Which COVID policies are most effective? A Bayesian analysis of COVID-19 by jurisdiction

Forecasting Epidemic Spread With Recurrent Graph Gate Fusion Transformers

Predicting the unprecedented, nonlinear nature of COVID-19 presents a significant public health challenge. Recent advances in deep learning, such as graph neural networks (GNNs), recurrent neural networks (RNNs), and Transformers, have enhanced predictions by modeling regional interactions, managing autoregressive time series, and identifying long-term dependencies. However, prior works often feature shallow integration of these models, leading to simplistic graph embeddings and inadequate analysis across different graph types. Additionally, excessive reliance on historical COVID-19 data limits the potential of utilizing time-lagged data, such as intervention policy information. To address these challenges, we introduce ReGraFT, a novel sequence-to-sequence (Seq2Seq) model designed for robust long-term forecasting of COVID-19. ReGraFT integrates multigraph-gated recurrent units (MGRU) with adaptive graphs, leveraging data from individual states, including infection rates, policy changes, and interstate travel. First, ReGraFT employs adaptive MGRU cells within an RNN framework to capture inter-regional dependencies, dynamically modeling complex transmission dynamics. Second, the model features a self-normalizing priming (SNP) layer using Scaled Exponential Linear Units (SeLU) to enhance stability and accuracy across short, medium, and long-term forecasts. Third, ReGraFT systematically compares and integrates various graph types, such as fully connected layers, pooling mechanisms, and attention-based structures, to provide a nuanced representation of inter-regional relationships. By incorporating lagged COVID-19 policy data, ReGraFT refines forecasts, demonstrating a 2.39% to 35.92% reduction in the root mean square error (RMSE) compared to state-of-the-art models. This work provides accurate long-term predictions, aiding in better public health decisions.

The Impact of Public Health and Social Measures (PHSMs) on SARS-CoV-2 Transmission in the WHO European Region (2020-2022)

BACKGROUND

Between 2020 and 2022, countries used a range of different public health and social measures (PHSMs) to reduce the transmission of SARS-CoV-2. The impact of these PHSMs varied as the pandemic progressed, variants of concern (VOCs) emerged, vaccines rolled out and acceptance/uptake rates evolved. In this study, we assessed the impact of PHSMs in the World Health Organization (WHO) European Region during VOC phases.

METHODS

We relied on time series data on genome sequencing, PHSMs, health outcomes and physical contacts. Panel regression models were used to assess the association between PHSMs and SARS-CoV-2 transmission (approximated using time-varying reproduction numbers). The interpretation of these regression models was assisted by hierarchical clustering, which was used to detect the temporal co-occurrence of PHSMs. Generalised linear models were used to check if PHSMs are associated with physical contacts.

RESULTS

We identified four phases based on the dominating VOC in the WHO European Region: wild type (before early 2021), Alpha (early to mid-2021), Delta (mid-to-late 2021) and Omicron (after late 2021). 'School closure', 'stay-at-home requirement' and 'testing policy' were consistently associated with lower transmission across VOC phases. The impact of most PHSMs varied by VOC phases without clear increasing or decreasing trends as the pandemic progressed. Several PHSMs associated with lower transmission were not associated with fewer physical contacts.

CONCLUSIONS

The impact of PHSMs evolved as the pandemic progressed-although without clear trends. The specific mechanisms by which some PHSMs reduce SARS-CoV-2 transmission require further research.

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.

Determining the nurses' perception regarding the effectiveness of COVID-19 protocols implemented in Eastern Province: Saudi Arabia

Background

The global impact of Coronavirus Disease 2019 (COVID-19) has been profound, affecting public health, the global economy, and overall human life. Past experiences with global pandemics underscored the significance of understanding the perception of HCWs and hospital staff in developing and implementing preventive measures. The World Health Organization (WHO) provided protocols to manage the spread of COVID-19 and assist healthcare workers and health systems globally in maintaining high-quality health services.

