Epidemic Prevention Policies Research Articles

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

The Impact of COVID-19 on Pedestrian Flow Patterns in Urban POIs—An Example from Beijing

The COVID-19 pandemic is a major challenge for society as a whole, and analyzing the impact of the spread of the epidemic and government control measures on the travel patterns of urban residents can provide powerful help for city managers to designate top-level epidemic prevention policies and specific epidemic prevention measures. This study investigates whether it is more appropriate to use groups of POIs with similar pedestrian flow patterns as the unit of study rather than functional categories of POIs. In this study, we analyzed the hour-by-hour pedestrian flow data of key locations in Beijing before, during, and after the strict epidemic prevention and control period, and we found that the pedestrian flow patterns differed greatly in different periods by using a composite clustering index; we interpreted the clustering results from two perspectives: groups of pedestrian flow patterns and functional categories. The results show that depending on the specific stage of epidemic prevention and control, the number of unique pedestrian flow patterns decreased from four before the epidemic to two during the strict control stage and then increased to six during the initial resumption of work. The restrictions on movement are correlated with most of the visitations, and the release of restrictions led to an increase in the variety of unique pedestrian flow patterns compared to that in the pre-restriction period, even though the overall number of visitations decreased, indicating that social restrictions led to differences in the flow patterns of POIs and increased social distance.

Impact of COVID-19 on Urban Energy Consumption of Commercial Tourism City

In 2020, the COVID-19 pandemic has spread worldwide. To alleviate this spread, various blockade policies have been implemented in many areas. This has led to a sluggish demand in the world's major economies, sharp drop in the trade index, and negative growth in energy consumption. To formulate a better epidemic prevention policy for urban energy consumption of commercial tourism cities, this study summarizes the major statistics of energy supply and demand before and during the epidemic period based on actual data. The characteristics of energy consumption in different sectors, including hotels, transportation, tourism culture, and public utilities, are then analyzed in detail. Finally, the energy consumption features of commercial tourism cities represented by Macao are compared to those of other typical countries (e.g., Italy, United States, Japan, and Brazil). These analyses demonstrate the impact of COVID-19 on the energy consumption in commercial tourism cities, which provides insights for the government or energy providers to formulate policies to adapt to this pandemic.

Climate change, environmental factors, and COVID-19: Current evidence and urgent actions

Climate change, environmental factors, and COVID-19: Current evidence and urgent actions

Short-term prediction of COVID-19 spread using grey rolling model optimized by particle swarm optimization

The prediction of the spread of coronavirus disease 2019 (COVID-19) is vital in taking preventive and control measures to reduce human health damage. The Grey Modelling (1,1) is a popular approach used to construct a predictive model with a small-sized dataset.​ In this study, a hybrid model based on grey prediction and rolling mechanism optimized by particle swarm optimization algorithm (PSO) was applied to create short-term estimates of the total number of confirmed COVID-19 cases for three countries, Germany, Turkey, and the USA. A rolling mechanism that updates data in equal dimensions was applied to improve the forecasting accuracy of the models. The PSO algorithm was used to optimize the Grey Modelling parameters (1,1) to provide more robust and efficient solutions with minimum errors. To compare the accuracy of the predictive models, a nonlinear autoregressive neural network (NARNN) was also developed. According to the analysis results, Grey Rolling Modelling (1,1) optimized by PSO algorithm performs better than the classical Grey Modelling (1,1), Grey Rolling Modelling (1,1), and NARNN models for predicting the total number of confirmed COVID-19 cases. The present study can provide an important basis for countries to allocate health resources and formulate epidemic prevention policies effectively.

Evaluation and prediction of the status of COVID-19 epidemic prevention and control based on the SEIR+CAQ model

This paper analyzes the spread of the COVID-19 epidemic in China, establishes a [Formula: see text] epidemiologic model that classifies the diagnosed population into asymptomatic, mild, and severe, and considers the condition of an isolated population. First of all, we theoretically analyzed the key problems of the disease-free equilibrium, stability, and basic reproduction number of the model. Then, combined with the actual data, the development trend of the epidemic situation is simulated and compared, and the influence of some important parameters is discussed in the model evaluation part. Finally, based on the theoretical analysis and numerical simulation results, we forecast the future development of the COVID-19 epidemic and put forward some reasonable scientific suggestions for the government to formulate a future epidemic prevention policy.

Mechanisms of recurrent outbreak of COVID-19: a model-based study.

