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Infectious diseases are among the leading causes of mortality worldwide. A new coronavirus named severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) was identified in Wuhan, China in 2019, and the World Health Organization (WHO) declared its outbreak, coronavirus disease 2019 (COVID-19), as a global pandemic in 2020. COVID-19 can spread quickly from person to person. One of the most challenging issues is to identify the infected individuals and prevent potential spread of SARS-CoV-2. Recently, anti-SARS-CoV-2 immunoglobulin M (IgM) and immunoglobulin G (IgG) antibody tests using immunochromatographic methods have been used as a complement to current detection methods and have provided information of the approximate course of COVID-19 infection. However, blood sampling causes pain and poses risks of infection at the needle puncture site. In this study, a novel patch sensor integrating porous microneedles and an immunochromatographic assay (PMNIA) was developed for the rapid detection of anti-SARS-CoV-2 IgM/IgG in dermal interstitial fluid (ISF), which is a rich source of protein biomarkers, such as antibodies. Biodegradable porous microneedles (MNs) made of polylactic acid were fabricated to extract ISF from human skin by capillary effect. The extracted ISF was vertically transported and flowed into the affixed immunoassay biosensor, where specific antibodies could be detected colorimetrically on-site. Anti-SARS-CoV-2 IgM/IgG antibodies were simultaneously detected within 3 min in vitro. Moreover, the limit of detection of anti-SARS-CoV-2 IgM and IgG concentrations was as low as 3 and 7 ng/mL, respectively. The developed device integrating porous MNs and immunochromatographic biosensors is expected to enable minimally invasive, simple, and rapid anti-SARS-CoV-2 IgM/IgG antibody testing. Furthermore, the compact size of the MN and biosensor-integrated device is advantageous for its widespread use. The proposed device has great potential for rapid screening of various infectious diseases in addition to COVID-19 as an effective complementary method with other diagnostic tests., (© 2022. The Author(s).)

Paper-based biosensors featuring immunoconjugated gold nanoparticles have gained extraordinary momentum in recent times as the platform of choice in key cases of field applications, including the so-called rapid antigen tests for SARS-CoV-2. Here, we propose a revision of this format, one that may leverage on the most recent advances in materials science and data processing. In particular, we target an amplifiable DNA rather than a protein analyte, and we replace gold nanospheres with anisotropic nanorods, which are intrinsically brighter by a factor of ~ 10, and multiplexable. By comparison with a gold-standard method for dot-blot readout with digoxigenin, we show that gold nanorods entail much faster and easier processing, at the cost of a higher limit of detection (from below 1 to 10 ppm in the case of plasmid DNA containing a target transgene, in our current setup). In addition, we test a complete workflow to acquire and process photographs of dot-blot membranes with custom-made hardware and regression tools, as a strategy to gain more analytical sensitivity and potential for quantification. A leave-one-out approach for training and validation with as few as 36 sample instances already improves the limit of detection reached by the naked eye by a factor around 2. Taken together, we conjecture that the synergistic combination of new materials and innovative tools for data processing may bring the analytical sensitivity of paper-based biosensors to approach the level of lab-grade molecular tests., (© 2022. The Author(s).)

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%., (© 2024. The Author(s).)

Molecular diagnostics involving nucleic acids (DNA and RNA) are regarded as extremely functional tools. During the 2020 global health crisis, efforts intensified to optimize the production and delivery of molecular diagnostic kits for detecting SARS-CoV-2. During this period, RT-LAMP emerged as a significant focus. However, the thermolability of the reagents used in this technique necessitates special low-temperature infrastructure for transport, storage, and conservation. These requirements limit distribution capacity and necessitate cost-increasing adaptations. Consequently, this report details the development of a lyophilization protocol for reagents in a colorimetric RT-LAMP diagnostic kit to detect SARS-CoV-2, facilitating room-temperature transport and storage. We conducted tests to identify the ideal excipients that maintain the molecular integrity of the reagents and ensure their stability during room-temperature storage and transport. The optimal condition identified involved adding 5% PEG 8000 and 75 mM trehalose to the RT-LAMP reaction, which enabled stability at room temperature for up to 28 days and yielded an analytical and diagnostic sensitivity and specificity of 83.33% and 90%, respectively, for detecting SARS-CoV-2. This study presents the results of a lyophilized colorimetric RT-LAMP COVID-19 detection assay with diagnostic sensitivity and specificity comparable to RT-qPCR, particularly in samples with high viral load., (© 2024. The Author(s).)

During the start of the COVID-19 pandemic in 2020, lockdowns and movement restrictions were thought to negatively impact population mental health, since depression and anxiety symptoms were frequently reported. This study investigates the effect of COVID-19 mitigation measures on mental health across the United States, at county and state levels using difference-in-differences analysis. It examines the effect on mental health facility usage and the prevalence of mental illnesses, drawing on large-scale medical claims data for mental health patients joined with publicly available state- and county-specific COVID-19 cases and lockdown information. For consistency, the main focus is on two types of social distancing policies, stay-at-home and school closure orders. Results show that lockdown has significantly and causally increased the usage of mental health facilities in regions with lockdowns in comparison to regions without such lockdowns. Particularly, resource usage increased by 18% in regions with a lockdown compared to 1% decline in regions without a lockdown. Also, female populations have been exposed to a larger lockdown effect on their mental health. Diagnosis of panic disorders and reaction to severe stress significantly increased by the lockdown. Mental health was more sensitive to lockdowns than to the presence of the pandemic itself. The effects of the lockdown increased over an extended time to the end of December 2020., (© 2024. The Author(s).)

Teachers in Japanese schools employ alphabetical surname lists that call students sooner, with surnames appearing early on these lists. We conducted Internet surveys nearly every month from March 2020 to September 2022 with the same participants, wherein we asked participants where the alphabetical columns of their childhood and adult surnames were located. We aimed to identify how surname order is important for the formation of noncognitive skills. During the data collection period, the COVID-19 vaccines became available; Japanese people could receive their third dose starting in December 2021. The 19th wave of the survey was conducted in January 2022. Therefore, to examine how a surname's alphabetical order could influence intention to revaccinate, we used a subsample of data from December 2021 to September 2022. The major findings were as follows. Women with early surnames had an approximately 4% stronger likelihood of having such intentions than men with early surnames. Early name order was more strongly correlated with revaccination intention among women than among men. The surname effect for women was larger when a mixed-gender list was used compared with when it was not used. This effect was only observed for childhood surnames and not for adult surnames., (© 2024. The Author(s).)

