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Background: The human hand has constant contact with the environment, hence requires regular hand hygiene. Hand hygiene has gained recognition because of the COVID-19 pandemic and is a largely effective, affordable preventive measure against infectious diseases. This study used both national and sub-national analyses to evaluate the effect of COVID-19 handwashing guidelines on instances of diarrhea in Ghana. Methods: Data on diarrhea cases spanning February 2018 and March 2022 were retrieved from the District Health Information Management System (DHIMS 2) using a data extraction guide. The data were summarized using descriptive statistics. The difference in diarrhea cases between the pre-COVID-19 and COVID-19 periods was measured using a two-sample t-test across Ghana's 16 administrative areas. Causal Impact package in R statistical software was employed to determine the impact of the introduction of COVID-19 hand hygiene protocols on diarrheal disease. Results: A total of 5,645,533 diarrheal cases reported between February 2018 and March 2022 through the routine MIS (DHIMS2) were examined. Fifty-three percent of the cases occurred before the introduction of the hand hygiene protocol. Descriptive statistics indicated a statistically significant decrease in average diarrheal cases during the hand hygiene implementation era (13,463 cases reduction, p<0.001). Sub-national analyses revealed significant reductions in various regions: Greater Accra, Ashanti, Ahafo, Central, Eastern, Northern, Upper East, Upper West, and Volta (p<0.05). Causal impact analysis confirmed 11.0% nationwide reduction in diarrheal cases attributed to the COVID-19 hand hygiene protocols (p<0.001). Conclusion: This study underscores the effectiveness of COVID-19 hand hygiene protocols in reducing diarrheal morbidity in Ghana, with varying regional impacts. These findings advocate for the sustenance of investments and commitments made at the COVID hand hygiene protocols, particularly in this era where the pandemic appears controlled. [ABSTRACT FROM AUTHOR]

To counter the spread of COVID-19, the French government imposed several stringent social and political measures across its entire population. We hereto assess the impact of these political decisions on healthcare access in 2020, focusing on patients who suffered from an ischemic stroke. We divide our analysis into four distinct periods: the pre-COVID-19 pandemic period, the lockdown period, the "in-between" or transitional period, and the shutdown period. Our methodology involves utilizing a retrospective dataset spanning 2019–2020, an exhaustive French national hospital discharge diagnosis database for stroke inpatients, integrated with income information from the reference year of 2019. The results reveal that the most affluent were more likely to forgo medical care, particularly in heavily affected areas. Moreover, the most disadvantaged exhibited even greater reluctance to seek care, especially in the most severely impacted regions. The data suggest a loss of opportunity for less severely affected patients to benefit from healthcares during this lockdown period, regardless of demographic, location, and socioeconomic determinants. Furthermore, our analysis reveals a notable discrepancy in healthcare-seeking behavior, with less affluent patients and seniors (over 75 years old) experiencing slower rates of return to healthcare access compared to pre-pandemic levels. This highlights a persistent gap in healthcare accessibility, particularly among socioeconomically disadvantaged groups, despite the easing of COVID-19 restrictions. [ABSTRACT FROM AUTHOR]

Although many studies highlighted the potential of COVID-19 to reshape existing models of wildlife management, empirical research on this topic has been scarce, particularly in Europe. We investigated the potential of COVID-19 pandemic to accelerate the ongoing decline in an aging population of recreational hunters in Italy. Namely, we modelled spatiotemporal trends between 2011 and 2021 in the number of recreational hunters in 50 Italian provinces with a varying incidence of COVID-19, and temporally delayed waves of infection. Compared to projections from 2011–2019 data, we detected a lower number of hunters who enrolled for the hunting season, both in 2020 (14 provinces) and in 2021 (15 provinces). The provinces with the highest incidence of COVID-19 in the Lombardy and Emilia-Romagna regions were also those experiencing the most marked decrease in hunting participation. Our findings revealed that a wildlife management system based on recreational hunting can be rapidly destabilized by epidemics and their associated public health measures, particularly when the average age of hunters is high, like in Italy. Considered the high incidence attained by COVID-19 in many European countries, where hunters are pivotal for the management of large ungulates and where they were already declining before the pandemic, our findings call for further large-scale research about the impact of COVID-19 on hunting participation. [ABSTRACT FROM AUTHOR]

Social contexts (e.g., family, friends) are important in predicting and preventing loneliness in middle childhood (MC) and adolescence; however, these social contexts were disrupted during the COVID-19 pandemic. Comparison of social context factors that may differentially contribute to loneliness at each developmental stage (MC vs. adolescence) during the COVID-19 pandemic have been overlooked. This study examined longitudinal predictors of loneliness, including social contexts and COVID-19 impact, within MC (8-12y) and adolescence (13-17y). Parents reported on demographic information, and their children completed surveys on COVID-19 impact, loneliness, and family functioning using the COVID-19 Exposure and Family Impact Survey (CEFIS), the NIH Toolbox Loneliness (Ages 8-17) measure, and the PROMIS Family Relationships Short Form 4a measure, respectively. Regression models examined time one (T1; May-June 2020) predictors of time two (T2; November 2020-January 2021) MC child (n=92, Mage=10.03) and adolescent (n=56, Mage=14.66) loneliness. For the MC child model, significant predictors of higher loneliness included worse family functioning as well as higher COVID-19 impact and lower family income. On the other hand, higher adolescent loneliness was significantly predicted by not having married/partnered parents and was marginally significantly predicted by higher COVID-19 impact. The regression model with the full sample and interaction terms revealed no significant interactions, but that lower family functioning and higher COVID-19 impact were significant predictors of higher loneliness. Lower family income and lower in-person communication were marginally significant predictors of higher loneliness in the combined interaction model. Lastly, further exploratory mediation analyses displayed that family functioning significantly mediated the relationship between COVID-19 impact and T2 loneliness only for MC children and the full sample. Results support future interventions focused on optimizing family functioning to help mitigate MC loneliness in the context of adversity, such as a global pandemic. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, digital contact-tracing has been employed in many countries to monitor and manage the spread of the disease. However, to be effective such a system must be adopted by a substantial proportion of the population; therefore, public trust plays a key role. This paper examines the NHS COVID-19 smartphone app, the digital contact-tracing solution in the UK. A series of interviews were carried out prior to the app's release (n = 12) and a large scale survey examining attitudes towards the app (n = 1,001) was carried out after release. Extending previous work reporting high level attitudes towards the app, this paper shows that prevailing negative attitudes prior to release persisted, and affected the subsequent use of the app. They also show significant relationships between trust, app features, and the wider social and societal context. There is lower trust amongst non-users of the app and trust correlates to many other aspects of the app, a lack of trust could hinder adoption and effectiveness of digital contact-tracing. The design of technology requiring wide uptake, e.g., for public health, should embed considerations of the complexities of trust and the context in which the technology will be used. [ABSTRACT FROM AUTHOR]

Issues related to malnutrition, broadly defined, have received a growing attention in recent years, not only in connection with the Sustainable Development Goals but also recently with the unprecedented Covid-19 pandemic. At the same time, there exists a complex interaction between institutions, capital flows, and food and nutrition security that has received less attention in the relevant literature. In this paper we estimate a series of dynamic panel data models to examine the impact of institutional quality and capital flows on food security, nutrition security and undernourishment by using panel data for 25 SSA countries over the period 1996 to 2018. One of the key contributions of the paper is the use of both aggregate and disaggregated capital flows to examine the impact on both food and nutrition security, a dimension that has been surprisingly neglected in most of the relevant literature. We combine this with the interaction of various types of capital flows with an institutional quality index we constructed from various governance indicators to examine the impact of institutions on the overall nexus. Finally, we examine the impact not only on food and nutrition security but also on undernourishment. Our findings clearly demonstrate the importance of a heterogeneity approach and reflect on earlier work regarding the role of institutional quality in the overall nexus between external capital flows and various measures of food and nutrition security which leads, and as expected, to an interesting variation in the results obtained, depending on the type of capital flows and the interaction with the governance indicators. [ABSTRACT FROM AUTHOR]