Recurrent outbreaks of the coronavirus disease 2019 (COVID-19) have occurred in many countries around the world. We developed a twofold framework in this study, which is composed by one novel descriptive model to depict the recurrent global outbreaks of COVID-19 and one dynamic model to understand the intrinsic mechanisms of recurrent outbreaks. We used publicly available data of cumulative infected cases from 1 January 2020 to 2 January 2021 in 30 provinces in China and 43 other countries around the world for model validation and further analyses. These time series data could be well fitted by the new descriptive model. Through this quantitative approach, we discovered two main mechanisms that strongly correlate with the extent of the recurrent outbreak: the sudden increase in cases imported from overseas and the relaxation of local government epidemic prevention policies. The compartmental dynamical model (Susceptible, Exposed, Infectious, Dead and Recovered (SEIDR) Model) could reproduce the obvious recurrent outbreak of the epidemics and showed that both imported infected cases and the relaxation of government policies have a causal effect on the emergence of a new wave of outbreak, along with variations in the temperature index. Meanwhile, recurrent outbreaks affect consumer confidence and have a significant influence on GDP. These results support the necessity of policies such as travel bans, testing of people upon entry, and consistency of government prevention and control policies in avoiding future waves of epidemics and protecting economy.

The dynamics of patient visits to traditional Chinese medicine during the 2019 coronavirus pandemic

BackgroundLarge-scale epidemics have changed people’s medical behavior, and patients tend to delay non-urgent medical needs. However, the impact of the pandemic on the use of complementary and alternative medicine remains unknown.MethodsThis retrospective study aimed to analyze the changes in the number of traditional Chinese medicine (TCM) patients and examine the epidemic prevention policy during the coronavirus disease 2019 (COVID-19) pandemic. We analyzed the number of TCM patients in Taipei City Hospital from January 2017 to May 2020. We tallied the numbers of patients in each month and compared them with those in the same months last year. We calculated the percentage difference in the number of patients to reveal the impact of the COVID-19 pandemic on TCM utilization. We used the Mann–Whitney U test to examine whether there was a significant difference in the number of patients during the COVID-19 pandemic.ResultsWe included a total of 1,935,827 TCM visits of patients from January 2017 to May 2020 in this study. During the COVID-19 pandemic, the number of patients decreased significantly, except in February 2020. The number of patients during the COVID-19 pandemic had fallen by more than 15% compared with those in the same months last year. March and April had the greatest number of patient losses, with falls of 32.8 and 40% respectively. TCM patients declined significantly during the COVID-19 pandemic, and mobile medicine provided to rural areas fell considerably. Among all the TCM specialties, pediatrics and traumatology, as well as infertility treatment, witnessed the most significant decline in the number of patients. However, the number of cancer patients has reportedly increased.ConclusionsThe COVID-19 pandemic decreased the utilization rate of TCM, especially for mobile healthcare in rural areas. We suggest that the government pay attention to the medical disparity between urban and rural areas, which are affected by the pandemic, as well as allocate adequate resources in areas deprived of medical care.

Two-Way Risk Communication, Public Value Consensus, and Citizens’ Policy Compliance Willingness About COVID-19: Multilevel Analysis Based on a Nudge View

COVID-19 has spawned a surge in the number of public epidemic prevention policies. More important than active policy-making is how to increase citizens’ policy compliance willingness. Based on public value theory and nudge theory, this study adopts a multilevel mediation and moderation model to analyze the relationships among the government’s authoritarian control, two-way risk communication, public value consensus, nudge, and citizens’ policy compliance willingness. A sample of 2,616 citizens nested in 86 Chinese cities was collected. The study found that (a) local governments can improve citizens’ policy compliance willingness by adopting both “stick” (authoritarian control) and “sermon” (two-way risk communication) policies, whereas two-way risk communication demonstrates a uniquely positive effect on public value consensus; and (b) the effect of two-way risk communication on policy compliance willingness is mediated by public value consensus. By enhancing public value consensus, two-way risk communication helps establish consensus between citizens and the government on “what needs/needs not to be done” and “what should/should not be done” to slow down the pandemic, thus coalescing efforts in a cooperative spirit; (c) nudge intervention positively moderates the effect of two-way risk communication on public value consensus, but has no effect on the authoritarian control–policy compliance willingness relationship. In other words, nudge intervention is a better companion to “sermons,” not “sticks.” Findings of the nuanced relationship manifested the differentiated effect of authoritarian control and two-way risk communication on citizens’ policy compliance willingness during a pandemic. The results provide theoretical implications for scholars to unlock the “black box” of public values and practical implications for strategies from behavioral public administration aspects to curb the ongoing COVID-19 pandemic.