During the COVID-19 pandemic, the world population faced various mental health challenges, highlighting a need for new community-based psychosocial interventions. This study aimed to investigate the effectiveness and feasibility of Nature-Based Therapy (NBT) for the community experiencing psychological distress during the pandemic. A multi-site trial comparing NBT and control groups was conducted in Korea with 291 participants exhibiting mild to severe depression or anxiety. A total of 192 participated in 30 sessions of therapeutic gardening, while 99 remained in the control group. Psychological distress and well-being were assessed using seven measures of depression, anxiety, daily activity, life satisfaction, mindfulness, stress, and loneliness. The effect sizes (Cohen's d) of NBT compared to the control group were medium to large: depression (0.583), anxiety (0.728), daily activity (1.002), life satisfaction (0.786), mindfulness (0.645), stress (0.903), and loneliness (0.695). Multilevel analysis revealed significant Time × Group interaction effects for all measures. Pearson correlation (r = - 0.28 to 0.71) showed that changes in all variables correlated significantly with each other, with small to large effect sizes. Therapeutic alliance at post-test positively moderated the intervention effects on the outcomes. We concluded that NBT is a promising psychosocial intervention for treating psychological distress for community dwellers., (© 2023. The Author(s).)

Following restrictions to control the spread of COVID-19, and subsequent vaccination campaigns, sentiments against such policies were quick to arise. While individual-level determinants that led to such attitudes have drawn much attention, there are also reasons to believe that the macro context in which these movements arose may contribute to their evolution. In this study, exploiting data on business activities which supported a major Italian anti-restriction and anti-vaccine movement, IoApro, using quantitative analysis that employs both a fractional response probit and logit model and a beta regression model, we investigate the relationship between socio-economic characteristics, institutional quality, and the flourishing of this movement. Our results suggest a U-shaped relationship between income and the proliferation of the movement, meaning that support for these movements increases the greater the degree of economic decline. Our results further indicate that the share of the population between 40 and 60 years old is positively related to support for such movements, as is institutional corruption., (© 2023. Springer Nature Limited.)

COVID-19 pandemic brought chemosensory impairment to the forefront of medicine, revealing gaps in the knowledge of pathophysiological mechanisms, true prevalence and preventive/therapeutic alternatives. This is a sub-study of the ORCHESTRA cohort focusing on post-COVID-19 chemosensory symptoms. Risk factors for neurosensorial cluster of post-COVID-19 syndrome (NSc-PCS) were assessed through multivariable analysis. Psychophysical validated tests were applied on a sub-population of 50 patients. Qualitative chemosensory symptoms as well as nasal and oral chemesthesis were evaluated through anamnestic interview and the quality of life through the SF-36 questionnaire. Chemosensory symptoms evolution and olfactory training's outcome were assessed through phone-call interviews. Out of 1187 patients (female, N = 630), 550 (47%) presented NSc-PCS, with a lower risk for older age and monoclonal antibodies treatment, and a higher risk in females (p < 0.001). Out of the 50 patients evaluated with psychophysical tests, 66% showed smell reduction with a qualitative alteration in 50% of hyposmic and 35% of normosmic patients. Hypogeusia was present in 14 (28%) of the patients assessed, with 56% showing a qualitative alteration; 53% of normogeusic patients presented qualitative disorders. NSc-PCS has a complex, fluctuating, multifaceted presentation. Quantifying and characterizing COVID-19-related chemosensory impairment is key to understand underlying mechanisms and to develop preventive and therapeutic treatment., (© 2024. The Author(s).)

COVID-19 has resulted in a significant global impact on health, the economy, education, and daily life. The disease can range from mild to severe, with individuals over 65 or those with underlying medical conditions being more susceptible to severe illness. Early testing and isolation are vital due to the virus's variable incubation period. Chest radiographs (CXR) have gained importance as a diagnostic tool due to their efficiency and reduced radiation exposure compared to CT scans. However, the sensitivity of CXR in detecting COVID-19 may be lower. This paper introduces a deep learning framework for accurate COVID-19 classification and severity prediction using CXR images. U-Net is used for lung segmentation, achieving a precision of 0.9924. Classification is performed using a Convulation-capsule network, with high true positive rates of 86% for COVID-19, 93% for pneumonia, and 85% for normal cases. Severity assessment employs ResNet50, VGG-16, and DenseNet201, with DenseNet201 showing superior accuracy. Empirical results, validated with 95% confidence intervals, confirm the framework's reliability and robustness. This integration of advanced deep learning techniques with radiological imaging enhances early detection and severity assessment, improving patient management and resource allocation in clinical settings., (© 2024. The Author(s).)

The enhancement of regional comprehensive development ability is significantly impacted by the study on the implementation effect of regional integration strategies. The integration strategy's impact on urban development during COVID-19 in the Yangtze River Delta(YRD) is unclear. According to prior industrial transfer theory, Hefei, Anhui's capital, is difficult to transfer industries, and other YRD cities push industry integration in Anhui. This study employs the theory of economic and land resource use to examine the resilience of the industrial economy during an epidemic by using industrial land as a representation of industrial economic development. The three cities in Anhui-Wuhu, Maanshan, and Chuzhou (Wu-ma-Chu) were selected as the research area. The study employed the UNet deep learning method to detect the land use types in Wu-ma-Chu. The land transfer matrix and the standard deviation ellipse were utilised to research the alterations in industrial land use and the spatial distribution of industrial output value, respectively. The results showed that the industrial land in Machu continued to grow during the outbreak, highlighting the resilience of the region's industrial economy. During 2019-2022, the elliptical ring of industrial output value is distributed in Nanjing, revealing the radiating role of Nanjing in integrating into the integration of the YRD. This confirms China's YRD integration strategy, strengthens regional economic resilience, and encourages coordinated regional economic development., (© 2024. The Author(s).)

We conducted a large-scale online survey in February 2023 to investigate the public's perceptions of COVID-19 infection and fatality risks in Japan. We identified two key findings. First, univariate analysis comparing perceived and actual risk suggested overestimation and nonnegligible underestimation of COVID-19 risk. Second, multivariate logistic regression analyses revealed that age, income, education levels, health status, information sources, and experiences related to COVID-19 were associated with risk perceptions. Given that risk perceptions are closely correlated with daily socioeconomic activities and well-being, it is important for policy-makers and public health experts to understand how to communicate COVID-19 risk to the public effectively., (© 2024. The Author(s).)

