Assessment of changes in air quality in Bangalore, India, during the Covid-19 pandemic lockdown: Statistical modelling

COVID-19 pandemic has resulted in a halt to the daily lifestyle of people around the world and bound them to abide by the lockdown measures enforced to prevent the disease from further spreading. In India also, lockdown has been enforced from March 2020. As a result, the level of air pollutants in the atmosphere goes on decreasing. To know the air quality pattern of Bangalore city, ten stations around the city were selected. Air quality data of these stations has been availed from the Central Pollution Control Board (CPCB) of India website. Box chart concept of graphical representation has been applied to show the range of temporal variation of the air pollutants selected (CO, NO2, Ozone, PM2.5, PM10 and SO2) for the study area over two distinct periods (pre-lockdown and post-lockdown). It has been observed that all the pollutants level were drastically or significantly reduced except for SO2 which showed mixed behavior during the entire study period probably due to no restriction on the operation of power plants. GIS based contour mapping is done for each pollutant over the entire study area and separately for two distinct periods (pre-lockdown and post-lockdown). It was found that, change in CO level over the entire study area was significant and the reason behind it was complete restriction on vehicular movement which is the primary reason for CO emission in atmosphere. Reduction in PMs and ozone was also noticeable, but change in SO2 over the entire study area was almost insignificant. To find out the probable sources of pollution during the lockdown and before the lockdown period and the most significant parameters statistical approach has been adopted. The whole data set has been grouped based on similarity and divided into three distinct clusters for both pre-lockdown and post-lockdown period separately using Hierarchical Agglomerative Cluster Analysis (HACA) concept. Principal Component Analysis (PCA) was done for each of the clusters and each time period considered. From the results of PCA it can be confirmed that the most significant parameters were PM10, PM2.5, ozone and SO2. Results suggest that the probable sources of pollution during pre-lockdown period were vehicular emissions, power plants, industrial activities etc. In contrast, during post-lockdown period the sources of pollution were power plants, construction sites and household pollution only. MLR (Multiple Linear Regression) models were developed to predict Air Quality Index (AQI). Most of the models showed good fit with adjusted R2 value more than 0.9. Regression coefficient (R2) values for PM10 followed PM2.5 were highest in each cluster. © 2023 Author(s).

The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model

Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks;each part has its disease transmission pattern. Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique. Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time. Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869;0.1513;and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3. Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area. © 2023 The Authors. Published by Universitas Airlangga.

Stock return anomalies identification during the Covid-19 with the application of a grouped multiple comparison procedure.

This study investigates the impact of COVID-19 pandemic on the Chinese stock market in 2020. Using daily data of three industries, this study addresses the identification of abnormal stock returns as a multiple hypothesis testing problem and proposes to apply a grouped comparison procedure for better detection. By comparing the numbers of daily signals and numbers of stocks with abnormal positive and negative returns, the empirical result shows that the three industries perform differently under the pandemic. Compared to the non-grouped testing procedure, the signals found by the grouped procedure are more prominent, which is advantageous for some situations when there tends to be abnormal performance clustering at the occurrence of major event. This paper on stock return anomalies gives a new perspective on the impact of major events to the stock market, like the global outbreak disease.

Association between children's home-schooling and parental psychological distress during the COVID-19 pandemic in Taiwan: Risk and protective factors in a multilevel approach.

