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Background: Hygiene behaviors in public toilets are important to prevent the transmission of infectious diseases, especially during the pandemic. All through the novel coronavirus (COVID-19) pandemic, governments in many countries published guidance on personal hygiene for the general population to prevent disease transmission. This study aimed to investigate improvements in residents' hygiene awareness and behaviors in public toilets before and during the pandemic.Methods: We recruited 316 residents between November and December 2018 before the pandemic, and 314 residents between December 2020 and January 2021 during the pandemic in the same study sites in Hangzhou, a well-developed city in China. Residents' hygiene behaviors in public toilets, hygiene awareness, risk perception, and sociodemographic factors were collected. Bivariate analysis and multivariable logistic regressions were used to test the differences between the two rounds. We conducted an observational study to record the provision of hygiene amenities at toilets during the pandemic.Results: After controlling for sociodemographic factors (gender, marital status, age, education level, and monthly household income), compared with respondents recruited before the pandemic, respondents recruited during the pandemic were more likely to perceive the risks of infection when using public toilets (aOR = 1.77, 95%CI [1.20, 2.60]), and were more likely to be aware of the risks of touching contaminated toilet facilities (aOR = 1.72, 95%CI [1.17, 2.54]) and the risks of not using soap to wash one's hands after using the toilet (aOR = 1.93, 95%CI [1.38, 2.72]). They were more likely to always clean their toilet seat with alcohol (aOR = 1.88, 95%CI [1.01, 3.51]), wash hands with soap (aOR = 1.52, 95%CI [1.09, 2.10]) and dry their hands with a dryer (aOR = 1.78, 95%CI [1.16, 2.71]), but they were less likely to always wash their hands after using the toilets (aOR = 0.57, 95%CI [0.32, 1.00]). Among 70 public toilets observed, 9 provided alcohol for toilet seat disinfection, 52 provided soap, 33 provided paper towels, and 41 had working hand dryers.Conclusions: Despite the overall improvement, residents' hygiene behaviors in public toilets and the supply of hygiene amenities were still suboptimal during the pandemic. Further hygiene education and an adequate supply of hygiene amenities in public toilets are needed to promote residents' hygiene behaviors. [ABSTRACT FROM AUTHOR]

Since December 2019, the COVID-19 pandemic has quickly spread across the world. The traditional understanding of the relationship between wealth and the spread of contagious diseases is that similar to many precedent epidemics, the pandemic spread easily in poor neighborhoods in many countries. The environmental and socioeconomic implications of the COVID-19 pandemic are still poorly understood, thus this paper examines the relationship between neighborhood characteristics and the spread of the pandemic through a case study of Shenzhen, a Chinese megacity with many low-income rural migrants. The major finding is that wealthier and larger neighborhoods in Shenzhen were more likely to be infected in the first wave of the pandemic in 2020. This spread pattern is likely to result from China's strict control to prevent the pandemic, human mobility, and demographic characteristics such as income. This finding reveals a new phenomenon that contrasts with the traditional understanding of the influence of wealth on the spread of epidemics. This paper enriches the understanding of the role of neighborhoods in the spread of the pandemic, and it has important public policy implications. [ABSTRACT FROM AUTHOR]

After three years of global rampage, the COVID-19 epidemic, the most serious infectious disease to occur worldwide since the 1918 influenza pandemic, is nearing its end. From the global experience, medical control and social control are the two main dimensions in the prevention and control of COVID-19. From the perspective of "two types of control", namely medical control and social control, this paper finds that the political system, economic structure, and cultural values of the United States greatly limit the government's ability to impose social control, forcing it to adopt medical control to fight the virus in a single dimension. In contrast, China's political system, economic structure, and cultural values allow its government to adopt stringent, extensive, and frequent social control, as well as medical control to fight the virus. This approach departs from the traditional pathway of fighting the epidemic, i.e., "infection–treatment–immunization", thereby outpacing the evolution of the virus and controlling its spread more rapidly. This finding helps explain why the Chinese government adopted a strict "zeroing" and "dynamic zeroing" policy during the first three years, at the cost of enormous economic, social, and even political legitimacy. It was not until late 2022, when the Omicron variant with the waning virulence became prevalent, that China chose to "coexist" with the virus, thus avoiding a massive epidemic-related death. While the United States adopted a pulsed-style strategy at the beginning of the epidemic, i.e., "relaxation–suppression–relaxation–suppression", and began to "coexist" with the virus in just one year, resulting in a large number of excess deaths associated with the epidemic. The study contributes to explaining the difference in the interplay between public health priorities and COVID-19 response strategies in China and the United States, based on the specific public health context and the perspective of "medical control" and "social control". [ABSTRACT FROM AUTHOR]

