Methodology of emergency medical logistics for public health emergencies

A bed allocation model for pandemic situation considering general demand: A case study of Iran

Pandemics place a new type of demand from patients affected by the pandemic, imposing significant strain on hospital departments, particularly the intensive care unit. A crucial challenge during pandemics is the imbalance in addressing the needs of both pandemic patients and general patients. Often, the community's focus shifts toward the pandemic patients, causing an imbalance that can result in severe issues. Simultaneously considering both demands, pandemic-related and general healthcare needs, has been largely overlooked. In this article, we propose a bi-objective mathematical model for locating temporary hospitals and allocating patients to existing and temporary hospitals, considering both demand types during pandemics. Hospital departments, such as emergency beds, serve both demand types, but due to infection risks, accommodating a pandemic patient and a general patient in the same department is not feasible. The first objective function is to minimize the bed shortages considering both types of demands, whereas the second objective is cost minimization, which includes the fixed and variable costs of temporary facilities, the penalty cost of changing the allocation of existing facilities (between general and pandemic demand), the cost of adding expandable beds to existing facilities, and the service cost for different services and beds. To show the applicability of the model, a real case study has been conducted on the COVID-19 pandemic in the city of Qom, Iran. Comparing the model results with real data reveals that using the proposed model can increase demand coverage by 16%.

A multi-period optimal distribution model of emergency resources for responding to COVID-19 under uncertain conditions

Ideally, optimal emergency resource allocation would have been vital for effective relief work during the COVID-19 outbreak. However, the suddenness of the epidemic and uncertainty of its spread added some difficulties to distributing emergency resources. First, this study introduces triangular fuzzy numbers to describe the uncertainty of supply and demand of emergency resources, and interval numbers to describe the time required for resource transportation under disaster conditions. To minimize the total delivery time and difference in the total satisfaction rate, this study constructs an optimal model for emergency resource distribution under uncertain conditions that considers both efficiency and equity. Subsequently, an improved genetic algorithm (IMGA) is proposed to obtain the optimal decision scheme. Finally, a case study on emergency resource distribution during the COVID-19 pandemic is conducted for model verification. The results demonstrate that the proposed model can improve the efficiency and effect of emergency resource distribution. The model allocates some emergency resources to each demand site during each emergency period, which can help avoid large losses caused by extreme shortages of resources at a certain demand point. The emergency resource allocation scheme considers the transportation time and degree of impact, which is beneficial for enhancing the flexibility of decision-making and practical applicability of distribution operations. A comparative analysis of the algorithms shows that the proposed IMGA is an effective method for managing emergency resource distribution optimization problems because it has higher solving efficiency, better convergence, and stronger stability. These findings can provide decision support for the optimal distribution of large-scale, multiperiod emergency resources during the COVID-19 pandemic.

Risk-based allocation of COVID-19 personal protective equipment under supply shortages

The COVID-19 outbreak put healthcare systems across the globe under immense pressure to meet the unprecedented demand for critical supplies and personal protective equipment (PPE). The traditional cost-effective supply chain paradigm failed to respond to the increased demand, putting healthcare workers (HCW) at a much higher infection risk relative to the general population. Recognizing PPE shortages and high infection risk for HCWs, the World Health Organization (WHO) recommends allocations based on ethical principles. In this paper, we model the infection risk for HCWs as a function of usage and use it as the basis for distribution planning that balances government procurement decisions, hospitals' PPE usage policies, and WHO ethical allocation guidelines. We propose an infection risk model that integrates PPE allocation decisions with disease progression estimates to quantify infection risk among HCWs. The proposed risk function is used to derive closed-form allocation decisions under WHO ethical guidelines in both deterministic and stochastic settings. The modelling is then extended to dynamic distribution planning. Although nonlinear, we reformulate the resulting model to make it solvable using off-the-shelf software. The risk function successfully accounts for virus prevalence in space and in time and leads to allocations that are sensitive to the differences between regions. Comparative analysis shows that the allocation policies lead to significantly different levels of infection risk, especially under high virus prevalence. The best-outcome allocation policy that aims to minimize the total infected cases outperforms other policies under this objective and that of minimizing the maximum number of infections per period.

