University of Cheikh Anta Diop, Dakar 10700, Senegal
Badria Aggoug: Emergency Department, SAMU 02, CHU Ibn Rochd, Casablanca 20100, Morocco
Abdessamad El Ballouti: Laboratory of Engineering Sciences for Energy, National School of Applied Sciences ENSAJ, UCD, El Jadida 24000, Morocco
Said Jidane: Emergency Department, Mohammed V Military Hospital, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat 10100, Morocco
Lahcen Belyamani: Emergency Department, Mohammed V Military Hospital, Faculty of Medicine and Pharmacy, Mohammed V University, Rabat 10100, Morocco
Eduardo Souza de Cursi: Laboratory of Mechanics of Normandy, National Institute of Applied Sciences INSA of Rouen-Normandy, 76800 Saint Etienne du Rouvray, France

IJERPH, 2023, vol. 20, issue 5, 1-29

Abstract: In this paper, we propose a new method for epidemic risk modelling and prediction, based on uncertainty quantification (UQ) approaches. In UQ, we consider the state variables as members of a convenient separable Hilbert space, and we look for their representation in finite dimensional subspaces generated by truncations of a suitable Hilbert basis. The coefficients of the finite expansion can be determined by approaches established in the literature, adapted to the determination of the probability distribution of epidemic risk variables. Here, we consider two approaches: collocation (COL) and moment matching (MM). Both are applied to the case of SARS-CoV-2 in Morocco, as an epidemic risk example. For all the epidemic risk indicators computed in this study (number of detections, number of deaths, number of new cases, predictions and human impact probabilities), the proposed models were able to estimate the values of the state variables with precision, i.e., with very low root mean square errors (RMSE) between predicted values and observed ones. Finally, the proposed approaches are used to generate a decision-making tool for future epidemic risk management, or, more generally, a quantitative disaster management approach in the humanitarian supply chain.

Keywords: uncertainty quantification; Hilbert expansion; risk management; epidemic risk; SARS-CoV-2; moment matching; collocation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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