A study on deep learning model based on global-local structure for crowd flow prediction.

Crowd flow prediction has been studied for a variety of purposes, ranging from the private sector such as location selection of stores according to the characteristics of commercial districts and customer-tailored marketing to the public sector for social infrastructure design such as transportation networks. Its importance is even greater in light of the spread of contagious diseases such as COVID-19. In many cases, crowd flow can be divided into subgroups by common characteristics such as gender, age, location type, etc. If we use such hierarchical structure of the data effectively, we can improve prediction accuracy of crowd flow for subgroups. But the existing prediction models do not consider such hierarchical structure of the data. In this study, we propose a deep learning model based on global-local structure of the crowd flow data, which utilizes the overall(global) and subdivided by the types of sites(local) crowd flow data simultaneously to predict the crowd flow of each subgroup. The experiment result shows that the proposed model improves the prediction accuracy of each sub-divided subgroup by 5.2% (Table 5 Cat #9)-45.95% (Table 11 Cat #5), depending on the data set. This result comes from the comparison with the related works under the same condition that use target category data to predict each subgroup. In addition, when we refine the global data composition by considering the correlation between subgroups and excluding low correlated subgroups, the prediction accuracy is further improved by 5.6-48.65%.

Knowledge sharing among academics from Egyptian medical schools during the COVID-19 pandemic.

BACKGROUND: Sharing knowledge among scientists during global health emergencies is a critical issue. So, this study investigates knowledge-sharing behavior and attitude among staff members of 19 Medical schools in Egyptian universities during the COVID-19 pandemic. METHODS: Across-sectional study was conducted using a web-based questionnaire. A total of 386 replies from the 10,318 distributed questionnaires were analyzed. Descriptive statistics were computed using SPSS (version 22) to summarize the demographic data. Inferential statistics such as the independent and chi-square test were used to achieve the study aims. RESULTS: More than half of the respondents (54.4%) indicated that their levels of knowledge of COVID-19 were good. Most participants (72.5%) reported that scientific publications and international websites were the most reliable source of their knowledge concerning COVID-19. More than 46% stated they sometimes share their knowledge. The lack of time to share and organizational culture were the most important factors that could affect their knowledge sharing. Additionally, about 75% of participants shared knowledge about treatment.

Mycoplasma pneumoniae detections in children with lower respiratory infection before and during the COVID-19 pandemic: a large sample study in China from 2019 to 2022.

BACKGROUND: Nonpharmaceutical interventions (NPIs) implemented to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have suppressed the spread of other respiratory viruses during the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to explore the epidemiological trends and clinical characteristics of Mycoplasma pneumoniae (MP) infection among inpatient children with lower respiratory tract infection (LRTI) before and during the COVID-19 pandemic, and investigate the long-term effects of China's NPIs against COVID-19 on the epidemiology of MP among inpatient children with LRTI. METHODS: Children hospitalised for LRTI at the Department of Pulmonology, The Children's Hospital, Zhejiang University School of Medicine (Hangzhou, China) between January 2019 and December 2022 were tested for common respiratory pathogens, including Mycoplasma pneumoniae (MP), Chlamydia trachomatis (CT) and other bacteria. Clinical data on age, sex, season of onset, disease spectrum, and combined infection in children with MP-induced LRTI in the past 4 years were collected and analysed. RESULTS: Overall, 15909 patients were enrolled, and MP-positive cases were 1971 (34.0%), 73 (2.4%), 176 (5.8%), and 952 (20.6%) in 2019, 2020, 2021, and 2022, respectively, with a significant statistical difference in the MP-positive rate over the 4 years (p <0.001). The median age of these children was preschool age (3-6 years), except for 2022, when they were school age (7-12 years), with statistical differences. Comparing the positive rates of different age groups, the school-age children (7-12 years) had the highest positive rate, followed by the preschoolers (3-6 years) in each of the 4 years. Compared among different seasons, the positive rate of MP in children with LRTI was higher in summer and autumn, whereas in 2020, it was highest in spring. The monthly positive rate peaked in July 2019, remained low from 2020 to 2021, and rebounded until 2022. Regarding the disease spectrum, severe pneumonia accounted for the highest proportion (46.3%) pre-pandemic and lowest (0%) in 2020. CONCLUSION: Trends in MP detection in children with LRTIs suggest a possible correlation between COVID-19 NPIs and significantly reduced detection rates. The positivity rate of MP gradually rose after 2 years. The epidemic season showed some differences, but school-age children were more susceptible to MP before and during the COVID-19 pandemic.

