Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study

Cost-based COVID-19 vaccination and willingness to pay: A post-pandemic review

The primary objective of this paper is to serve as a valuable resource for policymakers who are confronted with the evolving landscape of the coronavirus disease 2019 (COVID-19), considering both free and cost-based vaccination approaches. The potential consequences of shifting from free to cost-based vaccination are explored, encompassing its impact on global vaccine equity and prioritization, economic well-being, healthcare systems and delivery, public health policies, and vaccine distribution strategies. Examining past studies on willingness to pay for the initial COVID-19 vaccine dose and booster shots provides insights into how individuals value COVID-19 vaccinations and underscores the significance of addressing issues related to affordability. If COVID-19 vaccinations incur expenses, using effective communication strategies that emphasize the importance of vaccination and personal health benefits can increase willingness to pay. Making COVID-19 vaccines accessible through public health programs or health insurance can help alleviate financial barriers and increase vaccination rates.

Screen time and social-emotional skills in preschoolers with developmental, behavioural or emotional issues in Singapore

Introduction

This study aimed to determine patterns of screen viewing time (SVT) in preschool children with developmental, behavioural or emotional (DBE) issues, and to identify its relationship with social-emotional development.

Method

This cross-sectional study involved children aged 0-5 years who were referred to a developmental paediatric clinic for DBE issues. Parents completed a screen time questionnaire, and the Devereux Early Childhood Assessment-Clinical (DECA-C) question-naire which assessed the social-emotional competence of the children. Data were analysed using logistic regression, correlational analyses and tests of comparison.

Results

Among 225 children (mean age: 32.4 months), mean daily SVT was 138 minutes. More than half (51.1%) of the children had clinical features of language delay, while 26.6% had features suggestive of autism spectrum disorder. Screen time was first introduced at a mean age of 13.8 months, with 32.4% of children previously experiencing higher SVT. Compared to SVT introduction after 1 year of age, SVT in the first 12 months was primarily to facilitate feeding (P<0.05). Children with higher past SVT had poorer attention, more aggression, and increased behavioural concerns. Children with DBE issues have significantly more screen time than same-aged peers.

Conclusion

Children with DBE issues are exposed to SVT at a very young age and have significantly more screen time than their peers. It is crucial to guide parents to reduce SVT in early childhood, particularly around mealtimes.

Prevalence, under-reporting, and epidemiological surveillance of COVID-19 in the Araguaína City of Brazil

Asymptomatic and underreported individuals remain a source of coronafig disease 2019 (COVID-19) transmission to others. Data on the prevalence and epidemiological factors influencing transmission are fundamental for establishing control measures, especially in vulnerable regions such as the Amazon. This study aimed to determine the point prevalence and active infection of COVID-19 among the population in Araguaína, a Brazilian city located in the Amazon region, analyzed the socioeconomic and behavioral variables of a statistically representative sample of this population using an epidemiological survey, and identify the viral genomic diversity in the region. During the sixth epidemiological week of 2021 (February 8 to 12), samples of 497 inhabitants of the municipality asymptomatic for respiratory syndromes underwent reverse transcription-quantitative polymerase chain reaction and serological tests (immunoglobulin M and immunoglobulin G). A questionnaire collated data on socioeconomic factors, prevention measures, and health status history. The active infection rate was 6.2%, and the prevalence was 13.5% of the study population. Active infection cases were under-reported; each reported positive case represented 14-28 under-reported cases. Lineages P.2, P.1, and B.1.1 were detected. Working from home was a protective factor against the infection, and clinical signs of fever, dry cough, and loss of taste or smell were associated with testing positive (p <0.05). A descriptive analysis of the indicators revealed that the entire population was susceptible to the disease. Intensified vaccination strategies are required regardless of socioeconomic factors, health conditions, and preventive measures. Implementation of objective, comprehensive, and efficient management tools to minimize the spread of COVID-19 in this municipality can serve as a model for other regions of Brazil.

Informing Public Health Policies with Models for Disease Burden, Impact Evaluation, and Economic Evaluation

Conducting real-world public health experiments is often costly, time-consuming, and ethically challenging, so mathematical models have a long-standing history of being used to inform policy. Applications include estimating disease burden, performing economic evaluation of interventions, and responding to health emergencies such as pandemics. Models played a pivotal role during the COVID-19 pandemic, providing early detection of SARS-CoV-2's pandemic potential and informing subsequent public health measures. While models offer valuable policy insights, they often carry limitations, especially when they depend on assumptions and incomplete data. Striking a balance between accuracy and timely decision-making in rapidly evolving situations such as disease outbreaks is challenging. Modelers need to explore the extent to which their models deviate from representing the real world. The uncertainties inherent in models must be effectively communicated to policy makers and the public. As the field becomes increasingly influential, it needs to develop reporting standards that enable rigorous external scrutiny.

Knowledge, perceptions and practices of COVID19 among the came-roonian population

COVID19 is an emerging infectious disease that has spread all over the world and became a pandemic. Cameroon is the 7th most affected country in Africa, with most of the cases in metropoles. The main objective was to assess the knowledge, perceptions and practices of the Cameroonian populations about COVID19 infection. A cross-sectional study was conducted from May 15th to July 15th, 2020; targeting all Cameroonians over 15 years old living in Cameroon. A standard Google Forms® questionnaire was submitted via social media (WhatsApp and Facebook particularly). The sampling was consecutive and not exhaustive. The data were processed on Excel 2016 and analyzed through EPI info 3.5. The questionnaire included knowledge, perceptions and practices about the disease's existence, its functional signs, prevention and control. Knowledge, perceptions and practices were considered good for a response score ≥75% and poor for a score ≤25%. Intermediate scores were considered average and insufficient. Univariate and multivariate analyzes were performed to identify the factors associated with a significance level (P#x003C;0.05). Of the 996 (507 males) participants, the median age was 26±8 years. Health workers represented 20.8% of participants. A large proportion of the population (78.6%) had good knowledge, while more than half (56.5%) had good perceptions, but only (23.1%) had good practices. Having reached higher education (P=0.007), and being a health worker (P=0.0008) were associated with a good level of knowledge. High school education (P=0.040) and being a health worker (P=0.049) were associated with positive perceptions. Being employed (P=0.003) and having secondary education level (P=0.033) were associated with good practices. Knowledge and perceptions were good among the study population, but the practice level is still insufficient to effectively fight the COVID19 pandemic. Educational level, being employed or being healthcare workers were associated with good knowledge, perceptions and practices.

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Predicting COVID-19 pandemic waves including vaccination data with deep learning

Introduction

During the recent COVID-19 pandemics, many models were developed to predict the number of new infections. After almost a year, models had also the challenge to include information about the waning effect of vaccines and by infection, and also how this effect start to disappear.

Methods

We present a deep learning-based approach to predict the number of daily COVID-19 cases in 30 countries, considering the non-pharmaceutical interventions (NPIs) applied in those countries and including vaccination data of the most used vaccines.

Results

We empirically validate the proposed approach for 4 months between January and April 2021, once vaccination was available and applied to the population and the COVID-19 variants were closer to the one considered for developing the vaccines. With the predictions of new cases, we can prescribe NPIs plans that present the best trade-off between the expected number of COVID-19 cases and the social and economic cost of applying such interventions.

Discussion

Whereas, mathematical models which include the effect of vaccines in the spread of the SARS-COV-2 pandemic are available, to the best of our knowledge we are the first to propose a data driven method based on recurrent neural networks that considers the waning effect of the immunization acquired either by vaccine administration or by recovering from the illness. This work contributes with an accurate, scalable, data-driven approach to modeling the pandemic curves of cases when vaccination data is available.

COVID-19 positive cases in a pediatric surgery department from Romania: Case series from 2 years of pandemics

RATIONALE

The COVID-19 pandemic had a dramatic effect on various health systems in terms of admissions and outcomes, including pediatric surgery activity. The aim of this paper was to analyze the outcome of SARS-CoV-2-positive patients admitted to our department during the regional COVID-19 pandemic in North-Eastern Romania. We also evaluated the changes generated in our daily practice by the COVID-19 pandemic and the dynamic response to this major challenge.