Objective

This study aims to assess nurses' perception, awareness, and compliance regarding the implementation of COVID-19 protocols and explore factors influencing their perception.

Methodology

A quantitative cross-sectional survey-based study was conducted, distributing a constructed survey among nurses in the Eastern Province of Saudi Arabia.

Results

Out of 141 participants, most adhered to protocols such as hand sanitization, social distancing, and proper personal protective equipment (PPE) usage. The predominant age group among respondents was 31 to 40 years (n = 71, 50%). A significant portion of participants reported holding a bachelor's degree (n = 86, 61%), with only 14% possessing advanced degrees (n = 19). Nearly a third of the nurses in the study had accumulated 6 to 10 years of professional experience (n = 49, 34.8%). A noteworthy percentage of nurses were engaged in daily shifts exceeding 8 h (n = 98, 70%). Gender differences were observed, with females exhibiting a higher tendency to avoid shaking hands and social gatherings. Saudi nationals were more inclined to shake hands and engage in gatherings. Non-Saudi nurses and those aged between <25 to 40 years demonstrated proper donning/doffing practices. Nurses with over 6 years of experience avoided social gatherings, while those working >8 h adhered better to PPE usage, proper donning/doffing, and disposal of PPE in designated bins.

Conclusion

Understanding COVID-19 protocols is crucial for tailoring interventions and ensuring effective compliance with COVID-19 preventive measures among nurses. More efforts should be made toward preparing the healthcare nursing to deal with the outbreak. Preparing healthcare nursing with the right knowledge, attitude, and precautionary practices during the COVID-19 outbreak is very essential to patient and public safety.

The impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia: Simulation approach

Objectives

This paper aims to measure the impact of the implemented nonpharmaceutical interventions (NPIs) in the Kingdom of Saudi Arabia (KSA) during the pandemic using simulation modeling.

Methods

To measure the impact of NPI, a hybrid agent-based and system dynamics simulation model was built and validated. Data were collected prospectively on a weekly basis. The core epidemiological model is based on a complex Susceptible-Exposed-Infectious-Recovered and Dead model of epidemic dynamics. Reverse engineering was performed on a weekly basis throughout the study period as a mean for model validation which reported on four outcomes: total cases, active cases, ICU cases, and deaths cases. To measure the impact of each NPI, the observed values of active and total cases were captured and compared to the projected values of active and total cases from the simulation. To measure the impact of each NPI, the study period was divided into rounds of incubation periods (cycles of 14 days each). The behavioral change of the spread of the disease was interpreted as the impact of NPIs that occurred at the beginning of the cycle. The behavioral change was measured by the change in the initial reproduction rate (R0).

Results

After 18 weeks of the reverse engineering process, the model achieved a 0.4 % difference in total cases for prediction at the end of the study period. The results estimated that NPIs led to 64 % change in The R0. Our breakdown analysis of the impact of each NPI indicates that banning going to schools had the greatest impact on the infection reproduction rate (24 %).

Conclusion

We used hybrid simulation modeling to measure the impact of NPIs taken by the KSA government. The finding further supports the notion that early NPIs adoption can effectively limit the spread of COVID-19. It also supports using simulation for building mathematical modeling for epidemiological scenarios.

Using a Bayesian hierarchical approach to study the association between non-pharmaceutical interventions and the spread of Covid-19 in Germany