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper aims to reveal the complete silent transmission dynamics of COVID-19 by agent-based simulations overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, by inferring individual professions, visited locations and age group, we found that retailing, catering and hotel staff are more likely to get infected than other professions, and elderly and retirees are more likely to get infected at home than outside home. Author summary: Since 2019, we have witnessed the worldwide battle against COVID-19. As infected and vaccinated population increases, some countries chose not to impose any non-pharmaceutical measures and to live with COVID-19. In the literature, an extensive effort has been made to understand the effectiveness of intervention measures against COVID-19 and to predict its prevalence, but much less is known about the risk of choosing to coexist with COVID-19. Here, we conduct agent-based simulations overlaying a high-frequency mobility big data which records individual mobility tracks up to every second in a large-scale city in northern China, revealing the comprehensive transmission dynamics from a few initial infected individuals to city-wise infections. Such comprehensive understanding will provide useful insights into our battle against COVID-19. [ABSTRACT FROM AUTHOR]

Background: The global spread of the novel coronavirus pneumonia is still continuing, and a new round of more serious outbreaks has even begun in some countries. In this context, this paper studies the dynamics of a type of delayed reaction-diffusion novel coronavirus pneumonia model with relapse and self-limiting treatment in a temporal-spatial heterogeneous environment.Methods: First, focus on the self-limiting characteristics of COVID-19, incorporate the relapse and self-limiting treatment factors into the diffusion model, and study the influence of self-limiting treatment on the diffusion of the epidemic. Second, because the traditional Lyapunov stability method is difficult to determine the spread of the epidemic with relapse and self-limiting treatment, we introduce a completely different method, relying on the existence conditions of the exponential attractor of our newly established in the infinite-dimensional dynamic system to determine the diffusion of novel coronavirus pneumonia. Third, relapse and self-limiting treatment have led to a change in the structure of the delayed diffusion COVID-19 model, and the traditional basic reproduction number [Formula: see text] no longer has threshold characteristics. With the help of the Krein-Rutman theorem and the eigenvalue method, we studied the threshold characteristics of the principal eigenvalue and found that it can be used as a new threshold to describe the diffusion of the epidemic.Results: Our results prove that the principal eigenvalue [Formula: see text] of the delayed reaction-diffusion COVID-19 system with relapse and self-limiting treatment can replace the basic reproduction number [Formula: see text] to describe the threshold effect of disease transmission. Combine with the latest official data and the prevention and control strategies, some numerical simulations on the stability and global exponential attractiveness of the diffusion of the COVID-19 epidemic in China and the USA are given.Conclusions: Through the comparison of numerical simulations, we find that self-limiting treatment can significantly promote the prevention and control of the epidemic. And if the free activities of asymptomatic infected persons are not restricted, it will seriously hinder the progress of epidemic prevention and control. [ABSTRACT FROM AUTHOR]