Analysis of COVID-19 outbreak in Hubei province based on Tencent's location big data

Rapid urbanization has gradually strengthened the spatial links between cities, which greatly aggravates the possibility of the spread of an epidemic. Traditional methods lack the early and accurate detection of epidemics. This study took the Hubei province as the study area and used Tencent's location big data to study the spread of COVID-19. Using ArcGIS as a platform, the urban relation intensity, urban centrality, overlay analysis, and correlation analysis were used to measure and analyze the population mobility data of 17 cities in Hubei province. The results showed that there was high similarity in the spatial distribution of urban relation intensity, urban centrality, and the number of infected people, all indicating the spatial distribution characteristics of "one large and two small" distributions with Wuhan as the core and Huanggang and Xiaogan as the two wings. The urban centrality of Wuhan was four times higher than that of Huanggang and Xiaogan, and the urban relation intensity of Wuhan with Huanggang and Xiaogan was also the second highest in the Hubei province. Meanwhile, in the analysis of the number of infected persons, it was found that the number of infected persons in Wuhan was approximately two times that of these two cities. Through correlation analysis of the urban relation intensity, urban centrality, and the number of infected people, it was found that there was an extremely significant positive correlation among the urban relation intensity, urban centrality, and the number of infected people, with an R2 of 0.976 and 0.938, respectively. Based on Tencent's location big data, this study conducted the epidemic spread research for "epidemic spatial risk classification and prevention and control level selection" to make up for the shortcomings in epidemic risk analysis and judgment. This could provide a reference for city managers to effectively coordinate existing resources, formulate policy, and control the epidemic.

Are the epidemic prevention facilities effective? How cities should choose epidemic prevention facilities: Taking Wuhan as an example

The COVID-19 pandemic highlighted the limitations of urban public health emergency response capabilities. Taking Wuhan as an example, this study used breakpoint regression, kernel density analysis, overlay analysis, and accessibility analysis from Stata and ArcGIS, and divided epidemic prevention facilities into the basic epidemic prevention facilities (hospitals), and the emergency epidemic prevention facilities (mobile cabin hospitals) for further analysis. The results showed that over 70% of the basic epidemic prevention facilities in Wuhan were located in high density population areas. On the contrary, most of the emergency epidemic prevention facilities were located in low density population areas. The local treatment effect of the implementation of the emergency epidemic prevention facility policy is about 1, indicating that there was a significant impact of emergency epidemic prevention facilities on outbreak control, which passed the bandwidth test. What’s more, the analysis of the accessibility of residential points revealed that more than 67.3% of people from the residential points could arrive at the epidemic prevention facilities within 15 min, and only 0.1% of them took more than 20 min to arrive. Therefore, the epidemic prevention facilities can effectively curb the spread of the epidemic, and people from residential areas can quickly get there. This study summarized the spatial characteristics of epidemic prevention facilities in Wuhan and analyzed the importance of them, thus providing a new perspective for future research on upgrading the city’s comprehensive disaster prevention system.

Allocation of emergency medical resources for epidemic diseases considering the heterogeneity of epidemic areas

Background

The resources available to fight an epidemic are typically limited, and the time and effort required to control it grow as the start date of the containment effort are delayed. When the population is afflicted in various regions, scheduling a fair and acceptable distribution of limited available resources stored in multiple emergency resource centers to each epidemic area has become a serious problem that requires immediate resolution.

Methods

This study presents an emergency medical logistics model for rapid response to public health emergencies. The proposed methodology consists of two recursive mechanisms: (1) time-varying forecasting of medical resources and (2) emergency medical resource allocation. Considering the epidemic's features and the heterogeneity of existing medical treatment capabilities in different epidemic areas, we provide the modified susceptible-exposed-infected-recovered (SEIR) model to predict the early stage emergency medical resource demand for epidemics. Then we define emergency indicators for each epidemic area based on this. By maximizing the weighted demand satisfaction rate and minimizing the total vehicle travel distance, we develop a bi-objective optimization model to determine the optimal medical resource allocation plan.

Results

Decision-makers should assign appropriate values to parameters at various stages of the emergency process based on the actual situation, to ensure that the results obtained are feasible and effective. It is necessary to set up an appropriate number of supply points in the epidemic emergency medical logistics supply to effectively reduce rescue costs and improve the level of emergency services.

Conclusions

Overall, this work provides managerial insights to improve decisions made on medical distribution as per demand forecasting for quick response to public health emergencies.