Non-Pharmaceutical Interventions (NPIs) are community mitigation strategies, aimed at reducing the spread of illnesses like the coronavirus pandemic, without relying on pharmaceutical drug treatments. This study aims to evaluate the effectiveness of different NPIs across sixteen states of Germany, for a time period of 21 months of the pandemic. We used a Bayesian hierarchical approach that combines different sub-models and merges information from complementary sources, to estimate the true and unknown number of infections. In this framework, we used data on reported cases, hospitalizations, intensive care unit occupancy, and deaths to estimate the effect of NPIs. The list of NPIs includes: "contact restriction (up to 5 people)", "strict contact restriction", "curfew", "events permitted up to 100 people", "mask requirement in shopping malls", "restaurant closure", "restaurants permitted only with test", "school closure" and "general behavioral changes". We found a considerable reduction in the instantaneous reproduction number by "general behavioral changes", "strict contact restriction", "restaurants permitted only with test", "contact restriction (up to 5 people)", "restaurant closure" and "curfew". No association with school closures could be found. This study suggests that some public health measures, including general behavioral changes, strict contact restrictions, and restaurants permitted only with tests are associated with containing the Covid-19 pandemic. Future research is needed to better understand the effectiveness of NPIs in the context of Covid-19 vaccination.

Effectiveness of testing, contact tracing and isolation interventions among the general population on reducing transmission of SARS-CoV-2: a systematic review

We conducted a systematic literature review of general population testing, contact tracing, case isolation and contact quarantine interventions to assess their effectiveness in reducing SARS-CoV-2 transmission, as implemented in real-world settings. We designed a broad search strategy and aimed to identify peer-reviewed studies of any design provided there was a quantitative measure of effectiveness on a transmission outcome. Studies that assessed the effect of testing or diagnosis on disease outcomes via treatment, but did not assess a transmission outcome, were not included. We focused on interventions implemented among the general population rather than in specific settings; these were from anywhere in the world and published any time after 1 January 2020 until the end of 2022. From 26 720 titles and abstracts, 1181 were reviewed as full text, and 25 met our inclusion criteria. These 25 studies included one randomized control trial (RCT) and the remaining 24 analysed empirical data and made some attempt to control for confounding. Studies included were categorized by the type of intervention: contact tracing (seven studies); specific testing strategies (12 studies); strategies for isolating cases/contacts (four studies); and 'test, trace, isolate' (TTI) as a part of a package of interventions (two studies). None of the 25 studies were rated at low risk of bias and many were rated as serious risk of bias, particularly due to the likely presence of uncontrolled confounding factors, which was a major challenge in assessing the independent effects of TTI in observational studies. These confounding factors are to be expected from observational studies during an on-going pandemic, when the emphasis was on reducing the epidemic burden rather than trial design. Findings from these 25 studies suggested an important public health role for testing followed by isolation, especially where mass and serial testing was used to reduce transmission. Some of the most compelling analyses came from examining fine-grained within-country data on contact tracing; while broader studies which compared behaviour between countries also often found TTI led to reduced transmission and mortality, this was not universal. There was limited evidence for the benefit of isolation of cases/contacts away from the home environment. One study, an RCT, showed that daily testing of contacts could be a viable strategy to replace lengthy quarantine of contacts. Based on the scarcity of robust empirical evidence, we were not able to draw any firm quantitative conclusions about the quantitative impact of TTI interventions in different epidemic contexts. While the majority of studies found that testing, tracing and isolation reduced transmission, evidence for the scale of this impact is only available for specific scenarios and hence is not necessarily generalizable. Our review therefore emphasizes the need to conduct robust experimental studies that help inform the likely quantitative impact of different TTI interventions on transmission and their optimal design. Work is needed to support such studies in the context of future emerging epidemics, along with assessments of the cost-effectiveness of TTI interventions, which was beyond the scope of this review but will be critical to decision-making. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings

Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.

Governmental anti-pandemic policies, vaccination, population mobility, Twitter narratives, and the spread of COVID-19: Evidence from the European Union countries

We provide large-scale empirical evidence on the effects of multiple governmental regulatory and health policies, vaccination, population mobility, and COVID-19-related Twitter narratives on the spread of a new coronavirus infection. Using multiple-level fixed effects panel data model with weekly data for 27 European Union countries in the period of March 2020-June 2021, we show that governmental response policies were effective both in reducing the number of COVID-19 infection cases and deaths from it, particularly, in the countries with higher level of rule of law. Vaccination expectedly helped to decrease the number of virus cases. Reductions in population mobility in public places and workplaces were also powerful in fighting the pandemic. Next, we identify four core pandemic-related Twitter narratives: governmental response policies, people's sad feelings during the pandemic, vaccination, and pandemic-related international politics. We find that sad feelings' narrative helped to combat the virus spread in EU countries. Our findings also reveal that while in countries with high rule of law international politics' narrative helped to reduce the virus spread, in countries with low rule of law the effect was strictly the opposite. The latter finding suggests that trust in politicians played an important role in confronting the pandemic.

Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm

Analyzing the COVID-19 pandemic is a critical factor in developing effective policies to deal with similar challenges in the future. However, many parameters (e.g., the actual number of infected people, the effectiveness of vaccination) are still subject to considerable debate because they are unobservable. To model a pandemic and estimate unobserved parameters, researchers use compartmental models. Most often, in such models, the transition rates are considered as constants, which allows simulating only one epidemiological wave. However, multiple waves have been reported for COVID-19 caused by different strains of the virus. This paper presents an approach based on the reconstruction of real distributions of transition rates using genetic algorithms, which makes it possible to create a model that describes several pandemic peaks. The model is fitted on registered COVID-19 cases in four countries with different pandemic control strategies (Germany, Sweden, UK, and US). Mean absolute percentage error (MAPE) was chosen as the objective function, the MAPE values of 2.168%, 2.096%, 1.208% and 1.703% were achieved for the listed countries, respectively. Simulation results are consistent with the empirical statistics of medical studies, which confirms the quality of the model. In addition to observables such as registered infected, the output of the model contains variables that cannot be measured directly. Among them are the proportion of the population protected by vaccines, the size of the exposed compartment, and the number of unregistered cases of COVID-19. According to the results, at the peak of the pandemic, between 14% (Sweden) and 25% (the UK) of the population were infected. At the same time, the number of unregistered cases exceeds the number of registered cases by 17 and 3.4 times, respectively. The average duration of the vaccine induced immune period is shorter than claimed by vaccine manufacturers, and the effectiveness of vaccination has declined sharply since the appearance of the Delta and Omicron strains. However, on average, vaccination reduces the risk of infection by about 65-70%.

Interaction between travel restriction policies and the spread of COVID-19

To investigate the interaction between travel restriction policies and the spread of COVID-19, we collected data on human mobility trends, population density, Gross Domestic Product (GDP) per capita, daily new confirmed cases (or deaths), and the total confirmed cases (or deaths), as well as governmental travel restriction policies from 33 countries. The data collection period was from April 2020 to February 2022, resulting in 24,090 data points. We then developed a structural causal model to describe the causal relationship between these variables. Using the Dowhy method to solve the developed model, we found several significant results that passed the refutation test. Specifically, travel restriction policies played an important role in slowing the spread of COVID-19 until May 2021. International travel controls and school closures had an impact on reducing the spread of the pandemic beyond the impact of travel restrictions. Additionally, May 2021 marked a turning point in the spread of COVID-19 as it became more infectious, but the mortality rate gradually decreased. The impact of travel restriction policies on human mobility and the pandemic diminished over time. Overall, the cancellation of public events and restrictions on public gatherings were more effective than other travel restriction policies. Our findings provide insights into the effects of travel restriction policies and travel behavioral changes on the spread of COVID-19, while controlling for informational and other confounding variables. This experience can be applied in the future to respond to emergent infectious diseases.