BACKGROUND: The role home-schooling of children in parental mental health during the COVID-19 pandemic in Taiwan remains unknown. This study aimed to assess the association between parental psychological distress and home-schooling in a socio-ecological context during the peak of the first wave of the COVID-19 pandemic in Taiwan. METHODS: This was a prospective cohort study. In total, 902 parents (father: n = 206, mother: n = 696) who home-schooled children under 18 years of age were recruited by purposive sampling from 17 cities in Taiwan. Data were collected between 19 July and 30 September 2021 through a survey. Multilevel regression models were used to examine the association between parents' psychological distress and home-schooling considering the characteristics at the person and city levels. RESULTS: Parental psychological distress was positively associated with difficulty in setting up electronic devices and increased disputes between parents and children, and it was negatively associated with time management and increased time spent bonding with their children during home-schooling (Ps < 0.05). Parents who had a child with health conditions, lived in an extended family, worked from home, lived during the Level 3 alert level, and lived with a median/sporadic level of the COVID-19 community spread by city also reported greater psychological distress (Ps < 0.05). However, parents who had greater household family support reported less psychological distress (P < .05). CONCLUSIONS: Clinicians and policy makers must carefully consider parental mental health while home-schooling during the COVID-19 pandemic in a broader socio-ecological context. A focus is advised on the home-schooling experiences of parents and other risk and protective factors for parental psychological distress at the person and city levels, especially for those with children who require medical interventions and have a medical condition.

Towards improved thermal comfort predictions and higher energy savings: building energy model of an open-plan office based on indoor CO2 and temperature controls

In recent work, a Hierarchical Bayesian model was developed to predict occupants' thermal comfort as a function of thermal indoor environmental conditions and indoor CO2 concentrations. The model was trained on two large IEQ field datasets consisting of physical and subjective measurements of IEQ collected from over 900 workstations in 14 buildings across Canada and the US. Posterior results revealed that including measurements of CO2 in thermal comfort modelling credibly increases the prediction accuracy of thermal comfort and in a manner that can support future thermal comfort prediction. In this paper, the predictive model of thermal comfort is integrated into a building energy model (BEM) that simulates an open-concept mechanically-ventilated office space located in Vancouver. The model predicts occupants' thermal satisfaction and heating energy consumption as a function of setpoint thermal conditions and indoor CO2 concentrations such that, for the same thermal comfort level, higher air changes per hour can be achieved by pumping a higher amount of less-conditioned fresh air. The results show that it is possible to reduce the energy demand of increasing fresh air ventilation rates in winter by decreasing indoor air temperature setpoints in a way that does not affect perceived thermal satisfaction. This paper presents a solution for building managers that have been under pressure to increase current ventilation rates during the COVID-19 pandemic. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Mediating role of psychological resilience in relationship between stress and anxiety symptoms of medical students in post COVID-19 period

[Background] In the post-pandemic period, attention has been paid to the study of psychological stage changes in various groups. Under the stress of pandemics, how to control negative emotions such as anxiety symptoms will have an important impact on medical students' professional identity and future professional competence. [Objective] This study is designed to explore the characteristics of stress and anxiety symptoms of medical students in different stages of the post COVID-19 period, and potential mediating role of psychological resilience in the relationship between stress and anxiety symptoms. [Methods] By convenience sampling method, 3 000 medical students from three medical colleges in Shaanxi Province were selected and completed an online survey reporting the Self-Rating Anxiety Scale (SAS), Stress Scale for College Student (SSCS), and Resilience Scale of Adults (RSA) to assess their stress, psychological resilience, and anxiety symptoms in September and November 2020. SPSS 25.0 software was used to perform dependent-sample t test, variance analysis, Pearson correlation analysis, and mediating effect test (hierarchical regression analysis). [Results] A total of 2 894 valid questionnaires were recovered and the valid recovery rate was 96.5%. The overall scores of stress, psychological resilience, and anxiety symptoms of selected medical students were 56.61+/-17.17, 166.88+/-28.55, and 40.45+/-9.67, respectively in the post COVID-19 period. The positive rate of high stress was 72.2%, and the positive rate of anxiety symptoms was 16.0%. There were significant differences in anxiety symptoms scores between the high and the low stress level groups (42.16+/-9.92, 35.99+/-7.30) (P < 0.01). There were significant differences in scores of stress, psychological resilience, and anxiety symptoms among different grade groups (P < 0.01). The pearson correlation analysis results showed that the stress score was positively correlated with the anxiety symptom score (r=0.417, P < 0.01) and negatively correlated with the psychological resilience score (r=-0.344, P < 0.01);the psychological resilience score was negatively correlated with the anxiety symptom score (r=-0.495, P < 0.01). The hierarchical regression analysis results found that stress had a positive effect on anxiety symptoms (b=0.280, P < 0.01), and a negative effect on psychological resilience (b=-0.344, P < 0.01);psychological resilience negatively affected anxiety symptoms (b=-0.398, P < 0.01), and played a partial mediating role in the relationship between stress and anxiety symptoms (effect value was 0.137) that accounted for 32.8% of the total effect. [Conclusion] In the post COVID-19 period, medical students have a superposition of high stress and high anxiety symptoms. Psychological resilience is a protective factor for anxiety symptoms and plays a partial mediating role in the relationship between stress and anxiety symptoms.Copyright © 2021, Shanghai Municipal Center for Disease Control and Prevention. All rights reserved.

