COVID-19 Government Response Event Dataset (CoronaNet v.1.0)

A data-driven analysis on the mediation effect of compartment models between control measures and COVID-19 epidemics

By collecting various control policies taken by 127 countries/territories during the first wave of COVID-19 pandemic until July 2nd, 2020, we evaluate their impacts on the epidemic dynamics quantitatively through a combination of the multiple linear regression, neural-network-based nonlinear regression and sensitivity analysis. Remarkable differences in the public health policies are observed across these countries, which affect the spreading rate and infected population size to a great extent. Several key dynamical features, like the normalized cumulative numbers of confirmed/cured/death cases on the 100th day and the half time, show statistically significant linear correlations with the control measures, which thereby confirms their dramatic impacts. Most importantly, we perform the mediation analysis on the SEIR-QD model, a representative of general compartment models, by using the structure equation modeling for multiple mediators operating in parallel. This, to the best of our knowledge, is the first of its kind in the field of epidemiology. The infection rate and the protection rate of the SEIR-QD model are confirmed to exhibit a statistically significant mediation effect between the control measures and dynamical features of epidemics. The mediation effect along the pathway from control measures in Category 2 to four dynamical features through the infection rate, highlights the crucial role of nucleic acid testing and suspected cases tracing in containing the spread of the epidemic. Our data-driven analysis offers a deeper insight into the inherent correlations between the effectiveness of public health policies and the dynamic features of COVID-19 epidemics.

Geography versus sociodemographics as predictors of changes in daily mobility across the USA during the COVID-19 pandemic: a two-stage regression analysis across 26 metropolitan areas

OBJECTIVE

We investigated whether a zip code's location or demographics are most predictive of changes in daily mobility throughout the course of the COVID-19 pandemic.

DESIGN

We used a population-level study to examine the predictability of daily mobility during the COVID-19 pandemic using a two-stage regression approach, where generalised additive models (GAM) predicted mobility trends over time at a large spatial level, then the residuals were used to determine which factors (location, zip code-level features or number of non-pharmaceutical interventions (NPIs) in place) best predict the difference between a zip code's measured mobility and the average trend on a given date.

SETTING

We analyse zip code-level mobile phone records from 26 metropolitan areas in the USA on 15 March-31 September 2020, relative to October 2020.

RESULTS

While relative mobility had a general trend, a zip code's city-level location significantly helped to predict its daily mobility patterns. This effect was time-dependent, with a city's deviation from general mobility trends differing in both direction and magnitude throughout the course of 2020. The characteristics of a zip code further increased predictive power, with the densest zip codes closest to a city centre tended to have the largest decrease in mobility. However, the effect on mobility change varied by city and became less important over the course of the pandemic.

CONCLUSIONS

The location and characteristics of a zip code are important for determining changes in daily mobility patterns throughout the course of the COVID-19 pandemic. These results can determine the efficacy of NPI implementation on multiple spatial scales and inform policy makers on whether certain NPIs should be implemented or lifted during the ongoing COVID-19 pandemic and when preparing for future public health emergencies.

Epidemic modelling suggests that in specific circumstances masks may become more effective when fewer contacts wear them

BACKGROUND

The effectiveness of non-pharmaceutical interventions to control the spread of SARS-CoV-2 depends on many contextual factors, including adherence. Conventional wisdom holds that the effectiveness of protective behaviours, such as wearing masks, increases with the number of people who adopt them. Here we show in a simulation study that this is not always true.

METHODS

We use a parsimonious network model based on the well-established empirical facts that adherence to such interventions wanes over time and that individuals tend to align their adoption strategies with their close social ties (homophily).

RESULTS

When these assumptions are combined, a broad dynamic regime emerges in which the individual-level reduction in infection risk for those adopting protective behaviour increases as adherence to protective behaviour decreases. For instance, at 10 % coverage, we find that adopters face nearly a 30 % lower infection risk than at 60 % coverage. Based on surgical mask effectiveness estimates, the relative risk reduction for masked individuals ranges from 5 % to 15 %, or a factor of three. This small coverage effect occurs when the outbreak is over before the pathogen is able to invade small but closely knit groups of individuals who protect themselves.

CONCLUSIONS

Our results confirm that lower coverage reduces protection at the population level while contradicting the common belief that masking becomes ineffective at the individual level as more people drop their masks.

Evolving policies, enduring impacts: Cross-sectional surveys of mental health, risk-related perceptions, and coping behaviors throughout China's U-turn in its stringent zero-COVID policy

OBJECTIVE

China's early encounter with COVID-19 and protracted prevention policies, presents an ideal case to study psychological changes during a prolonged and evolving crisis. This study aims to examine the shifts in mental health symptoms, risk-related perceptions, and associated coping behaviors within two large-scale samples of Chinese respondents, spanning from the pandemic's onset to the relaxation of the zero-COVID policy. Moreover, the study strives to identify protective factors that could potentially mitigate the pandemic's impact.

METHODS

Two online surveys were conducted during China's initial pandemic phase (February 25-28, 2020) and the relaxation of the zero-COVID policy (March 30-April 18, 2023). Participants' mental health indicators, risk-related perceptions, and coping behaviors were assessed using the Depression, Anxiety, and Stress Scale-21 Items, the 9-item Bergen Burnout Inventory, and other adopted scales. Multivariable linear models were employed to examine the enduring psychological impact of the pandemic and identify potential protective factors.

RESULTS

Analysis of two datasets comprising 1946 and 1878 participants from the onset and the remission of China's COVID-19 pandemic revealed an upward trend in various mental health indicators of Chinese respondents between 2020 and 2023. Similarly, risk-related perceptions, encompassing perceived severity, susceptibility, and self-efficacy, and risk-related information sharing witnessed an increase. Being female, single, residing in rural areas, having higher education, and lacking acquaintances with COVID-19 are protective factors against mental health risks. Additionally, being female, married, over 30, living in big cities, having higher education, and lacking personal or acquaintances' infection history are associated with engaging in protective behaviors and reduced information avoidance.

CONCLUSION

The study investigated the changes in mental health symptoms, risk-related perceptions, and coping behaviors of Chinese respondents between 2020 and 2023 and identified protective factors against the pandemic's impact, including demographic (gender, age), social (education, marital status, residence), and exposure (infection history) elements. Understanding these fluctuations and protective elements is crucial for policymakers, as it can inform the development of targeted strategies to alleviate negative psychological impacts while effectively managing future pandemics.

Global Political Logics and Mainstream Discourses on Illness in the Declarations of the State of Exception in the Context of the Covid-19 Pandemic: The Case of the USA, France, and Spain

At the outbreak of the Covid-19 pandemic, several countries declared "states of exception," that is, authorized legal devices that, in the face of circumstances deemed catastrophic, permit the implementation of extraordinary measures and the temporary suspension of some rights in order to restore the previous state of affairs as soon as possible. This paper offers a comparative textual analysis of the different states of exception declared in the USA, France, and Spain. I argue that these texts constitute a privileged site to explore how prevalent global political logics and mainstream discourses on illness are interwoven. Regarding the global political logics in play, I hold that these declarations constitute an instantiation of democracy's autoimmune character; it attacks itself in order to protect itself. Regarding mainstream discourses on illness, I explore how illness is regarded as a threat to one's self (by something seemingly other) and the notion that therapy must consist of securing the self's triumph over anything seemingly other. This twofold analysis reveals that an aporetic dialectic between self and other-as regards politics and illness-operates in these declarations, most likely because it is, in fact, one and the same dialectic, upon which Western epistemology rests. Furthermore, I suggest that these texts reflect and promote these dominant logics, contributing to shape human relationships around the globe in a certain dangerous way.