The outburst of the pandemic COVID-19 in 2019 from the land of China to the entire world has brought a major change in people's living style. The severity of the disease is so much that more than 20 million people are affected by it worldwide. The recommended preventive measure to avoid the spread of this fatal disease is to maintain social distance leading to a significant economic disruption that could submerge the economies globally. However in order to keep the economies moving, the work from culture is adopted globally wherein the employees are connected via the internet and the work continues to happen. This paper aims to discover how people adopt such an attempt in the working culture. Also, the purpose of the paper is to find out its bearing on the employees' working capacity and whether they would like to continue with this arrangement in the future. The research employs primary data collected from 400 employees across various industries who are currently working from home. The research proposes advantages and challenges in working from home with a majority of employees preferring the method. [ABSTRACT FROM AUTHOR]

In response to the outbreak of COVID-19, the Chinese government imposed stringent lockdown measures to minimize the spread of the disease. This paper shows that under these measures, the PM2.5 concentrations were lower in February 2020 than in February 2019. However, after the measures were removed, the PM2.5 concentration returned to the same level as in the previous year, thus implicating that the reduction was temporary. [ABSTRACT FROM AUTHOR]

Since the emergence of COVID-19, migration of people has transferred the virus to new locations, causing the epidemic to expand, and local governments have put in place control measures to prevent the virus from spreading further. As of January 24, 2020, we calculated the population immigration from Wuhan to the rest of mainland China using migration statistics from the Gaode Map. In addition, we utilized machine learning methods to simulate the curve of the COVID-19 epidemic in different regions and over different time periods. Furthermore, we used machine learning methods to simulate the COVID-19 epidemic curve in various regions and over various time periods. Based on the Wuhan exodus, we built a migration transmission risk model. From January 24 to February 19, 2020, we predicted the location, severity, and timing of epidemics in various parts of mainland China. We showed how discrepancies in model predictions might be utilized to measure transmission load in different parts of the country. Higher transmission risk indices suggest more community transmission in the region. According to the study, states with lower transmission risk indices but fewer cases than expected may have taken highly effective public health measures. [ABSTRACT FROM AUTHOR]

Coronavirus spread in Wuhan, China, in December 2019. A few weeks later, the virus was present in over 100 countries around the globe. Governments have adopted extreme measures to contain the spreading virus. Quarantine is considered the most effective way to control the spreading speed of COVID-19. In this study, a mathematical model is developed to explore the influence of quarantine and the latent period on the spatial spread of COVID-19. We use the mathematical model with quarantine, and delay to predict the spreading speed of the virus. In particular, we transform the model to a single integral equation and then apply the Laplace transform to find implicit equations for the spreading speeds. The basic reproduction number of COVID-19 is also found and calculated. Numerical simulations are performed to confirm our theoretical results. To validate the proposed model, we compare our outcomes with the actual reported data published by the National Health Commission of China and the Health Commission of local governments. The model demonstrates good qualitative agreement with the actual data reported. The results show that delay and quarantine highly influence the spreading speeds of COVID-19. Also, we can only contain the disease if we quarantine 75% of the infected people. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic erupted from Wuhan/China, has impacted the whole world in many ways drastically. Understanding such a pandemic, taking necessary precautions, and controlling a potential epidemic is essential. Modules and communities are typically and frequently represented in networks and analyzed with specific methods. Epidemic network analysis is a powerful method that provides us different information to interpret and make accurate decisions for pandemic events. The randomness of epidemic networks is considerably high; hence, it is essential to obtain consistent information from such networks. In this study, we proposed the network-based community detection of the early-stage COVID-19 associated with the basic reproduction number and the geographical distance between country locations. Worldwide confirmed cases between 22/01/2020 and 08/06/2020 had been analyzed for tenday periods. For this purpose, the Community Detection method was used. Therefore, (i) the community regions, (ii) the change of these regions and weighted nodal degrees of confirmed cases during the COVID-19, (iii) the relations between locations, and (iv) locations or regions which played an important role during the spread of this disease were obtained. The results of this study may help to reduce the reproduction number by lowering the average rate of contact in the early stage of a new pandemic. [ABSTRACT FROM AUTHOR]