COVID-19 seroprevalence cohort survey among health care workers and their household members in Kinshasa, DR Congo, 2020-2022.

INTRODUCTION: Serological surveys offer the most direct measurement to define the immunity status for numerous infectious diseases, such as COVID-19, and can provide valuable insights into understanding transmission patterns. This study describes seroprevalence changes over time in the context of the Democratic Republic of Congo, where COVID-19 case presentation was apparently largely oligo- or asymptomatic, and vaccination coverage remained extremely low. METHODS: A cohort of 635 health care workers (HCW) from 5 health zones of Kinshasa and 670 of their household members was interviewed and sampled in 6 rounds between July 2020 and January 2022. At each round, information on risk exposure and a blood sample were collected. Serology was defined as positive when binding antibodies against SARS-CoV-2 spike and nucleocapsid proteins were simultaneously present. RESULTS: The SARS-CoV-2 antibody seroprevalence was high at baseline, 17.3% (95% CI 14.4-20.6) and 7.8% (95% CI 5.5-10.8) for HCW and household members, respectively, and fluctuated over time, between 9% and 62.1%. Seropositivity was heterogeneously distributed over the health zones (p < 0.001), ranging from 12.5% (95% CI 6.6-20.8) in N'djili to 33.7% (95% CI 24.6-43.8) in Bandalungwa at baseline for HCW. Seropositivity was associated with increasing rounds adjusted Odds Ratio (aOR) 1.75 (95% CI 1.66-1.85), with increasing age aOR 1.11 (95% CI 1.02-1.20), being a female aOR 1.35 (95% CI 1.10-1.66) and being a HCW aOR 2.38 (95% CI 1.80-3.14). There was no evidence that HCW brought the COVID-19 infection back home, with an aOR of 0.64 (95% CI 0.46-0.91) of seropositivity risk among household members in subsequent surveys. There was seroreversion and seroconversion over time, and HCW had a lower risk of seroreverting than household members (aOR 0.60 (95% CI 0.42-0.86)). CONCLUSION: SARS-CoV-2 IgG antibody levels were high and dynamic over time in this African setting with low clinical case rates. The absence of association with health profession or general risk behaviors and with HCW positivity in subsequent rounds in HH members, shows the importance of the time-dependent, and not work-related, force of infection. Cohort seroprevalence estimates in a 'new disease' epidemic seem insufficient to guide policy makers for defining control strategies.

The COVID-19 pandemic "anthropause" decreased plastic ingestion in neotropic cormorants Nannopterum brasilianus in Lima, Peru.

Background: The anthropause during the recent COVID-19 pandemic provided a unique opportunity to examine the impact of human activity on seabirds. Lockdowns in Peru prevented people from visiting coastal areas, thereby reducing garbage disposal on beaches and the movement of microplastics into the ocean. This cessation of activities likely led to a temporary decrease in plastic pollution in coastal regions. We aimed to investigate this phenomenon in inshore-feeding neotropic cormorants (Nannopterum brasilianus) along the Circuito de Playas Costa Verde (CPCV), situated on the coastal strip of Lima, Peru (∼ 11 million people). Methods: We collected and analyzed fresh pellets along the CPCV before (over 11 months) and during the pandemic lockdowns (over 8 months). Results: Our findings revealed a significant reduction in the occurrence of plastic in pellets during the pandemic period (% Oc = 2.47, n = 647 pellets) compared to pre-pandemic conditions (% Oc = 7.13, n = 800 pellets). The most common plastic debris item found in the pellets was threadlike microplastic. Additionally, our study highlights the direct correlation between human presence on beaches and the quantity of microplastics (mainly threadlike) found in cormorant pellets. We suggest that the reintroduction of these materials into the sea, previously accumulated on the coast, is likely facilitated by the movement and activity of beachgoers toward the ocean.

Indirect Effects of PM2.5 Exposure on COVID-19 Mortality in Greater Jakarta, Indonesia: An Ecological Study.