Mapping Life Expectancy Loss in Barcelona in 2020

We use a Bayesian spatio-temporal model, first to smooth small-area initial life expectancy estimates in Barcelona for 2020, and second to predict what small-area life expectancy would have been in 2020 in absence of covid-19 using mortality data from 2007 to 2019. This allows us to estimate and map the small-area life expectancy loss, which can be used to assess how the impact of covid-19 varies spatially, and to explore whether that loss relates to underlying factors, such as population density, educational level, or proportion of older individuals living alone. We find that the small-area life expectancy loss for men and for women have similar distributions, and are spatially uncorrelated but positively correlated with population density and among themselves. On average, we estimate that the life expectancy loss in Barcelona in 2020 was of 2.01 years for men, falling back to 2011 levels, and of 2.11 years for women, falling back to 2006 levels.

Characteristics of Traditional Chinese Medicine Syndromes and Treatments of COVID-19 Patients from Two Hospitals Based on "Treatment of Disease in Accordance with Three Conditions".

Objective: To explore the guidance value of "treatment of disease in accordance with three conditions" theory in the prevention and treatment of corona virus disease 2019 (COVID-19) based on the differences of syndromes and traditional Chinese medicine (TCM) treatments in COVID-19 patients from Xingtai Hospital of Chinese Medicine of Hebei province and Ruili Hospital of Chinese Medicine and Dai Medicine of Yunnan province and discuss its significance in the prevention and treatment of the unexpected acute infectious diseases. Method(s): Demographics data and clinical characteristics of COVID-19 patients from the two hospitals were collected retrospectively and analyzed by SPSS 18.0. The information on formulas was obtained from the hospital information system (HIS) of the two hospitals and analyzed by the big data intelligent processing and knowledge service system of Guangdong Hospital of Chinese Medicine for frequency statistics and association rules analysis. Heat map-hierarchical clustering analysis was used to explore the correlation between clinical characteristics and formulas. Result(s): A total of 175 patients with COVID-19 were included in this study. The 70 patients in Xingtai, dominated by young and middle-aged males, had clinical symptoms of fever, abnormal sweating, and fatigue. The main pathogenesis is stagnant cold-dampness in the exterior and impaired yin by depressed heat, with manifest cold, dampness, and deficiency syndromes. The therapeutic methods highlight relieving exterior syndrome and resolving dampness, accompanied by draining depressed heat. The core Chinese medicines used are Poria, Armeniacae Semen Amarum, Gypsum Fibrosum, Citri Reticulatae Pericarpium, and Pogostemonis Herba. By contrast, the 105 patients in Ruili, dominated by young females, had atypical clinical symptoms, and most of them were asymptomatic patients or mild cases. The main pathogenesis is dampness obstructing the lung and the stomach, with obvious dampness and heat syndromes. The therapeutic methods are mainly invigorating the spleen, resolving dampness, and dispersing Qi with light drugs. The core Chinese medicines used are Poria, Atractylodis Macrocephalae Rhizoma, Glycyrrhizae Radix et Rhizoma, Coicis Semen, Platycodonis Radix, Lonicerae Japonicae Flos, and Pogostemonis Herba. Conclusion(s): The differences in clinical characteristics, TCM syndromes, and medication of COVID-19 patients from the two places may result from different regions, population characteristics, and the time point of the COVID-19 outbreak. The "treatment of disease in accordance with three conditions" theory can help to understand the internal correlation and guide the treatments.Copyright © 2022, China Academy of Chinese Medical Sciences Institute of Chinese Materia Medica. All rights reserved.