PATIENT CONCERNS

The patients presented with symptoms related to their primary diagnosis: local pain and deformity in case of fractures; pain, swelling, and erythema in case of abscess; pain and decreased range of motion (ROM) in case of intolerance to metal implants. Other specific concerns are mentioned on an individual basis.

DIAGNOSES

Eighteen patients (of which 4 had acute appendicitis and were included in a previous article), representing 1.18% of the total number of admissions, tested positive for SARS-CoV-2. There were 4 patients with fractures, 3 patients with soft tissue abscess or cellulitis, 2 patients with intolerance to metal implants, 1 patient with facial burn, 1 patient with thumb laceration, 1 patient with liver trauma, 1 patient with undescende testis, and 1 patient with symptomatic inguinal hernia, respectively. Boys represented 11/ 14 (78.57%) of the cases. The mean age of the patients was 9 years 11 months. There were only mild COVID-19 cases.

INTERVENTIONS

Surgery was performed in 13/ 14 (95.71%) of cases. The fractures were treated with open reduction internal fixation (ORIF); incision and drainage (I & D) were performed in case of soft tissue abscess; the metal implants were removed in case of local intolerance. Other conditions (burn, inguinal hernia, undescended testis, skin laceration) were treated specifically. Only 1 patient with liver laceration was treated conservatively under close hemodynamic monitoring.

OUTCOMES

The mean length of stay (LoS) was 2.71 days. The infection with the SARS-CoV-2 virus had no deleterious effect on the surgical outcome among the 14 patients included in the study. There were no surgical complications during admission and no patient returned for late complications related to their primary disease or SARS-CoV-2 infection.

LESSONS

The SARS-CoV-2 infection had no significant influence on the outcome of pediatric surgical cases included in the study. We noticed a significant (31.54%) decrease in the number of admissions compared to the previous 2-year interval before the COVID-19 pandemic. Fast and adequate adjustment of the daily activity imposed by the COVID-19 pandemic was feasible and may be used in the future should similar epidemiological emergencies occur.

Evaluation of COVID-19 intervention policies in South Korea using the stochastic individual-based model

The COVID-19 pandemic has swept the globe, and countries have responded with various intervention policies to prevent its spread. In this study, we aim to analyze the effectiveness of intervention policies implemented in South Korea. We use a stochastic individual-based model (IBM) with a synthetic population to simulate the spread of COVID-19. Using statistical data, we make the synthetic population and assign sociodemographic attributes to each individual. Individuals go about their daily lives based on their assigned characteristics, and encountering infectors in their daily lives stochastically determines whether they are infected. We reproduce the transmission of COVID-19 using the IBM simulation from November 2020 to February 2021 when three phases of increasingly stringent intervention policies were implemented, and then assess their effectiveness. Additionally, we predict how the spread of infection would have been different if these policies had been implemented in January 2022. This study offers valuable insights into the effectiveness of intervention policies in South Korea, which can assist policymakers and public health officials in their decision-making process.

Dynamic variations in and prediction of COVID-19 with omicron in the four first-tier cities of mainland China, Hong Kong, and Singapore

Background

The COVID-19 pandemic, which began in late 2019, has resulted in the devastating collapse of the social economy and more than 10 million deaths worldwide. A recent study suggests that the pattern of COVID-19 cases will resemble a mini-wave rather than a seasonal surge. In general, COVID-19 has more severe impacts on cities than on rural areas, especially in cities with high population density.

Methods

In this study, the background situation of COVID-19 transmission is discussed, including the population number and population density. Moreover, a widely used time series autoregressive integrated moving average (ARIMA) model is applied to simulate and forecast the COVID-19 variations in the six cities. We comprehensively analyze the dynamic variations in COVID-19 in the four first-tier cities of mainland China (BJ: Beijing, SH: Shanghai, GZ: Guangzhou and SZ: Shenzhen), Hong Kong (HK), China and Singapore (SG) from 2020 to 2022.

Results

The major results show that the six cities have their own temporal characteristics, which are determined by the different control and prevention measures. The four first-tier cities of mainland China (i.e., BJ, SH, GZ, and SZ) have similar variations with one wave because of their identical "Dynamic COVID-19 Zero" strategy and strict Non-Pharmaceutical Interventions (NPIs). HK and SG have multiple waves primarily caused by the input cases. The ARIMA model has the ability to provide an accurate forecast of the COVID-19 pandemic trend for the six cities, which could provide a useful approach for predicting the short-term variations in infectious diseases.Accurate forecasting has significant value for implementing reasonable control and prevention measures.

Conclusions

Our main conclusions show that control and prevention measures should be dynamically adjusted and organically integrated for the COVID-19 pandemic. Moreover, the mathematical models are proven again to provide an important scientific basis for disease control.

Checkpoint Travel Numbers as a Proxy Variable in Population-Based Studies During the COVID-19 Pandemic: Validation Study

BACKGROUND

The COVID-19 pandemic had wide-ranging systemic impacts, with implications for social and behavioral factors in human health. The pandemic may introduce history bias in population-level research studies of other health topics during the COVID-19 period.

OBJECTIVE

We sought to identify and validate an accessible, flexible measure to serve as a covariate in research spanning the COVID-19 pandemic period.

METHODS

Transportation Security Administration checkpoint travel numbers were used to calculate a weekly sum of daily passengers and validated against two measures with strong face validity: (1) a self-reported item on social distancing practices drawn from a continuous tracking survey among a national sample of youths and young adults (15-24 years) in the United States (N=45,080, approximately 280 unique respondents each week); and (2) Google's Community Mobility Reports, which calculate daily values at the national level to represent rates of change in visits and length of stays to public spaces. For the self-reported survey data, an aggregated week-level variable was calculated as the proportion of respondents who did not practice social distancing that week (January 1, 2019, to May 31, 2022). For the community mobility data, a weekly estimate of change was calculated using daily values compared to a 5-week prepandemic baseline period (January 3, 2020, to February 6, 2020). Spearman rank correlation coefficients were calculated for each comparison.

RESULTS

Checkpoint travel data ranged from 668,719 travelers in the week of April 8, 2020, to nearly 15.5 million travelers in the week of May 18, 2022. The weekly proportion of survey respondents who did not practice social distancing ranged from 18.1% (n=42; week of April 15, 2020) to 70.9% (n=213; week of May 25, 2022). The measures were strongly correlated from January 2019 to May 2022 (ρ=0.90, P<.001) and March 2020 to May 2022 (ρ=0.87, P<.001). Strong correlations were observed when analyses were restricted to age groups (15-17 years: ρ=0.90; P<.001; 18-20 years: ρ=0.87; P<.001; 21-24 years: ρ=0.88; P<.001), racial or ethnic minorities (ρ=0.86, P<.001), and respondents with lower socioeconomic status (ρ=0.88, P<.001). There were also strong correlations between the weekly change from the baseline period for checkpoint travel data and community mobility data for transit stations (ρ=0.92, P<.001) and retail and recreation (ρ=0.89, P<.001), and moderate significant correlations for grocery and pharmacy (ρ=0.68, P<.001) and parks (ρ=0.62, P<.001). A strong negative correlation was observed for places of residence (ρ=-0.78, P<.001), and a weak but significant positive correlation was found for workplaces (ρ=0.24, P<.001).

CONCLUSIONS

The Transportation Security Administration's travel checkpoint data provide a publicly available flexible time-varying metric to control for history bias introduced by the pandemic in research studies spanning the COVID-19 period in the United States.

Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19

Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies.