The ongoing SARS-CoV-2 pandemic was initially managed by non-pharmaceutical interventions such as diagnostic testing, isolation of positive cases, physical distancing and lockdowns. The advent of vaccines has provided crucial protection against SARS-CoV-2. Neutralising antibody (nAb) responses are a key correlate of protection, and therefore measuring nAb responses is essential for monitoring vaccine efficacy. Fingerstick dried blood spots (DBS) are ideal for use in large-scale sero-surveillance because they are inexpensive, offer the option of self-collection and can be transported and stored at ambient temperatures. Such advantages also make DBS appealing to use in resource-limited settings and in potential future pandemics. In this study, nAb responses in sera, venous blood and fingerstick blood stored on filter paper were measured. Samples were collected from SARS-CoV-2 acutely infected individuals, SARS-CoV-2 convalescent individuals and SARS-CoV-2 vaccinated individuals. Good agreement was observed between the nAb responses measured in eluted DBS and paired sera. Stability of nAb responses was also observed in sera stored on filter paper at room temperature for 28 days. Overall, this study provides support for the use of filter paper as a viable sample collection method to study nAb responses., (© 2023. Springer Nature Limited.)

During the outbreak of coronavirus, RT-PCR was the premier gold standard method for severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) diagnosis. However, the sophisticated procedure of RT-PCR persuades researchers to develop sustainable point-of-need immunoassay methods for tracing unwitting carriers of SARSCoV-2. Herein, by fabricating a modified polysulfone (MPSF) membrane, we developed an integrated radial flow immunoassay (IRFIA) platform as a point-of-care system, capable of multiplying the immunoassays at a short run time. The target molecule is the SARSCoV-2 IgM in separated plasma. Although the lateral flow immunoassay kits for the rapid identification of Covid-19 have already been commercially developed but, the proposed method is superior to the conventional lateral flow immunoassay. In the newly designed membrane system, we have combined the five membranes of prevalent lateral flow immunoassay (LFIA) strips in one polymeric membrane. The MPSF membrane is capable of separating plasma from whole blood sample, which will reduce the interference of red colour of hemoglobin with generated signal and enhance the immunoassay precision. The efficiency of plasma separation, reached the mean value of 97.34 v/v% in 5 s. Furthermore, the gel electrophoresis results of the separated plasma contrasted with centrifuged plasma sample, demonstrated more efficient separation by the membrane. Using the MPSF membrane, signal generation time reduced from about 20 min in conventional rapid test strip for Covid-19 to about 7 min in IRFIA platform. The sensitivity and specificity of the membrane platform were determined to be 89% and 90%, respectively and a Kappa coefficient of 0.79 showed reliable agreement between the RT-PCR and the membrane system., (© 2023. Springer Nature Limited.)

In this study, we have presented a novel probabilistic model called the neutrosophic Burr-III distribution, designed for applications in neutrosophic surface analysis. Neutrosophic analysis allows for the incorporation of vague and imprecise information, reflecting the reality that many real-world problems involve ambiguous data. This ability to handle vagueness can lead to more robust and realistic models especially in situation where classical models fall short. We have also explored the neutrosophic Burr-III distribution in order to deal with the ambiguity and vagueness in the data where the classical Burr-III distribution falls short. This distribution offers valuable insights into various reliability properties, moment expressions, order statistics, and entropy measures, making it a versatile tool for analyzing complex data. To assess the practical relevance of our proposed distribution, we applied it to real-world data sets and compared its performance against the classical Burr-III distribution. The findings revealed that the neutrosophic Burr-III distribution outperformed than the classical Burr-III distribution in capturing the underlying data characteristics, highlighting its potential as a superior modeling toolin various fields., (© 2024. The Author(s).)

The present study aims to investigate the prevalence of loneliness and its associated factors among older adults during the COVID-19 pandemic in Bangladesh. This cross-sectional study was conducted in October 2020 among 1032 older Bangladeshi adults aged 60 years and above through telephone interviews. A semi-structured questionnaire was used to collect information on participants' characteristics and COVID-19-related information. Meanwhile, the level of loneliness was measured using a 3-item UCLA Loneliness scale. More than half (51.5%) of the older adults experienced loneliness. We found that participants formally schooled [adjusted odds ratio (aOR = 0.62, 95% CI 0.43-0.88)] and received COVID-19-related information from health workers (aOR = 0.33, 95% CI 0.22-0.49) had lower odds of being lonely during the pandemic. However, older adults living alone (aOR: 2.57, 95% CI 1.34-4.94), residing distant from a health facility (aOR = 1.46, 95% CI 1.02-2.08) and in rural areas (aOR = 1.53, 95% CI 1.02-2.23) had higher odds of loneliness than their counterparts. Likewise, odds of loneliness were higher among those overwhelmed by COVID-19 (aOR = 1.93, 95% CI 1.29-2.86), who faced difficulty in earning (aOR = 1.77, 95% CI 1.18-2.67) and receiving routine medical care during pandemic (aOR = 2.94, 95% CI 1.78-4.87), and those perceiving requiring additional care during the pandemic (aOR = 6.01, 95% CI 3.80-9.49). The findings suggest that policies and plans should be directed to reduce loneliness among older adults who require additional care., (© 2022. The Author(s).)

This paper investigates the air quality in 107 Italian provinces in the period 2014-2019 and the association between exposure to nine outdoor air pollutants and the COVID-19 spread and related mortality in the same areas. The methods used were negative binomial (NB) regression, ordinary least squares (OLS) model, and spatial autoregressive (SAR) model. The results showed that (i) common air pollutants-nitrogen dioxide (NO 2 ), ozone (O 3 ), and particulate matter (PM 2.5 and PM 10 )-were highly and positively correlated with large firms, energy and gas consumption, public transports, and livestock sector; (ii) long-term exposure to NO 2 , PM 2.5 , PM 10 , benzene, benzo[a]pyrene (BaP), and cadmium (Cd) was positively and significantly correlated with the spread of COVID-19; and (iii) long-term exposure to NO 2, O 3 , PM 2.5 , PM 10 , and arsenic (As) was positively and significantly correlated with COVID-19 related mortality. Specifically, particulate matter and Cd showed the most adverse effect on COVID-19 prevalence; while particulate matter and As showed the largest dangerous impact on excess mortality rate. The results were confirmed even after controlling for eighteen covariates and spatial effects. This outcome seems of interest because benzene, BaP, and heavy metals (As and Cd) have not been considered at all in recent literature. It also suggests the need for a national strategy to drive down air pollutant concentrations to cope better with potential future pandemics., (© 2022. The Author(s).)