Design of control strategies to help prevent the spread of COVID-19 pandemic

This paper proposes control strategies to allocate COVID-19 patients to screening facilities, health facilities, and quarantine facilities for minimizing the spread of the virus by these patients. To calculate the transmission rate, we propose a function that accounts for contact rate, duration of the contact, age structure of the population, susceptibility to infection, and the number of transmission events per contact. Moreover, the COVID-19 cases are divided into different groups according to the severity of their disease and are allocated to appropriate health facilities that provide care tailored to their needs. The multi-stage fuzzy stochastic programming approach is applied to cope with uncertainty, in which the probability associated with nodes of the scenario tree is treated as fuzzy variables. To handle the probabilistic model, we use a more flexible measure, M e measure, which allows decision-makers to adopt varying attitudes by assigning the optimistic-pessimistic parameter. This measure does not force decision-makers to hold extreme views and obtain the interval solution that provides further information in the fuzzy environment. We apply the proposed model to the case of Tehran, Iran. The results of this study indicate that assigning patients to appropriate medical centers improves the performance of the healthcare system. The result analysis highlights the impact of the demographic differences on virus transmission, and the older population has a greater influence on virus transmission than other age groups. Besides, the results indicate that behavioral changes in the population and their vaccination play a key role in curbing COVID-19 transmission.

Optimizing two-dose vaccine resource allocation to combat a pandemic in the context of limited supply: The case of COVID-19

The adequate vaccination is a promising solution to mitigate the enormous socio-economic costs of the ongoing COVID-19 pandemic and allow us to return to normal pre-pandemic activity patterns. However, the vaccine supply shortage will be inevitable during the early stage of the vaccine rollout. Public health authorities face a crucial challenge in allocating scarce vaccines to maximize the benefits of vaccination. In this paper, we study a multi-period two-dose vaccine allocation problem when the vaccine supply is highly limited. To address this problem, we constructed a novel age-structured compartmental model to capture COVID-19 transmission and formulated as a nonlinear programming (NLP) model to minimize the total number of deaths in the population. In the NLP model, we explicitly take into account the two-dose vaccination procedure and several important epidemiologic features of COVID-19, such as pre-symptomatic and asymptomatic transmission, as well as group heterogeneity in susceptibility, symptom rates, severity, etc. We validated the applicability of the proposed model using a real case of the 2021 COVID-19 vaccination campaign in the Midlands of England. We conducted comparative studies to demonstrate the superiority of our method. Our numerical results show that prioritizing the allocation of vaccine resources to older age groups is a robust strategy to prevent more subsequent deaths. In addition, we show that releasing more vaccine doses for first-dose recipients could lead to a greater vaccination benefit than holding back second doses. We also find that it is necessary to maintain appropriate non-pharmaceutical interventions (NPIs) during the vaccination rollout, especially in low-resource settings. Furthermore, our analysis indicates that starting vaccination as soon as possible is able to markedly alleviate the epidemic impact when the vaccine resources are limited but are currently available. Our model provides an effective tool to assist policymakers in developing adaptive COVID-19 likewise vaccination strategies for better preparedness against future pandemic threats.

Designing an optimal sequence of non-pharmaceutical interventions for controlling COVID-19

The COVID-19 pandemic has had an unprecedented impact on global health and the economy since its inception in December, 2019 in Wuhan, China. Non-pharmaceutical interventions (NPI) like lockdowns and curfews have been deployed by affected countries for controlling the spread of infections. In this paper, we develop a Mixed Integer Non-Linear Programming (MINLP) epidemic model for computing the optimal sequence of NPIs over a planning horizon, considering shortages in doctors and hospital beds, under three different lockdown scenarios. We analyse two strategies - centralised (homogeneous decisions at the national level) and decentralised (decisions differentiated across regions), for two objectives separately - minimization of infections and deaths, using actual pandemic data of France. We linearize the quadratic constraints and objective functions in the MINLP model and convert it to a Mixed Integer Linear Programming (MILP) model. A major result that we show analytically is that under the epidemic model used, the optimal sequence of NPIs always follows a decreasing severity pattern. Using this property, we further simplify the MILP model into an Integer Linear Programming (ILP) model, reducing computational time up to 99%. Our numerical results show that a decentralised strategy is more effective in controlling infections for a given severity budget, yielding up to 20% lesser infections, 15% lesser deaths and 60% lesser shortages in healthcare resources. These results hold without considering logistics aspects and for a given level of compliance of the population.

Supply of basic necessities to vulnerable populations during the COVID-19 pandemic: Empirical evidence from Shanghai, China

Background

In spite of initial widespread skepticism, city lockdown has been proved to be an effective short-term tool in containing and delaying the spread of a viral epidemic. The measures to ensure the supply of the basic necessities adequately and equitably, especially for those vulnerable ones has become a major challenge faced by all countries taking a city lockdown measure during the epidemic.

Methods

Data was collected through relevant government documents, work records, policy reports, media reports and the online-work information platform designed by the research group. Based on these references, the study analyzed the mainly technical difficulties and the countermeasures of the supply process, and summarized the key characteristics of basic necessities supply strategy for vulnerable groups in Shanghai.