Background: Air pollution, including PM2.5, was suggested as one of the primary contributors to COVID-19 fatalities worldwide. Jakarta, the capital city of Indonesia, was recognized as one of the ten most polluted cities globally. Additionally, the incidence of COVID-19 in Jakarta surpasses that of all other provinces in Indonesia. However, no study has investigated the correlation between PM2.5 concentration and COVID-19 fatality in Jakarta. Objective: To investigate the correlation between short-term and long-term exposure to PM2.5 and COVID-19 mortality in Greater Jakarta area. Methods: An ecological time-trend study was implemented. The data of PM2.5 ambient concentration obtained from Nafas Indonesia and the National Institute for Aeronautics and Space (LAPAN)/National Research and Innovation Agency (BRIN). The daily COVID-19 death data obtained from the City's Health Office. Findings: Our study unveiled an intriguing pattern: while short-term exposure to PM2.5 showed a negative correlation with COVID-19 mortality, suggesting it might not be the sole factor in causing fatalities, long-term exposure demonstrated a positive correlation. This suggests that COVID-19 mortality is more strongly influenced by prolonged PM2.5 exposure rather than short-term exposure alone. Specifically, our regression analysis estimate that a 50 µg/m3 increase in long-term average PM2.5 could lead to an 11.9% rise in the COVID-19 mortality rate. Conclusion: Our research, conducted in one of the most polluted areas worldwide, offers compelling evidence regarding the influence of PM2.5 exposure on COVID-19 mortality rates. It emphasizes the importance of recognizing air pollution as a critical risk factor for the severity of viral respiratory infections.

Occupational Health Barriers in South Africa: A Call for Ubuntu.

Many low- and middle-income countries (LMICs) grapple with shortages of health workers, a crucial component of robust health systems. The COVID-19 pandemic underscored the imperative for appropriate staffing of health systems and the occupational health (OH) threats to health workers. Issues related to accessibility, coverage, and utilization of OH services in public sector health facilities within LMICs were particularly accentuated during the pandemic. This paper draws on the observations and experiences of researchers engaged in an international collaboration to consider how the South African concept of Ubuntu provides a promising way to understand and address the challenges encountered in establishing and sustaining OH services in public sector health facilities. Throughout the COVID-19 pandemic, the collaborators actively participated in implementing and studying OH and infection prevention and control measures for health workers in South Africa and internationally as part of the World Health Organizations' Collaborating Centres for Occupational Health. The study identified obstacles in establishing, providing, maintaining and sustaining such measures during the pandemic. These challenges were attributed to lack of leadership/stewardship, inadequate use of intelligence systems for decision-making, ineffective health and safety committees, inactive trade unions, and the strain on occupational health professionals who were incapacitated and overworked. These shortcomings are, in part, linked to the absence of the Ubuntu philosophy in implementation and sustenance of OH services in LMICs.

Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases.

Background: Emerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging. Methods: We introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable. Results: To validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature. Conclusion: This study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.

Mental health inequality between urban and rural youth under COVID-19 from survey data from China.

Background: While health inequality has been the focus of past scholarly discussions, COVID-19's outbreak and spread have provided a new arena for discussing health inequality, particularly in the context of urban-rural disparities in China. This paper explores the impact of COVID-19 on urban-rural health inequality, and the roles played by socioeconomic status and social capital. Methods: A cross-sectional observational collected data on demographics, mental health, socioeconomic status, and social capital. An online survey was administered from August 27 to August 30, 2020, and, 1936 valid samples were received. Mental health was measured using the Brief Symptom Inventory (BSI-18). This study applied the ordinary least squares regression (OLS) model, and data analysis was performed using STATA. Results: There were 1936 participants, with an equal distribution of genders. Multiple regression analysis showed that the mental health levels of rural youth were superior to those of urban youth (p = 0.049), especially when the epidemic was not severe (p = 0.013). Socioeconomic status had a significant positive promotion effect on mental health (p = 0.008), but the interaction effect between socioeconomic status and the urban-rural divide indicated that the promotion effect of socioeconomic status on the mental health of urban youth was greater than that of rural youth (p = 0.04). Social capital had a significant positive promotion effect on mental health (p = 0.000), and the interaction effect indicated that this promoting effect did not differ between urban and rural areas (p > 0.05).