To determine the number of patients with acute respiratory distress syndrome (ARDS) who would be eligible to receive veno-venous extracorporeal membrane oxygenation (VV-ECMO). We conducted a retrospective observational study of ARDS patients admitted to Regina General Hospital Intensive Care Unit (ICU). VV-ECMO eligibility was assessed using selection criteria from the Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Syndrome trial (EOLIA), the Extracorporeal Life Support Organization (ELSO), New South Wales (NSW), Critical Care Services Ontario (CCSO) and a Regina-restrictive criteria. Of 415 patients admitted between October 16, 2018, and January 21, 2021, 103 (25%) had mild, 175 (42%) had moderate, and 64 (15%) had severe ARDS. Of the cohort, 144 (35%) had bacterial pneumonia, 86 (21%) had viral pneumonia (including COVID-19), and 72 (17%) had aspiration pneumonia. Using the EOLIA, ELSO, NSW, CCSO and Regina-restrictive criteria, 7/415 (1.7%), 6/415 (1.5%), 19/415 (4.6%), 26/415 (6.3%) and 12/415 (2.9%) were eligible for VV-ECMO, respectively. Of all ECMO-eligible patients, only one (2.4%) actually received VV-ECMO, 20/42 (48%) received prone positioning and 21/42 (50%) received neuromuscular blockade. There is potential for service expansion of VV-ECMO in Regina; however, there is still a need to improve the delivery of evidence-based ARDS therapies., (© 2023. Springer Nature Limited.)

The link between aerosol dynamics and viral exposure risk is not fully understood, particularly during movement and face-to-face interactions. To investigate this, we employed Particle Trace Velocimetry with a laser sheet and a high-speed camera to measure microparticles from a human mannequin's mouth. The average peak time in the non-ventilated condition (expiratory volume, 30 L; passing speed, 5 km/h) was 1.33 s (standard deviation = 0.32 s), while that in the ventilated condition was 1.38 s (standard deviation = 0.35 s). Our results showed that the peak of viral exposure risk was within 5 s during face-to-face encounters under both ventilated and non-ventilated conditions. Moreover, the risk of viral exposure greatly decreased in ventilated conditions compared to non-ventilated conditions. Based on these findings, considering a risk mitigation strategy for the duration of 5 s during face-to-face encounters is expected to significantly reduce the risk of virus exposure in airborne transmission., (© 2023. Springer Nature Limited.)

Injuries to the dentofacial region caused by third parties can affect physiological, sensory and esthetic functions with legal repercussions. The personal and social circumstances generated by Covid-19 and the governmental measures taken to control it, have increased the risk factors for violence and with it, the resulting injury rate. The aim of the present investigation was to compare the amount of civil liability for dental injury crimes agreed by Spanish courts, in certain Autonomous Communities, before and during the pandemic situation caused by Covid-19. For this purpose, a analytic cross-sectional study was carried out by analyzing sentences from the database of the Judicial Documentation Center. A comparison of means (one-way ANOVA) was used on the amount of compensation between the different years, and between the Autonomous Communities of Madrid, Catalonia Cataluña, Andalusia, the Canary Islands and the Valencian Community. It was observed that the year 2020 stood out for the increase in the number of cases of dental injury offenses. For its part, the Autonomous Community of Andalusia showed the highest amount of compensation during the pandemic, although the highest number of cases corresponded to the Community of Madrid. The statistical analysis yielded a probability of more than 0.05, which eliminated the possibility of significant differences in each of the comparisons., (© 2023. Springer Nature Limited.)

The SARS-CoV-2 (COVID-19) pandemic impacted the health systems between and within countries, and in the course of the pandemic sexual and reproductive health services were the most disrupted. Findings from high-income settings have reported significant changes in preterm birth prevalence during the pandemic period. To understand the possible effects of the COVID-19 pandemic on preterm birth numbers at the Brazilian national level. We compare the number of preterm deliveries during the COVID-19 pandemic period (2020 and 2021) with previous years. We conducted a population-based cross-sectional study taking the period from January 2017 to December 2021 to account. We use individual-level live births data from the Brazilian Live Birth Information System (SINASC), and we estimate the odds ratio (OR) of preterm deliveries using propensity score weighting analysis in Brazil and its regions. During the study period (from 2017 to 2021), about 2.7 million live births were recorded per year, and the missing value for gestational age at delivery was less than 1.5%. The preterm birth prevalence slightly increased during the COVID-19 pandemic compared to the pre-pandemic period (11.32% in 2021 vs 11.09% in 2019, p-value < 0.0001). After adjusting for sociodemographic variables, the OR of preterm births in Brazil has significantly increased, 4% in 2020 (OR: 1.04 [1.03-1.05] 95% CI, p-value < 0.001), and 2% in 2021(OR: 1.02 [1.01-1.03] 95% CI, p-value < 0.001), compared to 2019. At the regional level, the preterm birth pattern in the South, Southeast and Northeast regions show a similar pattern. The highest odds ratio was observed in the South region (2020 vs 2019, OR: 1.07 [1.05-1.10] 95% CI; 2021 vs 2019, OR: 1.03 [1.01-1.06] 95% CI). However, we also observed a significant reduction in the ORs of preterm births in the northern region during the COVID-19 pandemic (2020 vs 2019, OR: 0.96 [0.94-0.98] 95% CI) and (2021 vs 2019, OR: 0.97 [0.95-0.99] 95% CI). Our analysis shows that the pandemic has increased regional variation in the number of preterm births in Brazil in 2020 and 2021 compared to the pre-pandemic years., (© 2023. Springer Nature Limited.)

Few studies have identified the metabolic consequences of the post-acute phase of nonsevere COVID-19. This prospective study examined metabolic outcomes and associated factors in nonsevere, RT-PCR-confirmed COVID-19. The participants' metabolic parameters, the prevalence of long-term multiple metabolic abnormalities (≥ 2 components), and factors influencing the prevalence were assessed at 1, 3, and 6 months post-onset. Six hundred individuals (mean age 45.5 ± 14.5 years, 61.7% female, 38% high-risk individuals) with nonsevere COVID-19 attended at least one follow-up visit. The prevalence of worsening metabolic abnormalities was 26.0% for BMI, 43.2% for glucose, 40.5% for LDL-c, 19.1% for liver, and 14.8% for C-reactive protein. Except for lipids, metabolic-component abnormalities were more prevalent in high-risk hosts than in healthy individuals. The prevalence of multiple metabolic abnormalities at the 6-month follow-up was 41.3% and significantly higher in high-risk than healthy hosts (49.2% vs 36.5%; P = 0.007). Factors independently associated with a lower risk of these abnormalities were being female, having dyslipidemia, and receiving at least 3 doses of the COVID-19 vaccine. These findings suggest that multiple metabolic abnormalities are the long-term consequences of COVID-19. For both high-risk and healthy individuals with nonsevere COVID-19, healthcare providers should monitor metabolic profiles, encourage healthy behaviors, and ensure complete vaccination., (© 2023. Springer Nature Limited.)