Who is More Likely (Not) to Make Home-Based Work Trips During the COVID-19 Pandemic? The Case of Scotland

In this study, we used survey data (n = 6,000) to investigate the work trip patterns of Scottish residents at various points of the COVID-19 pandemic. We focused specifically on the reported patterns of weekly work trips made during the government-enforced lockdown and subsequent phases of restriction easing. This was of particular importance given the widespread changes in work trips prompted by COVID-19, including a significant rise in telecommuting and a reduction in public transport commuting trips. The survey data showed that the vast majority of respondents (;85%) made no work trips during lockdown, dropping to;77% following the easing of some work-related restrictions. Zero-inflated hierarchical ordered probit models were estimated to determine the sociodemographic and behavioral factors affecting the frequency of work trips made during three distinct periods. The model estimation results showed that the socioeconomic characteristics of respondents influenced work trips made throughout the pandemic. In particular, respondents in households whose main income earner was employed in a managerial/professional occupation were significantly more likely to make no work trips at all stages of the pandemic. Those with a health problem or disability were also significantly more likely to make no work trips throughout the pandemic. Other interesting findings concern respondents' gender, as males were more likely to complete frequent work trips than females throughout the pandemic, and differences between densely populated areas and the rest of Scotland, as respondents from a large city (Edinburgh or Glasgow) were significantly more likely to make frequent work trips as restrictions were eased. © National Academy of Sciences: Transportation Research Board 2021.

Hierarchical Bayesian hedonic regression analysis of Japanese rice wine: is the price right?

PurposeThis paper aims to formulate a hedonic pricing model for Japanese rice wine, sake, via hierarchical Bayesian modeling estimated using an efficient Markov chain Monte Carlo (MCMC) method. Using the estimated model, the authors examine how producing regions, rice breeds and taste characteristics affect sake prices.Design/methodology/approachThe datasets in the estimation consist of cross-sectional observations of 403 sake brands, which include sake prices, taste indicators, premium categories, rice breeds and regional dummy variables. Data were retrieved from Rakuten, Japan's largest online shopping site. The authors used the Bayesian estimation of the hedonic pricing model and used an ancillarity–sufficiency interweaving strategy to improve the sampling efficiency of MCMC.FindingsThe estimation results indicate that Japanese consumers value sweeter sake more, and the price of sake reflects the cost of rice preprocessing only for the most-expensive category of sake. No distinctive differences were identified among rice breeds or producing regions in the hedonic pricing model.Originality/valueTo the best of the authors' knowledge, this study is the first to estimate a hedonic pricing model of sake, despite the rich literature on alcoholic beverages. The findings may contribute new insights into consumer preference and proper pricing for sake breweries and distributors venturing into the e-commerce market.

Machine learning uncovers blood test patterns subphenotypes at hospital admission discerning increased 30-day ICU mortality rates in COVID-19 elderly patients