The prediction system EpiSIX was used to study the COVID-19 epidemic in mainland China between November 2022 and January 2023, based on reported data from December 9, 2022, to January 30, 2023, released by The Chinese Center for Disease Control and Prevention on February 1, 2023. Three kinds of reported data were used for model fitting: the daily numbers of positive nucleic acid tests and deaths, and the daily number of hospital beds taken by COVID-19 patients. It was estimated that the overall infection rate was 87.54% and the overall case fatality rate was 0.078%-0.116% (median 0.100%). Assuming that a new COVID-19 epidemic outbreak would start in March or April of 2023, induced by a slightly more infectious mutant strain, we predicted a possible large rebound between September and October 2023, with a peak demand of between 800,000 and 900,000 inpatient beds. If no such new outbreak was induced by other variants, then the current COVID-19 epidemic course in mainland China would remain under control until the end of 2023. However, it is suggested that the necessary medical resources be prepared to manage possible COVID-19 epidemic emergencies in the near future, especially for the period between September and October 2023. [ABSTRACT FROM AUTHOR]

A range of public health measures have been implemented to suppress local transmission of coronavirus disease 2019 (COVID-19) in Shenzhen. We examined the effect of these measures on the prevalence of respiratory pathogens in children. Clinical and respiratory pathogen data were collected for routine care from hospitalized children with acute respiratory infections in Shenzhen Children's Hospital from July 2018 to January 2022. Nasopharyngeal swabs were collected and respiratory pathogens were detected using standardized clinical diagnostics as part of routine care. Data were analyzed to describe the effects of COVID-19 prevention procedures on other common pathogens. A total of 56,325 children under 14 years of age were hospitalized with an acute respiratory infection during the study period, 33,909 were tested from July 2018 to January 2020 (pre-lockdown), 1168 from February 2020 to May 2020 (lockdown) and 21,248 from July 2020 to January 2022 (post-lockdown). We observed a 37.3% decline of routine care in respiratory infection associated hospital admission in the 19 months' post-lockdown vs. the 19 months' pre-lockdown. There were 99.4%, 16.0% and 1.26% reductions measured for Mycoplasma pneumoniae, influenza virus A and adenovirus, respectively. However, a 118.7% and 75.8% rise was found for respiratory syncytial virus (RSV) and human para-influenza virus (HPIV) during the 19 months' post-lockdown in comparison to the pre-pandemic period. The detection of RSV especially increased in toddlers after the lockdown. Lockdown measures during the COVID-19 pandemic led to a significant reduction of Mycoplasma pneumoniae, influenza virus A and adenovirus infection. In contrast, RSV and HPIV infection increased. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these intervention strategies influences morbidity in other human infectious diseases, such as tuberculosis.Methods: In this study, three prediction models were constructed to compare variations in PTB incidences after January 2020 without or with intervention includes strict and regular interventions, when the COVID-19 outbreak began in China. The non-interventional model was developed with an autoregressive integrated moving average (ARIMA) model that was trained with the monthly incidence of PTB in China from January 2005 to December 2019. The interventional model was established using an ARIMA model with a continuing intervention function that was trained with the monthly PTB incidence in China from January 2020 to December 2020.Results: Starting with the assumption that no COVID-19 outbreak had occurred in China, PTB incidence was predicted, and then the actual incidence was compared with the predicted incidence. A remarkable overall decline in PTB incidence from January 2020 to December 2020 was observed, which was likely due to the potential influence of intervention policies for COVID-19. If the same intervention strategy is applied for the next 2 years, the monthly PTB incidence would reduce on average by about 1.03 per 100,000 people each month compared with the incidence predicted by the non-interventional model. The annual incidence estimated 59.15 under regular intervention per 100,000 in 2021, and the value would decline to 50.65 with strict interventions.Conclusions: Our models quantified the potential knock-on effect on PTB incidence of the intervention strategy used to control the transmission of COVID-19 in China. Combined with the feasibility of the strategies, these results suggested that continuous regular interventions would play important roles in the future prevention and control of PTB. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) is a pulmonary disease produced by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and has become a global pandemic March 2019. The aim of this research is to study a modelling approach on the spread of the COVID-19 pandemic utilizing analysis that is based on the well-known susceptible-infectious-removed (SIR) model. Since the declaration of the epidemic in Wuhan-city in China and other places around the world, several studies have addressed the development and evolution of the disease in different countries and have shown good stability of the use of such model. The United Arab Emirates is among the countries affected by the spread of the virus. Using collected data from 1 March 2020 to 31 August 2020 collected by John Hopkins University and the Federal Competitiveness and Statistics Authority (FCSA) in the UAE, we apply the SIR model to simulate the evolution of the virus in the United Arab Emirates. Our model predicts a peak in the daily infected cases on 19 May 2020, and the cumulative number of infected individuals to reach 100,000 cases by the end of October 2020. [ABSTRACT FROM AUTHOR]