The intervention of local public authorities and the impact of the COVID-19 pandemic in Romania: a subnational analysis.

The COVID-19 pandemic had a strong territorial dimension, with a highly asymmetric impact among Romanian counties, depending on pre-existing vulnerabilities, regions' economic structure, exposure to global value chains, specialization, and overall ability to shift a large share of employees to remote working. The aim of this paper is to assess the role of Romanian local authorities during this unprecedented global medical emergency by capturing the changes of public spending at the local level between 2010 and 2021 and amid the COVID-19 pandemic, and to identify clusters of Romanian counties that shared similar characteristics in this period, using a panel data quantitative model and hierarchical cluster analysis. Our empirical analysis shows that between 2010-2021, the impact of social assistance expenditures was higher than public investment (capital spending and EU funds) on the GDP per capita at county level. Additionally, based on various macroeconomic and structural indicators (health, labour market performance, economic development, entrepreneurship, and both local public revenues and several types of expenditures), we determined seven clusters of counties. The research contributes to the discussion regarding the increase of economic resilience but also to the evidence-based public policies implementation at local level.

Support for Mandatory COVID-19 Vaccines for 5- to 11-Year-Old Children: Cross-sectional study of Omani mothers.

Objectives: This study aimed to investigate the variables that influenced a sample of Omani mothers' support for mandatory COVID-19 vaccines for children. The vaccination against COVID-19 averted millions of fatalities during the COVID-19 pandemic. Nevertheless, a considerable number of parents and caregivers opposed mandating COVID-19 vaccines for children. Methods: This cross-sectional study was conducted at several healthcare facilities in Oman using a structured questionnaire between February and March 2022. Univariable and multivariable logistic regression models were used to analyse the data. Results: A total of 700 Omani mothers (response rate = 73.4%) who had children aged 5-11 years old were included. The median age of the mothers was 38 ± 5.19 years. The results of multivariable logistic regression were generally consistent with those of the univariable analysis except for age (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 0.58-1.93; P = 0.86) and income (OR = 1.09, 95% CI: 0.58-2.03; P = 0.79). Mothers who were vaccine hesitant (OR = 9.82, 95% CI: 5.27-18.28; P <0.001), tested positive for COVID-19 (OR = 3.25, 95% CI: 1.80-5.86; P <0.001) and had one or two doses of COVID-19 vaccines (OR = 5.41, 95% CI: 2.92-10.03; P <0.001) were more likely to refuse mandating COVID-19 vaccines for children 5-11 years old. Conclusion: Mothers who were vaccine hesitant, tested positive for COVID-19 and had one or two doses of COVID-19 vaccines were more likely to oppose mandatory COVID-19 vaccines for young children. The findings should aid public health authorities in designing future childhood vaccine literacy programmes with specific attention to some subgroups in Oman to help reduce opposition to vaccines in future pandemics among mothers.

Factors associated with COVID-19 in children aged 0 to 15 in Niger, 2020.

On January 30, 2020, the WHO declared COVID-19 a global health emergency. Children were affected in less severe forms. Niger had implemented measures in a context where children were a source of contamination. The aim was to determine the factors associated with COVID-19 in children in Niger from February to August 2020 through an analysis of the national database. We conducted an analytical cross-sectional study including all COVID-19 suspects in the database. We used Excel and Epi Info 7.2.4. software for data extraction and analysis. Frequencies and proportions were calculated, and in a logistic regression, we estimated the ORs of association with their 95% confidence intervals, the factors associated with COVID-19 at the threshold of p<0.05. Of 572 notified cases of suspected COVID-19 in children aged 0-15, 11.36% were positive. The median age of infected children was 10 years [IQR: 5- 13 years]. The male/female sex ratio was 2.1. Children aged 11 to 15 accounted for 49.2%, 61.5% lived in Niamey, 4.6% had comorbidities. The notion of travel was 12.3% and 40% had a notion of contact, 24.4% had a fever, 23.2% had a cough, 18% were hospitalized, and a case-fatality rate of 1.5%. In etiological analysis, the factors associated with COVID-19 were sex ORa=0.51 [0.28-0.93] p=0.028, presence of symptoms ORa=2.29 [1.23-4.25] p=0.008 and notion of contact ORa=0.32 [0.13-0.77] p=0.011. Exposed children were sensitive to COVID-19, and all age groups were affected, with a predominance of males. We recommend barrier measures adapted to young people, and early detection and management of infected children.