Introduction: Since March 2020, a number of SARS-CoV-2 patients have frequently required intensive care unit (ICU) admission, associated with moderate survival outcomes and an increasing economic burden. Elderly patients are among the most numerous, due to previous comorbidities and complications they develop during hospitalization [1]. For this reason, a reliable early risk stratification tool could help estimate an early prognosis and allow for an appropriate resources allocation in favour of the most vulnerable and critically ill patients. Method(s): This retrospective study includes data from two Spanish hospitals, HU12O (Madrid) and HCUV (Valencia), from 193 patients aged > 64 with COVID-19 between February and November 2020 who were admitted to the ICU. Variables include demographics, full-blood-count (FBC) tests and clinical outcomes. Machine learning applied a non-linear dimensionality reduction by t-distributed stochastic neighbor embedding (t-SNE) [2];then hierarchical clustering on the t-SNE output was performed. The number of clinically relevant subphenotypes was chosen by combining silhouette and elbow coefficients, and validated through exploratory analysis. Result(s): We identified five subphenotypes with heterogeneous interclustering age and FBC patterns (Fig. 1). Cluster 1 was the 'healthiest' phenotype, with 2% 30-day mortality and characterized by moderate leukocytes and eosinophils. Cluster 5, the severe phenotype, showed 44% 30-day mortality and was characterized by the highest leukocyte, neutrophil and platelet count and minimal monocytes and lymphocyte count. Clusters 2-4 displayed intermediate mortality rates (20-28%). Conclusion(s): The findings of this preliminary report of Eld-ICUCOV19 patients suggest the patient's FBC and age can display discriminative patterns associated with disparate 30-day ICU mortality rates.

Cytokine Profile in Different Post-Covid-19 Condition Phenotypes

Background: At least 10% of SARS-CoV-2 infected patients suffer from persistent symptoms for >12 weeks, known as post-COVID-19 condition (PCC) or Long Covid. Reported symptomatology is diverse with >200 physical and neurological debilitating symptoms. Here, we analyzed pro-inflammatory cytokine levels as a potential mechanism underlying persistent symptomatology. Method(s): Clinical data and samples used belong to the KING cohort extension, which includes clinically well characterized PCC (N=358, 59 persistent symptoms evaluated), COVID-19 recovered and uninfected subjects. We used Gower distances to calculate symptom's similarity between PCC and Ward's hierarchical clustering method to identify different symptom patterns among PCC patients. Cytokine levels of randomly selected PCC, recovered and uninfected subjects (N=193) were measured on plasma samples collected >6 months after acute infection using the 30-Plex Panel for Luminex. Mann- Whitney t-test was used to compare PCC vs recovered groups and Kruskal-Wallis t-test for >2 groups comparisons (PCC vs recovered vs Uninfected and within PCC clusters). FDR correction was applied for statistical significance (p-adj). Result(s): Hierarchical clustering identified 5 different PCC clusters according to their symptomatology, where PCC3 and PCC5 clusters showed higher prevalence of women ( >80%) and more persistent symptoms, while acute COVID-19 was mild in >80% of the patients. We selected 91 PCC (belonging to each cluster), 57 recovered and 45 uninfected subjects for cytokine profiling (Table 1). 13 soluble markers were significantly elevated (IL-1beta, Eotaxin, MIP-1beta, MCP-1, IL-15, IL-5, HGF, IFN-alpha, IL-1RA, IL-7, MIG, IL-4 and IL-8) in PCC and recovered groups compared to uninfected subjects (all p-adj< 0.04). In addition, PCC subjects tended towards higher levels of IL-1RA compared to recovered group (padj= 0.071). Within PCC clusters, FGF-basic and RANTES were elevated while IL-2 and MIG were decreased in PCC3 and PCC5 compared to the other PCC clusters (all p-adj< 0.04). TNF-alpha, IP-10, G-CSF and MIP-1alpha were decreased in PCC3 and PCC5 not reaching statistical significance (all p-adj=0.07). Conclusion(s): Some cytokines remained altered in all SARS-CoV-2 infected subjects independently of persistent symptoms after 6 months from acute infection. Differences between PCC and recovered individuals are limited after correction. Importantly, PCC cytokine profiles showed differences between clusters, which suggests different PCC subsyndromes with distinct etiology. Subjects Characteristics (Table Presented).