At freedom's edge: Belief in free will during the COVID-19 pandemic

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.

Understanding COVID-19-Related Behaviors, Worries, and Attitudes among Chinese: Roles of Personality and Severity

During the COVID-19 pandemic, people exhibited various forms of adjustments. This study examines how situational factors (i.e., the severity of COVID-19) and individual differences (i.e., the HEXACO traits) affect one's COVID-19-related responses regarding behaviors (i.e., mask-wearing and hoarding), worries (i.e., worrying about infecting and spreading COVID-19), and attitudes (i.e., discrimination and empathy toward people infecting COVID-19) in China. With a sample of 927 participants, our results show that the severity of COVID-19 was predictive of all the responses, and its predictive value was more pronounced relative to personality traits. Concerning the association between personality traits and responses, Honesty-Humility and Conscientiousness were predictive of one's behaviors, Emotionality was predictive of one's worries, and almost all the HEXACO traits were associated with one's attitudes toward people infected with COVID-19. This study sheds some light on understanding how situations and individual differences shape one's responses in a time of emergency.

Harmonizing government responses to the COVID-19 pandemic

Public health and safety measures (PHSM) made in response to the COVID-19 pandemic have been singular, rapid, and profuse compared to the content, speed, and volume of normal policy-making. Not only can they have a profound effect on the spread of the disease, but they may also have multitudinous secondary effects, in both the social and natural worlds. Unfortunately, despite the best efforts by numerous research groups, existing data on COVID-19 PHSM only partially captures their full geographical scale and policy scope for any significant duration of time. This paper introduces our effort to harmonize data from the eight largest such efforts for policies made before September 21, 2021 into the taxonomy developed by the CoronaNet Research Project in order to respond to the need for comprehensive, high quality COVID-19 data. In doing so, we present a comprehensive comparative analysis of existing data from different COVID-19 PHSM datasets, introduce our novel methodology for harmonizing COVID-19 PHSM data, and provide a clear-eyed assessment of the pros and cons of our efforts.

Unraveling implicit human behavioral effects on dynamic characteristics of Covid-19 daily infection rates in Taiwan

We investigate the dynamic characteristics of Covid-19 daily infection rates in Taiwan during its initial surge period, focusing on 79 districts within the seven largest cities. By employing computational techniques, we extract 18 features from each district-specific curve, transforming unstructured data into structured data. Our analysis reveals distinct patterns of asymmetric growth and decline among the curves. Utilizing theoretical information measurements such as conditional entropy and mutual information, we identify major factors of order-1 and order-2 that influence the peak value and curvature at the peak of the curves, crucial features characterizing the infection rates. Additionally, we examine the impact of geographic and socioeconomic factors on the curves by encoding each of the 79 districts with two binary characteristics: North-vs-South and Urban-vs-Suburban. Furthermore, leveraging this data-driven understanding at the district level, we explore the fine-scale behavioral effects on disease spread by examining the similarity among 96 age-group-specific curves within urban districts of Taipei and suburban districts of New Taipei City, which collectively represent a substantial portion of the nation's population. Our findings highlight the implicit influence of human behaviors related to living, traveling, and working on the dynamics of Covid-19 transmission in Taiwan.

Religious Identity and its Relation to Health-Related Quality of Life and COVID-Related Stress of Refugee Children and Adolescents in Germany

Research shows that religious identity is associated with health. The aim of this study was to understand the role of religious identity for refugee minors' health in greater detail. Middle Eastern refugee minors resettled in Germany and aged 8-18 years completed questionnaires at baseline (T1, n = 246) and follow-up (T2, n = 122) measurement between 2019 and 2022. Religious identity was assessed with a 4-item measure (Cronbach's α = .89). Associations of religious identity at T1 with health-related quality of life (HRQoL) at T1, change in HRQoL from T1 to T2, and perceived COVID-related stress at T2, as well as the mediating role of resources were examined. The results showed a positive association between religious identity and HRQoL, which was partially mediated by integration into peer group, but not by ethnic identity, sense of coherence or religious practice. No significant associations between religious identity and change in HRQoL or COVID-related stress occurred. Therefore, cross-sectional analyses support the beneficial role of religious identity for HRQoL and the crucial mediating role of integration into peer group, suggesting the promotion of religious identity or peer group integration. However, the absence of significant effects on change in HRQoL from T1 to T2 and COVID-related stress at T2 do not allow drawing any long-term conclusions.

Caregiving Stress Experienced by Parents of Adult Children with Intellectual Disabilities During COVID-19 in Korea

Many of community supports and resources were shattered in the COVID-19 pandemic, leaving parents to navigate caring for their adult child with intellectual disabilities with little support. This study explored caregiving stress experienced by parents of adult children with intellectual disabilities during the COVID-19 pandemic in Korea. In-depth interviews were conducted with 19 parents of an adult child with intellectual disabilities. Thematic analysis yielded two themes: caregiving burden and deteriorating health. Parents bore the brunt of the caregiving burden, spending much of their time helping their adult child with daily activities and managing their challenging behaviors, leaving the caregivers struggling physically and mentally. The heightened caregiver burden and associated deteriorating health among these parents raise serious concerns, indicating a need for immediate support to alleviate these issues and help parents navigate caring for their adult child with intellectual disabilities during the COVID-19 pandemic.

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations ('claims') detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms 'physical distancing' and 'social distancing'. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.

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

Introduction

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

Methods

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

Results

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

Discussion

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

Is the effect of a health crisis symmetric for physical and digital financial assets? An assessment of gold and bitcoin during the pandemic

The emergence of the covid-19 health crisis, in this advanced technological era where connections between markets, nations, and economies have grown stronger than ever before, the shock of the COVID-19 pandemic quickly had an impact on both physical and digital financial assets. The Chinese financial market experienced the first consequences of the covid-19 pandemic, then spilled over to other financial markets, including those for cryptocurrencies and the precious metals. This study examines the impact of the covid-19 pandemic on the volatilities of the dynamics of bitcoin and gold. Both assets share some characteristics, such as online trading platforms, however, gold is a tangible financial asset unlike bitcoin, which is digitally generated without any physical form. This study argues that the similarities and differences between bitcoin and gold play major roles in how the covid-19 crisis affected their respective dynamics. Using daily data ranging from 9/22/2014 to 1/31/2023 and employing ARMA as the mean equation for GARCH model, the impact of the health crisis (covid-19) is examined on the volatilities of the prices and volumes of bitcoin and gold. Empirical evidence points out that, the pandemic has a symmetric impact on the volatilities of bitcoin and gold price returns, causing them to be more volatile. The impact of the covid-19 observed on the volume returns of the assets, however, is asymmetrical. The empirical results give evidence to the role that the vital differences existing between these assets played during the covid-19 pandemic.

Increase in presentations with new-onset psychiatric disorders in a psychiatric emergency department in Berlin, Germany during the second wave of the COVID-19 pandemic - a retrospective cross-sectional study

Introduction

While numerous studies have identified an increase in symptoms of depression as well as anxiety and distress due to the COVID-19 pandemic, relatively few studies have investigated the new-onset of psychiatric diseases during the pandemic.