Factors driving the implementation of the 'Local New Year' policy to prevent COVID-19 in China

This study examines the contradiction caused by the 'local new year' policy, that is, the conflict between the pandemic prevention policies and people's emotional demands during the Spring Festival, based on the normalisation of pandemic prevention and control. It focuses on the scientific logical relationship with the contradiction that people voluntarily support 'local new year', to explore the primary driving factors of their willingness. By evaluating the migrant workers in large cities, the primary influencing factors were screened, and the primary dynamic factors and their relationship were obtained using the Logit logical selection model and maximum-likelihood estimation. The study identified, 'whether social and entertainment activities are planned in migrant cities', as the primary driving factor, followed by 'whether there are relatives (elderly /children) at home', and 'contracting the infection during travel'. In view of this conclusion, this study further proposes corresponding policy suggestions: Relevant measures should be adopted according to different regions and the current situation of the pandemic in combination with the characteristics of the episodic and local nature of the pandemic. 'Local new year' is encouraged from the perspective of enriching people's emotional needs for spiritual entertainment and care. This study provides a new perspective and theoretical basis for the research and formulation of policies related to the normalisation of pandemic prevention and control in China and worldwide, and has a certain practical reference value.

How Did Governments Address the Needs of People With Disabilities During the COVID-19 Pandemic? An Analysis of 14 Countries' Policies Based on the UN Convention on the Rights of Persons With Disabilities

BACKGROUND

People with disabilities have experienced heightened social risks in the context of the pandemic, resulting in higher rates of infection and mortality. They have also borne elevated burdens associated with public health measures. The United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) obliges its 184 state parties to eliminate discrimination and ensure equality and inclusion for persons with disabilities, including protection and safety in situations of emergency. It remains unclear to what extent national COVID-19 policies have aligned with these commitments under the UNCRPD. Our objective in this exploratory study was to assess alignment between the UNCRPD indicators and COVID-19 policies from 14 countries with the goal of informing policy development that is inclusive of persons with disabilities and responsive to rights under the UNCRPD.

METHODS

We identified COVID-19 policy documents from 14 purposively selected countries. Country selection considered diversity based on geographic regions and national income levels, with restriction to those countries that had ratified the UNCRPD and had English or French as an official language. We used a computational text mining approach and developed a complex multilevel dictionary or categorization model based on the UNCRPD Bridging the Gap indicators proposed by the Office of the High Commissioner on Human Rights (OHCHR). This dictionary was used to assess the extent to which indicators across the entirety of the UNCRPD were represented in the selected policies. We analyzed frequency of associations with UNCRPD, as well as conducting 'key word in context' analyses to identify themes.

RESULTS

We identified 764 COVID-19 national policy documents from the period of January 2020 to June 2021. When analyzed in relation to the Articles of the UNCRPD, the most frequently identified were Articles 11 (risk and humanitarian emergencies), 23 (home and family), 24 (education), and 19 (community living). Six countries produced 27 policies that were specifically focused on disability. Common themes within these documents included continuation of services, intersectionality and equity, and disability considerations in regulations and public health measures.

CONCLUSION

Analyzing country policies in light of the UNCRPD offers important insights about how these policies do and do not align with states' commitments. As new policies are developed and existing ones revised, more comprehensive approaches to addressing the rights of persons with disabilities are urgently needed.

Predictors of changing patterns of adherence to containment measures during the early stage of COVID-19 pandemic: an international longitudinal study

BACKGROUND

Identifying common factors that affect public adherence to COVID-19 containment measures can directly inform the development of official public health communication strategies. The present international longitudinal study aimed to examine whether prosociality, together with other theoretically derived motivating factors (self-efficacy, perceived susceptibility and severity of COVID-19, perceived social support) predict the change in adherence to COVID-19 containment strategies.

METHOD

In wave 1 of data collection, adults from eight geographical regions completed online surveys beginning in April 2020, and wave 2 began in June and ended in September 2020. Hypothesized predictors included prosociality, self-efficacy in following COVID-19 containment measures, perceived susceptibility to COVID-19, perceived severity of COVID-19 and perceived social support. Baseline covariates included age, sex, history of COVID-19 infection and geographical regions. Participants who reported adhering to specific containment measures, including physical distancing, avoidance of non-essential travel and hand hygiene, were classified as adherence. The dependent variable was the category of adherence, which was constructed based on changes in adherence across the survey period and included four categories: non-adherence, less adherence, greater adherence and sustained adherence (which was designated as the reference category).