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. A major challenge in forecasting the transmission of COVID-19 is the accurate assessment of the multiscale human mobility and how it impacts infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of geographical places, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behavior and individual health conditions on the disease outbreak and the probability of zero-COVID in the population. Specifically, individuals perform power law-type local movements within a container and global transport between different-level containers. It is revealed that frequent long-distance movements inside a small-level container (e.g., a road or a county) and a small population size reduce both the local crowdedness and disease transmission. It takes only half of the time to induce global disease outbreaks when the population increases from 150 to 500 (normalized unit). When the exponent c 1 of the long-tail distribution of distance k moved in the same-level container, p ( k ) ∼ k - c 1 · level , increases, the outbreak time decreases rapidly from 75 to 25 (normalized unit). In contrast, travel between large-level containers (e.g., cities and nations) facilitates global spread of the disease and outbreak. When the mean traveling distance across containers 1 d increases from 0.5 to 1 (normalized unit), the outbreak occurs almost twice as fast. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or to a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing population size and restricting global travel help achieve zero-COVID-19. Specifically, when c 1 is smaller than 0.2, the ratio of people with low levels of mobility is larger than 80% and the population size is smaller than 400, zero-COVID can be achieved within fewer than 1000 time steps. In summary, the Mob-Cov model considers more realistic human mobility at a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to apply when investigating pandemic dynamics and when planning actions against disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11071-023-08489-5.

Psychological status of infertile men during the Coronavirus Disease 2019 Pandemic in China: a cross-sectional investigation

BACKGROUND

The coronavirus disease 2019 (COVID-19) outbreak has had a widespread and profound impact on people's mental health. The factors associated with mental symptoms among men diagnosed with infertility, a disease closely related to psychological conditions, remain unclear. The aim of this study is to investigate the risk factors associated with mental symptoms among infertile Chinese men during the pandemic.

RESULTS

A total of 4,098 eligible participants were recruited in this cross-sectional, nationwide study, including 2,034 (49.6%) with primary infertility and 2,064 (50.4%) with secondary infertility. The prevalence of mental health conditions was 36.3%, 39.6%, and 6.7% for anxiety, depression, and post-pandemic stress, respectively. Sexual dysfunction is associated with a higher risk with adjusted odds ratios (ORs) of 1.40 for anxiety, 1.38 for depression, and 2.32 for stress. Men receiving infertility drug therapy displayed a higher risk for anxiety (adjusted OR, 1.31) and depression (adjusted OR, 1.28) symptoms, while those receiving intrauterine insemination had a lower risk of anxiety (adjusted OR, 0.56) and depression (adjusted OR, 0.55) symptoms.

CONCLUSION

The COVID-19 pandemic has had a significant psychological impact on infertile men. Several psychologically vulnerable populations were identified, including individuals with sexual dysfunction, respondents receiving infertility drug therapy, and those experiencing control measures for COVID-19. The findings provide a comprehensive profile of the mental health status of infertile Chinese men during the COVID-19 outbreak and provide potential psychological intervention strategies.

Effects of media on preventive behaviour during the COVID-19 pandemic

The novel coronavirus 2019 (COVID-19) pandemic required implementation of a variety of measures. In addition to pharmaceutical measures, such as vaccines, changing individuals' nonpharmaceutical preventive behaviour is essential to prevent the spread of infection. In uncertain situations, such as a pandemic, media sources are important for guiding individuals' decision-making behaviour. In this study, we examined the effects of media use on preventive behaviour during COVID-19. Earlier studies have shown that social networking service (SNS) browsing promotes preventive behaviour. However, those studies only assessed a single point during the early stages of the pandemic; therefore, the effects on ongoing preventive behaviour are unclear. Thus, a two-wave panel survey was conducted in 2020 and 2021 for an exploratory analysis of changes in the effects of media on individuals' preventive behaviour over time. The results show that the effect of SNS browsing on preventing going out was confirmed only during the early stage of the pandemic and was not observed 1 year later. It is also shown that those who shifted from self-restraint to going out within 1 year were not affected by the type of media use, but by cognitive factors. As the situation changes during a pandemic, analyses that consider time-series changes are essential for gaining insights about the effects of media on the promotion and maintenance of continuous prevention behaviours.

Functional Textile Materials for Blocking COVID-19 Transmission

The outbreak of COVID-19 provided a warning sign for society worldwide: that is, we urgently need to explore effective strategies for combating unpredictable viral pandemics. Protective textiles such as surgery masks have played an important role in the mitigation of the COVID-19 pandemic, while revealing serious challenges in terms of supply, cross-infection risk, and environmental pollution. In this context, textiles with an antivirus functionality have attracted increasing attention, and many innovative proposals with exciting commercial possibilities have been reported over the past three years. In this review, we illustrate the progress of textile filtration for pandemics and summarize the recent development of antiviral textiles for personal protective purposes by cataloging them into three classes: metal-based, carbon-based, and polymer-based materials. We focused on the preparation routes of emerging antiviral textiles, providing a forward-looking perspective on their opportunities and challenges, to evaluate their efficacy, scale up their manufacturing processes, and expand their high-volume applications. Based on this review, we conclude that ideal antiviral textiles are characterized by a high filtration efficiency, reliable antiviral effect, long storage life, and recyclability. The expected manufacturing processes should be economically feasible, scalable, and quickly responsive.

A new method for spatio-temporal transmission prediction of COVID-19

COVID-19 is the most serious public health event of the 21st century and has had a huge impact across the world. The spatio-temporal pattern analysis and simulation of epidemic spread have become the focus of current research. LSTM model has made a lot of achievements in the prediction of infectious diseases by virtue of its advantages in time prediction, but lacks the spatial expression. CA model plays an important role in epidemic spatial propagation modeling due to its unique evolution characteristics from local to global. However, no existing studies of CA have considered long-term dependence due to the impact of time changes on the evolution of the epidemic, and few have modeled using location data from actual diagnosed patients. Therefore, we proposed a LSTM-CA model to solve above mentioned problems. Base on the advantages of LSTM in temporal level and CA in spatial level, LSTM and CA are integrated from the spatio-temporal perspective of geography based on the fine-grained characteristics of epidemic data. The method divides the study area into regular grids, simulates the spatial interactions between neighborhood cells with the help of CA model, and extracts the parameters affecting the transition probability in CA with the help of LSTM model to assist evolution. Simulations are conducted in Python 3.4 to model the propagation of COVID-19 between Feb, 6 to Mar 20, 2020 in China. Experimental results show that, LSTM-CA performs a higher statistical accuracy than LSTM and spatial accuracy than CA, which could demonstrate the effectiveness of the proposed model. This method could be universal for the temporal and spatial transmission of major public health events. Especially in the early stage of the epidemic, we can quickly understand its development trend and cycle, so as to provide an important reference for epidemic prevention and control and public sentiment counseling.

Malignant polyps in the COVID-19 era: a population-based analysis

BACKGROUND

Malignant polyps represent the early development of colorectal adenocarcinoma. During 2020, there was widescale rationing of health-care resources in response to the COVID-19 pandemic. In particular there was deferral of some colonoscopy procedures required for timely malignant polyp detection. This study sought to assess how these deferrals affected the diagnosis of malignant polyps.

METHODS

A population wide analysis was performed of 2079 malignant polyps, diagnosed in Queensland, Australia from 2011 to 2020. A regression analysis, with 95% prediction intervals, was produced to determine whether there was a significant impact on the number of malignant polyps diagnosed in 2020 compared to previous years. Univariate statistical analysis of patient, procedural, and pathological variables was also performed.

RESULTS

In 2020 there were 211 malignant polyps diagnosed, which was significantly lower than was predicted by the univariate regression analysis (r²  = 0.85, 95% prediction interval: 255.07-323.91, P < 0.001). These malignant polyps were less likely to be diagnosed in a private setting (P < 0.001), and exhibited significantly less depth of submucosal invasion (P = 0.017). There was no significant difference in the management strategy (polypectomy, resection or trans-anal resection) between 2011 and 2019 and 2020.

