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

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.

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

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.

SARS-CoV-2, recurrent ischemic strokes and carotid macrothrombosis: the other face of the cytokine storm (a case report).

Since December 2019, the world has experienced the emergence in China of a new infection called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This infection quickly has progressed to a global pandemic since March 2020, with very high human-to-human transmission rate. Besides lung injury, COVID-19 is also associated with cardio and neurovascular complications. Herein, we report the case of a 77-year-old female who presented with non-severe COVID-19 and multiple ischemic strokes secondary to an extensive carotid thrombosis. The ischemic stroke was supposed to have been caused by the cytokine storm related to COVID-19. The possibility of hemorrhagic transformation, based on the assessment of bleeding score, limited the use of anticoagulation, and probably explained the stroke recurrence and poor outcome in our patient. The pathogenic mechanism and the management of this complex situation are still lacking and further studies are needed.