Results

The supply strategy for vulnerable groups in Shanghai covers 16 districts, 232 streets and 6,028 neighborhood communities, which has already been in test running in April in some districts. The practical experience in Shanghai solved three key materials supply problems (lack of purchase channels, insufficient material reserves, insufficient transportation capacity) faced by government during the city lockdown, and showed three essential characteristics (overall coordination, community-centered intervention, technical support).

Conclusions

The findings in this study may provide some suggestions to other countries about how to better manage the preparation, dispatch and transportation of basic necessities in shortage for those vulnerable ones during the city lockdown.

Covid-19 PPE distribution planning with demand priorities and supply uncertainties

The recent Covid-19 outbreak put healthcare resources under enormous pressure. Governments and healthcare authorities faced major challenges in securing and delivering critical supplies such as personal protective equipment (PPE) and test kits. As timely distribution of critical supplies exceeded government resources, certain sectors, negatively impacted by the pandemic, offered their storage and distribution capabilities; both helping with the crisis and creating economic revenue. We investigate the problem of optimally leveraging the capacity and efficiency of underutilized distribution networks to enhance the capability of government supply networks to meet healthcare needs for critical supplies. We model the problem as a dynamic distribution planning problem that decides on the re-purposing of storage facilities, the allocation of demand, and the timely distribution of limited PPE supplies to different jurisdictions. From a resource provider's perspective, the goal is to maximize demand fulfillment based on priorities set out by the government, as well as maximize economic value to participating networks. As uncertainty is a prevalent feature of the problem, we adopt a robust framework due to the lack of historical data on such supply uncertainties. We provide a mixed integer programming formulation for the adversarial problem and present a cutting plane algorithm to solve the robust model efficiently under both polyhedral and ellipsoidal uncertainty sets. We build a case study for the province of Ontario, Canada, and run extensive analysis of the service and economic value trade-off, and the effects of modeling demand priorities and supply uncertainties.

A Multi-Regional Collaborative Optimization Model of Emergency Medical Materials for Responding to COVID-19

Medical materials are the most important, fundamental resources necessary for emergency relief of major infectious disease disasters. The scientific and optimal allocation of emergency medical materials is the key to reducing casualties and losses in epidemic regions, and to improving the effectiveness and efficiency of rescue operations. In response to the cross-border characteristics of major infectious diseases, the imbalance of material storage, and the differences of supply across regions, a multi-objective optimization model for a multi-regional collaborative allocation of emergency medical materials was developed. Then, an improved adaptive genetic algorithm (IAGA) was designed and applied to solve the proposed model. Finally, a case study of the collaborative response to the COVID-19 epidemic in the Yangtze River Delta of China was conducted for model verification. The results show that collaborative allocation can improve the material satisfaction rate at demand points, especially under peak demand pressure during the early stage of the response, and can meet all material needs at all demand points in the shortest possible amount of time. The proposed model can achieve the effective integration and mutual sharing of emergency materials across regions, and improve the efficiency of emergency material utilization and rescue efforts. The material allocation scheme considers the difference coefficients in different regions, which is conducive to enhancing the flexibility of decision-making and the practical applicability of collaborative allocation operations. A comparative analysis of the algorithms shows that the proposed IAGA is an effective method for managing large-scale multi-regional emergency material allocation optimization problems, as it has higher solving efficiency, better convergence, and stronger stability.

Multiobjective Emergency Resource Allocation under the Natural Disaster Chain with Path Planning

Public safety and health cannot be secured without the comprehensive recognition of characteristics and reliable emergency response schemes under the disaster chain. Distinct from emergency resource allocation that focuses primarily on a single disaster, dynamic response, periodic supply, and assisted decision-making are necessary. Therefore, we propose a multiobjective emergency resource allocation model considering uncertainty under the natural disaster chain. Resource allocation was creatively combined with path planning through the proposed multiobjective cellular genetic algorithm (MOCGA) and the improved A* algorithm with avoidance of unexpected road elements. Furthermore, timeliness, efficiency, and fairness in actual rescue were optimized by MOCGA. The visualization of emergency trips and intelligent avoidance of risk areas were achieved by the improved A* algorithm. The effects of logistics performance, coupling of disaster factors, and government regulation on emergency resource allocation were discussed based on different disaster chain scenarios. The results show that disruption in infrastructure support, cascading effect of disasters, and time urgency are additional environmental challenges. The proposed model and algorithm work in obtaining the optimal solution for potential regional coordination and resilient supply, with a 22.2% increase in the total supply rate. Cooperative allocation complemented by political regulation can be a positive action for successfully responding to disaster chains.