CONCLUSION

Because of the significant decrease in the number of malignant polyps, and the natural history of the disease, it is expected that there will be an increase in more advanced colorectal adenocarcinomas presenting in 2021 and beyond. This has implications for healthcare resources, particularly in light of the ongoing strain on health departments as a result of the COVID-19 pandemic.

The emerging significance of nanomedicine-based approaches to fighting COVID-19 variants of concern: A perspective on the nanotechnology’s role in COVID-19 diagnosis and treatment

COVID-19, one of the worst-hit pandemics, has quickly spread like fire across nations with very high mortality rates. Researchers all around the globe are making consistent efforts to address the main challenges faced due to COVID-19 infection including prompt diagnosis and therapeutics to reduce mortality. Conventional medical technology does not effectively contain the havoc caused by deadly COVID-19. This signals a crucial mandate for innovative and novel interventions in diagnostics and therapeutics to combat this ongoing pandemic and counter its successor or disease if it were ever to arise. The expeditious solutions can spring from promising areas such as nanomedicine and nanotechnology. Nanomedicine is a dominant tool that has a huge potential to alleviate the disease burden by providing nanoparticle-based vaccines and carriers. Nanotechnology encompasses multidisciplinary aspects including artificial intelligence, chemistry, biology, material science, physical science, and medicine. Nanoparticles offer many advantages compared to larger particles, including better magnetic properties and a multiplied surface-to-volume ratio. Given this, the present review focuses on promising nanomedicine-based solutions to combat COVID-19 and their utility to control a broad range of pathogens and viruses, along with understanding their role in the therapy, diagnosis, and prevention of COVID-19. Various studies, reports, and recent research and development from the nanotechnology perspective are discussed in this article.

Thyroid surgery during the COVID-19 pandemic: difficulties - how to improve

In December 2019, the new coronavirus infection (COVID-19) was declared a pandemic by the World Health Organization after rapidly spreading over the world in just a few months. All elective operations and nonemergency treatments have been postponed worldwide. However, some patients require surgical therapy as well, and the time spent waiting should not have a negative impact on the surgical outcome or disease course. Following the initial onset of the COVID-19 epidemic, instructions for proper and safe surgery for healthcare staff and patients should develop. Thyroid surgeries have decreased during the COVID-19 pandemic. Most of them can be postponed for a long time. Assessment of thyroid nodules recommends clinical examination, imaging studies, fine needle aspiration (FNA) and vocal cord examination. All these procedures are necessary, and sometimes they cannot be postponed. To determine the best timing, a thorough preoperative assessment should be undertaken, taking into account both oncological and anatomical features. Furthermore, COVID-19 status must be negative prior to any intervention, and hospital infrastructure must be ready to deal with the demanding situation.

Future behaviours decision-making regarding travel avoidance during COVID-19 outbreaks

Human behavioural changes are poorly understood, and this limitation has been a serious obstacle to epidemic forecasting. It is generally understood that people change their respective behaviours to reduce the risk of infection in response to the status of an epidemic or government interventions. We must first identify the factors that lead to such decision-making to predict these changes. However, due to an absence of a method to observe decision-making for future behaviour, understanding the behavioural responses to disease is limited. Here, we show that accommodation reservation data could reveal the decision-making process that underpins behavioural changes, travel avoidance, for reducing the risk of COVID-19 infections. We found that the motivation to avoid travel with respect to only short-term future behaviours dynamically varied and was associated with the outbreak status and/or the interventions of the government. Our developed method can quantitatively measure and predict a large-scale population's behaviour to determine the future risk of COVID-19 infections. These findings enable us to better understand behavioural changes in response to disease spread, and thus, contribute to the development of reliable long-term forecasting of disease spread.

Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Primary Health Care Electronic Records

BACKGROUND

A possible link between influenza immunization and susceptibility to the complications of COVID-19 infection has been previously suggested owing to a boost in the immunity against SARS-CoV-2.

OBJECTIVE

This study aimed to investigate whether individuals with COVID-19 could have benefited from vaccination against influenza. We hypothesized that the immunity resulting from the previous influenza vaccination would boost part of the immunity against SARS-CoV-2.

METHODS

We performed a population-based cohort study including all patients with COVID-19 with registered entries in the primary health care (PHC) electronic records during the first wave of the COVID-19 pandemic (March 1 to June 30, 2020) in Catalonia, Spain. We compared individuals who took an influenza vaccine before being infected with COVID-19, with those who had not taken one. Data were obtained from Information System for Research in Primary Care, capturing PHC information of 5.8 million people from Catalonia. The main outcomes assessed during follow-up were a diagnosis of pneumonia, hospital admission, and mortality.

RESULTS

We included 309,039 individuals with COVID-19 and compared them on the basis of their influenza immunization status, with 114,181 (36.9%) having been vaccinated at least once and 194,858 (63.1%) having never been vaccinated. In total, 21,721 (19%) vaccinated individuals and 11,000 (5.7%) unvaccinated individuals had at least one of their outcomes assessed. Those vaccinated against influenza at any time (odds ratio [OR] 1.14, 95% CI 1.10-1.19), recently (OR 1.13, 95% CI 1.10-1.18), or recurrently (OR 1.10, 95% CI 1.05-1.15) before being infected with COVID-19 had a higher risk of presenting at least one of the outcomes than did unvaccinated individuals. When we excluded people living in long-term care facilities, the results were similar.

CONCLUSIONS

We could not establish a protective role of the immunity conferred by the influenza vaccine on the outcomes of COVID-19 infection, as the risk of COVID-19 complications was higher in vaccinated than in unvaccinated individuals. Our results correspond to the first wave of the COVID-19 pandemic, where more complications and mortalities due to COVID-19 had occurred. Despite that, our study adds more evidence for the analysis of a possible link between the quality of immunity and COVID-19 outcomes, particularly in the PHC setting.

Effect of Human Behavior on the Evolution of Viral Strains During an Epidemic

It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here, we explore a complementary trend: the pathogen itself might experience a force of selection to become less “visible,” or less “symptomatic,” in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.

Sexual Desire in the Time of COVID-19: How COVID-Related Stressors Are Associated with Sexual Desire in Romantic Relationships

The COVID-19 pandemic and the resulting social distancing measures have caused widespread social and economic disruptions, resulting in spikes in unemployment and financial instability, along with drastic changes to people's ability to feel socially connected. Many of the changes resulting from the COVID-19 pandemic are risk factors for depressive symptoms, which are associated with lower levels of sexual desire. The current research (N = 4,993) examined whether responses to external stressors brought on by COVID-19 (i.e., financial concern, worry, loneliness, stress) were associated with sexual desire among a multi-national sample of people in relationships (Studies 1-2), and whether this association was, in part, due to reports of depressive symptoms (Study 2). In the period immediately following the onset of the pandemic, more financial concern (Study 1) and worry (Study 2) were associated with higher sexual desire, while other factors, like stress (Studies 1-2), were associated with lower desire. We also followed a subset of participants every two weeks during the initial stages of the pandemic and at times when people reported greater stress, loneliness, financial strain, or worry than their average, they reported greater depressive symptoms, which was, in turn, associated with lower sexual desire. Results suggest that the social isolation and stress resulting from the COVID-19 pandemic have mixed associations with sexual desire at the onset of the pandemic. But over time, when people report heightened COVID-related stressors, they tend to report lower sexual desire for their partner, in part because these stressors are associated with more depressive symptoms.

The pros and cons of entry restrictions: are entry restrictions really effective in preventing the spread of SARS-CoV-2?

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide, leading the World Health Organization to declare coronavirus disease 2019 (COVID-19) a pandemic. To curb the unchecked spread of SARS-CoV-2 infection, most countries have enforced travel restrictions. However, it is debatable whether such restrictions are effective in containing infections and preventing pandemics. Rather, they may negatively impact economies and diplomatic relationships. Each government should conduct an extensive and appropriate analysis of its national economy, diplomatic status, and COVID-19 preparedness to decide whether it is best to restrict entering travelers. Even if travelers from other countries are allowed entry, extensive contact tracing is required to prevent the spread of COVID-19. In addition, governments can implement “travel bubbles,” which allow the quarantine-free flow of people among countries with relatively low levels of community transmission. An accurate evaluation of the benefits and losses due to entry restrictions during the COVID-19 pandemic would be helpful in determining whether entry restrictions are an effective measure to reduce the spread of infection in future pandemics.