Methods

This study focuses on the number of psychiatric new-onset diagnoses in a psychiatric emergency department (pED) in Berlin, Germany during the second wave of the pandemic (i.e. from 09/15/2020 to 03/01/2021 = COVID-19-period) compared to pre-pandemic times (09/15/2019 to 03/01/2020 = control period). We focused on diagnostic subgroups and performed logistic regression analysis to investigate potential risk groups based on covariables such as age, gender, homelessness, attending in police custody and familial relationship.

Results

Overall, there was a 59.7% increase in new-onset psychiatric diagnoses during the COVID-19-period. Increases in the following diagnoses were observed: new-onset of substance-related and addictive disorders (+192.5%), depressive disorders (+115.8%), schizophrenia spectrum and psychotic disorders (+113.3%) and anxiety disorders (+63.6%). These diagnostic subgroups, together with attending in police custody, were found to predict pED presentations with new-onset during the COVID-19-period. Interestingly, in the group of new-onset psychiatric diseases in the COVID-19-period, higher amounts of job loss and living alone as well as a relative decrease in familial relationships were observed.

Discussion

COVID-19 infections and post-COVID-19 syndrome are unlikely to have played a substantial role in the increase of new-onset diseases in this study. Conclusion: Our findings underline the role of indirect factors in new-onset of psychiatric diseases during the pandemic and should be a caveat for future pandemic control policies.

The complex interplay between weather, social activity, and COVID-19 in the US

Empirical studies on the impact of weather and policy interventions on Covid-19 infections have dedicated little attention to the mediation role of social activity. In this study, we combine mobile locations, weather, and COVID-19 data in a two-way fixed effects mediation model to estimate the impact of weather and policy interventions on the COVID-19 infection rate in the US before the availability of vaccines, disentangling their direct impact from the part of the effect that is mediated by the endogenous response of social activity. We show that, while temperature reduces viral infectiousness, it also increases the amount of time individuals spend out of home, which instead favours the spread of the virus. This second channel substantially attenuates the beneficial effect of temperature in curbing the spread of the virus, offsetting one-third of the potential seasonal fluctuations in the reproduction rate. The mediation role of social activity is particularly pronounced when viral incidence is low, and completely offsets the beneficial effect of temperature. Despite being significant predictors of social activity, wind speed and precipitation do not induce sufficient variation to affect infections. Our estimates also suggest that school closures and lockdowns are effective in reducing infections. We employ our estimates to quantify the seasonal variation in the reproduction rate stemming from weather seasonality in the US.

Big data evidence of the impact of COVID-19 hospitalizations on mortality rates of non-COVID-19 critically ill patients

The COVID-19 virus caused a global pandemic leading to a swift policy response. While this response was designed to prevent the spread of the virus and support those with COVID-19, there is growing evidence regarding measurable impacts on non-COVID-19 patients. The paper uses a large dataset from administrative records of the Brazilian public health system (SUS) to estimate pandemic spillover effects in critically ill health care delivery, i.e. the additional mortality risk that COVID-19 ICU hospitalizations generate on non-COVID-19 patients receiving intensive care. The data contain the universe of ICU hospitalizations in SUS from February 26, 2020 to December 31, 2021. Spillover estimates are obtained from high-dimensional fixed effects regression models that control for a number of unobservable confounders. Our findings indicate that, on average, the pandemic increased the mortality risk of non-COVID-19 ICU patients by 1.296 percentage points, 95% CI 1.145-1.448. The spillover mortality risk is larger for non-COVID patients receiving intensive care due to diseases of the respiratory system, diseases of the skin and subcutaneous tissue, and infectious and parasitic diseases. As of July 2023, the WHO reports more than 6.9 million global deaths due to COVID-19 infection. However, our estimates of spillover effects suggest that the pandemic's total death toll is much higher.

SPRC19: A Database of State Policy Responses to COVID-19 in the United States

SPRC19 is a new database that seeks to capture a wide range of state policy actions in response to COVID-19 in the United States. Since March 2020 we have monitored state governments' and multi-state associations' websites for executive orders, agency rules, new legislation, and court decisions. We categorize each policy action into one of 206 distinct policies, then document the branch of government, source document, announcement date, implementation date, and expiration date (if applicable). We also record whether the action represents the introduction of a new policy or the expansion or contraction of an existing policy. The current release of SPRC19, v3.0, captures over 13,000 distinct policy actions through April 2020, which constitutes thousands more actions than similar resources over the same time period.

Open data for COVID-19 policy analysis and mapping

As the COVID-19 pandemic unfolded in the spring of 2020, governments around the world began to implement policies to mitigate and manage the outbreak. Significant research efforts were deployed to track and analyse these policies in real-time to better inform the response. While much of the policy analysis focused narrowly on social distancing measures designed to slow the spread of disease, here, we present a dataset focused on capturing the breadth of policy types implemented by jurisdictions globally across the whole-of-government. COVID Analysis and Mapping of Policies (COVID AMP) includes nearly 50,000 policy measures from 150 countries, 124 intermediate areas, and 235 local areas between January 2020 and June 2022. With up to 40 structured and unstructured characteristics encoded per policy, as well as the original source and policy text, this dataset provides a uniquely broad capture of the governance strategies for pandemic response, serving as a critical data source for future work in legal epidemiology and political science.

A comparative analysis of the effects of containment policies on the epidemiological manifestation of the COVID-19 pandemic across nine European countries

The COVID-19 pandemic has been a catastrophic event that has seriously endangered the world's population. Governments have largely been unprepared to deal with such an unprecedented calamity, partially due to the lack of sufficient or adequately fine-grained data necessary for forecasting the pandemic's evolution. To fill this gap, researchers worldwide have been collecting data about different aspects of COVID-19's evolution and government responses to them so as to provide the foundation for informative models and tools that can be used to mitigate the current pandemic and possibly prevent future ones. Indeed, since the early stages of the pandemic, a number of research initiatives were launched with this goal, including the PERISCOPE (Pan-European Response to the ImpactS of COVID-19 and future Pandemics and Epidemics) Project, funded by the European Commission. PERISCOPE aims to investigate the broad socio-economic and behavioral impacts of the COVID-19 pandemic, with the goal of making Europe more resilient and prepared for future large-scale risks. The purpose of this study, carried out as part of the PERISCOPE project, is to provide a first European-level analysis of the effect of government policies on the spread of the virus. To do so, we assessed the relationship between a novel index, the Policy Intensity Index, and four epidemiological variables collected by the European Centre for Disease Control and Prevention, and then applied a comprehensive Pan-European population model based on Multilevel Vector Autoregression. This model aims at identifying effects that are common to some European countries while treating country-specific policies as covariates, explaining the different evolution of the pandemic in nine selected countries due to data availability: Spain, France, Netherlands, Latvia, Slovenia, Greece, Ireland, Cyprus, Estonia. Results show that specific policies' effectiveness tend to vary consistently within the different countries, although in general policies related to Health Monitoring and Health Resources are the most effective for all countries.

Early impact of COVID-19 social distancing on social determinants of health and their effects on mental health and quality of life of Korean undergraduate students

Introduction

This study aimed to investigate the association between social determinants of health and perception of COVID-19 social distancing/mental health/quality of life during COVID-19 social distancing in Korean undergraduate students using online survey data augmented with natural language processing.

