ABSTRACT
The coronavirus disease 2019(COVID-19) pandemic spreads across borders with the frequent global population movement. To explore the impact of the COVID-19 pandemic on China's domestic epidemic prevention and control, based on the classical infectious disease dynamics model this paper proposes an infectious disease model that considers oversea imported cases. The model can simulate three situations:national pandemic without imported cases, no domestic cases with only imported cases, and domestic cases with international travellers entering simultaneously. By calculating the peak case number and range of infection spread duration in these situations, as well as the amount of medical resources invested, the model has shown the different results of impact of entry type on the domestic pandemic and different pressures on medical resources. Finally, the paper suggests that testing measures should be taken according to the degree of pandemic risk and resource conditions, that strict prevention and control should be applied to the people not entering through customs, and closed-loop management to the people entering through customs, that entry quarantine measures and quarantine periods should be dynamically adjusted and international exchanges should be gradually resumed in the context of ensuring domestic and overseas epidemic prevention and control in advance, and that it is necessary to integrate medical resources, improve allocation efficiency, and relieve the pressure of resource occupation.
ABSTRACT
This paper constructs an index system for evaluating the efficiency of COVID-19 sporadic outbreak prevention and control from three dimensions (epidemic prevention and control, epidemic traceability, and epidemic background) and with nine sub-indicators (pidemic duration, confirmed cases, detection route, mobile population management policy, flow investigation and tracing, nucleic acid detection, etc.) This evaluation index system is used to calculate and evaluate the control of epidemic in all the 32 regions of Chian since June 1, 2020 to June 1, 2021. Results show that there were significant differences in the efficiency of epidemic prevention and control among these regions, that the intensity of prevention and control measures did not match the severity of the epidemic, and that the management policies for mobile populations across regions lacked a unified yet scientific basis. Based on these, certain epidemic prevention and control management optimization suggestions are presented.
ABSTRACT
We define the concept and analyze the connotation of the post-pandemic era by reviewing the recent foreign research on social impacts, risk prevention and control, and emergency management in the post-pandemic era. The current state of foreign research in the post-pandemic era is outlined, and the progress of foreign research on social impacts like urban planning and travel patterns, as well as core issues like risk prevention and control and emergency management in the post-pandemic era is analyzed in detail. The characteristics and shortcomings of existing research are summarized and future research in the postpandemic era is also forecasted. COVID-19 has had a huge impact on how people socialize, travel, and work, as well as changing industry trends, technical advancements, and social governance. Uncertainty is the most significant risk feature of the post-pandemic era, and the coupling of numerous hazards poses a new set of challenges to emergency management practices.
ABSTRACT
To mitigate the spread of novel coronavirus, how to optimise COVID-19 medical waste location-transport strategies remains an open but urgent issue. In this paper, a novel digital twin-driven conceptual framework is proposed to improve the strategic decision on the location of temporary disposal centres and, subsequently, the operational decision on the transport of COVID-19 medical waste in the presence of hierarchical relationships amongst stakeholders, circular economy, uncertainty in infection probability, and service level. The circular economy aspect is measured by the reduction of infection risks and costs, as well as limiting exhaust emissions. The polyhedral uncertainty set is introduced to characterise stochastic infection probability. Digital twin technology is further used to estimate the upper and lower bound of the uncertainty set. Such a problem is formulated as a digital twin-driven robust bi-level mixed-integer programming model to minimise total infection risks on the upper level and total costs on the lower level. A hybrid solution strategy is designed to combine dual theory, Karush-Kuhn-Tucker (KKT) conditions, and a branch-and-bound approach. Finally, a real case study from Maharashtra in India is presented to evaluate the proposed model. Results demonstrate that the solution strategy performs well for such a complex problem because the CPU time required to conduct all experiments is less than one hour. Under a given uncertainty level of 36 and perturbation ratio of 20%, a regional transport strategy is preferred from generation points to transfer points, while a cross-regional one is usually implemented from transfer points to disposal centres. It is of significance to determine the bound of available temporary disposal centres. Using digital technology (e.g., digital twin) to accurately estimate the amount of COVID-19 medical waste is beneficial for controlling the pandemic. Reducing infection risks relative to cost is the prioritised goal in cleaning up COVID-19 medical waste within a relatively long period.