In the name of health security, individual freedoms were constrained in an unprecedented way in many countries, democratic or authoritarian, all over the world during the COVID-19 pandemic. Yet the constraints have not been consistent across countries, which motivates this paper to examine the relevance of value preferences towards freedom or security in the society for COVID-19 policies. Based on data for 40 democratic and authoritarian countries, the analyses show that the variation in the stringency of COVID-19 policies can be explained by value preferences of the population only in autocracies. In democracies, however, we do not find such a relationship. Governments in democratic political systems, we argue, are responsive to their constitutions and face prosecution by the judiciary if they violate the law or provisions of the constitution, limiting their capacity to implement strong COVID-19 policies. Nevertheless, their COVID-19 policies restricted citizens' freedoms and liberties, which means that these policies were rather not responsive to citizens' preferences for freedom, democratic rights and liberties. By highlighting how autocracies respond to their citizens' value preferences for security, this paper contributes to a better understanding of how autocracies might gain legitimacy in times of crises. [ABSTRACT FROM AUTHOR]

This paper aims to deal with the impact of COVID-19 on the livelihoods of disadvantaged persons living in slums in Kenya. Months after the first case of COVID-19 was reported in Kenya in March 2020, most of the studies that have been carried out pertaining to its impact on slum dwellers have concentrated on narrowly defined concerns e.g. the impact of COVID-19 on youth, gender based violence and nutrition. It is thus difficult to get a clear global picture of the overall impact of COVID-19 on the livelihoods of slum dwellers in Kenya. This paper relies on information gathered during a comprehensive qualitative micro study covering numerous aspects of slum dwellers' livelihoods, as well as information that has been produced by the Ministry of Health, civil society organizations that work in specific slums, private research organizations as well as local and foreign media houses. The slums whose information is reported in this paper were selected to be indicative of the over 300 slums that are located in Nairobi and Mombasa, the two most important cities in Kenya. The analysis concludes that slum dwellers were potentially at a higher risk under the pressures of COVID-19 of deteriorating conditions with regard to the provision of health services, employment, gender-based violence, education and youth-related problems, and human rights violations, and offers several recommendations to the government. [ABSTRACT FROM AUTHOR]

Covid-19 outbreak led all organizations to reorganize their processes to prevent infection and contagion risk. All healthcare facilities, included penitentiary mental health services, had to redesign their processes to safely deliver care services. In this paper, the case of a Penitentiary Mental Health Division located in southern Italy is presented. Soft System Methodology and Business process management principles and techniques are adopted to analyse and redesign the detainees' mental health care process. The process, characterized by direct, close and prolonged contact with patients, exposes detainees and healthcare staff to a high Covid-19 infection risk. Through document analysis, interviews with the actors involved in the process and direct observation, the process's inefficiencies and criticalities are identified. The process is redesigned to make it compliant with Covid-19 prevention provisions and national penitentiary regulations and address the other criticalities. The proposed methodological approach–which innovatively combines Soft System Methodology and Business Process Management–constitutes a human-centered process-based redesign approach that can be used both in healthcare and other organizational settings. [ABSTRACT FROM AUTHOR]

The coronavirus (COVID-19) pandemic had wide-ranging negative impacts on mental health. The pandemic also placed extraordinary strain on frontline workers who were required to continue working and putting themselves at risk to provide essential services at a time when their normal support mechanisms may not have been available. This paper presents an evaluation of the Time for You service, a rapidly developed and implemented intervention aimed at providing frontline workers with quick access to flexible online mental health support. Time for You provided service users with three service options: self-guided online cognitive behavioural therapy (CBT) resources; guided engagement with online CBT resources; 1–1 psychological therapy with trainee sport and exercise psychologists and trainee health psychologists. A process evaluation informed by the Consolidated Framework for Implementation Research considered service fidelity, adaptations, perceived impact, reach, barriers, and facilitators. Interviews with project managers (n = 5), delivery staff (n = 10), and service users (n = 14) explored perceptions of the service implementation and outcomes, supported by data regarding engagement with the online CBT platform (n = 217). Findings indicated that service users valued the flexibility of the service and the speed with which they were able to access support. The support offered by Trainee Psychologists was perceived to be of high quality, and the service was perceived by service users to have improved mental health and wellbeing. The rapid implementation contributed to issues regarding appropriate service user screening that led to trainee psychologists being unable to provide the service users with the support they needed as the presenting issues were outside of trainees' competencies. Overall, the findings suggest that interventions offering flexible, online psychological support to frontline workers can be an effective model for future interventions. Trainee psychologists are also able to play an important role in delivering such services when clear screening processes are in place. [ABSTRACT FROM AUTHOR]

Since the beginning of the rapidly spreading COVID-19 pandemic, several mutations have occurred in the genetic sequence of the virus, resulting in emerging different variants of concern. These variants vary in transmissibility, severity of infections, and mortality rate. Designing models that are capable of predicting the future behavior of these variants in the societies can help decision makers and the healthcare system to design efficient health policies, and to be prepared with the sufficient medical devices and an adequate number of personnel to fight against this virus and the similar ones. Among variants of COVID-19, Alpha and Delta variants differ noticeably in the virus structures. In this paper, we study these variants in the geographical regions with different size, population densities, and social life styles. These regions include the country of Iran, the continent of Asia, and the whole world. We propose four deep learning models based on Long Short-Term Memory (LSTM), and examine their predictive power in forecasting the number of infections and deaths for the next three, next five, and next seven days in each variant. These models include Encoder Decoder LSTM (ED-LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional LSTM (Conv-LSTM), and Gated Recurrent Unit (GRU). Performance of these models in predictions are evaluated using the root mean square error, mean absolute error, and mean absolute percentage error. Then, the Friedman test is applied to find the leading model for predictions in all conditions. The results show that ED-LSTM is generally the leading model for predicting the number of infections and deaths for both variants of Alpha and Delta, with the ability to forecast long time intervals ahead. [ABSTRACT FROM AUTHOR]

This paper explores the impact of COVID-19 restrictions on gender biases in a STEM Faculty in an Irish University. For the purposes of this research, gender bias was understood as gender-based inclinations or prejudices which affect researchers' personal and professional opportunities as described by fifteen interviewees (nine women, six men). We used thematic interviews to explore participants' perspectives. Analyzing interviews with an inductive approach showed that gender biases experienced before COVID-19 restrictions were different from biases during restrictions. In discussing gender biases prior to the pandemic, interviewees mentioned institutional disparities, discrimination, implicit biases, stereotypes and gender roles. When discussing gender issues during COVID restrictions, interviewees mentioned disparities at home, challenges involved in parenting, support from colleagues and the University, as well as negative and positive impacts of restrictions on existing gender issues. Our results show that while COVID-19 restrictions and the associated consequences constrained all gender groups, they most negatively affected women researchers with caring responsibilities. Moving forward, reducing gender disparities requires promoting a horizontal organizational structure, and adjusting policies and work arrangements to cater for vulnerable cohorts such as parents, and international and early-career researchers. Offering a hybrid working model that grants more flexibility to those with caring responsibilities and which accommodates personal circumstances would help improve the working conditions for all researchers and nurture an organizational culture of care for the employees; thereby also fostering gender equity and tolerance. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB), as a respiratory infectious disease, has damaged public health globally for decades, and mainland China has always been an area with high incidence of TB. Since the outbreak of COVID-19, it has seriously occupied medical resources and affected medical treatment of TB patients. Therefore, the authenticity and reliability of TB data during this period have also been questioned by many researchers. In response to this situation, this paper excludes the data from 2019 to the present, and collects the data of TB incidence in mainland China and the data of 11 influencing factors from 2014 to 2018. Using spatial autocorrelation methods and multiscale geographically weighted regression (MGWR) model to study the temporal and spatial distribution of TB incidence in mainland China and the influence of selected influencing factors on TB incidence. The experimental results show that the distribution of TB patients in mainland China shows spatial aggregation and spatial heterogeneity during this period. And the R2 and the adjusted R2 of MGWR model are 0.932 and 0.910, which are significantly better than OLS model (0.466, 0.429) and GWR model (0.836, 0.797). The fitting accuracy indicators MAE, MSE and MAPE of MGWR model reached 5.802075, 110.865107 and 0.088215 respectively, which also show that the overall fitting effect is significantly better than OLS model (19.987574, 869.181549, 0.314281) and GWR model (10.508819, 267.176741, 0.169292). Therefore, this model is based on real and reliable TB data, which provides decision-making references for the prevention and control of TB in mainland China and other countries. [ABSTRACT FROM AUTHOR]

