How can process safety and a risk management approach guide pandemic risk management?

Abstract

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.

Keywords: Layers of protection; Neural network; Non-pharmaceutical interventions; Pandemic; Process monitoring; Risk.

Fig. 1

Schematic representation of the SEIQRD model.

Fig. 2

Parameter learning of the SEIQRD model using parameter tweaking and ANN-based calibration.

Fig. 3

Schematic Representation of the parameter fitting model.

Fig. 4

Forecast of the infected population of COVID-19 at selected regions using the SEIQRD model (The dash line at T = 40 represent the training period of the models for the forecast of the extended period).

Fig. 5

Predicting the infection risk considering randomness in the incubation, infection, and recovery periods (a) Incubation period, (b) infection period, (c) recovery period, (d) peak infection per day, (e) cumulative infection.

Fig. 6

Effect of interventions on controlling the epidemic risk; a: Without any intervention, b: School and non-essential business closure after one week of the first mortality), c: Enforcing public emergency/lockdown after one week of the first mortality d: School and non-essential business closure after one month of the first mortality), e: Enforcing public emergency/lockdown after one month of the first mortality.

Fig. 7

a: Variation of peak values of the number of infections in terms of the most probable impact and the calculated risk b: Variation of cumulative values of the number of infections in terms of the most probable impact and the calculated risk.

Fig. 8

Effect of relaxing regulations on the impact of the pandemic, a: Existing scenario, b: Relaxing regulations on opening of school/university/non-essential business at T = 70, c: Relaxing regulations on social gatherings at T = 70, d: Phasewise relaxing regulations: school openings at T = 70 and social gatherings at T = 100.

Fig. 9

Layer of protection analysis (LOPA) for epidemic and abnormal situation management in process systems; a. for epidemic management; b. for safety of a process system.