Efficacy of the confinement policies on the COVID-19 spread dynamics in the early period of the pandemic

Spread dynamics and the confinement policies of COVID-19 exhibit different patterns for different countries. Numerous factors affect such patterns within each country. Examining these factors, and analyzing the confinement practices allow government authorities to implement effective policies in the future. In addition, they help the authorities to distribute healthcare resources optimally without overwhelming their systems. In this empirical study, we use a clustering-based approach, Hierarchical Cluster Analysis (HCA) on time-series data to capture the spread patterns at various countries. We particularly investigate the confinement policies adopted by different countries and their impact on the spread patterns of COVID-19. We limit our investigation to the early period of the pandemic, because many governments tried to respond rapidly and aggressively in the beginning. Moreover, these governments adopted diverse confinement policies based on trial-and-error in the beginning of the pandemic. We found that implementations of the same confinement policies may exhibit different results in different countries. Specifically, lockdowns become less effective in densely populated regions, because of the reluctance to comply with social distancing measures. Lack of testing, contact tracing, and social awareness in some countries forestall people from self-isolation and maintaining social distance. Large labor camps with unhealthy living conditions also aid in high community transmissions in countries depending on foreign labor. Distrust in government policies and fake news instigate the spread in both developed and under-developed countries. Large social gatherings play a vital role in causing rapid outbreaks almost everywhere. An early and rapid response at the early period of the pandemic is necessary to contain the spread, yet it is not always sufficient.

Distinct clinical phenotypes of critically ill COVID-19 patients: a cohort observational study

Introduction: COVID-19 presents a complex pathophysiology and evidence collected points towards an intricated interaction of viraldependent and individual immunological mechanisms. The identification of phenotypes, through clinical and biological markers, may provide a better understanding of the subjacent mechanisms and an early patient-tailored characterization of illness severity. Method(s): Multicenter prospective cohort study performed in 5 hospitals of Portugal and Brazil, during one year, between 2020-2021. All adult patients with an Intensive Care Unit admission with SARS-CoV-2 pneumonia were eligible. COVID-19 was diagnosed using clinical and radiologic criteria with a SARS-CoV-2 positive RT-PCR test. A two-step hierarchical cluster analysis was made using several class-defining variables. Result(s): 814 patients were included. The cluster analysis revealed a three-class model, allowing for the definition of three distinct COVID- 19 phenotypes: 244 patients in phenotype A, 163 patients in phenotype B, and 407 patients in phenotype C. Patients included in the phenotype C were significantly older, with higher baseline inflammatory biomarkers profile, and significantly higher requirement of organ support and mortality rate (Table 1 ( P062)). Phenotypes A and B demonstrated some overlapping clinical characteristics but different outcomes. Phenotype B patients presented a lower mortality rate, with consistently lower C-reactive protein, but higher procalcitonin and interleukin-6 serum levels, describing an immunological profile significantly different from phenotype A (Table 1). Conclusion(s): Severe COVID-19 patients exhibit three different clinical phenotypes with distinct profiles and outcomes. Their identification could have an impact in patients' care, justifying different therapy responses and inconsistencies identified across different randomized control trials results.

THE SPREAD OF THE COVID-19 INFECTION IN RUSSIA'S BALTIC MACRO-REGION: INTERNAL DIFFERENCES

This article explores the spread of the COVID-19 infection in Russia's Baltic macro-region. The monthly excess mortality rate in the Baltic region is analysed along with regional and municipal COVID-19 response acts to identify regional features affecting the spread of the disease. The spatial characteristics of Russia's Baltic regions, germane to the propagation of COVID-19, were distinguished by examining selected social and economic statistical indicators. Based on the space of places/space of flows dichotomy, Russia's Baltic regions can be divided into three spaces: 1) St. Petersburg, the Leningrad and Kaliningrad regions (dominated by spaces of flows;highly permeable space);2) the Republic of Karelia and the Murmansk region (the key factors are rotational employment and the introduction of the virus from without);3) the Novgorod and Pskov regions (lowly permeable spaces of places;the central role of local foci of the disease). The principal risk factor for the space of flows is the rapid spread of COVID-19 along transport arteries, whilst, within the space of places, the coronavirus spreads through spatial diffusion from isolated foci along short radii. In the former case, local authorities counteracted spatial diffusion by restricting movement in the local labour market;in the latter, by limiting travel between the centre and the periphery. The traditional ideas about positive (openness, centrality) and negative (closedness, peripherality) characteristics of space are reversed in the context of the pandemic: periphery gains the benefit of natural protection from the pandemic, whilst centres become acutely vulnerable © Alov, I. N., Pilyasov, A. N., 2023

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control.

Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a Hierarchical Reinforcement Learning decision framework for multi-mode Epidemic Control with multiple interventions called HRL4EC. We devise an epidemiological model, referred to as MID-SEIR, to describe multiple interventions' impact on transmission explicitly, and use it as the environment for HRL4EC. Besides, to address the complexity introduced by multiple interventions, this work transforms the multi-mode intervention decision problem into a multi-level control problem, and employs hierarchical reinforcement learning to find the optimal strategies. Finally, extensive experiments are conducted with real and simulated epidemic data to validate the effectiveness of our proposed method. We further analyze the experiment data in-depth, conclude a series of findings on epidemic intervention strategies, and make a visualization accordingly, which can provide heuristic support for policymakers' pandemic response.

Coping with COVID: Performance of China's hierarchical medical system during the COVID-19 pandemic.

Objective: The COVID-19 pandemic has challenged the health system worldwide. This study aimed to assess how China's hierarchical medical system (HMS) coped with COVID-19 in the short-and medium-term. We mainly measured the number and distribution of hospital visits and healthcare expenditure between primary and high-level hospitals during Beijing's 2020-2021 pandemic relative to the 2017-2019 pre-COVID-19 benchmark period. Methods: Hospital operational data were extracted from Municipal Health Statistics Information Platform. The COVID-19 period in Beijing was divided into five phases, corresponding to different characteristics, from January 2020 to October 2021. The main outcome measures in this study include the percentage change in inpatient and outpatient emergency visits, and surgeries, and changing distribution of patients between different hospital levels across Beijing's HMS. In addition, the corresponding health expenditure in each of the 5 phases of COVID-19 was also included. Results: In the outbreak phase of the pandemic, the total visits of Beijing hospitals declined dramatically, where outpatient visits fell 44.6%, inpatients visits fell 47.9%; emergency visits fell 35.6%, and surgery inpatients fell 44.5%. Correspondingly, health expenditures declined 30.5% for outpatients and 43.0% for inpatients. The primary hospitals absorbed a 9.51% higher proportion of outpatients than the pre-COVID-19 level in phase 1. In phase 4, the number of patients, including non-local outpatients reached pre-pandemic 2017-2019 benchmark levels. The proportion of outpatients in primary hospitals was only 1.74% above pre-COVID-19 levels in phases 4 and 5. Health expenditure for both outpatients and inpatients reached the baseline level in phase 3 and increased nearly 10% above pre-COVID-19 levels in phases 4 and 5. Conclusion: The HMS in Beijing coped with the COVID-19 pandemic in a relatively short time, the early stage of the pandemic reflected an enhanced role for primary hospitals in the HMS, but did not permanently change patient preferences for high-level hospitals. Relative to the pre-COVID-19 benchmark, the elevated hospital expenditure in phase 4 and phase 5 pointed to hospital over-treatment or patient excess treatment demand. We suggest improving the service capacity of primary hospitals and changing the preferences of patients through health education in the post-COVID-19 world.

Exploring COVID-19 transmission patterns and key factors during epidemics caused by three major strains in Asia.