Scholars have identified a range of variables that predict public health compliance during COVID-19, including: psychological, institutional and situational variables as well as demographic characteristics, such as gender, location and age. In this paper, we argue that religious affiliation is also a clear predictor for compliance with public health guidelines. Based on a sample representative survey (N = 800) of Haredi Jews in Israel, we found that Haredi Jews mostly followed COVID-19 health regulations. Among the respondents who were non-compliant, however, we found large divergences which mostly reflected religious affiliation. While members of Lithuanian and Sephardi communities reported following guidelines, Hasidim, a more charismatic sub-group, were 12% and 14% more likely to flout public health guidelines than their Lithuanian and Sephardi counterparts, respectively. Despite this inner diversity, all Haredim were portrayed in Israeli media as one homogeneous group that was blamed for flouting public health guidelines and spreading COVID-19. Based on these findings, we argue for the importance of public health messaging that attends to diverse aspects of religious dogma, practice and observance by creating partnerships and sustainable relationships between different actors and stakeholders. In addition, we found that compliance was also shaped by knowledge about COVID-19 and public concern. Taking these findings together, health communication that acknowledges religious diversity while providing critical knowledge about the pandemic is key to developing and implementing community-focused interventions and public health programs. Practically, these insights help to improve pandemic governance as well as contributing theoretically to the study of public health relations and religion by highlighting how discourses around health vary and how differently positioned actors shape representations of responsiveness and health compliance. [ABSTRACT FROM AUTHOR]

This paper studies the updated estimation method for estimating the transmission rate changes over time. The models for the population dynamics under SEIR epidemic models with stochastic perturbations are analysed the dynamics of the COVID-19 pandemic in Bogotá, Colombia. We performed computational experiments to interpret COVID-19 dynamics using actual data for the proposed models. We estimate the model parameters and updated their estimates for reported infected and recovered data. [ABSTRACT FROM AUTHOR]

Due to the outbreak of COVID-19, the last couple of years have been drastic in terms of human behavioural patterns. The pandemic has taught us key lessons about crisis, communication and misinformation. People were forced to stay at home for a very long duration because of the strict lockdown measures imposed by governments all over the globe. India was no exception, wherein the Indian government imposed several very strict lockdowns all across the country, which restricted human activities and their social behaviours. However, such restrictions were seen to have a positive impact on environment and ecology. In this paper, we aim to study the changes in House sparrow sightings, as a result of the lockdowns. It is postulated that the lockdowns give rise to increased House sparrow numbers, which we try to argue, using appropriate exploratory analysis and statistical modelling. We apply a specific "zero-inflated Poisson" regression model in this regard. [ABSTRACT FROM AUTHOR]

Topic modelling with innovative deep learning methods has gained interest for a wide range of applications that includes COVID-19. It can provide, psychological, social and cultural insights for understanding human behaviour in extreme events such as the COVID-19 pandemic. In this paper, we use prominent deep learning-based language models for COVID-19 topic modelling taking into account data from the emergence (Alpha) to the Omicron variant in India. Our results show that the topics extracted for the subsequent waves had certain overlapping themes such as governance, vaccination, and pandemic management while novel issues aroused in political, social and economic situations during the COVID-19 pandemic. We also find a strong correlation between the major topics with news media prevalent during the respective time period. Hence, our framework has the potential to capture major issues arising during different phases of the COVID-19 pandemic which can be extended to other countries and regions. [ABSTRACT FROM AUTHOR]

Introduction: The Covid-19 pandemic had a tremendous impact on healthcare but uncertainty remains about the extent to which primary care provision was affected. Therefore, this paper aims to assess the impact on primary care provision and the evolution of the incidence of disease during the first year of the Covid-19 pandemic in Flanders (Belgium). Methods: Care provision was defined as the number of new entries added to a patient's medical history. Pre-pandemic care provision (February 1, 2018–January 31, 2020) was compared with care provision during the pandemic (February 1, 2020-January 31, 2021). A large morbidity registry (Intego) was used. Regression models compared the effect of demographic characteristics on care provision and on acute and chronic diagnoses incidence both prior and during the pandemic. Results: During the first year of the Covid-19 pandemic, overall care provision increased with 9.1% (95%CI 8.5%;9.6%). There was an increase in acute diagnoses of 5.1% (95%CI 4.2%;6.0%) and a decrease in the selected chronic diagnoses of 12.8% (95% CI 7.0%;18.4%). Obesity was an exception with an overall incidence increase. The pandemic led to strong fluctuations in care provision that were not the same for all types of care and all demographic groups in Flanders. Relative to other groups in the population, the pandemic caused a reduction in care provision for children aged 0–17 year and patients from a lower socio-economic situation. Conclusion: This paper strengthened the claim that Covid-19 should be considered as a syndemic instead of a pandemic. During the first Covid-19 year, overall care provision and the incidence of acute diagnoses increased, whereas chronic diseases' incidence decreased, except for obesity diagnoses which increased. More granular, care provision and chronic diseases' incidence decreased during the lockdowns, especially for people with a lower socio-economic status. After the lockdowns they both returned to baseline. [ABSTRACT FROM AUTHOR]

Capacity limitations are indispensable measures of social distancing in fighting COVID-19 and other pandemics. The paper at hand analyzes these restrictions from the viewpoint of fairness, understood as the possibility of equal access to the scarce resource. To this end, it employs the so-called El Farol Bar problem in conjunction with an adaptive learning approach. Particular emphasis is given to the distribution of information. Our results show that information is, indeed, central to the situation. Policy recommendations are derived. [ABSTRACT FROM AUTHOR]

Background: It is critical to capture data and modeling from the COVID-19 pandemic to understand as much as possible and prepare for future epidemics and possible pandemics. The Hawaiian Islands provide a unique opportunity to study heterogeneity and demographics in a controlled environment due to the geographically closed borders and mostly uniform pandemic-induced governmental controls and restrictions. Objective: The goal of the paper is to quantify the differences and similarities in the spread of COVID-19 among different Hawaiian islands as well as several other archipelago and islands, which could potentially help us better understand the effect of differences in social behavior and various mitigation measures. The approach should be robust with respect to the unavoidable differences in time, as the arrival of the virus and promptness of mitigation measures may vary significantly among the chosen locations. At the same time, the comparison should be able to capture differences in the overall pandemic experience. Methods: We examine available data on the daily cases, positivity rates, mobility, and employ a compartmentalized model fitted to the daily cases to develop appropriate comparison approaches. In particular, we focus on merge trees for the daily cases, normalized positivity rates, and baseline transmission rates of the models. Results: We observe noticeable differences among different Hawaiian counties and interesting similarities between some Hawaiian counties and other geographic locations. The results suggest that mitigation measures should be more localized, that is, targeting the county level rather than the state level if the counties are reasonably insulated from one another. We also notice that the spread of the disease is very sensitive to unexpected events and certain changes in mitigation measures. Conclusions: Despite being a part of the same archipelago and having similar protocols for mitigation measures, different Hawaiian counties exhibit quantifiably different dynamics of the spread of the disease. One potential explanation is that not sufficiently targeted mitigation measures are incapable of handling unexpected, localized outbreak events. At a larger-scale view of the general spread of the disease on the Hawaiian island counties, we find very interesting similarities between individual Hawaiian islands and other archipelago and islands. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has impacted urban transportation mobility throughout the world. In this paper, we investigate the impact of COVID-19 on the urban mobility network's structural characteristics. We contribute to the literature by discussing how various community areas in the city traffic network are impacted by the pandemic. We analyze a large dataset on urban mobility from the city of Chicago and derive various insights. Our analysis of the mobility network structure is important because a better understanding of such networks can help control the spread of the disease by reducing interactions among individuals. We find that the pandemic significantly impacted the structure of the mobility network of taxis in Chicago. Our study reveals some important pointers for policymakers that could potentially aid in developing urban transportation policies during the pandemic. [ABSTRACT FROM AUTHOR]