The coronavirus disease (COVID-19) pandemic has emphasized the paucity of non-contact and non-invasive methods for the objective evaluation of dry eye disease (DED). However, robust evidence to support the implementation of mHealth- and app-based biometrics for clinical use is lacking. This study aimed to evaluate the reliability and validity of app-based maximum blink interval (MBI) measurements using DryEyeRhythm and equivalent traditional techniques in providing an accessible and convenient diagnosis. In this single-center, prospective, cross-sectional, observational study, 83 participants, including 57 with DED, had measurements recorded including slit-lamp-based, app-based, and visually confirmed MBI. Internal consistency and reliability were assessed using Cronbach's alpha and intraclass correlation coefficients. Discriminant and concurrent validity were assessed by comparing the MBIs from the DED and non-DED groups and Pearson's tests for each platform pair. Bland-Altman analysis was performed to assess the agreement between platforms. App-based MBI showed good Cronbach's alpha coefficient, intraclass correlation coefficient, and Pearson correlation coefficient values, compared with visually confirmed MBI. The DED group had significantly shorter app-based MBIs, compared with the non-DED group. Bland-Altman analysis revealed minimal biases between the app-based and visually confirmed MBIs. Our findings indicate that DryEyeRhythm is a reliable and valid tool that can be used for non-invasive and non-contact collection of MBI measurements, which can assist in accessible DED detection and management., (© 2023. Springer Nature Limited.)

The Coronavirus Disease 2019 (COVID-19) is transitioning into the endemic phase. Nonetheless, it is crucial to remain mindful that pandemics related to infectious respiratory diseases (IRDs) can emerge unpredictably. Therefore, we aimed to develop and validate a severity assessment model for IRDs, including COVID-19, influenza, and novel influenza, using CT images on a multi-centre data set. Of the 805 COVID-19 patients collected from a single centre, 649 were used for training and 156 were used for internal validation (D1). Additionally, three external validation sets were obtained from 7 cohorts: 1138 patients with COVID-19 (D2), and 233 patients with influenza and novel influenza (D3). A hybrid model, referred to as Hybrid-DDM, was constructed by combining two deep learning models and a machine learning model. Across datasets D1, D2, and D3, the Hybrid-DDM exhibited significantly improved performance compared to the baseline model. The areas under the receiver operating curves (AUCs) were 0.830 versus 0.767 (p = 0.036) in D1, 0.801 versus 0.753 (p < 0.001) in D2, and 0.774 versus 0.668 (p < 0.001) in D3. This study indicates that the Hybrid-DDM model, trained using COVID-19 patient data, is effective and can also be applicable to patients with other types of viral pneumonia., (© 2023. Springer Nature Limited.)

At the height of the COVID-19 pandemic, K-12 schools struggled to safely operate under the fast-changing pandemic situation. However, little is known about the impact of different school operating scenarios considering the ongoing efforts of vaccination. In this study, we deployed an agent-based simulation model to mimic disease transmission in a mid-sized community consisting of 10,000 households. A total of eight school operating scenarios were simulated, in decreasing order of restrictiveness regarding COVID-19 mitigation measures. When masks were worn at school, work, and community environments, increasing in-person education from 50% to 100% would result in only 1% increase in cumulative infections. When there were no masks nor contact tracing while schools were 100% in person, the cumulative infection increased by 86% compared to the scenario when both masking and contact tracing were in place. In the sensitivity analysis for vaccination efficacy, we found that higher vaccination efficacy was essential in reducing overall infections. Our findings showed that full in-person education was safe, especially when contact tracing, masking, and widespread vaccination were in place. If no masking nor contact tracing was practiced, the transmission would rose dramatically but eventually slow down due to herd immunity., (© 2023. Springer Nature Limited.)

Covid-19 has spurred a renewed interest in decontamination techniques for air, objects and surfaces. Beginning in 2020, urgent effort was done to permit the reuse of UV-C for inactivating SARS-CoV-2. However, those studies diverged widely on the dose necessary to reach this goal; until today, the real value of the sensitivity of the virus to a 254-nm illumination is not known precisely. In this study, decontamination was performed in an original UV-C large decontamination chamber (UVCab, ON-LIGHT, France) delivering an omnidirectional irradiation with an average dose of 50 mJ/cm 2 in 60 s. Viral inactivation was checked by both cell culture and PCR test. SARS-CoV-2 was inactivated by UV-C light within 3 s on both porous (disposable gown) and non-porous (stainless steel and apron) surfaces. For the porous surface, an irradiation of 5 min was needed to achieve a completely negative PCR signal. The Z value estimating the sensitivity of SARS-CoV-2 to UV-C in the experimental conditions of our cabinet was shown to be > 0.5820 m 2 /J. These results illustrate the ability of this apparatus to inactivate rapidly and definitively high loads of SARS-CoV-2 deposited on porous or non-porous supports and opens new perspectives on material decontamination using UV-C., (© 2023. Springer Nature Limited.)

COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots., (© 2023. The Author(s).)

The pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we developed a mathematical model to analyze the effects of the interaction between supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. Analysis of the supply chain model suggests that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviates the societal impact of the disease. A lean resource allocation strategy can reduce the impact of supply chain shortages from 11.91 to 1.11% in North America. Our model highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic and provides a framework that could advance the containment and model-based decision making for future pandemics., (© 2021. The Author(s).)

We aimed to assess the potential role of Asymmetric dimethylarginine (ADMA) in conditioning respiratory function and pulmonary vasoregulation during Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) infection. Within 72 h from admission, samples from 90 COVID-19 patients were assessed for ADMA, SDMA, L-arginine concentrations. In addition to classical statistics, patients were also clustered by a machine learning approach according to similar features. Multivariable analysis showed that C-reactive protein (OR 1.012), serum ADMA (OR 4.652), white blood cells (OR = 1.118) and SOFA (OR = 1.495) were significantly associated with negative outcomes. Machine learning-based clustering showed three distinct clusters: (1) patients with low severity not requiring invasive mechanical ventilation (IMV), (2) patients with moderate severity and respiratory failure whilst not requiring IMV, and (3) patients with highest severity requiring IMV. Serum ADMA concentration was significantly associated with disease severity and need for IMV although less pulmonary vasodilation was observed by CT scan. High serum levels of ADMA are indicative of high disease severity and requirement of mechanical ventilation. Serum ADMA at the time of hospital admission may therefore help to identify COVID-19 patients at high risk of deterioration and negative outcome., (© 2023. The Author(s).)