The Allocation Method for Personal Protective Equipment in the Emerging Infectious Disease Environment

The COVID-19 pandemic gives humankind a lesson that the outbreak of an emerging infectious disease (EID) is sudden and uncertain. Accurately mastering its dynamics and putting forward an efficient and fair humanitarian logistics plan for personal protective equipment (PPE) remains difficult. This study examines the decision making for humanitarian logistics to answer the question that how to coordinate fairness and efficiency when facing supply-demand imbalance during humanitarian logistics planning in an EID environment. The main contributions include two aspects: (1) The victims' losses in terms of fairness and efficiency in receiving PPE are jointly explored by evaluating their bearing capacity evolution, and then a novel loss function is built to search for a reasonable compromise between fairness and efficiency. (2) A multi-objective optimization model is built, which is solved using the combined use of goal programming approach and improved branch and bound method. Finally, the practicability of the proposed model is tested by an EID case study. The potential advantages of the proposed model and improved approach are discussed.

Data-Driven Dynamic Adjustment and Optimization Model of Emergency Logistics Network in Public Health

Background and Aim

In the long-term prevention of the COVID-19 pandemic, parameters may change frequently for various reasons, such as the emergence of mutant strains and changes in government policies. These changes will affect the efficiency of the current emergency logistics network. Public health emergencies have typical unstructured characteristics such as blurred transmission boundaries and dynamic time-varying scenarios, thus requiring continuous adjustment of emergency logistics network to adapt to the actual situation and make a better rescue.

Practical Significance

The infectivity of public health emergencies has shown a tendency that it first increased and then decreased in the initial decision-making cycle, and finally reached the lowest point in a certain decision-making cycle. This suggests that the number of patients will peak at some point in the cycle, after which the public health emergency will then be brought under control and be resolved. Therefore, in the design of emergency logistics network, the infectious ability of public health emergencies should be fully considered (ie, the prediction of the number of susceptible population should be based on the real-time change of the infectious ability of public health emergencies), so as to make the emergency logistics network more reasonable.

Methods

In this paper, we build a data-driven dynamic adjustment and optimization model for the decision-making framework with an innovative emergency logistics network in this paper. The proposed model divides the response time to emergency into several consecutive decision-making cycles, and each of them contains four repetitive steps: (1) analysis of public health emergency transmission; (2) design of emergency logistics network; (3) data collection and processing; (4) adjustment and update of parameters.

Results

The result of the experiment shows that dynamic adjustment and update of parameters help to improve the accuracy of describing the evolution of public health emergency transmission. The model successively transforms the public health emergency response into the co-evolution of data learning and optimal allocation of resources.

Conclusion

Based on the above results, it is concluded that the model we designed in this paper can provide multiple real-time and effective suggestions for policy adjustment in public health emergency management. When responding to other emergencies, our model can offer helpful decision-making references.

An Evolutionary Game Model of the Supply Decisions between GNPOs and Hospitals during a Public Health Emergency

The distribution of medical supplies tied to the government-owned nonprofit organizations (GNPOs) is crucial to the sustainable and high-quality development of emergency response to public health emergencies. This paper constructs a two-sided GNPO–hospital game model in a Chinese context, and explores the strategies and influencing factors of medical supply distribution in public health emergencies based on evolutionary game theory. The results show that: (1) GNPOs, as the distributor of medical supplies, should choose strategies that balance efficiency and equity as much as possible. (2) Hospitals, as the recipient of medical supplies, should actively choose strategies that maximize the total benefit to society and strengthen trust in GNPOs. Meanwhile, hospital managers need to pay attention to reducing the impact of communication and coordination costs and strive for the reduction of conflicts between different values. (3) The government should strengthen supervision to avoid conflicts between medical distributors and receivers during a public health emergency and ensure the rescue efficiency. This study provides some reference for the sustainable development of emergency relief in public health emergencies.

Emergency and disaster response strategies to support mother-infant dyads during COVID-19

The COVID-19 pandemic has produced an unprecedented global health crisis. Vulnerable populations, such as breastfeeding mother-infant dyads, are in a particularly delicate situation. Before, during, and after birth mothers and their infants could be exposed to the virus. Due to fear of infection transmission, there has been an increase in separation of COVID-positive mothers and their infants and a decline in breastfeeding, despite research supporting the provision of mother's milk for her infant. During this crisis, evidence-based education counseling and resources can support healthful infant feeding which is necessary for short- and long-term infant growth and development. Using a framework of disaster preparedness and response, we delineate operational guidelines and policy recommendations to support maternal-infant dyads during the COVID pandemic outbreak. Key recommendations include promotion of breastfeeding and milk expression, avoiding the use of formula, engaging healthcare providers in supporting lactation, and incorporating evidence-based breastfeeding and lactation protocols and practices in disaster preparedness and disaster response plans.

