Assessing the Outbreak Risk of Epidemics Using Fuzzy Evidential Reasoning

Abstract

Epidemic diseases (EDs) present a significant but challenging risk endangering public health, evidenced by the outbreak of COVID‐19. Compared to other risks affecting public health such as flooding, EDs attract little attention in terms of risk assessment in the current literature. It does not well respond to the high practical demand for advanced techniques capable of tackling ED risks. To bridge this gap, an adapted fuzzy evidence reasoning method is proposed to realize the quantitative analysis of ED outbreak risk assessment (EDRA) with high uncertainty in risk data. The novelty of this article lies in (1) taking the lead to establish the outbreak risk evaluation system of epidemics covering the whole epidemic developing process, (2) combining quantitative and qualitative analysis in the fields of epidemic risk evaluation, (3) collecting substantial first‐hand data by reviewing transaction data and interviewing the frontier experts and policymakers from Chinese Centers for Disease Control and Chinese National Medical Products Administration. This work provides useful insights for the regulatory bodies to (1) understand the risk levels of different EDs in a quantitative manner and (2) the sensitivity of different EDs to the identified risk factors for their effective control. For instance, in the case study, we use real data to disclose that influenza has the highest breakout risk level in Beijing. The proposed method also provides a potential tool for evaluating the outbreak risk of COVID‐19.

Port vulnerability assessment from a supply Chain perspective

“…given the recent successful use of fuzzy ER on the outbreak risk analysis of epidemics (e.g. Shi et al, 2021). Within the port vulnerability study, the model should take into account different attribute weight assignments to reflect the feature of low-frequency-high-consequence events such as COVID-19.…”