The COVID-19 epidemic has lasted for more than two years since the outbreak in late 2019. An urgent and challenging question is how to systematically evaluate epidemic developments in different countries, during different periods, and to determine which measures that could be implemented are key for successful epidemic prevention. In this study, SBD distance-based K-shape clustering and hierarchical clustering methods were used to analyse epidemics in Asian countries. For the hierarchical clustering, epidemic time series were divided into three periods (epidemics induced by the Original/Alpha, Delta and Omicron variants separately). Standard deviations, the Hurst index, mortality rates, peak value of confirmed cases per capita, average growth rates, and the control efficiency of each period were used to characterize the epidemics. In addition, the total numbers of cases in the different countries were analysed by correlation and regression in relation to 15 variables that could have impacts on COVID-19. Finally, some suggestions on prevention and control measures for each category of country are given. We found that the total numbers of cases per million of a population, total deaths per million and mortality rates were highly correlated with the proportion of people aged over 65 years, the prevalence of multiple diseases, and the national GDP. We also found significant associations between case numbers and vaccination rates, health expenditures, and stringency of control measures. Vaccinations have played a positive role in COVID-19, with a gradual decline in mortality rates in later periods, and are still playing protective roles against the Delta and Omicron strains. The stringency of control measures taken by a government is not an indicator of the appropriateness of a country's response to the outbreak, and a higher index does not necessarily mean more effective measures; a combination of factors such as national vaccination rates, the country's economic foundation and the availability of medical equipment is also needed. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index.

The outbreak of the COVID-19 epidemic has had a huge impact on a global scale and its impact has covered almost all human industries. The Chinese government enacted a series of policies to restrict the transportation industry in order to slow the spread of the COVID-19 virus in early 2020. With the gradual control of the COVID-19 epidemic and the reduction of confirmed cases, the Chinese transportation industry has gradually recovered. The traffic revitalization index is the main indicator for evaluating the degree of recovery of the urban transportation industry after being affected by the COVID-19 epidemic. The prediction research of traffic revitalization index can help the relevant government departments to know the state of urban traffic from the macro level and formulate relevant policies. Therefore, this study proposes a deep spatial-temporal prediction model based on tree structure for the traffic revitalization index. The model mainly includes spatial convolution module, temporal convolution module and matrix data fusion module. The spatial convolution module builds a tree convolution process based on the tree structure that can contain directional features and hierarchical features of urban nodes. The temporal convolution module constructs a deep network for capturing temporal dependent features of the data in the multi-layer residual structure. The matrix data fusion module can perform multi-scale fusion of COVID-19 epidemic data and traffic revitalization index data to further improve the prediction effect of the model. In this study, experimental comparisons between our model and multiple baseline models are conducted on real datasets. The experimental results show that our model has an average improvement of 21%, 18%, and 23% in MAE, RMSE and MAPE indicators, respectively.

Factors Affecting the Public Intention to Repeat the COVID-19 Vaccination: Implications for Vaccine Communication.

Although most of the pandemic-related mandatory restrictions have been lifted or eased, vaccination is still recommended as an effective measure to minimize the damage from COVID-19 infection. Since COVID-19 eradication is unlikely, it is necessary to understand the factors affecting the public's vaccination intention when COVID-19 vaccination is continuously recommended. This study aims to explore the factors that affect the intention to repeat the COVID-19 vaccination in South Korea. An online survey was conducted in January 2022 with adults living in Gyeonggi-do, South Korea. In a hierarchical logistic regression analysis, sociodemographic factors, COVID-19 infection-related factors, COVID-19 vaccination-related factors, sociocultural factors, and communication factors were taken into account. In this study, more than three-quarters (78.1%) of Koreans were willing to repeat the COVID-19 vaccination. People who had high-risk perceptions, had been vaccinated against COVID-19 at least once, had more authoritarian attitudes, regarded the vaccination as a social responsibility, and had positive attitudes toward health authorities' regular briefings were more likely to repeat the vaccination. In contrast, those who directly or indirectly experienced COVID-19 vaccine side effects and who showed psychological reactance against the government's vaccination recommendation were less likely to repeat the vaccination. Our research indicates that empathetic communication, promotion of the prosocial aspect of vaccination, and regular and transparent provision of vaccine information are essential for promoting the intention to repeat the COVID-19 vaccination.