Methodology of emergency medical logistics for multiple epidemic areas in public health emergency

Coronavirus disease 2019(COVID-19) has brought great disasters to humanity, and its influence continues to intensify. In response to the public health emergencies, prompt relief supplies are key to reduce the damage. This paper presents a method of emergency medical logistics to quick response to emergency epidemics. The methodology includes two recursive mechanisms: (1) the time-varying forecasting of medical relief demand according to a modified susceptible-exposed-infected- Asymptomatic- recovered (SEIAR) epidemic diffusion model, (2) the relief supplies distribution based on a multi-objective dynamic stochastic programming model. Specially, the distribution model addresses a hypothetical network of emergency medical logistics with considering emergency medical reserve centers (EMRCs), epidemic areas and e-commerce warehousing centers as the rescue points. Numerical studies are conducted. The results show that with the cooperation of different epidemic areas and e-commerce warehousing centers, the total cost is 6% lower than without considering cooperation of different epidemic areas, and 9.7% lower than without considering cooperation of e-commerce warehousing centers. Particularly, the total cost is 20% lower than without considering any cooperation. This study demonstrates the importance of cooperation in epidemic prevention, and provides the government with a new idea of emergency relief supplies dispatching, that the rescue efficiency can be improved by mutual rescue between epidemic areas in public health emergency.

Nosocomial infection of COVID‑19: A new challenge for healthcare professionals (Review)

Nosocomial infections, also known as hospital-acquired infections, pose a serious challenge to healthcare professionals globally during the Coronavirus disease 2019 (COVID‑19) pandemic. Nosocomial infection of COVID‑19 directly impacts the quality of life of patients, as well as results in extra expenditure to hospitals. It has been shown that COVID‑19 is more likely to transmit via close, unprotected contact with infected patients. Additionally, current preventative and containment measures tend to overlook asymptomatic individuals and superspreading events. Since the mode of transmission and real origin of COVID‑19 in hospitals has not been fully elucidated yet, minimizing nosocomial infection in hospitals remains a difficult but urgent task for healthcare professionals. Healthcare professionals globally should form an alliance against nosocomial COVID‑19 infections. The fight against COVID‑19 may provide valuable lessons for the future prevention and control of nosocomial infections. The present review will discuss some of the key strategies to prevent and control hospital‑based nosocomial COVID‑19 infections.

Research on the Allocation of 3D Printing Emergency Supplies in Public Health Emergencies

Significant public health emergencies greatly impact the global supply chain system of production and cause severe shortages in personal protective and medical emergency supplies. Thus, rapid manufacturing, scattered distribution, high design degrees of freedom, and the advantages of the low threshold of 3D printing can play important roles in the production of emergency supplies. In order to better realize the efficient distribution of 3D printing emergency supplies, this paper studies the relationship between supply and demand of 3D printing equipment and emergency supplies produced by 3D printing technology after public health emergencies. First, we fully consider the heterogeneity of user orders, 3D printing equipment resources, and the characteristics of diverse production objectives in the context of the emergent public health environment. The multi-objective optimization model for the production of 3D printing emergency supplies, which was evaluated by multiple manufacturers and in multiple disaster sites, can maximize time and cost benefits of the 3D printing of emergency supplies. Then, an improved non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model is developed and compared with the traditional NSGA-II algorithm analysis. It contains more than one solution in the Pareto optimal solution set. Finally, the effectiveness of 3D printing is verified by numerical simulation, and it is found that it can solve the matching problem of supply and demand of 3D printing emergency supplies in public health emergencies.

The Interactive Effects of Cognition on Coping Styles among Chinese during the COVID-19 Pandemic

BACKGROUND

The outbreak of Coronavirus Disease 2019 (COVID-19) has seriously affected people's life. The main aim of our investigation was to determine the interactive effects of disease awareness on coping style among Chinese residents during the COVID-19 pandemic.

METHODS

A total of 616 Chinese residents from 28 provinces were recruited to participate in this investigation. A questionnaire was used to collect demographic characteristics, cognition of COVID-19, and disease-related stress sources. Coping styles were assessed via the Simplified Coping Style Questionnaire (SCSQ).