The COVID-19 pandemic continues to emphasize the importance of epidemiological modelling in guiding timely and systematic responses to public health threats. Nonetheless, the predictive qualities of these models remain limited by their underlying assumptions of the factors and determinants shaping national and regional disease landscapes. Here, we introduce epidemiological feature detection, a novel latent variable mixture modelling approach to extracting and parameterizing distinct and localized features of real-world trends in daily COVID-19 cases and deaths. In this approach, we combine methods of peak deconvolution that are commonly used in spectroscopy with the susceptible-infected-recovered-deceased model of disease transmission. We analyze the second wave of the COVID-19 pandemic in Israel, Canada, and Germany and find that the lag time between reported cases and deaths, which we term case-death latency, is closely correlated with adjusted case fatality rates across these countries. Our findings illustrate the spatiotemporal variability of both these disease metrics within and between different disease landscapes. They also highlight the complex relationship between case-death latency, adjusted case fatality rate, and COVID-19 management across various degrees of decentralized governments and administrative structures, which provides a retrospective framework for responding to future pandemics and disease outbreaks., (© 2023. The Author(s).)

China detected the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with Delta variant in May 2021. We assessed control strategies against this variant of concern. We constructed a robust transmission model to assess the effectiveness of interventions against the Delta variant in Guangzhou with initial quarantine/isolation, followed by social distancing. We also assessed the effectiveness of alternative strategies and that against potentially more infectious variants. The effective reproduction number (R t ) fell below 1 when the average daily number of close contacts was reduced to ≤ 7 and quarantine/isolation was implemented on average at the same day of symptom onset in Guangzhou. Simulations showed that the outbreak could still be contained when quarantine is implemented on average 1 day after symptom onset while the average daily number of close contacts was reduced to ≤ 9 per person one week after the outbreak's beginning. Early quarantine and reduction of close contacts were found to be important for containment of the outbreaks. Early implementation of quarantine/isolation along with social distancing measures could effectively suppress spread of the Delta and more infectious variants., (© 2022. The Author(s).)

The fans' importance in sports is acknowledged by the term 'the 12th man', a figurative extra player for the home team. Sport teams are indeed more successful when they play in front of their fans than when they play away. The supposed mechanism behind this phenomenon, termed Home Advantage (HA), is that fans' support spurs home players to better performance and biases referees, which in turn determines the outcome. The inference about the importance of fans' support is, however, indirect as there is normally a 12th man of this kind, even if it is an opponent's. The current pandemic, which forced sporting activities to take place behind closed doors, provides the necessary control condition. Here we employ a novel conceptual HA model on a sample of over 4000 soccer matches from 12 European leagues, some played in front of spectators and some in empty stadia, to demonstrate that fans are indeed responsible for the HA. However, the absence of fans reduces the HA by a third, as the home team's performance suffers and the officials' bias disappears. The current pandemic reveals that the figurative 12th man is no mere fan hyperbole, but is in fact the most important player in the home team., (© 2021. The Author(s).)

The Coronavirus Disease (COVID-19) pandemic imposed a high burden of morbidity and mortality. In COVID-19, direct lung parenchymal involvement and pulmonary microcirculation dysfunction may entail pulmonary hypertension (PH). PH and direct cardiac injury beget right ventricular dysfunction (RVD) occurrence, which has been frequently reported in COVID-19 patients; however, the prevalence of RVD and its impact on outcomes during COVID-19 are still unclear. This study aims to evaluate the prevalence of RVD and associated outcomes in patients with COVID-19, through a Systematic Review and Meta-Analysis. MEDLINE and EMBASE were systematically searched from inception to 15th July 2021. All studies reporting either the prevalence of RVD in COVID-19 patients or all-cause death according to RVD status were included. The pooled prevalence of RVD and Odds Ratio (OR) for all-cause death according to RVD status were computed and reported. Subgroup analysis and meta-regression were also performed. Among 29 studies (3813 patients) included, pooled prevalence of RVD was 20.4% (95% CI 17.1-24.3%; 95% PI 7.8-43.9%), with a high grade of heterogeneity. No significant differences were found across geographical locations, or according to the risk of bias. Severity of COVID-19 was associated with increased prevalence of RVD at meta-regression. The presence of RVD was found associated with an increased likelihood of all-cause death (OR 3.32, 95% CI 1.94-5.70). RVD was found in 1 out of 5 COVID-19 patients, and was associated with all-cause mortality. RVD may represent one crucial marker for prognostic stratification in COVID-19; further prospective and larger are needed to investigate specific management and therapeutic approach for these patients., (© 2021. The Author(s).)

COVID-19 pandemic continues to obstruct social lives and the world economy other than questioning the healthcare capacity of many countries. Weather components recently came to notice as the northern hemisphere was hit by escalated incidence in winter. This study investigated the association between COVID-19 cases and two components, average temperature and relative humidity, in the 16 states of Germany. Three main approaches were carried out in this study, namely temporal correlation, spatial auto-correlation, and clustering-integrated panel regression. It is claimed that the daily COVID-19 cases correlate negatively with the average temperature and positively with the average relative humidity. To extract the spatial auto-correlation, both global Moran's [Formula: see text] and global Geary's [Formula: see text] were used whereby no significant difference in the results was observed. It is evident that randomness overwhelms the spatial pattern in all the states for most of the observations, except in recent observations where either local clusters or dispersion occurred. This is further supported by Moran's scatter plot, where states' dynamics to and fro cold and hot spots are identified, rendering a traveling-related early warning system. A random-effects model was used in the sense of case-weather regression including incidence clustering. Our task is to perceive which ranges of the incidence that are well predicted by the existing weather components rather than seeing which ranges of the weather components predicting the incidence. The proposed clustering-integrated model associated with optimal barriers articulates the data well whereby weather components outperform lag incidence cases in the prediction. Practical implications based on marginal effects follow posterior to model diagnostics.

The COVID-19 pandemic has had a significant impact on students' academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits. Students getting used to online learning routines might find it even more challenging to perform well in face to face learning. Therefore, assessing various potential risk factors associated with students low performance and its prediction is important for early intervention. As students' performance data encompass diverse behaviors, standard machine learning methods find it hard to get useful insights for beneficial practical decision making and early interventions. Therefore, this research explores regularized ensemble learning methods for effectively analyzing students' performance data and reaching valid conclusions. To this end, three pruning strategies are implemented for the random forest method. These methods are based on out-of-bag sampling, sub-sampling and sub-bagging. The pruning strategies discard trees that are adversely affected by the unusual patterns in the students data forming forests of accurate and diverse trees. The methods are illustrated on an example data collected from university students currently studying on campus in a face-to-face modality, who studied during the COVID-19 pandemic through online learning. The suggested methods outperform all the other methods considered in this paper for predicting students at the risk of academic failure. Moreover, various factors such as class attendance, students interaction, internet connectivity, pre-requisite course(s) during the restrictions, etc., are identified as the most significant features. [ABSTRACT FROM AUTHOR]