Mathematical Models Supporting Control of COVID-19

Mathematical models have played an important role in the management of the coronavirus disease 2019 (COVID-19) pandemic. The aim of this review is to describe the use of COVID-19 mathematical models, their classification, and the advantages and disadvantages of different types of models. We conducted subject heading searches of PubMed and China National Knowledge Infrastructure with the terms "COVID-19," "Mathematical Statistical Model," "Model," "Modeling," "Agent-based Model," and "Ordinary Differential Equation Model" and classified and analyzed the scientific literature retrieved in the search. We categorized the models as data-driven or mechanism-driven. Data-driven models are mainly used for predicting epidemics, and have the advantage of rapid assessment of disease instances. However, their ability to determine transmission mechanisms is limited. Mechanism-driven models include ordinary differential equation (ODE) and agent-based models. ODE models are used to estimate transmissibility and evaluate impact of interventions. Although ODE models are good at determining pathogen transmission characteristics, they are less suitable for simulation of early epidemic stages and rely heavily on availability of first-hand field data. Agent-based models consider influences of individual differences, but they require large amounts of data and can take a long time to develop fully. Many COVID-19 mathematical modeling studies have been conducted, and these have been used for predicting trends, evaluating interventions, and calculating pathogen transmissibility. Successful infectious disease modeling requires comprehensive considerations of data, applications, and purposes.

Influence of Face Mask and Tropical Climate on Subjective States: Affect, Motivation, and Selective Attention

We investigated whether face mask wearing in a hot and humid environment would influence subjective states: motivation, affect, and the performance of cognitive tasks requiring attentional processes. Forty volunteers performed the tasks under 4 repeated conditions: in an air-conditioned (AC) room or a tropical climate (TC; hot and wet environment) while wearing a mask or not (counterbalanced). For each condition, they completed questionnaires on subjective states (affect, motivation, and attentional processes) by performing the Bells and d2 tests, and a comparison of men and women was an indirect focus of this study. Results showed that the participants had higher sensations of fatigue, thermal discomfort, and a negative affect when wearing a mask in the TC condition. Additionally, lower performance scores were noted in the Bells test when participants wore a mask in a TC compared with all other conditions. Lastly, the participants’ performance on the d2 test involving selective and sustained attention improved without a mask in AC and in TC conditions. Consequently, the results revealed that wearing a mask in a TC can induce anxiety, headaches, discomfort, and lower motivation.

THE COVID-19 PANDEMIC AND ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN HEALTH: HOW MUCH ARE WE INTERESTED IN?

Objective New viruses have emerged, causing global damage and mass deaths that can spread to international borders, the latest of which is the new coronavirus (COVID-19). After the Second International Congress on Artificial Intelligence in Health, themed "Artificial Intelligence in Health During COVID-19 Pandemic Process" organized online by İzmir Bakırçay University and İzmir Provincial Health Directorate with the contributions of the International Association of Artificial Intelligence in Health, a questionnaire was conducted to evaluate the knowledge of the participants about artificial intelligence applications. Materials and Methods: This study aimed to evaluate the interest of the congress participants in this field with the questions which form the questionnaire such as the duration of the interest of the participants in the field of artificial intelligence in health, their publication status, the development of studies on artificial intelligence with the COVID-19 pandemic, demographic structures such as age and gender, and educational level. 130 participants answered the questionnaire consisting of 23 questions. Questionnaire responses were analyzed in a statistical setting. Results: We found that 130 people filled out the questionnaire and the majority of the participants were female, with participation from many organizations, but university staff showed more interest. We have seen that the 30-39 age group is more interested in artificial intelligence than the other age groups, but the majority of the participants do not have academic studies in this field. We found that the technical terms related to artificial intelligence were not well known by the participants, and that the number of participants who tended to this field, especially in the recent year, was high. Another important point was that people working in this field stated that they would definitely follow up if scientific activities continued. Conclusion: We know how important congresses, symposiums, courses and other meetings are, especially for scientist candidates, which will be held to raise awareness about the usage areas of artificial intelligence-based health technologies, to develop new communication and work networks by bringing together different disciplines, to create an agenda and to lay the groundwork for new studies, and we think that there is a need for many repetitive activities in this field and that these activities should be continued.

Agent Simulation Model of COVID-19 Epidemic Agent-Based on GIS: A Case Study of Huangpu District, Shanghai

Since the COVID-19 outbreak was detected and reported at the end of 2019, the pandemic continues worldwide, with public health authorities and the general public in each country struggling to balance safety and normal travel activities. However, the complex public health environment and the complexity of human behaviors, as well as the constant mutation of the COVID-19 virus, requires the development of theoretical and simulation tools to accurately model all segments of society. In this paper, an agent-based model is proposed, the model constructs the real geographical environment of Shanghai Huangpu District based on the building statistics data of Shanghai Huangpu District, and the real population data of Shanghai Huangpu District based on the data of China's seventh Population census in 2020. After incorporating the detailed elements of COVID-19 transmission and the real data of WHO, the model forms various impact parameters. Finally, the model was validated according to the COVID-19 data reported by the official, and the model is applied to a hypothetical scenario. Shanghai is one of the places hardest hit by the current outbreak, Huangpu District is the "heart, window and name card" of Shanghai, and its importance to Shanghai is self-evident. so we used one-to-one population modeling to simulate the spread of COVID-19 in Huangpu District of Shanghai, In addition to the conventional functions of crowd movement, detection and treatment, the model also takes into account the burden of nucleic acid detection on the model caused by diseases similar to COVID-19, such as seasonal cold. The model validation results show that we have constructed a COVID-19 epidemic agent risk assessment system suitable for the individual epidemiological characteristics of COVID-19 in China, which can adjust and reflect on the existing COVID-19 epidemic intervention strategies and individual health behaviors. To provide scientific theoretical basis and information decision-making tools for effective prevention and control of COVID-19 and public health intervention in China.

Evaluation of the role of vaccination in the COVID-19 pandemic based on the data from the 50 U.S. States

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The use of vaccines can significantly reduce COVID-19 deaths and mortality.

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The use of vaccines cannot reduce the risk of COVID-19 infection.

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The SARS-CoV-2 Delta variant may weaken the effect of vaccines on lessening the severity of COVID-19.

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Vaccine usage remains effective in reducing the severity of COVID-19 caused by the SARS-CoV-2 Delta variant infection.

Vaccination is considered as the ultimate weapon to end the pandemic. However, the role of vaccines in the pandemic remains controversial. To explore the impact of vaccination on the COVID-19 pandemic, we used logistic regression models to predict numbers of population-adjusted confirmed cases, deaths, intensive care unit (ICU) cases, case fatality rates and ICU admission rates of COVID-19 in the 50 U.S. states, based on 17 related variables. The logistic regression analysis showed that percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths and case fatality rates but showed no significant correlation with numbers of confirmed cases or ICU cases, or ICU admission rates. The Spearman correlation analysis showed that the percentages of people vaccinated correlated inversely with the numbers of COVID-19 deaths, ICU cases, ICU case rates, and case fatality rates but showed no significant correlation with numbers of confirmed cases. The number of deaths and mortality in the group after the vaccine usage were significantly lower than those in the group before the vaccine usage. However, after delta became the dominant strain, there were no longer significant differences in the number of deaths and the mortality rate between before and after delta became the dominant strain, although vaccines were used in both periods. Vaccination can significantly reduce COVID-19 deaths and mortality, while it cannot reduce the risk of COVID-19 infection. In addition to vaccination, other measures, such as social distancing, remain important in containing COVID-19 transmission and lower the risk of COVID-19 severe outcomes.