RESULTS

The survey showed that the main source of information on COVID-19 was different in relation to gender, age, educational level, and occupation (p < 0.001). People's knowledge of the disease, preventive measures, and stress factors were different in relation to demographic characteristics (p < 0.001). Compared with the baseline values, the scores of positive coping and negative coping based on SCSQ in relation to gender, age, educational level, and occupation were statistically significant (p < 0.001, except for participants older than 60 years). Different educational levels corresponded to statistical significant differences in positive coping (p = 0.004) but not in negative coping.

CONCLUSIONS

During the pandemic, people with different characteristics had different levels of preventive measures' awareness, which influenced their coping styles. Therefore, during public health emergencies, knowledge of prevention and control measures should be efficiently provided to allow more effective coping styles.

A new scheduling method based on sequential time windows developed to distribute first-aid medicine for emergency logistics following an earthquake

After an earthquake, affected areas have insufficient medicinal supplies, thereby necessitating substantial distribution of first-aid medicine from other supply centers. To make a proper distribution schedule, we considered the timing of supply and demand. In the present study, a "sequential time window" is used to describe the time to generate of supply and demand and the time of supply delivery. Then, considering the sequential time window, we proposed two multiobjective scheduling models with the consideration of demand uncertainty; two multiobjective stochastic programming models were also proposed to solve the scheduling models. Moreover, this paper describes a simulation that was performed based on a first-aid medicine distribution problem during a Wenchuan earthquake response. The simulation results show that the methodologies proposed in this paper provide effective schedules for the distribution of first-aid medicine. The developed distribution schedule enables some supplies in the former time windows to be used in latter time windows. This schedule increases the utility of limited stocks and avoids the risk that all the supplies are used in the short-term, leaving no supplies for long-term use.

Optimization-driven distribution of relief materials in emergency disasters

The distribution of relief materials is an important part of post-disaster emergency rescue. To meet the needs of the relief materials in the affected areas after a sudden disaster and ensure its smooth progress, an optimized dispatch model for multiple periods and multiple modes of transportation supported by the Internet of Things is established according to the characteristics of relief materials. Through the urgent production of relief materials, market procurement, and the use of inventory collection, the needs of the disaster area are met and the goal of minimizing system response time and total cost is achieved. The model is solved using CPLX software, and numerical simulation and results are analyzed using the example of the COVID-19 in Wuhan City, and the dispatching strategies are given under different disruption scenarios. The results show that the scheduling optimization method can meet the material demand of the disaster area with shorter time and lower cost compared with other methods, and can better cope with the supply interruptions that occur in post-disaster rescue.

Intelligent Evaluation System of Government Emergency Management Based on BP Neural Network

With the deepening of the global economic community, various emergencies emerge in endlessly, and the risks gradually increase. People's lives and property are threatened, which also causes a great burden on the social economy. Hitherto unknown novel coronavirus events occurred in China after the outbreak of the new coronavirus in 2019. The emergency management system is not perfect, so we start to study and improve the deficiencies of the emergency management system, but it is still difficult to effectively prevent and deal with all kinds of sudden and frequent social problems. Therefore, this paper puts forward the research of intelligent evaluation system of government emergency management based on BP neural network. In this paper, an intelligent evaluation system of government emergency management based on Internet of things environment is established, and then the system is deepened by BP neural network algorithm to avoid the interference of human factors. An objective intelligent evaluation system of government emergency management is constructed and verified by an example. We applied the system in a province, and proved that the system has strong executive ability, outstanding big data computing ability, and can objectively evaluate and analyze the government emergency management. The operability and accuracy of the intelligent evaluation system are verified. The effective evaluation content provides a new idea and method for government emergency management. And then continuously improve the emergency management measures to achieve the effect of dealing with things smoothly without panic.

Emotional and Cognitive Responses and Behavioral Coping of Chinese Medical Workers and General Population during the Pandemic of COVID-19

BACKGROUND

The outbreak of Corona Virus Disease 2019 (COVID-19) might affect the psychological health of population, especially medical workers. We aimed to investigate the impact of the COVID-19 pandemic on emotional and cognitive responses and behavioral coping among Chinese residents.

METHODS

An online investigation was run from 5 February to 25 February 2020, which recruited a total of 616 Chinese residents. Self-designed questionnaires were used to collect demographic information, epidemic knowledge and prevention of COVID-19 and characteristics of medical workers. The emotional and cognitive responses were assessed via the Symptom Check List-30 (SCL-30) and Yale-Brown Obsessive Compulsive Scale (Y-BOCS). Behavioral coping was assessed via Simplified Coping Style Questionnaire (SCSQ).