Text mining enables search, extraction, categorisation and information visualisation. This study aimed to identify oral manifestations in patients with COVID-19 using text mining to facilitate extracting relevant clinical information from a large set of publications. A list of publications from the open-access COVID-19 Open Research Dataset was downloaded using keywords related to oral health and dentistry. A total of 694,366 documents were retrieved. Filtering the articles using text mining yielded 1,554 oral health/dentistry papers. The list of articles was classified into five topics after applying a Latent Dirichlet Allocation (LDA) model. This classification was compared to the author's classification which yielded 17 categories. After a full-text review of articles in the category "Oral manifestations in patients with COVID-19", eight papers were selected to extract data. The most frequent oral manifestations were xerostomia (n = 405, 17.8%) and mouth pain or swelling (n = 289, 12.7%). These oral manifestations in patients with COVID-19 must be considered with other symptoms to diminish the risk of dentist-patient infection. [ABSTRACT FROM AUTHOR]

Lockdowns and work-from-home arrangements became abrupt realities for people at an unprecedented scale during the COVID-19 pandemic. Here, considering the case of Singapore, we study how peoples' sleep behaviors-which are closely linked to their mental health-varied as a result. However, different from most studies, this paper uses household electricity consumption data to estimate the sleeping behaviors of nearly 10,000 households in the city-state. With this, we study how the residents' daily sleep durations changed dynamically during the lockdown and afterwards when restrictions were progressively eased, and show their strong connection to major changes in the public health policy and current events during this period. Our results add to the evidence for the stress endured by the populace during the lockdown; we find that sleep durations for all demographics, while higher than before the lockdown, became more fluctuating across days. A major, and surprising, finding is that it was the lockdown that determined the residents' sleeping duration, rather than simply working-from-home arrangements. That is, the sleeping durations largely reverted back to their pre-pandemic levels when the lockdown was lifted-with small variations based on demographic factors-although a vast majority of people continued to work from home. This highlights the resilience of the daily routines of the Singapore populace. While providing insights into how a pandemic influences the dynamics of urban sleep patterns, our finding also has broader implications regarding the efficiency of the workforce, suggesting that concerns about asynchronous work routines and productivity may be overblown., (© 2024. The Author(s).)

Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proactive intervention, and targeted preventive measures. This paper introduces Sybil, a framework that integrates machine learning and variant-aware compartmental models, leveraging a fusion of data-centric and analytic methodologies. To validate and evaluate Sybil's forecasts, we employed COVID-19 data from several European and U.S. states. The dataset included the number of new and recovered cases, fatalities, and variant presence over time. We evaluate the forecasting precision of Sybil in periods in which there is a change in the trend of the pandemic evolution or a new variant appears. Results demonstrate that Sybil outperforms conventional data-centric approaches, being able to forecast accurately the changes in the trend, the magnitude of these changes, and the future prevalence of new variants., (© 2024. The Author(s).)

Peripheral Capillary Oxygen Saturation (SpO 2 ) has received increasing attention during the COVID-19 pandemic. Clinical investigations have demonstrated that individuals afflicted with COVID-19 exhibit notably reduced levels of SpO 2 before the deterioration of their health status. To cost-effectively enable individuals to monitor their SpO 2 , this paper proposes a novel neural network model named "ITSCAN" based on Temporal Shift Module. Benefiting from the widespread use of smartphones, this model can assess an individual's SpO 2 in real time, utilizing standard facial video footage, with a temporal granularity of seconds. The model is interweaved by two distinct branches: the motion branch, responsible for extracting spatiotemporal data features and the appearance branch, focusing on the correlation between feature channels and the location information of feature map using coordinate attention mechanisms. Accordingly, the SpO 2 estimator generates the corresponding SpO 2 value. This paper summarizes for the first time 5 loss functions commonly used in the SpO 2 estimation model. Subsequently, a novel loss function has been contributed through the examination of various combinations and careful selection of hyperparameters. Comprehensive ablation experiments analyze the independent impact of each module on the overall model performance. Finally, the experimental results based on the public dataset (VIPL-HR) show that our model has obvious advantages in MAE (1.10%) and RMSE (1.19%) compared with related work, which implies more accuracy of the proposed method to contribute to public health., (© 2024. The Author(s).)

The COVID-19 pandemic has imposed significant challenges on global health, emphasizing the persistent threat of large-scale infectious diseases in the future. This study addresses the need to enhance pooled testing efficiency for large populations. The common approach in pooled testing involves consolidating multiple test samples into a single tube to efficiently detect positivity at a lower cost. However, what is the optimal number of samples to be grouped together in order to minimize costs? i.e. allocating ten individuals per group may not be the most cost-effective strategy. In response, this paper introduces the hierarchical quotient space, an extension of fuzzy equivalence relations, as a method to optimize group allocations. In this study, we propose a cost-sensitive multi-granularity intelligent decision model to further minimize testing costs. This model considers both testing and collection costs, aiming to achieve the lowest total cost through optimal grouping at a single layer. Building upon this foundation, two multi-granularity models are proposed, exploring hierarchical group optimization. The experimental simulations were conducted using MATLAB R2022a on a desktop with Intel i5-10500 CPU and 8G RAM, considering scenarios with a fixed number of individuals and fixed positive probability. The main findings from our simulations demonstrate that the proposed models significantly enhance the efficiency and reduce the overall costs associated with pooled testing. For example, testing costs were reduced by nearly half when the optimal grouping strategy was applied, compared to the traditional method of grouping ten individuals. Additionally, the multi-granularity approach further optimized the hierarchical groupings, leading to substantial cost savings and improved testing efficiency., (© 2024. The Author(s).)

Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness., (© 2024. The Author(s).)

The process of implementation of the UN Sustainable Development Goals (SDGs) which were approved by the UN General Assembly in 2015 has not been simple, being influenced by variety of social, economic, and logistical problems. It has also been negatively affected by the COVID-19 pandemic. There are to date no specific studies aimed at assessing the extent to which higher universities institutions in Europe are active in the SDGs implementation process. Departing from this research need, this paper reports on a study aimed at examining the current degree of engagement of European universities in the implementing the SDGs. By using a multi-methods approach, which entails a review of existing documents, a survey involving participants from 22 countries and case studies, the paper maps, documents and disseminates examples of what European universities are doing to implement the SDGs, the challenges they face, and the solutions being deployed to overcome them., (© 2024. The Author(s).)