Methods

An online cross-sectional survey including sociodemographic characteristics, students' perceptions of COVID-19 social distancing, and social determinants of health was conducted between July and November in 2020. We conducted logistic regression analysis to investigate the relationship between social determinants of health (independent variables) and perceptions of COVID-19 social distancing, mental health, and quality of life (dependent variables). This association was augmented using sentiment analysis and word clouds by visualizing open-ended comments on COVID-19 social-distancing policies.

Results

Data were collected from 1,276 undergraduate students. Participants who experienced negative impacts on their social-networking activities due to COVID-19 social distancing were at significantly higher odds to perceive COVID-19 social distancing as not being beneficial [odds ratio (OR) = 1.948, 95% confidence interval (CI) 1.254-3.027], to have increased stress levels (OR = 1.619, 95% CI 1.051-2.496), and to experience decreased quality of life over 5 weeks (OR = 2.230, 95% CI 1.448-3.434) against those who answered neutrally. In contrast, Participants who reported positive perceptions of social-networking activities during the COVID-19 pandemic had lower odds of feeling depressed or anxious (OR = 0.498, 95% CI 0.278-0.894) and reporting a low quality of life over 5 weeks (OR = 0.461, 95% CI 0.252-0.842) compared to those who reported neutral perceptions. Furthermore, the results of the word cloud and sentiment analyses showed that most students perceived social distancing negatively.

Conclusions

The government's social-distancing policy to prevent the spread of COVID-19 may have had a negative impact, particularly on undergraduate students' social-networking activities. This highlights the need for greater social support for this population, including access to psychotherapeutic resources, and improvements in policies to prevent infectious diseases while still maintaining social connections.

COVID-19 and food security risk in Nkambe Cameroon: perceptions, drivers and household response measures

This study focuses on COVID-19 and food security risk, capturing perceptions, drivers and household response measures. Using a mixed research design, the researchers investigated food security risk in Nkambe Cameroon during peak periods of COVID-19. Data were collected through a structured questionnaire distributed to 400 respondents and key informant interviews, and it was analysed using both descriptive and inferential statistics. Results showed significant differences in the food security dynamics among COVID-19-infected and non-infected households (19% and 33%, respectively, p = 0.002), with non-infected households being more food secure than COVID-19-infected ones. Significant positive effects between variables, like age of respondent, household size, level of education and food security of affected households were identified. The regression model explains 82.8% of the food security determinants in the midst of COVID-19's peak period. Both COVID-19-infected and non-infected households responded to food insecurity through food rationing and consumption frequency rationing, instead of reducing food consumption frequency. The researchers recommend increasing the efficacy of safety nets and other social assistance programmes in the face of shocks by targeting households most vulnerable to COVID-19-induced food insecurity. Expanding this research agenda to other study sites with a gender lense can provide insights for food security policies post COVID-19.

Restricting Human Movement During the COVID-19 Pandemic: New Research Avenues in the Study of Mobility, Migration, and Citizenship

Every government in the world introduced restrictions to human mobility - that is, the movement of persons across and within state borders - in response to the COVID-19 pandemic. Such restrictions thus constituted a global phenomenon, but they were by no means globally uniform; rather, they varied significantly between and within states, as well as over time. This research note presents different data sources for studying the drivers and outcomes of mobility restrictions, highlighting specific ways in which the data can be used. We begin by surveying seven new databases capturing various aspects of the regulation of human movement during the COVID-19 pandemic. Drawing inspiration from research on previous pandemics, we then outline five possible research avenues prompted by these data. We suggest that explaining the causes and consequences of such restrictions, as well as the differences between them, can significantly advance research on the governance of mobility, migration, and citizenship.

An interactive national digital surveillance system to fight against COVID-19 in Bangladesh

Background

COVID-19 has affected many people globally, including in Bangladesh. Due to a lack of preparedness and resources, Bangladesh has experienced a catastrophic health crisis, and the devastation caused by this deadly virus has not yet been halted. Hence, precise and rapid diagnostics and infection tracing are essential for managing the condition and limiting its spread. The conventional screening procedure, such as reverse transcription polymerase chain reaction (RT-PCR), is not available in most rural areas and is time-consuming. Therefore, a data-driven intelligent surveillance system can be advantageous for rapid COVID-19 screening and risk estimation.

Objectives

This study describes the design, development, implementation, and characteristics of a nationwide web-based surveillance system for educating, screening, and tracking COVID-19 at the community level in Bangladesh.

Methods

The system consists of a mobile phone application and a cloud server. The data is collected by community health professionals via home visits or telephone calls and analyzed using rule-based artificial intelligence (AI). Depending on the results of the screening procedure, a further decision is made regarding the patient. This digital surveillance system in Bangladesh provides a platform to support government and non-government organizations, including health workers and healthcare facilities, in identifying patients at risk of COVID-19. It refers people to the nearest government healthcare facility, collecting and testing samples, tracking and tracing positive cases, following up with patients, and documenting patient outcomes.

Results

This study began in April 2020, and the results are provided in this paper till December 2022. The system has successfully completed 1,980,323 screenings. Our rule-based AI model categorized them into five separate risk groups based on the acquired patient information. According to the data, around 51% of the overall screened populations are safe, 35% are low risk, 9% are high risk, 4% are mid risk, and the remaining 1% is very high risk. The dashboard integrates all collected data from around the nation onto a single platform.

Conclusion

This screening can help the symptomatic patient take immediate action, such as isolation or hospitalization, depending on the severity. This surveillance system can also be utilized for risk mapping, planning, and allocating health resources to more vulnerable areas to reduce the virus's severity.

Community review: a robust and scalable selection system for resource allocation within open science and innovation communities

Resource allocation is essential to selection and implementation of innovative projects in science and technology. Current "winner-take-all" models for grant applications require significant researcher time in writing extensive project proposals, and rely on the availability of a few time-saturated volunteer experts. Such processes usually carry over several months, resulting in high effective costs compared to expected benefits. We devised an agile "community review" system to allocate micro-grants for the fast prototyping of innovative solutions. Here we describe and evaluate the implementation of this community review across 147 projects from the "Just One Giant Lab's OpenCOVID19 initiative" and "Helpful Engineering" open research communities. The community review process uses granular review forms and requires the participation of grant applicants in the review process. Within a year, we organised 7 rounds of review, resulting in 614 reviews from 201 reviewers, and the attribution of 48 micro-grants of up to 4,000 euros. The system is fast, with a median process duration of 10 days, scalable, with a median of 4 reviewers per project independent of the total number of projects, and fair, with project rankings highly preserved after the synthetic removal of reviewers. Regarding potential bias introduced by involving applicants in the process, we find that review scores from both applicants and non-applicants have a similar correlation of r=0.28 with other reviews within a project, matching traditional approaches. Finally, we find that the ability of projects to apply to several rounds allows to foster the further implementation of successful early prototypes, as well as provide a pathway to constructively improve an initially failing proposal in an agile manner. Overall, this study quantitatively highlights the benefits of a frugal, community review system acting as a due diligence for rapid and agile resource allocation in open research and innovation programs, with implications for decentralised communities.