Consistent pattern of epidemic slowing across many geographies led to longer, flatter initial waves of the COVID-19 pandemic

To define appropriate planning scenarios for future pandemics of respiratory pathogens, it is important to understand the initial transmission dynamics of COVID-19 during 2020. Here, we fit an age-stratified compartmental model with a flexible underlying transmission term to daily COVID-19 death data from states in the contiguous U.S. and to national and sub-national data from around the world. The daily death data of the first months of the COVID-19 pandemic was qualitatively categorized into one of four main profile types: "spring single-peak", "summer single-peak", "spring/summer two-peak" and "broad with shoulder". We estimated a reproduction number R as a function of calendar time tc and as a function of time since the first death reported in that population (local pandemic time, tp). Contrary to the diversity of categories and range of magnitudes in death incidence profiles, the R(tp) profiles were much more homogeneous. We found that in both the contiguous U.S. and globally, the initial value of both R(tc) and R(tp) was substantial: at or above two. However, during the early months, pandemic time R(tp) decreased exponentially to a value that hovered around one. This decrease was accompanied by a reduction in the variance of R(tp). For calendar time R(tc), the decrease in magnitude was slower and non-exponential, with a smaller reduction in variance. Intriguingly, similar trends of exponential decrease and reduced variance were not observed in raw death data. Our findings suggest that the combination of specific government responses and spontaneous changes in behaviour ensured that transmissibility dropped, rather than remaining constant, during the initial phases of a pandemic. Future pandemic planning scenarios should include models that assume similar decreases in transmissibility, which lead to longer epidemics with lower peaks when compared with models based on constant transmissibility.

Investigating the Effectiveness of Government Public Health Systems against COVID-19 by Hybrid MCDM Approaches

To elucidate the effectiveness of the containment strategies against the pandemic, a Multi-Criteria Decision Making (MCDM) model is established to evaluate the government’s performance against COVID-19. In this study, the Analytic Hierarchy Process (AHP), Entropy, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method are used in determining the performance of the public health system. We adopt both subjective and objective weighting methods for a more accurate evaluation. In addition, the evaluation of performance against COVID-19 is conducted in various aspects and divided into different periods. Data Envelopment Analysis (DEA) is applied to evaluate the sustainability of the public health system. Composite scores of the public health system are determined based on the performance and sustainability assessment. The five countries, South Korea, Japan, Germany, Australia, and China are rated with higher composite scores. On the country, the US, Indonesia, Egypt, South Africa, and Brazil receive lower rating scores among the countries for evaluation. This modeling study can provide a practical quantitative justification for developing containment policies and suggestions for improving the public health system in more countries or areas.

Effects of Psychological Discomfort on Social Networking Site (SNS) Usage Intensity During COVID-19

To cope with the COVID-19 pandemic, many countries are implementing social measures. Social distancing, working from home, and non-face-to-face lectures have led to major changes in people's activities. Since face-to-face classes are restricted, students in higher education become to feel psychological and cognitive discomforts such as isolation and risk perception. The purpose of this study is to explore the effects of psychological discomforts on the social network site (SNS) usage intensity of University students. Using structural equation modeling (SEM), this study applied SmartPLS 3.3.9 to analyze 271 valid samples. The results show that the affective risk perception significantly affects social distancing attitude. Cognitive risk perception is positively related to social distancing intention. In addition, cabin fever syndrome influences SNS usage intensity, affective risk perception, and cognitive risk perception. In conclusion, psychological discomfort partially affects the intensity of SNS use. Therefore, the government should set policies by reflecting citizens' mental difficulties and SNS activities together. Moreover, companies are needed to carefully consider the risk of the sense of isolation when marketing to SNS users.

From Policy to Prediction: Forecasting COVID-19 Dynamics Under Imperfect Vaccination

Understanding the joint impact of vaccination and non-pharmaceutical interventions on COVID-19 development is important for making public health decisions that control the pandemic. Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model (Wang et al. in Bull Math Biol 84:57, 2022). In this paper, we extend the method to the post-vaccination period, accordingly obtain a retrospective forecast of COVID-19 daily confirmed cases in the US, and identify the relative influence of the policies used as the predictor variables. In particular, our ODE model contains both partially and fully vaccinated compartments and accounts for the breakthrough cases, that is, vaccinated individuals can still get infected. Our results indicate that the inclusion of data on non-pharmaceutical interventions can significantly improve the accuracy of the predictions. With the use of policy data, the model predicts the number of daily infected cases up to 35 days in the future, with an average mean absolute percentage error of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20.15\%$$\end{document}20.15%, which is further improved to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$14.88\%$$\end{document}14.88% if combined with human mobility data. Moreover, the most influential predictor variables are the policies of restrictions on gatherings, testing and school closing. The modeling approach used in this work can help policymakers design control measures as variant strains threaten public health in the future.

Self-reported side effects of the Oxford AstraZeneca COVID-19 vaccine among healthcare workers in Ethiopia, Africa: A cross-sectional study

Introduction

Ethiopia is the second most populous country in Africa. Ethiopia received most of its COVID-19 vaccines through donations. The Oxford AstraZeneca vaccine is the first to be donated to Ethiopia by the COVAX facility. Healthcare workers were the priority population that received the Oxford AstraZeneca COVID-19 vaccine. However, there was no nationwide study on the safety of the vaccine in Ethiopia. This study aimed to measure the prevalence and predictors of self-reported side effects of the Oxford AstraZeneca vaccine.

Materials and methods

The study employed a cross-sectional design. A sample of healthcare workers who took Oxford AstraZeneca COVID-19 vaccine was drawn from four regions of Ethiopia; namely, Amhara, Oromia, Somali, and Southwest. Data were collected on sociodemographic characteristics, medical anamnesis, COVID-19 related anamnesis, and COVID-19 vaccine anamnesis via telephone interview. Descriptive and inferential analyses were done. The software, IBM SPSS Statistics v21.0, was used for analyses of data.

Results

Out of 384 people, 346 responded (response rate: 90.1%). Female accounted for 34.1% of the respondents. The mean age of the respondents was 31.0 years (Standard Deviation (SD) = 7.4). Nurses accounted for 43.7% of the respondents. The prevalence of at least one local- and systemic-side effect was 50.6 and 44.5%, respectively. The most frequent local- and systemic- side effect were injection site pain and headache, respectively. Both types of side effects mostly subsided in the first 3 days. A third of healthcare workers with side effects took at least one medication. Paracetamol followed by diclofenac sodium were taken by healthcare workers to overcome side effects. There was no independent predictor of local side effect. After controlling for age and chronic diseases, the odds of healthcare workers with COVID-19 like symptoms to experience systemic side effects was 1.38 (Confidence Interval (CI): 1.04–1.82) times more than that of healthcare workers without COVID-19 like symptoms.

Conclusions

The prevalence of local- and systemic-side effects of the Oxford AstraZeneca COVID-19 vaccine was modest. As the symptoms were mostly common in the first 3 days, it is preferable to monitor healthcare workers at least in the first 3 days following the administration of the vaccine.

The Association Between Previous Influenza Vaccination and COVID-19 Infection Risk and Severity: A Systematic Review and Meta-analysis

INTRODUCTION

The association between influenza vaccination and COVID-19 remains controversial. This meta-analysis aimed to investigate whether influenza vaccination reduces the susceptibility and severity of SARS-CoV-2 infection.

METHODS

A systematic literature search of PubMed, Web of Science, the Cochrane Library, Embase, China National Knowledge Infrastructure, SinoMed, Wanfang Data Knowledge Service Platform, and China Science and Technology Journal VIP Database was conducted from database inception to August 2021. The pooled RR with 95% CI was used to estimate the effect of influenza vaccination on COVID-19. The I² value was used to assess heterogeneity. If I²>50%, the random-effects model was used as the pooling method.

