No abstract

Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models.

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.