RESULTS

In total, 131 (21.3%) medical workers and 485 (78.7%) members of the general public completed the structured online survey. The structural equation models showed that emotional response interacted with cognitive response, and both emotional response and cognitive response affected the behavioral coping. Multivariate regression showed that positive coping enhanced emotional and cognitive responses, while negative coping reduced emotional and cognitive responses. The emotional response (depression, anxiety and photic anxiety) scores of the participants were higher than the norm (all p < 0.001); in particular, the panic scores of members of the general public were higher than those of medical workers (p < 0.05), as well as the cognitive response (paranoia and compulsion). Both positive and negative coping scores of the participants were lower than the norm (p < 0.001), and the general public had higher negative coping than medical workers (p < 0.05).

CONCLUSION

During the preliminary stage of COVID-19, our study confirmed the significance of emotional and cognitive responses, which were associated with behavioral coping and significantly influenced the medical workers and the general public's cognition and level of public health emergency preparedness. These results emphasize the importance of psychological health at times of widespread crisis.

A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)

The disasters caused by epidemic outbreaks is different from other disasters due to two specific features: their long-term disruption and their increasing propagation. Not controlling such disasters brings about severe disruptions in the supply chains and communities and, thereby, irreparable losses will come into play. Coronavirus disease 2019 (COVID-19) is one of these disasters that has caused severe disruptions across the world and in many supply chains, particularly in the healthcare supply chain. Therefore, this paper, for the first time, develops a practical decision support system based on physicians' knowledge and fuzzy inference system (FIS) in order to help with the demand management in the healthcare supply chain, to reduce stress in the community, to break down the COVID-19 propagation chain, and, generally, to mitigate the epidemic outbreaks for healthcare supply chain disruptions. This approach first divides community residents into four groups based on the risk level of their immune system (namely, very sensitive, sensitive, slightly sensitive, and normal) and by two indicators of age and pre-existing diseases (such as diabetes, heart problems, or high blood pressure). Then, these individuals are classified and are required to observe the regulations of their class. Finally, the efficiency of the proposed approach was measured in the real world using the information from four users and the results showed the effectiveness and accuracy of the proposed approach.

Reverse Logistics Network Design for Effective Management of Medical Waste in Epidemic Outbreaks: Insights from the Coronavirus Disease 2019 (COVID-19) Outbreak in Wuhan (China)

The outbreak of an epidemic disease may pose significant treats to human beings and may further lead to a global crisis. In order to control the spread of an epidemic, the effective management of rapidly increased medical waste through establishing a temporary reverse logistics system is of vital importance. However, no research has been conducted with the focus on the design of an epidemic reverse logistics network for dealing with medical waste during epidemic outbreaks, which, if improperly treated, may accelerate disease spread and pose a significant risk for both medical staffs and patients. Therefore, this paper proposes a novel multi-objective multi-period mixed integer program for reverse logistics network design in epidemic outbreaks, which aims at determining the best locations of temporary facilities and the transportation strategies for effective management of the exponentially increased medical waste within a very short period. The application of the model is illustrated with a case study based on the outbreak of the coronavirus disease 2019 (COVID-19) in Wuhan, China. Even though the uncertainty of the future COVID-19 spread tendency is very high at the time of this research, several general policy recommendations can still be obtained based on computational experiments and quantitative analyses. Among other insights, the results suggest installing temporary incinerators may be an effective solution for managing the tremendous increase of medical waste during the COVID-19 outbreak in Wuhan, but the location selection of these temporary incinerators is of significant importance. Due to the limitation on available data and knowledge at present stage, more real-world information are needed to assess the effectiveness of the current solution.

Epidemic-Logistics Network Considering Time Windows and Service Level

In this chapter, we present two optimization models for optimizing the epidemic-logistics network. In the first one, we formulate the problem of emergency materials distribution with time windows to be a multiple traveling salesman problem. Knowledge of graph theory is used to transform the MTSP to be a TSP, then such TSP route is analyzed and proved to be the optimal Hamilton route theoretically. Besides, a new hybrid genetic algorithm is designed for solving the problem. In the second one, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level. We formulate the problem to be a mixed-integer nonlinear programming model and develop an effective algorithm to solve the model. In this chapter, we present two optimization models for optimizing the epidemic-logistics network. In the first one, we formulate the problem of emergency materials distribution with time windows to be a multiple traveling salesman problem. Knowledge of graph theory is used to transform the MTSP to be a TSP, then such TSP route is analyzed and proved to be the optimal Hamilton route theoretically. Besides, a new hybrid genetic algorithm is designed for solving the problem. In the second one, we propose an improved location-allocation model with an emphasis on maximizing the emergency service level. We formulate the problem to be a mixed-integer nonlinear programming model and develop an effective algorithm to solve the model.