RESULTS

In total, 2189 adult participants (82% female, 57.2% aged 31-59 years) from East Asia (217 [9.7%]), West Asia (246 [11.2%]), North and South America (131 [6.0%]), Northern Europe (600 [27.4%]), Western Europe (322 [14.7%]), Southern Europe (433 [19.8%]), Eastern Europe (148 [6.8%]) and other regions (96 [4.4%]) were analyzed. Adjusted multinomial logistic regression analyses showed that prosociality, self-efficacy, perceived susceptibility and severity of COVID-19 were significant factors affecting adherence. Participants with greater self-efficacy at wave 1 were less likely to become non-adherence at wave 2 by 26% (adjusted odds ratio [aOR], 0.74; 95% CI, 0.71 to 0.77; P < .001), while those with greater prosociality at wave 1 were less likely to become less adherence at wave 2 by 23% (aOR, 0.77; 95% CI, 0.75 to 0.79; P = .04).

CONCLUSIONS

This study provides evidence that in addition to emphasizing the potential severity of COVID-19 and the potential susceptibility to contact with the virus, fostering self-efficacy in following containment strategies and prosociality appears to be a viable public health education or communication strategy to combat COVID-19.

Comparative effectiveness of contact tracing interventions in the context of the COVID-19 pandemic: a systematic review

Contact tracing is a non-pharmaceutical intervention (NPI) widely used in the control of the COVID-19 pandemic. Its effectiveness may depend on a number of factors including the proportion of contacts traced, delays in tracing, the mode of contact tracing (e.g. forward, backward or bidirectional contact training), the types of contacts who are traced (e.g. contacts of index cases or contacts of contacts of index cases), or the setting where contacts are traced (e.g. the household or the workplace). We performed a systematic review of the evidence regarding the comparative effectiveness of contact tracing interventions. 78 studies were included in the review, 12 observational (ten ecological studies, one retrospective cohort study and one pre-post study with two patient cohorts) and 66 mathematical modelling studies. Based on the results from six of the 12 observational studies, contact tracing can be effective at controlling COVID-19. Two high quality ecological studies showed the incremental effectiveness of adding digital contact tracing to manual contact tracing. One ecological study of intermediate quality showed that increases in contact tracing were associated with a drop in COVID-19 mortality, and a pre-post study of acceptable quality showed that prompt contact tracing of contacts of COVID-19 case clusters / symptomatic individuals led to a reduction in the reproduction number R. Within the seven observational studies exploring the effectiveness of contact tracing in the context of the implementation of other non-pharmaceutical interventions, contact tracing was found to have an effect on COVID-19 epidemic control in two studies and not in the remaining five studies. However, a limitation in many of these studies is the lack of description of the extent of implementation of contact tracing interventions. Based on the results from the mathematical modelling studies, we identified the following highly effective policies: (1) manual contact tracing with high tracing coverage and either medium-term immunity, highly efficacious isolation/quarantine and/ or physical distancing (2) hybrid manual and digital contact tracing with high app adoption with highly effective isolation/ quarantine and social distancing, (3) secondary contact tracing, (4) eliminating contact tracing delays, (5) bidirectional contact tracing, (6) contact tracing with high coverage in reopening educational institutions. We also highlighted the role of social distancing to enhance the effectiveness of some of these interventions in the context of 2020 lockdown reopening. While limited, the evidence from observational studies shows a role for manual and digital contact tracing in controlling the COVID-19 epidemic. More empirical studies accounting for the extent of contact tracing implementation are required.

Communicating risk during early phases of COVID-19: Comparing governing structures for emergency risk communication across four contexts

Background

Emergency risk communication (ERC) is key to achieving compliance with public health measures during pandemics. Yet, the factors that facilitated ERC during COVID-19 have not been analyzed. We compare ERC in the early stages of the pandemic across four socio-economic settings to identify how risk communication can be improved in public health emergencies (PHE).