RESULTS

A total of 23 published articles with 1,037,445 participants were identified. This meta-analysis showed that influenza vaccination was associated with reduced risk of COVID-19 infection (RR=0.83, 95% CI=0.76, 0.90) and hospitalization (RR=0.71, 95% CI=0.59, 0.84), although not significantly associated with intensive care unit admission and death (risk of intensive care unit admission: RR=0.93, 95% CI=0.64, 1.36; risk of death: RR=0.83, 95% CI=0.68, 1.01). Further analysis suggested that the tetravalent influenza vaccine may be associated with a reduced risk of COVID-19 infection (RR=0.74, 95% CI=0.65, 0.84).

DISCUSSION

The results suggest that influenza vaccination is associated with reduced susceptibility to or disease severity of COVID-19 and that influenza vaccination may reduce the risk of COVID-19 and improve clinical outcomes.

Optimize data-driven multi-agent simulation for COVID-19 transmission

Background

Multi-Agent Simulation is an essential technique for exploring complex systems. In research of contagious diseases, it is widely exploited to analyze their spread mechanisms, especially for preventing COVID-19. Nowadays, transmission dynamics and interventions of COVID-19 have been elaborately established by this method, but its computation performance is seldomly concerned. As it usually suffers from inadequate CPU utilization and poor data locality, optimizing the performance is challenging and important for real-time analyzing its spreading.

Results

This paper explores approaches to optimize multi-agent simulation for COVID-19 disease. The focus of this work is on the algorithm and data structure designs for improving performance, as well as its parallelization strategies. We propose two successive methods to optimize the computation. We construct a case-focused iteration algorithm to improve data locality, and propose a fast data-mapping scheme called hierarchical hash table to accelerate hash operations. As a result, The case-focused method degrades \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim 90 \%$$\end{document}∼90% cache references and achieves \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times 4.3$$\end{document}×4.3 speedup. Hierarchical hash table can further boost computation speed by 47%. And parallel implementation with 20 threads on CPU achieves \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times 80$$\end{document}×80 speedup consequently.

Conclusions

In this work, we propose optimizations for multi-agent simulation of COVID-19 transmission from aspects of algorithm and data structure. Benefit from improvement of locality and multi-thread implementation, our methods can significantly accelerate the simulation computation. It is promising in supporting real-time prevention of COVID-19 and other infectious diseases in the future.

Decision-making framework for identifying regions vulnerable to transmission of COVID-19 pandemic

At the beginning of 2020, the World Health Organization (WHO) identified an unusual coronavirus and declared the associated COVID-19 disease as a global pandemic. We proposed a novel hybrid fuzzy decision-making framework to identify and analyze these transmission factors and conduct proactive decision-making in this context. We identified thirty factors from the extant literature and classified them into six major clusters (climate, hygiene and safety, responsiveness to decision-making, social and demographic, economic, and psychological) with the help of domain experts. We chose the most relevant twenty-five factors using the Fuzzy Delphi Method (FDM) screening from the initial thirty. We computed the weights of those clusters and their constituting factors and ranked them based on their criticality, applying the Fuzzy Analytic Hierarchy Process (FAHP). We found that the top five factors were global travel, delay in travel restriction, close contact, social cohesiveness, and asymptomatic. To evaluate our framework, we chose ten different geographically located cities and analyzed their exposure to COVID-19 pandemic by ranking them based on their vulnerability of transmission using Fuzzy Technique for Order of Preference by Similarity To Ideal Solution (FTOPSIS). Our study contributes to the disciplines of decision analytics and healthcare risk management during a pandemic through these novel findings. Policymakers and healthcare officials will benefit from our study by formulating and improving existing preventive measures to mitigate future global pandemics. Finally, we performed a sequence of sensitivity analyses to check for the robustness and generalizability of our proposed hybrid decision-making framework.

Love in the Time of COVID: Perceived Partner Responsiveness Buffers People From Lower Relationship Quality Associated With COVID-Related Stressors

External stressors can erode relationship quality, though little is known about what can mitigate these effects. We examined whether COVID-related stressors were associated with lower relationship quality, and whether perceived partner responsiveness—the extent to which people believe their partner understands, validates, and cares for them—buffers these effects. When people in relationships reported more COVID-related stressors they reported poorer relationship quality at the onset of the pandemic ( N = 3,593 from 57 countries) and over the subsequent 3 months ( N = 1,125). At the onset of the pandemic, most associations were buffered by perceived partner responsiveness, such that people who perceived their partners to be low in responsiveness reported poorer relationship quality when they experienced COVID-related stressors, but these associations were reduced among people who perceived their partners to be highly responsive. In some cases, these associations were buffered over the ensuing weeks of the pandemic.

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers

In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19.

Isolating the net effect of multiple government interventions with an extended Susceptible-Exposed-Infectious-Recovered (SEIR) framework: empirical evidence from the second wave of COVID-19 pandemic in China

OBJECTIVE

By using a data-driven statistical approach, we isolated the net effect of multiple government interventions that were simultaneously implemented during the second wave of COVID-19 pandemic in China.

DESIGN, DATA SOURCES AND ELIGIBILITY CRITERIA

We gathered epidemiological data and government interventions data of nine cities with local outbreaks during the second wave of COVID-19 pandemic in China. We employed the Susceptible-Exposed-Infectious-Recovered (SEIR) framework model to analyse the different pathways of transmission between cities with government interventions implementation and those without. We introduced new components to the standard SEIR model and investigated five themes of government interventions against COVID-19 pandemic.

DATA EXTRACTION AND SYNTHESIS

We extracted information including study objective, design, methods, main findings and implications. These were tabulated and a narrative synthesis was undertaken given the diverse research designs, methods and implications.

RESULTS

Supported by extensive empirical validation, our results indicated that the net effect of some specific government interventions (including masks, environmental cleaning and disinfection, tracing, tracking and 14-day centralised quarantining close contacts) had been significantly underestimated in the previous investigation. We also identified important moderators and mediators for the effect of certain government interventions, such as closure of shopping mall and restaurant in the medium-risk level areas, etc. Linking the COVID-19 epidemiological dynamics with the implementation timing of government interventions, we detected that the earlier implementation of some specific government interventions (including targeted partial lockdown, tracing, tracking and 14-day centralised quarantining close contacts) achieved the strongest and most timely effect on controlling COVID-19, especially at the early period of local outbreak.

CONCLUSIONS

These findings provide important scientific information for decisions regarding which and when government interventions should be implemented to fight against COVID-19 in China and beyond. The proposed analytical framework is useful for policy-making in future endemic and pandemic as well.

Risk Assessment and Prevention Strategy of Virus Infection in the Context of University Resumption

The risk assessment system of virus infection probability and the prevention measures for virus transmission are keys to controlling epidemics. In the context of university resumption, this study identifies the risk elements in terms of the mechanism of virus transmission. The effect of two recognized effective measures, i.e., occupancy constraints and ventilation intervention, on the infection risk are quantified and compared using the improved Wells–Riley model. Considering the priority of these two measures, the controlling quantity are determined, and the optimal schemes are proposed based on the targeted infection risk. The results show that the effect of reducing infection risk by constraining occupancy within 25% of all public campus buildings is better than that achieved by increasing the ventilation rate alone. If the ventilation system of the building type is operated by occupiers, it is a priority to prevent the risk of virus infection by restricting occupancy and ensuring the distance between occupants, while if the ventilation system of the building type is centrally controlled, it is a priority to increase the ventilation rate and then limit the occupancy rate during peak periods to 75%.

An Agile Systems Modeling Framework for Bed Resource Planning During COVID-19 Pandemic in Singapore

Background

The COVID-19 pandemic has had a major impact on health systems globally. The sufficiency of hospitals' bed resource is a cornerstone for access to care which can significantly impact the public health outcomes.

Objective

We describe the development of a dynamic simulation framework to support agile resource planning during the COVID-19 pandemic in Singapore.