Infectious diseases attack humans from time to time and threaten the lives and survival of people all around the world. An important strategy to prevent the spatial spread of infectious diseases is to restrict population travel. With the reduction of the epidemic situation, when and where travel restrictions can be lifted, and how to organize orderly movement patterns become critical and fall within the scope of this study. We define a novel diffusion distance derived from the estimated mobility network, based on which we provide a general model to describe the spatiotemporal spread of infectious diseases with a random diffusion process and a deterministic drift process of the population. We consequently develop a multi-source data fusion method to determine the population flow in epidemic areas. In this method, we first select available subregions in epidemic areas, and then provide solutions to initiate new travel flux among these subregions. To verify our model and method, we analyze the multi-source data from mainland China and obtain a new travel flux triggering scheme in the selected 29 cities with the most active population movements in mainland China. The testable predictions in these selected cities show that reopening the borders in accordance with our proposed travel flux will not cause a second outbreak of COVID-19 in these cities. The finding provides a methodology of re-triggering travel flux during the weakening spread stage of the epidemic. Author summary: Human infectious diseases spread from their origins to other places with population movements. In order to curb the spatial spread of infectious diseases, many countries and regions may introduce some travel restrictions when the epidemic is severe, and reopen the borders as the epidemic eases. This process involves some important issues such as the start and end time of travel restrictions, the geographical scope of the implementation of the exit strategy, and the allowable passenger flow on traffic lines. Here, we integrate multi-source data with a mathematical model, and consequently develop a new method to determine the travel flux in epidemic areas. As an application, we use this method to calculate when and where the travel restrictions targeting COVID-19 in China in early 2020 could be lifted, and how to optimize passenger flow along the traffic lines among the reopened cities. The testable predictions indicate that the population flow in accordance with our proposed movement pattern will not cause a resurgent outbreak of COVID-19 in the cities studied. [ABSTRACT FROM AUTHOR]

Background: The incubation period is a crucial index of epidemiology in understanding the spread of the emerging Coronavirus disease 2019 (COVID-19). In this study, we aimed to describe the incubation period of COVID-19 globally and in the mainland of China. Methods: The searched studies were published from December 1, 2019 to May 26, 2021 in CNKI, Wanfang, PubMed, and Embase databases. A random-effect model was used to pool the mean incubation period. Meta-regression was used to explore the sources of heterogeneity. Meanwhile, we collected 11 545 patients in the mainland of China outside Hubei from January 19, 2020 to September 21, 2020. The incubation period fitted with the Log-normal model by the coarseDataTools package. Results: A total of 3235 articles were searched, 53 of which were included in the meta-analysis. The pooled mean incubation period of COVID-19 was 6.0 days (95% confidence interval [CI] 5.6–6.5) globally, 6.5 days (95% CI 6.1–6.9) in the mainland of China, and 4.6 days (95% CI 4.1–5.1) outside the mainland of China (P = 0.006). The incubation period varied with age (P = 0.005). Meanwhile, in 11 545 patients, the mean incubation period was 7.1 days (95% CI 7.0–7.2), which was similar to the finding in our meta-analysis. Conclusions: For COVID-19, the mean incubation period was 6.0 days globally but near 7.0 days in the mainland of China, which will help identify the time of infection and make disease control decisions. Furthermore, attention should also be paid to the region- or age-specific incubation period. [ABSTRACT FROM AUTHOR]