Why People Stayed Home During the COVID-19 Pandemic: Implications for Health Communication Across Four Countries

Staying at home substantially reduces the spread of COVID-19. Moreover, understanding why people stayed at home by addressing its social cognitive determinants can help create more effective communication to change behaviors. This study analyzed this outcome through an extended model of the theory of planned behavior based on risk perception and personal norms in four countries: the United States, Japan, Brazil, and Taiwan. 1,196 individuals participated in this study through a questionnaire focused on planned behavior, moral norms, and risk perception. The data showed that intention and perceived behavioral control influenced behavior significantly, while attitude, injunctive norms, perceived behavioral control, personal norms, and risk perception influenced intention. With multigroup analysis and ANOVA, we verified significant differences in the estimates and mean scores across cultures, revealing the need for scholars to analyze outcomes based on geography and local political culture. Given that health communications played a key role in managing the pandemic, this study clarifies the social cognitive determinants of staying at home and how the local political culture can impact their influence. Thus, we provide an evidence-based prescription for focused communications.

A dataset of governments' economic responses to COVID-19

We introduce a new dataset of ten economic measures in percentage of gross domestic product implemented by governments worldwide between January 2020 and June 2021 to fight COVID-19. The measures coded include fiscal measures (wage support, cash transfers, in-kind transfers, tax cuts, sectorial support and credit schemes), tax deferrals, off-budget measures, and main policy rate cuts. The data can be used to study the impact of economic measures on different outcomes, and to understand the diffusion of economic policies during crises.

Divergent COVID-19 vaccine policies: policy mapping of ten European countries

Background

The COVID-19 pandemic highlighted the fragmented nature of governmental policy decisions in Europe. However, the extent to which COVID-19 vaccination policies differed in various European countries remains unclear. Here, we mapped the COVID-19 vaccination policies that were in force in January 2022 as well as booster regulations as of April 2022 in Austria, Denmark, England, France, Germany, Ireland, Italy, the Netherlands, Poland, and Spain.

Methods

National public health and health policy experts from these ten European nations developed and completed an electronic questionnaire. The questionnaire included a series of questions that addressed six critical components of vaccine implementation, including (1) authorization, (2) prioritization, (3) procurement and distribution, (4) data collection, (5) administration, and (6) mandate requirements.

Results

Our findings revealed significant variations in COVID-19 vaccination policies across Europe. We observed critical differences in COVID-19 vaccine formulations that were authorized for use as well as the specific groups that were provided with priority access. We also identified discrepancies in how vaccination-related data were recorded in each country and what vaccination requirements were implemented.

Conclusion

Each of the ten European nations surveyed in this study reported different COVID-19 vaccination policies. These differences complicated efforts to provide a coordinated pandemic response. These findings might alert policymakers in Europe of the need to coordinate their efforts to avoid fostering divergent and socially disruptive policies.

COVID-19 non-pharmaceutical interventions: data annotation for rapidly changing local policy information

Understanding the scope, prevalence, and impact of the COVID-19 pandemic response will be a rich ground for research for many years. Key to the response to COVID-19 was the non-pharmaceutical intervention (NPI) measures, such as mask mandates or stay-in-place orders. For future pandemic preparedness, it is critical to understand the impact and scope of these interventions. Given the ongoing nature of the pandemic, existing NPI studies covering only the initial portion provide only a narrow view of the impact of NPI measures. This paper describes a dataset of NPI measures taken by counties in the U.S. state of Virginia that include measures taken over the first two years of the pandemic beginning in March 2020. This data enables analyses of NPI measures over a long time period that can produce impact analyses on both the individual NPI effectiveness in slowing the pandemic spread, and the impact of various NPI measures on the behavior and conditions of the different counties and state.

Sleep in Children With Neurodevelopmental Disabilities During COVID-19: An Integrative Review

INTRODUCTION

Sleep issues occur at higher rates in children with neurodevelopmental disorders than in the typical population. Little is known about the impact of COVID-19 on sleep issues in this population METHOD: This integrative review aimed to characterize studies during the COVID-19 pandemic (2020-2022) addressing the prevalence and management of sleep issues in children and youth with neurodevelopmental disorders.Comprehensive database searches were used to identify articles, and 31 studies were considered suitable for this review.

RESULTS

Most studies reported worsening sleep disturbances during COVID-19 restrictions. All studies were conducted when clinics were closed; only two studies addressed treatment options such as music therapy and sleep hygiene education and found improvement in sleep issues.

DISCUSSION

Future research needs to concentrate on developing interventions to assist families remotely and empower families with a toolkit of preparedness in times of crisis.

Education data needs and challenges for building back from COVID-19

The COVID-19 pandemic has led governments worldwide to impose extensive restrictions on citizens, some of which may have long-term impact after their removal. Education is arguably the policy domain where closure policies are anticipated to lead to greatest lasting loss, in this case learning loss. Currently, limited data exists from which researchers and practitioners can draw insightful conclusions about how to remedy the problem. In this paper, we outline the global pattern in pandemic school-closure periods and illustrate data needs through the examples drawn from Brazil and India, two large countries which experienced prolonged periods of school closures during the pandemic. We conclude with a series of recommendations for building an improved data environment at government, school and household levels, to serve the building back agenda in education, and to provide better opportunities for evidence-based policymaking thereafter.

The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: An energy-economic modelling perspective

In the EU, COVID-19 and associated policy responses led to economy-wide disruptions and shifts in services demand, with considerable energy-system implications. The European Commission's response paved the way towards enhancing climate ambition through the European Green Deal. Understanding the interactions among environmental, social, and economic dimensions in climate action post-COVID thus emerged as a key challenge. This study disaggregates the implications of climate ambition, speed of economic recovery from COVID-19, and behavioural changes due to pandemic-related measures and/or environmental concerns for EU transition dynamics, over the next decade. It soft-links two large-scale energy-economy models, EU-TIMES and NEMESIS, to shed light on opportunities and challenges related to delivering on the EU's 2030 climate targets. Results indicate that half the effort required to reach the updated 55% emissions reduction target should come from electricity decarbonisation, followed by transport. Alongside a post-COVID return to normal, the European Green Deal may lead to increased carbon prices and fossil-fuel rebounds, but these risks may be mitigated by certain behavioural changes, gains from which in transport energy use would outweigh associated consumption increases in the residential sector. Finally, the EU recovery mechanism could deliver about half the required investments needed to deliver on the 2030 ambition.

Efficient social distancing during the COVID-19 pandemic: Integrating economic and public health considerations

Although social distancing can effectively contain the spread of infectious diseases by reducing social interactions, it may have economic effects. Crises such as the COVID-19 pandemic create dilemmas for policymakers because the long-term implementation of restrictive social distancing policies may cause massive economic damage and ultimately harm healthcare systems. This paper proposes an epidemic control framework that policymakers can use as a data-driven decision support tool for setting efficient social distancing targets. The framework addresses three aspects of the COVID-19 pandemic that are related to social distancing or community mobility data: modeling, financial implications, and policy-making. Thus, we explore the COVID-19 pandemic and concurrent economic situation as functions of historical pandemic data and mobility control. This approach allows us to formulate an efficient social distancing policy as a stochastic feedback control problem that minimizes the aggregated risks of disease transmission and economic volatility. We further demonstrate the use of a deep learning algorithm to solve this control problem. Finally, by applying our framework to U.S. data, we empirically examine the efficiency of the U.S. social distancing policy.

The Effects of Demographic, Training, and Specialization Differences of Physicians in COVID-19 Treatment in Turkey

INTRODUCTION

The study aimed to determine how physicians' professional and institutional characteristics affect their treatment approaches for COVID-19 antiviral and supportive treatment, as well as their demographic characteristics and participation in COVID-19 trainings that may influence these orientations.