Purpose: Although the outbreak of the Corona Virus Disease 2019 (COVID-19) occurred on a global scale, governments from different countries adopted different policies and achieved different anti-epidemic effects. The purpose of this study is to investigate whether and how the government response affected the transmission scale of COVID-19 on the dynamic perspective. Methodology: This paper uses a dynamic generalized moment method to research the relationship between the government response and COVID-19 case fatality rate by using panel data from eight countries: China, United States, Canada, Australia, Italy, France, Japan, and South Korea. Findings: We have the following findings: 1. Government responses have a significant impact on the scale of COVID-19 transmission. 2. The rate of increase of government responses on the growth rate of COVID-19 case fatality rate has the characteristics of cyclicity and repeatability, that is, with the increase in the growth rate of government responses, the COVID-19 case fatality rate shows the following cyclical motion law: increasing first, reaching the maximum point, and then declining, and finally reaching the minimum point and then rising; ultimately, its convergence becomes 0. The cyclical fluctuations of COVID-19 in the long term may be caused by the decline in the level of government response, the mutation of the virus, and the violation of restrictive policies by some citizens. 3. The government response has a lag in controlling the spread of COVID-19. Originality/Value: Since there is a lack of literature on the impact of government responses on the development of COVID-19 from a long-term and dynamic perspective. This paper fills this gap in empirical research. We provide and expand new empirical evidence based on the current literature. This paper provides the basis for government decision-making and will help to formulate the response to other major public health events that may occur in the future. [ABSTRACT FROM AUTHOR]

Background: In response to the novel Coronavirus Disease 2019 (COVID-19) pandemic, the Department of Veterans Affairs (VA) Million Veteran Program (MVP) organized efforts to better understand the impact of COVID-19 on Veterans by developing and deploying a self-reported survey. Methods: The MVP COVID-19 Survey was developed to collect COVID-19 specific elements including symptoms, diagnosis, hospitalization, behavioral and psychosocial factors and to augment existing MVP data with longitudinal collection of key domains in physical and mental health. Due to the rapidly evolving nature of the pandemic, a multipronged strategy was implemented to widely disseminate the COVID-19 Survey and capture data using both the online platform and mailings. Results: We limited the findings of this paper to the initial phase of survey dissemination which began in May 2020. A total of 729,625 eligible MVP Veterans were invited to complete version 1 of the COVID-19 Survey. As of October 31, 2020, 58,159 surveys have been returned. The mean and standard deviation (SD) age of responders was 71 (11) years, 8.6% were female, 8.2% were Black, 5.6% were Hispanic, and 446 (0.8%) self-reported a COVID-19 diagnosis. Over 90% of responders reported wearing masks, practicing social distancing, and frequent hand washing. Conclusion: The MVP COVID-19 Survey provides a systematic collection of data regarding COVID-19 behaviors among Veterans and represents one of the first large-scale, national surveillance efforts of COVID-19 in the Veteran population. Continued work will examine the overall response to the survey with comparison to available VA health record data. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been particularly deadly for residents of nursing homes and other long-term care facilities. This paper analyzes COVID-19 deaths at nursing homes during the first wave of the pandemic in the United States during the spring and early summer 2020. By combining data on facility-level COVID-19 deaths during this period with data on the neighborhoods where nursing home staff reside for a sample of eighteen states, this paper finds that staff neighborhood characteristics were a large and significant predictor of COVID-19 nursing home deaths. Even after controlling for the county where a facility is located, one standard deviation increases in average staff neighborhood (Census tract) population density, public transportation use, and non-white share were associated with 1.3 (p <.001), 1.4 (p <.001), and 0.9 (p <.001) additional deaths per 100 beds, respectively. These effects are larger than all facility management or quality variables, and larger than the effect of the nursing home's own neighborhood characteristics. These results suggest COVID-19 outbreaks in staff communities can have large consequences for the facilities where they work, even in highly-rated facilities, and that disparities in nursing home outbreaks may be related to differences in the types of neighborhoods nursing home staff live in. [ABSTRACT FROM AUTHOR]

In most big cities, public transports are enclosed and crowded spaces. Therefore, they are considered as one of the most important triggers of COVID-19 spread. Most of the existing research related to the mobility of people and COVID-19 spread is focused on investigating highly frequented paths by analyzing data collected from mobile devices, which mainly refer to geo-positioning records. In contrast, this paper tackles the problem by studying mass mobility. The relations between daily mobility on public transport (subway or metro) in three big cities and mortality due to COVID-19 are investigated. Data collected for these purposes come from official sources, such as the web pages of the cities' local governments. To provide a systematic framework, we applied the IBM Foundational Methodology for Data Science to the epidemiological domain of this paper. Our analysis consists of moving averages with a moving window equal to seven days so as to avoid bias due to weekly tendencies. Among the main findings of this work are: a) New York City and Madrid show similar distribution on studied variables, which resemble a Gauss bell, in contrast to Mexico City, and b) Non-pharmaceutical interventions don't bring immediate results, and reductions to the number of deaths due to COVID are observed after a certain number of days. This paper yields partial evidence for assessing the effectiveness of public policies in mitigating the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The absence of reliable registration of Covid-19 deaths in India has prevented proper assessment and monitoring of the coronavirus pandemic. In addition, India's relatively young age structure tends to conceal the severity of Covid-19 mortality, which is concentrated in older age groups. In this paper, we present four different demographic samples of Indian populations for which we have information on both their demographic structures and death outcomes. We show that we can model the age distribution of Covid-19 mortality in India and use this modeling to estimate Covid-19 mortality in the country. Our findings point to a death toll of approximately 3.2–3.7 million persons by early November 2021. Once India's age structure is factored in, these figures correspond to one of the most severe cases of Covid-19 mortality in the world. India has recorded after February 2021 the second outbreak of coronavirus that has affected the entire country. The accuracy of official statistics of Covid-19 mortality has been questioned, and the real number of Covid-19 deaths is thought to be several times higher than reported. In this paper, we assembled four independent population samples to model and estimate the level of Covid-19 mortality in India. We first used a population sample with the age and sex of Covid-19 victims to develop a Gompertz model of Covid-19 mortality in India. We applied and adjusted this mortality model on two other national population samples after factoring in the demographic characteristics of these samples. We finally derive from these samples the most reasonable estimate of Covid-19 mortality level in India and confirm this result using a fourth population sample. Our findings point to a death toll of about 3.2–3.7 million persons by late May 2021. This is by far the largest number of Covid-19 deaths in the world. Once standardized for age and sex structure, India's Covid-19 mortality rate is above Brazil and the USA. Our analysis shows that existing population samples allow an alternative estimation of deaths due to Covid-19 in India. The results imply that only one out of 7–8 deaths appear to have been recorded as a Covid-19 death in India. The estimates also point to a very high Covid-19 mortality rate, which is even higher after age and sex standardization. The magnitude of the pandemic in India requires immediate attention. In the absence of effective remedies, this calls for a strong response based on a combination of non-pharmaceutical interventions and the scale-up of vaccination to make them accessible to all, with an improved surveillance system to monitor the progression of the pandemic and its spread across India's regions and social groups. [ABSTRACT FROM AUTHOR]