The entire world has suffered a lot since the outbreak of the novel coronavirus (COVID-19) in 2019, so simulation models of COVID-19 dynamics are urgently needed to understand and control the pandemic better. Meanwhile, emotional contagion, the spread of vigilance or panic, serves as a negative feedback to the epidemic, but few existing models take it into consideration. In this study, we proposed an innovative multi-layer hybrid modelling and simulation approach to simulate disease transmission and emotional contagion together. In each layer, we used a hybrid simulation method combining agent-based modelling (ABM) with system dynamics modelling (SDM), keeping spatial heterogeneity while reducing computation costs. We designed a new emotion dynamics model IWAN (indifferent, worried, afraid and numb) to simulate emotional contagion inside a community during an epidemic. Our model was well fit to the data of China, the UK and the US during the COVID-19 pandemic. If there weren't emotional contagion, our experiments showed that the confirmed cases would increase rapidly, for instance, the total confirmed cases during simulation in Guangzhou, China would grow from 334 to 2096, which increased by 528%. We compared the calibrated emotional contagion parameters of different countries and found that the suppression effect of emotional contagion in China is relatively more visible than that in the US and the UK. Due to the experiment results, the proposed multi-layer network model with hybrid simulation is valid and can be applied to the quantitative analysis of the epidemic trends and the suppression effect of emotional contagion in different countries. Our model can be modified for further research to study other social factors and intervention policies in the COVID-19 pandemic or future epidemics. [ABSTRACT FROM AUTHOR]

The world is facing a severe and acute public health emergency due to the ongoing COVID-19 global pandemic. How individual countries respond in the coming weeks will be critical in influencing the trajectory of national epidemics. The mortality and morbidity rates reported so far and healthcare demands anticipated, are based on data from China and other high-income countries; marked variations in underlying health conditions of general public, capacity and strength of healthcare systems and vision of policy makers will likely result in different patterns in low income settings. This review is focusing on variations in pattern and outcome of SARS-COV-2 in Asian region in comparison with some neighboring countries. In conclusion, the aged population with co-morbidities has deadly outcome. As asymptomatic carriers are source of transmission of disease to vulnerable subjects, therefore, we need to continuously develop, monitor and implement prophylactic and therapeutic strategies to combat this deadly virus over the next months to years. A possible explanation of different outcome in different country can be ascribed by the presence of comorbidities, average age of population that are infected with virus and the use of some drugs that induce the cell expression of receptors for the virus. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to quantify the impact of coronavirus disease 2019 (COVID-19) on pediatric operations, and establish preoperative, intraoperative, and postoperative protocols to improve the pediatric operations. Methods: We here compare the number of patients who underwent surgery in Chongqing Medical University Affiliated Children's Hospital during the pandemic (January 23–March 11), after the pandemic (March 12–April 30), after our measures were put in place (May 1–May 21), and the equivalent period in 2019. Result: During the COVID-19 pandemic, 62.68% fewer patients underwent surgery than during the homologous period of time 1 year earlier (P < 0.01). After the COVID-19 pandemic, the number of orchidopexy cases increased significantly from 175.14 to 504.57 per week (P < 0.01). The large number of patients that accrued in our hospital may have increased the risk of COVID-19 transmission. In response, hospitals and clinics have made protocols and reorganized healthcare facilities (e.g., performing nucleic acid tests (NAT), adding adequate personal protective equipment (PPE)) from May 1, 2020. After the measures were implemented, the number of operations performed remained stable and comparable to the pre-pandemic period. COVID-19 RNA detection was performed in 5104 cases and there were no new confirmed cases in our hospital. Conclusion: This outbreak of COVID-19 has affected not only individuals with COVID-19 but also patients seeking surgical operations. Understanding the present situation helps clinicians provide a high level of treatment to all children. [ABSTRACT FROM AUTHOR]