Future state prediction for nonlinear dynamical systems is a challenging task. Classical prediction theory is based on a, typically long, sequence of prior observations and is rooted in assumptions on statistical stationarity of the underlying stochastic process. These algorithms have trouble predicting chaotic dynamics, "Black Swans" (events which have never previously been seen in the observed data), or systems where the underlying driving process fundamentally changes. In this paper we develop (1) a global and local prediction algorithm that can handle these types of systems, (2) a method of switching between local and global prediction, and (3) a retouching method that tracks what predictions would have been if the underlying dynamics had not changed and uses these predictions when the underlying process reverts back to the original dynamics. The methodology is rooted in Koopman operator theory from dynamical systems. An advantage is that it is model-free, purely data-driven and adapts organically to changes in the system. While we showcase the algorithms on predicting the number of infected cases for COVID-19 and influenza cases, we emphasize that this is a general prediction methodology that has applications far outside of epidemiology. [ABSTRACT FROM AUTHOR]

Disaster, catastrophe, and cataclysm are some English terminologies that describe the severity of adverse events. Civilians, reporters, and professionals often use these terminologies to communicate and report any event's severity. This linguistic method is the most practical way to rapidly reach all levels of local/regional/national, and international stakeholders during disasters. Therefore, disaster terminologies play a significant role in disaster management. However, attaining the actual magnitude of a disaster's severity cannot be comprehended simply by using these terminologies because they are used interchangeably. Unfortunately, there is no consistent method to differentiate disaster terminologies from one another. Additionally, no globally accepted standard technique exists to communicate the severity level when disasters strike; one observer's 'disaster' can be another's 'catastrophe'. Hence, a nation's ability to manage extreme events is difficult when there are no agreed terminologies among emergency management systems. A standard severity classification system is required to understand, communicate, report, and educate stakeholders. This paper presents perceptions of people about disaster terminologies in different geographical regions, rankings and differences in disaster lexical and lexicon. It explores how people perceive major events (e.g., the Covid-19 pandemic), and proposes a ranking of disaster terminologies to create a severity classification system suitable for global use., (© 2024. The Author(s).)

This paper analyzes the determinants of COVID-19 mortality across over 140 countries in 2020, with a focus on healthcare expenditure and corruption. It finds a positive association between COVID-19 deaths and aging populations, obesity rates, and healthcare expenditure while noting a negative association with rural residency and corruption perception. The study further reveals that mortality is positively associated with aging populations in high-income countries and positively associated with obesity in upper-middle to high-income countries. Mortality is positively associated with healthcare expenditure, which likely reflects a country's preparedness and ability to better track, document, and report COVID-19 deaths. On the other hand, mortality is negatively associated with corruption perception in upper-middle-income countries. Further analyses based on 2021 data reveal COVID-19 deaths are positively associated with the proportion of the population aged 65 and older in low to lower-middle-income countries, with obesity in high-income countries, and with tobacco use across most countries. Interestingly, there is no evidence linking COVID-19 deaths to healthcare expenditure and corruption perception, suggesting a post-2020 convergence in preparedness likely due to proactive pandemic responses, which might have also mitigated corruption's impact. Policy recommendations are proposed to aid the elderly, address obesity, and combat tobacco use., (© 2024. The Author(s).)

This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation., (© 2024. The Author(s).)

A situation of emergency involving the whole population introduces changes in the dynamics of the health services that are provided. The magnitude of these shifts should be also linked to the medical speciality within which the health benefits are delivered. The aim of the paper was to identify changes in tendencies in inpatient medical service delivery during the COVID-19 pandemic by medical specialties. On the basis of a database extracted for in-patient treatment received from the public payer, a retrospective analysis was carried out. Comparing the values of the dynamics of the services provided in each medical speciality, the period before the COVID-19 pandemic was collated to the years of the pandemic (2020-2021). In the period before COVID-19, positive patient dynamics were observed in more than half of the specialities. Between 2020 and 2021, virtually all specialties reversed the trend and negative dynamics were recorded. The dynamics in 2021 indicate a process of return to the values from 2015 to 2019. Emergency situation has affected the dynamics of healthcare provision in different specialities to various extent. The most resistant to the negative impulses of the state of emergency were the areas that are strictly organisationally and financially defined (e.g. the group of "therapeutic and drug programmes")., (© 2024. The Author(s).)

In Rhineland-Palatinate, Germany, a system of three data sources has been established to track the Covid-19 pandemic. These sources are the number of Covid-19-related hospitalizations, the Covid-19 genecopies in wastewater, and the prevalence derived from a cohort study. This paper presents an extensive comparison of these parameters. It is investigated whether wastewater data and a cohort study can be valid surrogate parameters for the number of hospitalizations and thus serve as predictors for coming Covid-19 waves. We observe that this is possible in general for the cohort study prevalence, while the wastewater data suffer from a too large variability to make quantitative predictions by a purely data-driven approach. However, the wastewater data and the cohort study prevalence are able to detect hospitalizations waves in a qualitative manner. Furthermore, a detailed comparison of different normalization techniques of wastewater data is provided., (© 2024. The Author(s).)

Though COVID-19 is no longer a pandemic but rather an endemic, the epidemiological situation related to the SARS-CoV-2 virus is developing at an alarming rate, impacting every corner of the world. The rapid escalation of the coronavirus has led to the scientific community engagement, continually seeking solutions to ensure the comfort and safety of society. Understanding the joint impact of medical and non-medical interventions on COVID-19 spread is essential for making public health decisions that control the pandemic. This paper introduces two novel hybrid machine-learning ensembles that combine supervised and unsupervised learning for COVID-19 data classification and regression. The study utilizes publicly available COVID-19 outbreak and potential predictive features in the USA dataset, which provides information related to the outbreak of COVID-19 disease in the US, including data from each of 3142 US counties from the beginning of the epidemic (January 2020) until June 2021. The developed hybrid hierarchical classifiers outperform single classification algorithms. The best-achieved performance metrics for the classification task were Accuracy = 0.912, ROC-AUC = 0.916, and F1-score = 0.916. The proposed hybrid hierarchical ensemble combining both supervised and unsupervised learning allows us to increase the accuracy of the regression task by 11% in terms of MSE, 29% in terms of the area under the ROC, and 43% in terms of the MPP metric. Thus, using the proposed approach, it is possible to predict the number of COVID-19 cases and deaths based on demographic, geographic, climatic, traffic, public health, social-distancing-policy adherence, and political characteristics with sufficiently high accuracy. The study reveals that virus pressure is the most important feature in COVID-19 spread for classification and regression analysis. Five other significant features were identified to have the most influence on COVID-19 spread. The combined ensembling approach introduced in this study can help policymakers design prevention and control measures to avoid or minimize public health threats in the future., (© 2024. The Author(s).)