METHODS

After researching the literature and conducting interviews with the experts, a questionnaire was prepared. The convenience sampling method was used to distribute the questionnaire online to 408 physicians across the nation in January 2021. Exploratory factor analysis was used to verify the research scale, and the Cronbach's Alpha test was used to confirm its reliability. During the data analysis, descriptive and inferential statistical tests were used.

RESULTS

It was observed that the physicians were willing to give COVID-19 patients the antivirals but hesitant to provide and suggest supportive care. The training status for COVID-19 treatment was varied. Female physicians prescribed more antiviral drugs than male physicians, received more training, and scored higher across the scale than male physicians ( p = 0.001). The treatment approaches of physicians varied according to their specializations ( p = 0.002) all over the scale.

CONCLUSION

The publication of current Ministry of Health guidelines on a variety of themes related to COVID-19 treatment, as seen in Turkey, is both beneficial and important. However, because all physicians, regardless of specialty, are affected by current and will be in the future pandemics, it is necessary to design and maintain a continuous training program on pandemics and their management for all physicians.

Impact of combination preventative interventions on hospitalization and death under the pandemic of SARS-CoV-2 Omicron variant in China

With a large population most susceptible to Omicron and emerging SARS-CoV-2 variants, China faces uncertain scenarios if reopening its border. Thus, we aimed to predict the impact of combination preventative interventions on hospitalization and death. An age-stratified susceptible-infectious-quarantined-hospitalized-removed-susceptible (SIQHRS) model based on the new guidelines of COVID-19 diagnosis and treatment (the ninth edition) was constructed to simulate the transmission dynamics of Omicron within 365 days. At baseline, we assumed no interventions other than 60% booster vaccination in individuals aged ≤60 years and 80% in individuals aged >60 years, quarantine and hospitalization. Oral antiviral medications for COVID-19 and nonpharmaceutical interventions (NPIs) such as social distancing and antigen self-testing were considered in subsequent scenarios. Sensitivity analyses were conducted to reflect different levels of interventions. A total of 0.73 billion cumulative quarantines (95% CI 0.53-0.83), 33.59 million hospitalizations (22.41-39.31), and 0.62 million deaths (0.40-0.75) are expected in 365 days. The case fatality rate with pneumonia symptoms (moderate, severe and critical illness) is expected to be 1.83% (1.68-1.99%) and the infected fatality rate is 0.38‰ (0.33-0.4‰). The highest existing hospitalization and ICU occupations are 3.11 (0.30-3.85) and 20.33 (2.01-25.20) times of capacity, respectively. Sensitivity analysis showed that interventions can be adjusted to meet certain conditions to reduce the total number of infections and deaths. In conclusion, after sufficient respiratory and ICU beds are prepared and the relaxed NPIs are in place, the SARS-CoV-2 Omicron variant would not seriously impact the health system.

The COVID-19 Response in North America

In our Information Technology (IT) based societies, social media plays an important role in communications and social networks for COVID-19. This study explores social responses for COVID-19 in North America, which is the most severe continent affected by the COVID-19 pandemic. This study employs social network analysis for Twitter among the US, Canada, and Mexico. This study finds that the 3 countries show different characteristics of social networks for COVID-19. For example, the Prime Minister plays the second most important role in the Canadian networks, whereas the Presidents play the most significant role in them, in the US, and Mexico. WHO shows a pivotal effect on social networks of COVID-19 in Canada and the US, whereas it does not affect them in Mexico. Canadians are interested in COVID-19 apps, the American people criticize the president and administration as incompetent in terms of COVID-19, and the Mexican people search for COVID-19 cases and the pandemic in Mexico. This study shows that governments and disease experts should understand social networks and communications of social network services, to develop effective COVID-19 policies according to the characteristics of their country.

Attitudes toward the pandemic and COVID-19 vaccination intention among German university students and the general population: Results from two cross-sectional surveys

Background

The COVID-19 pandemic has had an impact on nearly all people. Vaccines provide an effective tool to combat the pandemic, however, vaccination hesitancy remains an issue. This study aims to investigate (a) students' attitudes toward the pandemic, (b) potential differences in attitudes between university students and the general population, and (c) to examine predictors of vaccination intention in both samples.

Methods

In this cross-sectional study data from two research projects were analyzed and compared. First, attitudes toward the COVID-19 pandemic in German university students were assessed within a cross-sectional anonymous online survey (March-April 2021, N = 5,639) and analyzed quantitatively and also qualitatively (free text field answers examined positive and negative aspects of the pandemic). Second, data from a cross-sectional survey within the COVID-19 Snapshot Monitoring project (COSMO; 29^th wave of data collection, December 2020, N = 1,387) in the German general population was analyzed. Both samples, were compared in sharedly used variables, regarding attitudes toward the pandemic and vaccination intention, and factors associated with vaccination (logistic regression analyses).

Results

In comparison to the general population, university students were significantly more likely to report being worried about/thinking about the coronavirus and to perceive the coronavirus as overrepresented in the media (all p < 0.001). University students reported a more supportive attitude toward vaccinations in general (students: M = 4.57, SD = 0.85; general population: M = 3.92, SD = 1.27) and a significantly higher vaccination intention (students: n = 4,438, 78.7%; general population: n = 635, 47.7%) than the general population (p < 0.001). Regression analyses revealed that in university students, vaccination intention was significantly predicted by not having children, a supporting attitude toward vaccinations in general, the belief that the coronavirus is overrepresented in the media, and less thinking about/worrying about the coronavirus (all p < 0.05). In the general population, vaccination intention was significantly associated with male gender, higher age, not having children, a supporting attitude toward vaccinations in general, and the belief that the coronavirus is overrepresented in the media (p < 0.05). The qualitative analysis among university students revealed that the most frequently stated positive aspect of the pandemic was to be more flexible due to digitalization (n = 1,301 statements, 22.2%) and the most frequently stated negative aspect was restriction in social life (n = 3,572 statements, 24.2%).

Conclusion

The results indicate differences in the attitudes toward the pandemic between university students and the general population. In addition, differences regarding factors associated with vaccination intention were found in both samples. These results could be important to be considered when designing and targeting vaccination campaigns aiming at informing different population or age groups.

Study registration

DRKS00022424.

Suit the Remedy to the Case-The Effectiveness of COVID-19 Nonpharmaceutical Prevention and Control Policies Based on Individual Going-Out Behavior

Nonpharmaceutical policies for epidemic prevention and control have been extensively used since the outbreak of COVID-19. Policies ultimately work by limiting individual behavior. The aim of this paper is to evaluate the effectiveness of policies by combining macro nonpharmaceutical policies with micro-individual going-out behavior. For different going out scenarios triggered by individual physiological safety needs, friendship needs, and family needs, this paper categorizes policies with significant differences in intensity, parameterizes the key contents of the policies, and simulates and analyzes the effectiveness of the policies in different going-out scenarios with simulation methods. The empirical results show that enhancing policy intensity can effectively improve policy effectiveness. Among different types of policies, restricting the times of going out is more effective. Further, the effect of controlling going out based on physiological safety needs is better than other needs. We also evaluate the policy effectiveness of 26 global countries or regions. The results show that the policy effectiveness varies among 26 countries or regions. The quantifiable reference provided by this study facilitates decision makers to establish policy and practices for epidemic prevention and control.