Background: Considering the COVID-19 pandemic, understanding the reliability, validity, social validity, and feasibility of using telehealth to diagnose ASD is a critical public health issue. This paper examines evidence supporting the use of telehealth methods to diagnose ASD and outlines the necessary modifications and adaptations to support telehealth diagnosis. Methods and procedures: Studies were identified by searching PubMed and PsychInfo electronic databases and references lists of relevant articles. Only peer reviewed articles published in English with a focus on using telehealth for the purposes of diagnosing ASD were included. Searches were conducted through June 3rd, 2021. Outcomes and results: A total of 10 studies were identified as meeting inclusion criteria. Of the eight papers that reported on reliability (e.g., accuracy), telehealth methods to diagnose ASD were between 80–91% accurate when compared with traditional in-person diagnosis. Six studies reported on validity (i.e., sensitivity and/or specificity). All six studies calculated sensitivity, with values ranging from 75% and 100%. Five of the six studies calculated specificity, with values ranging from 68.75% and 100%. The seven papers that reported social validity indicated that caregivers, as well as adult participants and clinicians, were mostly satisfied with telehealth. Feasibility was reported by seven studies and suggests that telehealth methods appear largely viable, though some challenges were reported. Conclusions and implications: Although findings reviewed here are promising, more research is needed to verify the accuracy, validity, and feasibility of utilizing telehealth to diagnose ASD. Studies with larger sample sizes and samples across sites will be critical, as these will allow clinicians to identify subjects most likely to benefit from telehealth as well as those more likely to require an in-person assessment. This research is important not only due to the current pandemic, but also due to increased prevalence rates of ASD and an insufficient number of diagnostic providers—particularly in rural and/or otherwise under-served communities. [ABSTRACT FROM AUTHOR]

Propose: To review published Physical Activity (PA) on the Mental Health of Children and Adolescents aged 5 to 18 years during COVID-19 pandemic-induced school closures. Methods: From the database creation to April 2022, 10 databases are retrieved, with 4427 records filtered, 14 included in this research. The research takes Agency for Healthcare Research and Quality (AHRQ) evaluation standards. Results: The thesis selects 14 studies from 6 countries, involving 400009 children and adolescents. These studies happened during the lockdown of COVID-19 (from December 2019 to April 2021). During the lockdown of COVID-19, schools were closed, which was considered part of a more extensive lockdown. Schools were closed for 1 to 4 weeks. There were 10 high quality studies (71.4%) and 4 medium quality studies (28.6%). 4 studies report that the pandemic reduces the time of PA but increases the time of watching screen and sitting. 10 studies (71.4%) identify that PA is positive for the mental health, because it helps reduce mental symptoms to a certain extent, especially anxiety, depression, and emotional disorders. 5 studies show that PA may not improve the mental health of children and adolescents under 12 during the pandemic. 4 studies indicate that the influence of PA on mental health of children and adolescents is determined by the amount of activity, including the extent, intensity, frequency, and duration, etc. Conclusions: In this narrative synthesis of reports from the class suspension period, reports that PA has a improve on the mental health of children and adolescents to a certain extent. it is found that PA may be helpful in reducing mental health symptoms of children and adolescents who are influenced by class suspension because of the COVID-19 pandemic. Therefore, stakeholders of the mental health of children and adolescents around the world should recommend PA because it is a practicable and beneficial way for long-term mental support. [ABSTRACT FROM AUTHOR]

Background: Children in custodial settings are a vulnerable group. Prior to the COVID-19 pandemic there were concerns about the safety of children in these settings. COVID-19 has had an impact on everyone but given the vulnerability of children in custody, there were concerns about the impact of COVID-19 restrictions. All custody settings for children are independently inspected and this research aimed to analyse data from inspection reports. Twenty-six inspection reports undertaken between March 2020 and October 2021 were analysed to understand the impact of COVID-19 on delivery of usual care/regime. Results: Data showed that across all site's children spent considerable amounts of time isolated and in some cases, this was deemed to amount to solitary confinement. There was evidence of some positive experiences, in the smaller sites, around COVID-19 slowing the pace of life allowing staff and children could foster relationships. However, in the larger sites, isolation was extreme and COVID-19 policies such as 'bubbles' appear to have created unintended consequences as sites have moved into recovery, leading to increased violence and stress. COVID-19 directly impacted staffing levels. This and the COVID-19 policies to reduce mixing also had an impact on how children's behaviour, welfare and safeguarding was managed. In some larger sites, being COVID-19 secure was prioritised over the needs of the children. Conclusions: This research highlights the importance of multi-site longitudinal research to understand how children, staff and institution's function. The experiences of children in custody during COVID-19 differed by site type. The research suggests that the larger sites are struggling to keep children safe and there should be a shift towards smaller, more therapeutic environments. More research is needed to understand the longer-term unintended consequences of COVID-19 policy in custody, for these vulnerable children. [ABSTRACT FROM AUTHOR]

Among life-and-death health concerns surrounding the COVID-19 pandemic were frustrations about the loss of personal freedom due to emergency quarantine. To test these perceptions, two studies examined whether belief in free will was resilient during different points of the pandemic. In Study 1, conducted in 2020, participants completed a writing task describing their lives before the COVID-19 pandemic, during the COVID-19 pandemic while under emergency quarantine, and during the COVID-19 pandemic while under state re-openings. Following each task, they completed belief in free will measures. Results indicated that free will beliefs were higher before the pandemic than during emergency quarantine. Free will beliefs were also greater during state re-opening than during emergency quarantine. Belief in free will did not differ between pre-pandemic and state re-opening. Study 2 replicated and extended these effects two years later. These findings highlight the brief loss of freedom during COVID-19 as well as the resiliency of agentic control. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic drastically affected many areas and contexts of today's society, including school and family. Several studies focused on the worldwide effects of school closures on students' learning outcomes, context, and well-being. However, the data emerging from these studies are often inconsistent and fragmentary, highlighting the need of a comprehensive analysis of the phenomenon. This need is especially urgent for the countries with the most severe school closure, like Italy. This systematic review aims to collect the opinions of parents, teachers, and students on: other dimensions of Italian primary school students affected by school closures, beyond academic performance; hypothetical agreement between the opinions of parents, teachers, and students regarding the different effects of school closures on Italian primary school students; possible differences between the effects of school closures on Italian primary school students and the students in other countries. Our search was conducted using PRISMA 2020 guidelines on Web of Science, Pubmed, Scopus, and EBSCOHost. The results obtained from 34 articles revealed a strong concern on the part of all stakeholders involved in learning during the pandemic, with evident negative effects for Italian school students. The constraint on distance learning led to a drastic change in everyone's routine, and a negative emotional change on the part of young students. Parents and teachers generally considered distance learning to be ineffective for the education of their children and students; they encountered technical-practical difficulties in the use of electronic devices for participation in school activities; overall learning deficits on the part of students, especially in mathematics, as confirmed by INVALSI results were also found. The investigation reveals a condition of shared emotional and academic performance difficulty, and a further challenging circumstance for students previously at risk of marginalization. Further research in this field is paramount to identify new and adequate recovery strategies. [ABSTRACT FROM AUTHOR]