Materials and Methods

The study data were derived from the Singapore General Hospital and public domain sources over the period from 1 January 2020 till 31 May 2020 covering the period when the initial outbreak and surge of COVID-19 cases in Singapore happened. The simulation models and its variants take into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes in Singapore.

Results

The models were calibrated against historical data for the Singapore COVID-19 situation. Several variants of the resource planning model were rapidly developed to adapt to the fast-changing COVID-19 situation in Singapore.

Conclusion

The agility in adaptable models and robust collaborative management structure enabled the quick deployment of human and capital resources to sustain the high level of health services delivery during the COVID-19 surge.

A database of US state policies to mitigate COVID-19 and its economic consequences

Background

Since COVID-19 first appeared in the United States (US) in January 2020, US states have pursued a wide range of policies to mitigate the spread of the virus and its economic ramifications. Without unified federal guidance, states have been the front lines of the policy response.

Main text

We created the COVID-19 US State Policy (CUSP) database (https://statepolicies.com/) to document the dates and components of economic relief and public health measures issued at the state level in response to the COVID-19 pandemic. Documented interventions included school and business closures, face mask mandates, directives on vaccine eligibility, eviction moratoria, and expanded unemployment insurance benefits. By providing continually updated information, CUSP was designed to inform rapid-response, policy-relevant research in the context of the COVID-19 pandemic and has been widely used to investigate the impact of state policies on population health and health equity. This paper introduces the CUSP database and highlights how it is already informing the COVID-19 pandemic response in the US.

Conclusion

CUSP is the most comprehensive publicly available policy database of health, social, and economic policies in response to the COVID-19 pandemic in the US. CUSP documents widespread variation in state policy decisions and implementation dates across the US and serves as a freely available and valuable resource to policymakers and researchers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13487-0.

Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.

Effectiveness of containment strategies in preventing SARS-CoV-2 transmission

BACKGROUND

Despite substantial resources deployed to curb SARS-CoV-2 transmission, controlling the COVID-19 pandemic has been a major challenge. New variants of the virus are frequently emerging leading to new waves of infection and re-introduction of control measures. In this study, we assessed the effectiveness of containment strategies implemented in the early phase of the pandemic.

METHODS

Real-world data for COVID-19 cases was retrieved for the period Jan 1 to May 1, 2020 from a number of different sources, including PubMed, MEDLINE, Facebook, Epidemic Forecasting and Google Mobility Reports. We analyzed data for 18 countries/regions that deployed containment strategies such as travel restrictions, lockdowns, stay-at-home requests, school/public events closure, social distancing, and exposure history information management (digital contact tracing, DCT). Primary outcome measure was the change in the number of new cases over 30 days before and after deployment of a control measure. We also compared the effectiveness of centralized versus decentralized DCT. Time series data for COVID-19 were analyzed using Mann-Kendall (M-K) trend tests to investigate the impact of these measures on changes in the number of new cases. The rate of change in the number of new cases was compared using M-K z-values and Sen's slope.

RESULTS

In spite of the widespread implementation of conventional strategies such as lockdowns, travel restrictions, social distancing, school closures, and stay-at-home requests, analysis revealed that these measures could not prevent the spread of the virus. However, countries which adopted DCT with centralized data storage were more likely to contain the spread.

CONCLUSIONS

Centralized DCT was more effective in containing the spread of COVID-19. Early implementation of centralized DCT should be considered in future outbreaks. However, challenges such as public acceptance, data security and privacy concerns will need to be addressed.

An Efficient Numerical Simulation for the Fractional COVID-19 Model Using the GRK4M Together with the Fractional FDM

In this research, we studied a mathematical model formulated with six fractional differential equations to characterize a COVID-19 outbreak. For the past two years, the disease transmission has been increasing all over the world. We included the considerations of people with infections who were both asymptomatic and symptomatic as well as the fact that an individual who has been exposed is either quarantined or moved to one of the diseased classes, with the chance that a susceptible individual could also migrate to the quarantined class. The suggested model is solved numerically by implementing the generalized Runge–Kutta method of the fourth order (GRK4M). We discuss the stability analysis of the GRK4M as a general study. The acquired findings are compared with those obtained using the fractional finite difference method (FDM), where we used the Grünwald–Letnikov approach to discretize the fractional differentiation operator. The FDM is mostly reliant on correctly converting the suggested model into a system of algebraic equations. By applying the proposed methods, the numerical results reveal that these methods are straightforward to apply and computationally very effective at presenting a numerical simulation of the behavior of all components of the model under study.

Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data

The outbreak of COVID-19 stimulated a new round of discussion on how to deal with respiratory infectious diseases. Influenza viruses have led to several pandemics worldwide. The spatiotemporal characteristics of influenza transmission in modern cities, especially megacities, are not well-known, which increases the difficulty of influenza prevention and control for populous urban areas. For a long time, influenza prevention and control measures have focused on vaccination of the elderly and children, and school closure. Since the outbreak of COVID-19, the public's awareness of measures such as vaccinations, mask-wearing, and home-quarantine has generally increased in some regions of the world. To control the influenza epidemic and reduce the proportion of infected people with high mortality, the combination of these three measures needs quantitative evaluation based on the spatiotemporal transmission characteristics of influenza in megacities. Given that the agent-based model with both demographic attributes and fine-grained mobility is a key planning tool in deploying intervention strategies, this study proposes a spatially explicit agent-based influenza model for assessing and recommending the combinations of influenza control measures. This study considers Shenzhen city, China as the research area. First, a spatially explicit agent-based influenza transmission model was developed by integrating large-scale individual trajectory data and human response behavior. Then, the model was evaluated across multiple intra-urban spatial scales based on confirmed influenza cases. Finally, the model was used to evaluate the combined effects of the three interventions (V: vaccinations, M: mask-wearing, and Q: home-quarantining) under different compliance rates, and their optimal combinations for given control objectives were recommended. This study reveals that adults were a high-risk population with a low reporting rate, and children formed the lowest infected proportion and had the highest reporting rate in Shenzhen. In addition, this study systematically recommended different combinations of vaccinations, mask-wearing, and home-quarantine with different compliance rates for different control objectives to deal with the influenza epidemic. For example, the “V45%-M60%-Q20%” strategy can maintain the infection percentage below 5%, while the “V20%-M60%-Q20%” strategy can maintain the infection percentage below 15%. The model and policy recommendations from this study provide a tool and intervention reference for influenza epidemic management in the post-COVID-19 era.

Sheet, Surveillance, Strategy, Salvage and Shield in global biodefense system to protect the public health and tackle the incoming pandemics

The pandemic of COVID-19 challenges the global health system and raises our concerns on the next waves of other emerging infectious diseases. Considering the lessons from the failure of world's pandemic warning system against COVID-19, many scientists and politicians have mentioned different strategies to improve global biodefense system, among which Sheet, Surveillance, Strategy, Salvage and Shield (5S) are frequently discussed. Nevertheless, the current focus is mainly on the optimization and management of individual strategy, and there are limited attempts to combine the five strategies as an integral global biodefense system. Sheet represents the biosafety datasheet for biohazards in natural environment and human society, which helps our deeper understanding on the geographical pattern, transmission routes and infection mechanism of pathogens. Online surveillance and prognostication network is an environmental Surveillance tool for monitoring the outbreak of pandemic diseases and alarming the risks to take emergency actions, targeting aerosols, waters, soils and animals. Strategy is policies and legislations for social distancing, lockdown and personal protective equipment to block the spread of infectious diseases in communities. Clinical measures are Salvage on patients by innovating appropriate medicines and therapies. The ultimate defensive Shield is vaccine development to protect healthy crowds from infection. Fighting against COVID-19 and other emerging infectious diseases is a long rocky journey, requiring the common endeavors of scientists and politicians from all countries around the world. 5S in global biodefense system bring a ray of light to the current darkest and future road from environmental and geographical perspectives.