Evolving perceptions of COVID-19 vaccines among remote Alaskan communities

Given the dynamic nature of the ongoing pandemic, public knowledge and perceptions about COVID-19 are evolving. Limited transportation options, inconsistent healthcare resources, and lack of water and sanitation infrastructure in many remote Alaskan communities located off the road system have contributed to the experience of the COVID-19 pandemic in these areas. We used longitudinal surveys to evaluate remote Alaskan residents' early vaccine acceptance, vaccine uptake and motivations, risk perceptions regarding COVID-19 vaccines, and likelihood of getting a booster. Slightly over half of respondents showed early vaccine acceptance (November/December 2020), with the highest rate among those over the age of 65 years. However, by March 2021, 80.7% of participants reported receiving the COVID-19 vaccine or planning to get one. Of the unvaccinated, reasons for not getting a vaccine included concerns about side effects and not trusting the vaccine. By September 2021, 88.5% of people had received two doses of a COVID-19 vaccine and 79.7% said they would get the booster (third dose) when it became available. There were misconceptions about vaccine recommendations for pregnant women and effects on fertility and DNA. Although initial vaccine concerns may have subsided, the booster rollout and forthcoming vaccines for youth under 12 years of age present new hurdles for vaccine communication efforts.

Terminated local subsidy on electric vehicle adoption during the COVID-19 pandemic: The case of Chongqing City

Chongqing, one of the four municipalities directly under the Central Government in China, terminated local subsidies for electric vehicles (EVs) on June 26, 2019. Shortly after the termination, EV adoption in China was affected by the coronavirus disease (COVID-19) pandemic. However, little research studies on whether the terminated local subsidy has a lasting impact on EV adoption, especially during the pandemic. Using EV adoption data from Chongqing and 44 other cities in China, this study aims to fill this gap by first proposing a new method to estimate the unobservable data of the treated unit in the preintervention periods to obtain accurate results. This study then also estimates unobservable data for more conservative results. The findings show that the terminated subsidy has had a significant positive impact on EV adoption during the COVID-19 pandemic compared to the situation where a local subsidy was never provided. The results show that in Chongqing, during the first five months of the pandemic, terminated local subsidy helped reduce the loss of EV adoption by approximately 3141 units when accurately estimated, and approximately 1696 units when more conservatively estimated. These findings help to understand the role of subsidies both during implementation and after their termination.

Exploring the Impact of Localized COVID-19 Events on Intercity Mobility during the Normalized Prevention and Control Period in China

Uncontrolled, large-scale human mobility can amplify a localized disease into a pandemic. Tracking changes in human travel behavior, exploring the relationship between epidemic events and intercity travel generation and attraction under policies will contribute to epidemic prevention efforts, as well as deepen understanding of the essential changes of intercity interactions in the post-epidemic era. To explore the dynamic impact of small-scale localized epidemic events and related policies on intercity travel, a spatial lag model and improved gravity models are developed by using intercity travel data. Taking the localized COVID-19 epidemic in Xi'an, China as an example, the study constructs the travel interaction characterization before or after the pandemic as well as under constraints of regular epidemic prevention policies, whereby significant impacts of epidemic events are explored. Moreover, indexes of the quantified policies are refined to the city level in China to analyze their effects on travel volumes. We highlight the non-negligible impacts of city events and related policies on intercity interaction, which can serve as a reference for travel management in case of such severe events.

Reliability of COVID-19 data: An evaluation and reflection

Importance

The rapid proliferation of COVID-19 has left governments scrambling, and several data aggregators are now assisting in the reporting of county cases and deaths. The different variables affecting reporting (e.g., time delays in reporting) necessitates a well-documented reliability study examining the data methods and discussion of possible causes of differences between aggregators.

Objective

To statistically evaluate the reliability of COVID-19 data across aggregators using case fatality rate (CFR) estimates and reliability statistics.

Design, setting, and participants

Cases and deaths were collected daily by volunteers via state and local health departments, as primary sources and newspaper reports, as secondary sources. In an effort to begin comparison for reliability statistical analysis, BroadStreet collected data from other COVID-19 aggregator sources, including USAFacts, Johns Hopkins University, New York Times, The COVID Tracking Project.

Main outcomes and measures

COVID-19 cases and death counts at the county and state levels.

Results

Lower levels of inter-rater agreement were observed across aggregators associated with the number of deaths, which manifested itself in state level Bayesian estimates of COVID-19 fatality rates.

Conclusions and relevance

A national, publicly available data set is needed for current and future disease outbreaks and improved reliability in reporting.

How to design virus containment policies? A joint analysis of economic and epidemic dynamics under the COVID-19 pandemic

We analyze the impact of different designs of COVID-19-related lockdown policies on economic loss and mortality using a micro-level simulation model, which combines a multi-sectoral closed economy with an epidemic transmission model. In particular, the model captures explicitly the (stochastic) effect of interactions between heterogeneous agents during different economic activities on virus transmissions. The empirical validity of the model is established using data on economic and pandemic dynamics in Germany in the first 6 months after the COVID-19 outbreak. We show that a policy-inducing switch between a strict lockdown and a full opening-up of economic activity based on a high incidence threshold is strictly dominated by alternative policies, which are based on a low incidence threshold combined with a light lockdown with weak restrictions of economic activity or even a continuous weak lockdown. Furthermore, also the ex ante variance of the economic loss suffered during the pandemic is substantially lower under these policies. Keeping the other policy parameters fixed, a variation of the consumption restrictions during the lockdown induces a trade-off between GDP loss and mortality. Furthermore, we study the robustness of these findings with respect to alternative pandemic scenarios and examine the optimal timing of lifting containment measures in light of a vaccination rollout in the population.

The Effect of Governmental Health Measures on Public Behaviour During the COVID-19 Pandemic Outbreak

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic resulted in radical changes in many aspects of life. To deal with this, each country has implemented continuous health measures from the beginning of the outbreak. Discovering how governmental actions impacted public behaviour during the outbreak stage is the purpose of this study.

METHODS

This study uses a hybrid large-scale data visualisation method to analyse public behaviour (epidemic concerns, self-protection, and mobility trends), using the data provided by multiple authorities. Meanwhile, a content analysis method is used to qualitatively code the health measures of three countries with severe early epidemic outbreaks from different continents, namely China, Italy, and the United States. Eight dimensions are coded to rate the mobility restrictions implemented in the above countries.

RESULTS

(1) Governmental measures did not immediately persuade the public to change their behaviours during the COVID-19 epidemic. Instead, the public behaviour proceeded in a three-phase rule, which is typically witnessed in an epidemic outbreak, namely the wait-and-see phase, the surge phase and the slow-release phase. (2) The strictness of the mobility restrictions of the three countries can be ranked as follows: Hubei Province in China (with an average score of 8.5 out of 10), Lombardy in Italy (7.125), and New York State in the United States (5.375). Strict mobility restrictions are more likely to cause a surge of population outflow from the epidemic area in the short term, whereas the effect of mobility restrictions is positively related to the stringency of policies in the long term.

CONCLUSION

The public showed generally lawful behaviour during regional epidemic outbreaks and blockades. Meanwhile public behaviour was deeply affected by the actions of local governments, rather than the global pandemic situation. The contextual differences between the various countries are important factors that influence the effects of the different governments' health measures.