Introduction: The World Health Organization declared COVID-19 as a pandemic in March 2020. COVID-19 has since caused a significant increase in mental health problems at national and global levels. This study assessed the views of key mental health stakeholders regarding the state of mental health service provision in Malawi and the pandemic's impact on the sector. Methods: The study utilised a qualitative approach through key informant interviews (KIIs) conducted using a semi-structured interview guide. The interviews were audio recorded in English language and were manually transcribed for thematic analysis by generating codes re-classified into themes, sub-themes and quotes. Results: The results are categorised into five themes. Firstly, the availability of mental health services. All experts confirmed the lack of availability of the mental health services especially at the lower levels of care. Currently, only 0.3% of facilities offer mental health services in Malawi. Moreover, although mental health services are part of the essential health care package and, therefore, are supposed to be provided for free in public facilities at all levels, the services are centralised and only functional at a tertiary level of care in public facilities. Secondly, funding sources for mental health in public and private facilities. We learnt that public facilities depend on donor sources and there is lack of prioritisation in budget allocation for mental health services. Whereas private facilities, their major source of funding is user fees. Thirdly, government's response in the provision of mental health services during COVID-19. Almost all experts echoed that government took a proactive approach to address the mental health needs of its population during the pandemic. There was increased collaboration between the government and the private sector to provide psychosocial and counselling services to health workers working directly with COVID-19 patients in isolation centres. Furthermore, to increase awareness of the general population on where to seek counselling services. Lastly, challenges in the provision of mental health services were highlighted and how the pandemic acerbated the challenges including shortage in human resources for health and inadequate funding. Conclusion: This study underscores the urgency of addressing mental health challenges in Malawi. Policymakers must prioritize the decentralization of mental health services, explore funding opportunities, and build on the successful collaboration with the private sector. These measures will not only enhance the accessibility and quality of mental health services but also ensure that mental well-being is a central component of public health efforts in Malawi. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, concerns emerged that vaccinated individuals might engage less in infection-preventive behaviors, potentially contributing to virus transmission. This study evaluates the causal effects of COVID-19 vaccination on such behaviors within Japan, highlighting the significance of understanding behavioral dynamics in public health strategies. Methods: Utilizing Japan's age-based vaccination priority for those born before April 1, 1957, this research employs a regression discontinuity design (RDD) to assess the vaccination's impact. Data from the fourth round of a longitudinal online survey, conducted from July 20 to 27, 2021, served as the basis for analyzing 14 infection-protective behaviors, including mask usage, handwashing, and avoiding crowds. Results: A total of 12067 participants completed the survey. The analyzed sample size varied by outcome variable, ranging from 1499 to 5233. The analysis revealed no significant differences in the 14 behaviors examined among fully vaccinated, partially vaccinated, and unvaccinated individuals. This consistency across groups suggests that vaccination status did not significantly alter engagement in protective behaviors during the observation period. Conclusions: Empirical findings highlight the complexity of behavioral responses following vaccination, indicating that such responses may be influenced by various factors, rather than by vaccination status alone. Additionally, this result underscores the importance of crafting public health policies that account for the intricate interplay between vaccination and behavior. This study contributes to the broader discourse on managing responses to the pandemic and tailoring interventions to sustain or enhance protective health behaviors amid vaccination rollouts. [ABSTRACT FROM AUTHOR]

This study investigates how the landscape of sex work in Dar es Salaam, Tanzania, evolved in the context of the COVID-19 epidemic. Using a mixed-methods approach, the analysis triangulates data from quantitative and qualitative sources to quantify shifts in income, demand, and client frequency and describe female sex workers' perspectives on their work environment. The COVID-19 restrictions introduced in early 2020 resulted in dramatic decreases in sex work income, leading to extreme financial vulnerability, food insecurity, and challenges in meeting other basic needs such as paying rent. However, in a 2021 follow-up survey, sex workers reported the summer of 2021 as a key turning point, with the demand for sex work rebounding to closer to pre-pandemic levels. Notably, despite the average number of unique weekly clients not yet having fully rebounded, by 2021 the price per client and the total monthly sex work income had returned to pre-pandemic levels. This may potentially be explained by an increased number of repeat clients, which represented a larger proportion of all clients during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

'Single Session Therapy' (SST) is a service delivery model that seeks to provide an evidence-based, solution-focused, brief intervention within a single therapy session. The stand-alone session affords the opportunity to provide brief psychological interventions while clients await access to longer-term services. The COVID-19 pandemic has adversely impacted individuals' mental health. However, the majority of research has investigated patient mental health within hospital settings and community organizations that offer long-term services, whereas minimal research has focused on mental health concerns during COVID-19 within an SST model. The primary aim of the study was to measure client experiences of a brief mental health service. The nature of client mental health concerns who access such services at various points during a pandemic was also investigated. The current study utilized client feedback forms and the Computerized Adaptive Testing—Mental Health (CAT-MH) to measure client experiences and mental health concerns. Qualitative analysis of client feedback forms revealed themes of emotional (e.g., safe space) and informational support (e.g., referrals). Clients also reported reduced barriers to accessing services (e.g., no appointment necessary, no cost), as well as limitations (e.g., not enough sessions) of the Walk-in clinic. Profile analysis of the CAT-MH data indicated that clients had higher rates of depression before COVID-19 (M = 64.2, SD = 13.07) as compared to during the pandemic (M = 59.78, SD = 16.87). In contrast, higher rates of positive suicidality flags were reported during the pandemic (n = 54) as compared to before (n = 29). The lower reported rates of depression but higher rate of suicidality during the pandemic was an unanticipated finding that contradicted prior research, to which possible explanations are explored. Taken together, the results demonstrate the positive experiences of clients who access a single session therapy. [ABSTRACT FROM AUTHOR]

Introduction: During the COVID-19 pandemic, ventilator shortages necessitated the development of new, low-cost ventilator designs. The fundamental requirements of a ventilator include precise gas delivery, rapid adjustments, durability, and user-friendliness, often achieved through solenoid valves. However, few solenoid-valve assisted low-cost ventilator (LCV) designs have been published, and gas exchange evaluation during LCV testing is lacking. This study describes the development and performance evaluation of a solenoid-valve assisted low-cost ventilator (SV-LCV) in vitro and in vivo, focusing on gas exchange and respiratory mechanics. Methods: The SV-LCV, a fully open ventilator device, was developed with comprehensive hardware and design documentation, utilizing solenoid valves for gas delivery regulation. Lung simulator testing calibrated tidal volumes at specified inspiratory and expiratory times, followed by in vivo testing in a porcine model to compare SV-LCV performance with a conventional ventilator. Results: The SV-LCV closely matched the control ventilator's respiratory profile and gas exchange across all test cycles. Lung simulator testing revealed direct effects of compliance and resistance changes on peak pressures and tidal volumes, with no significant changes in respiratory rate. In vivo testing demonstrated comparable gas exchange parameters between SV-LCV and conventional ventilator across all cycles. Specifically, in cycle 1, the SV-LCV showed arterial blood gas (ABG) results of pH 7.54, PCO2 34.5 mmHg, and PO2 91.7 mmHg, compared to the control ventilator's ABG of pH 7.53, PCO2 37.1 mmHg, and PO2 134 mmHg. Cycle 2 exhibited ABG results of pH 7.53, PCO2 33.6 mmHg, and PO2 84.3 mmHg for SV-LCV, and pH 7.5, PCO2 34.2 mmHg, and PO2 93.5 mmHg for the control ventilator. Similarly, cycle 3 showed ABG results of pH 7.53, PCO2 32.1 mmHg, and PO2 127 mmHg for SV-LCV, and pH 7.5, PCO2 35.5 mmHg, and PO2 91.3 mmHg for the control ventilator. Conclusion: The SV-LCV provides similar gas exchange and respiratory mechanic profiles compared to a conventional ventilator. With a streamlined design and performance akin to commercially available ventilators, the SV-LCV presents a viable, readily available, and reliable short-term solution for overcoming ventilator supply shortages during crises. [ABSTRACT FROM AUTHOR]