Women's Preventive Services Utilization in Illinois in the Aftermath of the ACA and the COVID-19 Pandemic.

This study explores the association between health system changes over the last decade and women's preventive care utilization in Illinois. A cross-sectional analysis using Illinois Behavioral Risk Factor Surveillance System (BRFSS) data from 2012-2020 among women aged 21-75 (n=21,258) examined well-woman visit (WWV) receipt and breast and cervical cancer screening overall and over several time periods. There was an increase in the prevalence of receiving a WWV for Illinois women overall from 2012-2020. However, the overall adjusted prevalence difference was only significant for the 2020 versus 2015-2019 comparison and not for 2015-2019 versus 2012-2014. The COVID-19 pandemic was not associated with a decrease in the prevalence of mammogram use but was manifest for cervical cancer screening, particularly for Black women. Finally, those reporting having a WWV in the past year had a significantly higher prevalence of being up to date with screening compared with those not reporting a WWV.

[Chronic hepatitis B in rheumatic diseases: issues of screening and reactivation of infection: A review].

Patients with rheumatic diseases infected with hepatitis B virus (HBV) are difficult to manage not only due to the presence of risk factors for the development and rapid progression of liver cirrhosis, but also due to the likelihood of reactivation of this infection. Despite the successes achieved in the fight against HBV, the virus cannot be completely defeated due to the presence of hidden forms of the disease, escaping the field of vision of a rheumatologist and an infectionist. Based on the results of the analysis of current publications, the paper presents the rationale for a complete immunological screening of patients with rheumatic diseases when prescribing antirheumatic therapy. The issues of the role of COVID-19 in the exacerbation of chronic viral hepatitis B, antiviral prevention and monitoring are discussed, the classification of antirheumatic drugs according to the risk of HBV reactivation is presented.

Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large population-based cohort study.

BACKGROUND: Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model. METHODS: A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort. CONCLUSIONS: We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.

Informing evidence-based policy during the COVID-19 pandemic and recovery period: learning from a national evidence centre.

BACKGROUND: The COVID-19 pandemic demonstrated the vital need for research to inform policy decision-making and save lives. The Wales COVID-19 Evidence Centre (WCEC) was established in March 2021 and funded for two years, to make evidence about the impact of the pandemic and ongoing research priorities for Wales available and actionable to policy decision-makers, service leads and the public. OBJECTIVES: We describe the approaches we developed and our experiences, challenges and future vision. PROGRAM IMPLEMENTATION: The centre operated with a core team, including a public partnership group, and six experienced research groups as collaborating partners. Our rapid evidence delivery process had five stages: 1. Stakeholder engagement (continued throughout all stages); 2. Research question prioritisation; 3. Bespoke rapid evidence review methodology in a phased approach; 4. Rapid primary research; and 5. Knowledge Mobilisation to ensure the evidence was available for decision-makers. MAIN ACHIEVEMENTS: Between March 2021-23 we engaged with 44 stakeholder groups, completed 35 Rapid Evidence Reviews, six Rapid Evidence Maps and 10 Rapid Evidence Summaries. We completed four primary research studies, with three published in peer reviewed journals, and seven ongoing. Our evidence informed policy decision-making and was cited in 19 Welsh Government papers. These included pandemic infection control measures, the Action Plan to tackle gender inequalities, and Education Renew and Reform policy. We conducted 24 Welsh Government evidence briefings and three public facing symposia. POLICY IMPLICATIONS: Strong engagement with stakeholder groups, a phased rapid evidence review approach, and primary research to address key gaps in current knowledge enabled high-quality efficient, evidence outputs to be delivered to help inform Welsh policy decision-making during the pandemic. We learn from these processes to continue to deliver evidence from March 2023 as the Health and Care Research Wales Evidence Centre, with a broader remit of health and social care, to help inform policy and practice decisions during the recovery phase and beyond.