Exploring early prevention and control of COVID-19 outbreak based on system dynamics model analysis.

To explore early prevention and control of coronavirus disease 2019 （COVID-19） outbreak based on system dynamics model analysis. The data of early outbreak of COVID-19 were collected from the World Health Organization，covering countries of the China，United States，United Kingdom，Australia，Serbia and Italy. The susceptible-exposed-infected-recovered （SEIR） model was generalized and then its parameters were optimized. According to the parameters in the basic infection number expression，the sensitivity in the system dynamics model was used to quantitatively analyze the influence of the protection rate，infection rate and average quarantine time on the early spread of the outbreak. Based on the analysis results，targeted prevention and control measures for the early outbreak of COVID-19 were proposed. The generalized SEIR model had a good fit for the early prediction and evaluation of COVID-19 outbreaks in six countries. The spread of COVID-19 was mainly affected by the protection rate，infection rate and average quarantine time. The improvement of the protection rate in the first ays was the most important:the greater the protection rate，the fewer the number of confirmed cases. The infection rate in the first 5 days was the most critical:the smaller the infection rate，the fewer the number of confirmed cases. The average quarantine time in the first 5 days was very important:the shorter the average quarantine time，the fewer the number of confirmed cases. Through the comparison of key parameters of six countries，Australia and China had implemented strict epidemic prevention policies，which had resulted in good epidemic prevention effects. In the early stage of the outbreak，it is necessary to improve the protection rate，shorten the average quarantine time，and implement strict isolation policies to curb the spread of COVID-19.

The Operational Mode of Grid-Based Management in Covid-19 Prevention and Control in China's Grassroots Communities

After the outbreak of COVID-19 in China, China's government has moved the epidemic prevention and control point forward and applied grid-based management in the community level to prevent the spread of the epidemic. Specifically, the grid-based management in the epidemic is to build a "seamless government" management mode of "horizontal to the edge, vertical to the end" through more refined spatial division, diversified staffing, more accurate information processing, more meticulous management and more thoughtful and active service. This mode makes the main body of epidemic prevention diverse and fight a people's war for epidemic prevention and control; Vertically, it has accelerated the flow of power, resources and information, and improved the efficiency of epidemic response; Horizontally, it breaks the boundaries of departments, levels and functions, so that government departments can form a joint force to formulate and implement epidemic prevention policies; When carrying out strict management , meanwhile, grid-based management provides warm and refined public services. Grid-based management, a Chinese "seamless government" mode, can provide some reference and thought for countries still fighting against the virus.

Research on COVID-19 Global Forecast based on SIR Model-ML regression

Under the background of the global COVID-19 pandemic, the global COVID-19 epidemic spread was modeled and analyzed, and its future trend was predicted. The dataset covers 163 countries and almost 2 full months from 2020, which is enough data to get some clues about the pandemic. The epidemic situation was predicted by the fusion of SIR model and ML regression, and the results showed that the model analysis was basically consistent with the real performance of the epidemic development. Finally, the development stage and trend of the epidemic situation are evaluated to provide a basis for the government to formulate relevant epidemic prevention policies.

A novel grey model based on traditional Richards model and its application in COVID-19.

In 2020, a new type of coronavirus is in the global pandemic. Now, the number of infected patients is increasing. The trend of the epidemic has attracted global attention. Based on the traditional Richards model and the differential information principle in grey prediction model, this paper uses the modified grey action quantity to propose a new grey prediction model for infectious diseases. This model weakens the dependence of the Richards model on single-peak and saturated S-shaped data, making Richards model more applicable, and uses genetic algorithm to optimize the nonlinear terms and the background value. To illustrate the effectiveness of the model, groups of slowly growing small-sample and large-sample data are selected for simulation experiments. Results of eight evaluation indexes show that the new model is better than the traditional GM(1,1) and grey Richards model. Finally, this model is applied to China, Italy, Britain and Russia. The results show that the new model is better than the other 7 models. Therefore, this model can effectively predict the number of daily new confirmed cases of COVID-19, and provide important prediction information for the formulation of epidemic prevention policies.

The impact of imported cases on the control of COVID-19 in China

To control the outbreak of COVID-19, the Chinese government has been carrying on a series of joint prevention and control measures. Current domestic situation shows an encouraging sign of improvement. However, the situation abroad is in a serious phase. Therefore, interdicting abroad inputs will be the key point at the next stage. In this paper, we establish a dynamical model incorporating with impulse to describe the transmission of SARS-CoV-2 and analyze the impact of overseas inputs on domestic prevention and control. Considering the imported cases from a typical neighboring country, we study the impacts of control measures under three different levels of control strategy. The simulations for the provinces with risk are given. The numerical experiments show that the current epidemic prevention policy can control the development of the epidemic well in the areas with less imported population; for the provinces with more imported population from the epidemic area, the effective screening and necessary isolation at immigration ports are crucial for preventing the further outbreak caused by imported cases.

Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data

During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.

Impact of Wuhan's Epidemic Prevention Policy on the Outbreak of COVID-19 in Wuhan, China

Background: In early December 2019 the first COVID-19 case appeared in Wuhan, China, and then turned into a nationwide epidemic one month later Besides Wuhan t

Risk Identification and Responses of Tunnel Construction Management during the COVID‐19 Pandemic

The COVID‐19 pandemic has brought about significant influences to the world, including tunnel construction. Based on the analysis of 12 tunnel construction projects, this paper identifies the specific risk factors related to the COVID‐19 pandemic, e.g., men, materials, machines, methods, social environment, and political epidemic prevention pressure. Among these risk factors, worker availability, site accessibility, shortage of construction materials, and inadequate epidemic prevention materials caused by the lockdown policy are the most fundamental challenges encountered by the projects. Social panic and epidemic prevention policy requirements are key issues needed to be addressed before the resumption of construction work. The special circumstances caused by the COVID‐19 pandemic called for flexible project management and coordination skills to raise suitable and effective response strategies, while local governments make substantial contributions in solving the difficulties. Although these measures have resulted in higher project costs, their effectiveness in catching up with project schedules is worthy of recognition. The findings of this study enrich the risk categories of tunnel construction and the risk response strategies from the perspective of a global pandemic. It implies that future construction schemes including design, budget, supply chain, and project management should consider the possible influence of an epidemic.

Exploring Coping Strategies of Nursing Staff for Influenza Vaccination Among Pregnant Women

Influenza vaccinations may decrease the risk of contracting influenza and its complications significantly in pregnant women and effectively help infants less than six months of age avoid hospitalization. Based on scientific evidence supporting the safety and benefits of the influenza vaccination for pregnant women and fetuses, the epidemic prevention policy of both the World Health Organization and Taiwan's Ministry of Health and Welfare recommend that pregnant women receive the influenza vaccination. However, low acceptance of the influenza vaccine among pregnant women is an ongoing concern in Taiwan. Nursing staffs play a key role in implementing the influenza vaccination program for pregnant women. Therefore, strengthening the professional competency and service effectiveness of nursing staffs may be expected to significantly improve the vaccination coverage rate. This article offers practical methods for achieving this, including promoting nursing staffs' professional knowledge and positive attitudes regarding influenza vaccinations for pregnant women, improving nursing staffs' related education and skills in communication with pregnant women and their families, and improving the implementation of the notification mechanism for disseminating accurate information regarding the vaccine and vaccine adverse events. Our ultimate goal is to enhance the confidence and willingness of pregnant women with regard to receiving influenza vaccinations in order to promote the health of mothers and their fetuses.

Feasibility study for epidemic prevention and control in a regional hospital

Epidemic prevention policies in hospitals address issues such as, indoor air quality control, cleanliness of medical staff clothing and employee hand-washing procedures. Our hospital employed Bio-Kil to treat air-conditioning filters and nursing staff uniforms. We also assessed the efficacy of different detergents. Using Bio-Kil technology, the mean bacterial count in the air was reduced from 108.8 CFU/h/plate (n=420) to 68.6 CFU/h/plate (n=630). On the lower hems of the Bio-Kil-treated gowns, the mean bacterial count was 1,201 CFU/100 cm(2), markedly lower than the bacterial count of 7,753 CFU/100 cm(2), found on the parts of the gowns not treated with Bio-Kil (p=0.0401). On the cuffs of sleeves treated with Bio-Kil, the mean count was 1,165 CFU/100 cm(2), markedly lower than that of 2,131 CFU/100 cm(2), found on the cuffs not treated with Bio-Kil (p=0.0073). With regard to the mean bacterial eradication rates of antimicrobial solutions, Steridal Solution, 75% alcohol and Bio-Kil (3rd generation) were shown to be the most effective, with rates exceeding 80%. Hibiscrub with paper towels and Fresh Protect Skin were the second most effective. Bio-Kil (1st generation), tap water with paper towels, liquid hand soap with paper towels and ozone water were the least effective. One important observation was that hand-washing without the use of paper towels increased the bacterial count by as much as 84% . Bio-Kil is effective in reducing bacterial counts in the air, on nursing staff uniforms and is an effective detergent.