Evidence suggests that reductions in healthcare utilization, including forgone care, during the COVID-19 pandemic may be contributing towards excess morbidity and mortality. The objective of this study was to describe individual and community-level correlates of forgone care during the COVID-19 pandemic. We conducted a cross-sectional, secondary data analysis of participants (n = 2,003) who reported needing healthcare in two population-representative surveys conducted in Baltimore, MD in 2021 and 2021–2022. Abstracted data included the experience of forgone care, socio-demographic data, comorbidities, financial strain, and community of residence. Participant's community of residence were linked with data acquired from the Baltimore Neighborhood Indicators Alliance relevant to healthcare access and utilization, including walkability and internet access, among others. The data were analyzed using weighted random effects logistic regression. Individual-level factors found to be associated with increased odds for forgone care included individuals age 35–49 (compared to 18–34), female sex, experiencing housing insecurity during the pandemic, and the presence of functional limitations and mental illness. Black/African American individuals were found to have reduced odds of forgone care, compared to any other race. No community-level factors were significant in the multilevel analyses. Moving forward, it will be critical that health systems identify ways to address any barriers to care that populations might be experiencing, such as the use of mobile health services or telemedicine platforms. Additionally, public health emergency preparedness planning efforts must account for the unique needs of communities during future crises, to ensure that their health needs can continue to be met. Finally, additional research is needed to better understand how healthcare access and utilization practices have changed during versus before the pandemic. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has not only caused tremendous loss of life and health but has also greatly disrupted the world economy. The impact of this disruption has been especially harsh in urban settings of developing countries. We estimated the impact of the pandemic on the occurrence of food insecurity in a cohort of women living in Mexico City, and the socioeconomic characteristics associated with food insecurity severity. Methods: We analyzed data longitudinally from 685 women in the Mexico City-based ELEMENT cohort. Food insecurity at the household level was gathered using the Latin American and Caribbean Food Security Scale and measured in-person during 2015 to 2019 before the pandemic and by telephone during 2020–2021, in the midst of the pandemic. Fluctuations in the average of food insecurity as a function of calendar time were modeled using kernel-weighted local polynomial regression. Fixed and random-effects ordinal logistic regression models of food insecurity were fitted, with timing of data collection (pre-pandemic vs. during pandemic) as the main predictor. Results: Food insecurity (at any level) increased from 41.6% during the pre-pandemic period to 53.8% in the pandemic stage. This increase was higher in the combined severe-moderate food insecurity levels: from 1.6% pre-pandemic to 16.8% during the pandemic. The odds of severe food insecurity were 3.4 times higher during the pandemic relative to pre-pandemic levels (p<0.01). Socioeconomic status quintile (Q) was significantly related to food insecurity (Q2 OR = 0.35 p<0.1, Q3 OR = 0.48 p = 0.014, Q4 OR = 0.24 p<0.01, and Q5 OR = 0.17 p<0.01), as well as lack of access to social security (OR = 1.69, p = 0.01), and schooling (OR = 0.37, p<0.01). Conclusions: Food insecurity increased in Mexico City households in the ELEMENT cohort as a result of the COVID-19 pandemic. These results contribute to the body of evidence suggesting that governments should implement well-designed, focalized programs in the context of economic crisis such as the one caused by COVID-19 to prevent families from the expected adverse health and well-being consequences associated to food insecurity, especially for the most vulnerable. [ABSTRACT FROM AUTHOR]

Despite extensive debate on the effects of COVID-19 mitigation measures in school settings, little evidence exists on trends in school children's mental health over the course of the COVID-19 pandemic. The objective of this paper was to identify factors affecting parent reports of school children's mental health during COVID-19 Omicron variant outbreak in a cohort of high-risk, socially vulnerable children attending public elementary schools. We analyzed four waves of cross-sectional, online-administered surveys completed by parents of children attending public elementary schools in San Diego between November 2021 and March of 2022. Children (n = 684) ranged in age from 2–17 years. We used multilevel linear mixed effects models to assess determinants of parent-reported child mental health status. The outcome was child mental health, as reported by the parent. Parents consistently rated their children's mental health as very good, though parents who experienced recent COVID-related challenges and who had older children reported lower levels of mental health in their children. Children's mental health was generally considered to be very good, as judged by their parents during a period of constant in-school masking and the Omicron variant outbreak. Structural support mechanisms aimed at mitigating COVID-related challenges for adults may offer benefit to children's mental health. [ABSTRACT FROM AUTHOR]

We present a novel approach to estimate the time-varying ascertainment rate in almost real-time, based on the surveillance of positively tested infectious and hospital admission data. We also address the age dependence of the estimate. The ascertainment rate estimation is based on the Bayes theorem. It can be easily calculated and used (i) as part of a mechanistic model of the disease spread or (ii) to estimate the unreported infections or changes in their proportion in almost real-time as one of the early-warning signals in case of undetected outbreak emergence. The paper also contains a case study of the COVID-19 epidemic in the Czech Republic. The case study demonstrates the usage of the ascertainment rate estimate in retrospective analysis, epidemic monitoring, explanations of differences between waves, usage in the national Anti-epidemic system, and monitoring of the effectiveness of non-pharmaceutical interventions on Czech nationwide surveillance datasets. The Czech data reveal that the probability of hospitalization due to SARS-CoV-2 infection for the senior population was 12 times higher than for the non-senior population in the monitored period from the beginning of March 2020 to the end of May 2021. In a mechanistic model of COVID-19 spread in the Czech Republic, the ascertainment rate enables us to explain the links between all basic compartments, including new cases, hospitalizations, and deaths. [ABSTRACT FROM AUTHOR]

Charities play an increasingly important role in helping people experiencing poverty. However, institutionalized charity shifts the burden of poverty reduction away from the state and exposes recipients to stress and stigma. In this paper, we examine whether the need for institutionalized charity can be offset through enhanced state support. As in other countries, the Australian government responded to the COVID-19 pandemic by substantially increasing the level of income support to citizens through several temporary payments. We draw on this natural experiment and time-series data from the two largest charity organizations in Queensland, Australia to examine how these payments altered the demand for institutionalized charity. We model these data using difference-in-difference regression models to approximate causal effects. By exploiting the timing and varying amounts of the payments, our analyses yield evidence that more generous income support reduces reliance on charity. Halving the demand for charity requires raising pre-pandemic income-support by AUD$42/day, with supplements of approximately AUD$18/day yielding the greatest return on investment. [ABSTRACT FROM AUTHOR]

The worldwide outbreak of the coronavirus was first identified in 2019 in Wuhan, China. Since then, the disease has spread worldwide. As it is currently spreading in the United States, policy makers, public health officials and citizens are racing to understand the impact of this virus on the United States healthcare system. They fear a rapid influx of patients overwhelming the healthcare system and leading to unnecessary fatalities. Most countries and states in America have introduced mitigation strategies, such as using social distancing to decrease the rate of newly infected people. This is what is usually meant by flattening the curve. In this paper, we use queueing theoretic methods to analyze the time evolution of the number of people hospitalized due to the coronavirus. Given that the rate of new infections varies over time as the pandemic evolves, we model the number of coronavirus patients as a dynamical system based on the theory of infinite server queues with time inhomogeneous Poisson arrival rates. With this model we are able to quantify how flattening the curve affects the peak demand for hospital resources. This allows us to characterize how aggressive societal policy needs to be to avoid overwhelming the capacity of healthcare system. We also demonstrate how curve flattening impacts the elapsed lag between the times of the peak rate of hospitalizations and the peak demand for the hospital resources. Finally, we present empirical evidence from Italy and the United States that supports the insights from our model analysis. [ABSTRACT FROM AUTHOR]

Based on the extensive data accumulated during the COVID-19 pandemic, we put forward simple to implement indicators, that should alert authorities and provide early warnings of an impending sanitary crisis. In fact, Testing, Tracing, and Isolation (TTI) in conjunction with disciplined social distancing and vaccination were expected to achieve negligible COVID-19 contagion levels; however, they proved to be insufficient, and their implementation has led to controversial social, economic and ethical challenges. This paper focuses on the development of simple indicators, based on the experience gained by COVID-19 data, which provide a sort of yellow light as to when an epidemic might expand, despite some short term decrements. We show that if case growth is not stopped during the 7 to 14 days after onset, the growth risk increases considerably, and warrants immediate attention. Our model examines not only the COVID contagion propagation speed, but also how it accelerates as a function of time. We identify trends that emerge under the various policies that were applied, as well as their differences among countries. The data for all countries was obtained from ourworldindata.org. Our main conclusion is that if the reduction spread is lost during one, or at most two weeks, urgent measures should be implemented to avoid scenarios in which the epidemic gains strong impetus. [ABSTRACT FROM AUTHOR]