Unraveling the dynamic impacts of COVID-19 on metro ridership: An empirical analysis of Beijing and Shanghai, China

The outbreak of coronavirus disease 2019 (COVID-19) has had severely disruptive impacts on transportation, particularly public transit. To understand metro ridership changes due to the COVID-19 pandemic, this study conducts an in-depth analysis of two Chinese megacities from January 1, 2020, to August 31, 2021. Generalized linear models are used to explore the impact of the COVID-19 pandemic on metro ridership. The dependent variable is the relative change in metro ridership, and the independent variables include COVID-19, socio-economic, and weather variables. The results suggested the following: (1) The COVID-19 pandemic has a significantly negative effect on the relative change in metro ridership, and the number of cumulative confirmed COVID-19 cases within 14 days performs better in regression models, which reflects the existence of the time lag effect of the COVID-19 pandemic. (2) Emergency responses are negatively associated with metro system usage according to severity and duration. (3) The marginal effects of the COVID-19 variables and emergency responses are larger on weekdays than on weekends. (4) The number of imported confirmed COVID-19 cases only significantly affects metro ridership in the weekend and new-normal-phase models for Beijing. In addition, the daily gross domestic product and weather variables are significantly associated with metro ridership. These findings can aid in understanding the usage of metro systems in the outbreak and new-normal phases and provide transit operators with guidance to adjust services.

Evidence in decision-making in the context of COVID-19 in Latin America

Background

The pace of the COVID-19 pandemic poses an unprecedented challenge to the evidence-to-decision process. Latin American countries have responded to COVID-19 by introducing interventions to both mitigate the risk of infection and to treat cases. Understanding how evidence is used to inform government-level decision-making at a national scale is crucial for informing country and regional actors in ongoing response efforts.

Objectives

This study was undertaken between February-May 2021 and aims to characterise the best available evidence (BAE) and assess the extent to which it was used to inform decision-making in 21 Latin American countries, in relation to pharmaceutical (PI) and non-pharmaceutical interventions (NPI) related to COVID-19, including the use of therapeutics (corticosteroids, hydroxychloroquine/chloroquine and ivermectin), facemask use in the community setting and the use of diagnostic tests as a requirement for international travel.

Method

A three-phase methodology was used to; (i) characterise the BAE for each intervention using an umbrella review, (ii) identify government-level decisions for each intervention through a document review and (iii) assess the use of evidence to inform decisions using a novel adapted framework analysis.

Findings

The BAE is characterized by 17 living and non-living systematic reviews as evolving, and particularly uncertain for NPIs. 107 country-level documents show variation in both content and timing of decision outcomes across intervention types, with the majority of decisions taken at a time of evidence uncertainty, with only 5 documents including BAE. Seven out of eight key indicators of an evidence-to-decision process were identified more frequently among PIs than either NPI of facemask use or testing prior to travel. Overall evidence use was reported more frequently among PIs than either NPI of facemask use or travel testing (92%, 28% and 29%, respectively).

Interpretation

There are limitations in the extent to which evidence use in decision-making is reported across the Latin America region. Institutionalising this process and grounding it in existing and emerging methodologies can facilitate the rapid response in an emergency setting.

Funding

No funding was sourced for this work.

The effects of daily growth in COVID-19 deaths, cases, and governments' response policies on stock markets of emerging economies

Since the beginning of COVID-19, human beings have been threatened by various aspects. As of February 14, 2022, this global pandemic has caused about 412 million cases and 5.8 million deaths worldwide. Stock markets are one of the most agile economic indicators. In this context, this study investigates how daily growth in deaths, daily growth in cases, and governmental interventions affect stock market returns in 21 emerging economies from January 22 to December 31, 2020. Our results indicate that government response policies to Covid-19 positively impact stock returns. Besides, the daily growths in deaths and cases negatively affect stock market returns. The results also indicate that government response policies also have an indirect positive effect on stock market returns by weakening the negative impact of the daily growth in COVID-19 confirmed cases and deaths.

Inequality and support for government responses to COVID-19

Despite a deep literature studying the impact of inequality on policy outcomes, there has been limited effort to bring these insights into the debates about comparative support for government responses to the COVID-19 pandemic. We fill this gap by analyzing rich survey data at the beginning of the pandemic in April 2020 from six countries spanning different income levels and geographical locations—China, Italy, Japan, South Korea, the United Kingdom, and the United States. We find that poorer individuals are less supportive of government responses. Furthermore, poorer individuals residing in more economically unequal countries offer even less government support. We also find that both economic and non-economic factors could affect the poor’s decisions to support stringent government policies. These findings suggest that greater transfers to the poor may offer an option to help increase support for strict policies and may reduce the potential deepening of social inequalities caused by the pandemic.

Public health research priorities for WHO on COVID-19 in the South-East Asia Region: results of a prioritization survey

Background

Effectively addressing the coronavirus disease 2019 (COVID-19) pandemic caused by the new pathogen requires continuous generation of evidence to inform decision-making. Despite an unprecedented amount of research occurring globally, the need to identify gaps in knowledge and prioritize a research agenda that is linked to public health action is indisputable. The WHO South-East Asia Region (SEAR) is likely to have region-specific research needs.

Methods

We aimed to identify a priority research agenda for guiding the regional and national response to the COVID-19 pandemic in SEAR countries. An online, anonymous research prioritization exercise using recent WHO guidance was conducted among the technical staff of WHO’s country and regional offices engaged with the national COVID-19 response during October 2020. They were each asked to contribute up to five priority research ideas across seven thematic areas. These research ideas were reviewed, consolidated and scored by a core group on six parameters: regional specificity, relevance to the COVID-19 response, feasibility within regional research capacity, time to availability for decision-making, likely impact on practice, and promoting equity and gender responsiveness. The total scores for individual suggestions were organized in descending order, and ideas in the upper tertile were considered to be of high priority.

Results

A total of 203 priority research ideas were received from 48 respondents, who were primarily research and emergency response focal points in country and regional offices. These were consolidated into 78 research ideas and scored. The final priority research agenda of 27 items covered all thematic areas—health system (n=10), public health interventions (n=6), disease epidemiology (n=5), socioeconomic and equity (n=3), basic sciences (n=1), clinical sciences (n=1) and pandemic preparedness (n=1).

Conclusions

This exercise, a part of WHO’s mandate to “shape the research agenda”, can help build a research roadmap ensuring efficient use of limited resources. This prioritized research agenda can act as a catalyst for Member States to accelerate research that could impact the COVID-19 response in SEAR.

Institutional development and the government response to COVID-19 in China

As COVID-19 is pervasive across the globe, governments in different countries face the dilemma of restricting the transmission risk of the virus by social distancing while yet maintaining economic activity. Inadequate social distancing policies lead to more infection cases and deaths, while over stringent social distancing policies have significant economic cost implications. This study investigates the role of local government institutions in striking the balance between saving lives and economic recovery. We based our study on a sample of 28 provincial governments in China during the early outbreak of 2020 when the emergency responses of local governments were synchronous. The findings show that local governments in those provinces with lower degrees of marketization, which were accustomed to directly intervene in the social system, mandatorily quarantined many more close contacts for each confirmed case than those in the more market-oriented provinces whose social distancing policies took economic considerations into account. The 'overdone' (over stringent) social distancing policies in the more state-oriented provinces led to lower human mobility and economic growth. This study highlights the importance of taking economic considerations into account when adopting policies and strategies to combat the spread of COVID-19 and how different institution management cultures lead to different outcomes.