Background: Kerala, a south Indian state, has a long and strong history of mobilisation of people's participation with institutionalised mechanisms as part of decentralisation reforms introduced three decades ago. This history formed the backdrop of the state's COVID-19 response from 2020 onwards. As part of a larger health equity study, we carried out an analysis to understand the contributions of people's participation to the state's COVID-19 response, and what implications this may have for health reform as well as governance more broadly. Methods: We employed in-depth interviews with participants from four districts of Kerala between July and October, 2021. Following written informed consent procedures, we carried out interviews of health staff from eight primary health care centres, elected Local Self Government (LSG, or Panchayat) representatives, and community leaders. Questions explored primary health care reforms, COVID responses, and populations left behind. Transliterated English transcripts were analysed by four research team members using a thematic analysis approach and ATLAS.ti 9 software. For this paper, we specifically analysed codes and themes related to experiences of community actors and processes for COVID mitigation activities. Results: A key feature of the COVID-19 response was the formation of Rapid Response Teams (RRTs), groups of lay community volunteers, who were identified and convened by LSG leaders. In some cases, pre-pandemic 'Arogya sena' (health army) community volunteer groups were merged with RRTs. RRT members were trained and supported by the health departments at the local level to distribute medicine and essential items, provided support for transportation to health facilities, and assisted with funerary rites during lockdown and containment period. RRTs often comprised youth cadres of ruling and opposition political parties. Existing community networks like Kudumbashree (Self Help Groups) and field workers from other departments have supported and been supported by RRTs. As pandemic restrictions eased, however, there was concern about the sustainability of this arrangement as well. Conclusion: Participatory local governance in Kerala allowed for the creation of invited spaces for community participation in a variety of roles as part of the COVID 19 response, with manifest impact. However, the terms of engagement were not decided by communities, nor were they involved more deeply in planning and organising health policy or services. The sustainability and governance features of such involvement warrant further study. [ABSTRACT FROM AUTHOR]

Single-measurement-point data collection to assess change has increased with studies assessing the impact of the Covid-19 pandemic and of its containment, despite evidence of its lack of validity. Retrospective change is not equivalent to change in repeated self-reported measures giving raise to questions about the validity of the former. This paper purports to investigate inconsistencies between change measures by confronting retrospective change to information from longitudinally self-reported measures from the C-SURF cohort study. The study sample consists of 2,279 young men who participated in C-SURF between 2020 and 2021, and completed between May and June 2021 a survey covering change in alcohol, cigarette, cannabis and other addictive behaviors related to the pandemic. The aforementioned behaviors were assessed longitudinally at two time points using self-reports, and retrospective change since the onset of the Covid-19 crisis was also assessed at the second measurement time. Information from both prospective and retrospective change measures were confronted to identify inconsistent information for each behavior. Additionally, multiple logistic regressions were performed to assess associations between socioeconomic status, impulsivity, depression, and different indicators of motivation to complete the study and inconsistency between both measures for each behavior of interest. Importantly, inconsistent information in at least one of the investigated behaviors was found in about 90% of the participants. Small associations were found between inconsistency and different factors with a consistent effect of impulsivity. In the absence of evidence of the validity of retrospective change measures, studies relying on retrospective change should be interpreted with caution. [ABSTRACT FROM AUTHOR]

While spillover across equity markets has been extensively investigated, volatility spillover across sectors has largely been under-examined in the current literature. This paper estimates the sectoral volatility using the ARMA-GARCH model and its spillover across Australian sectors on the VAR framework during the 2010–2021 period. We then identify breakpoints in market volatility during the Covid-19 pandemic using a wavelet methodology. We find that volatility spillover across Australian sectors is very significant at 60 per cent from 2010 to 2019, reaching 90 per cent during the Covid-19 pandemic in 2020. The spillover then reverts to its pre-pandemic level in 2021. Consumer Staples and Industrials are the significant risk transmitters, whereas Financials and Real estates are the most significant risk absorbers. Our findings also indicate that Real Estate, Health Care, and Financials record the most significant increase in volatility of more than 300 per cent. Policy implications regarding risk management across Australian sectors have emerged, particularly during extreme events such as the pandemic. [ABSTRACT FROM AUTHOR]

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future. [ABSTRACT FROM AUTHOR]

Background: The aim of this paper was to evaluate the change in 25-hidroxyvitamin D (25(OH)D) levels before and during the COVID-19 pandemic. Methods: In this retrospective, cross-sectional and methodological study included 86,772 patients (18–75 years) samples who were admitted to the Izmir Dokuz Eylul University Hospital (latitude and longitude (Turkey): 27 E 09; 38 N 25, respectively) for various reasons and whose 25(OH)D levels were measured in the biochemistry unit between 2019–2020 and 2020–2021 (before and during the COVID-19 outbreak). A time series analysis of monthly averages for 25(OH)D was performed. For the purpose of seasonal study, the mean levels of 25(OH)D are grouped by years. Data were modeled in terms of 25(OH)D levels using the MATLAB Curve Fitting Toolbox. Results: There was no significant difference between the sexes according to 25(OH)D levels (p>0.05). 25(OH)D levels were significantly higher in the summer months and lower in the winter months (p<0.001). When comparing the spring months, 25(OH)D levels in 2020 (18 ± 10) were found to be significantly lower than in 2019 (22 ± 12) (p<0.001); on the contrary, when examined based on the summer, autumn, and winter months, it was determined that 25(OH)D levels increased in 2020 (summer: 25 ± 13, autumn: 25 ± 14, and winter: 19 ± 10) compared to 2019 (summer: 23 ± 11, autumn: 22 ± 10, and winter: 19 ± 11) (p<0.001). In the estimates curve obtained with an error margin of 11% in the time series analysis, it was estimated that the 25(OH)D averages after the pandemic would be similar to those before the pandemic. Conclusions: Restrictions, partial or complete closures, and curfews can significantly affect individuals' 25(OH)D levels during the COVID-19 outbreak. There is a need for multicenter studies with larger populations covering different regions to strengthen and support our results. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the spread of COVID-19 and the impact of government measures at the early stage of the pandemic (before the introduction of the vaccines) in the Netherlands. We build a multiple linear regression model to predict the effective reproduction rate using key factors and measures and integrate it with a system dynamics model to predict the spread and the impact of measures against COVID-19. Data from February to November 2020 is used to train the model and data until December 2020 is used to validate the model. We use data about the key factors, e.g., disease specific such as basic reproduction rate and incubation period, weather related factors such as temperature, and controllable factors such as testing capacity. We consider particularly the following measures taken by the government: wearing facemasks, event allowance, school closure, catering services closure, and self-quarantine. Studying the strategy of the Dutch government, we control these measures by following four main policies: doing nothing, mitigation, curbing, elimination. We develop a systems dynamic model to simulate the effect of policies. Based on our numerical experiments, we develop the following main insights: It is more effective to implement strict, sharp measures earlier but for a shorter duration than to introduce measures gradually for a longer duration. This way, we can prevent a quick rise in the number of infected cases but also to reduce the number of days under measures. Combining the measures with a high testing capacity and with effective self-quarantine can significantly reduce the spread of COVID-19. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic necessitated a shift to virtual curriculum delivery at Canadian medical schools. At the NOSM University, some learners transitioned to entirely online learning, while others continued in-person, in-clinic learning. This study aimed to show that medical learners who transitioned to exclusively online learning exhibited higher levels of burnout compared to their peers who continued in-person, clinical learning. Analysis of factors that protect against burnout including resilience, mindfulness, and self-compassion exhibited by online and in-person learners at NOSM University during this curriculum shift were also explored. Methods: As part of a pilot wellness initiative, a cross-sectional online survey-based study of learner wellness was conducted at NOSM University during the 2020–2021 academic year. Seventy-four learners responded. The survey utilized the Maslach Burnout Inventory, the Brief Resilience Scale, Cognitive and Affective Mindfulness Scale–Revised, and the Self-Compassion Scale–Short Form. T-tests were utilized to compare these parameters in those who studied exclusively online and those who continued learning in-person in a clinical setting. Results: Medical learners who engaged in online learning exhibited significantly higher levels of burnout when compared with learners who continued in-person learning in a clinical setting, despite scoring equally on protective factors such as resilience, mindfulness, and self-compassion. Conclusion: The results discussed in this paper suggest that the increased time spent in a virtual learning environment during the COVID-19 pandemic might be associated with burnout among exclusively online learners, as compared to learners who were educated in clinical, in-person settings. Further inquiry should investigate causality and any protective factors that could mitigate negative effects of the virtual learning environment. [ABSTRACT FROM AUTHOR]