Methods

To map and assess the content, process, actors, and context of ERC in Germany, Guinea, Nigeria, and Singapore, we performed a qualitative document review, and thematically analyzed semi-structured key informant interviews with 155 stakeholders involved in ERC at national and sub-national levels. We applied Walt and Gilson's health policy triangle as a framework to structure the results.

Results

We identified distinct ERC strategies in each of the four countries. Various actors, including governmental leads, experts, and organizations with close contact to the public, collaborated closely to implement ERC strategies. Early integration of ERC into preparedness and response plans, lessons from previous experiences, existing structures and networks, and clear leadership were identified as crucial for ensuring message clarity, consistency, relevance, and an efficient use of resources. Areas of improvement primarily included two-way communication, community engagement, and monitoring and evaluation. Countries with recurrent experiences of pandemics appeared to be more prepared and equipped to implement ERC strategies.

Conclusion

We found that considerable potential exists for countries to improve communication during public health emergencies, particularly in the areas of bilateral communication and community engagement as well as monitoring and evaluation. Building adaptive structures and maintaining long-term relationships with at-risk communities reportedly facilitated suitable communication. The findings suggest considerable potential and transferable learning opportunities exist between countries in the global north and countries in the global south with experience of managing outbreaks.

Environmental determinants of infectious and chronic disease prevention behaviours: A systematic review and thematic synthesis of qualitative research

Regulatory health policies facilitate desired health behaviours in communities, and among them, smoke-free policies and COVID-19 restrictions have been widely implemented. Qualitative research studies have explored how these measures and other environmental influences shape preventive behaviours. The objective of this systematic review was to synthesize previously published qualitative research, generate across-study themes, and propose recommendations for behaviour change interventions. We used a comprehensive search strategy, relevance screening and confirmation, data extraction, quality assessment, thematic synthesis, and quality-of-evidence assessment. In total, 87 relevant studies were identified. Findings were grouped under six overarching themes and mapped under three categories: (i) the political environment, (ii) the sociocultural environment, and (iii) the physical environment. These findings provide insights into the environmental influences of behaviour and indicate future interventions may be more effective by considering moral norms, community norms, policy support, and group identity.

Pros and cons factors influence population attitudes toward non-pharmaceutical interventions and vaccination during post–COVID-19

Objectives

Population compliance greatly influences the effectiveness of vaccination and non-pharmaceutical interventions (NPIs) for the curtaining of COVID-19 transmission. We aimed to determine the conceptual framework of potential factors that influence compliance.

Study design

This was a cross-sectional study.

Methods

Questionnaires were used to survey population attitudes toward vaccination and NPIs in China. Confirmatory factor analysis of the survey data by structural equation model was used to define the pros and cons factors of attitudes. The strength and direction of each factor’s effect on population attitudes were illustrated by Bayesian network analysis.

Results

A total of 1700 respondents aged 18–70 years were surveyed with a panel of 34 questionnaires. Of these questionnaires, the confirmatory factor and structural equation model analysis identified five categories contributing to positive attitudes, including response efficiency, willingness and behavior, trust, cues to action, and knowledge, as well as four categories contributing to negative attitudes, including autonomy, perceived barriers, threat, and mental status. Bayesian networks revealed that cues to action produced a driving force for positive attitudes, followed by willingness and behavior, trust, response efficiency, and knowledge, whereas perceived barriers produced a driving force for negative attitudes, followed by autonomy and threat.

Conclusions

This study established a concise and representative list of questionnaires that could be applied to investigate the conceptual framework of potential pros and cons factors of attitudes toward vaccination and NPIs for COVID-19 prevention. The factors with driving forces should be addressed with a priority to effectively improve population compliance.

The fading impact of lockdowns: A data analysis of the effectiveness of Covid-19 travel restrictions during different pandemic phases

As countries struggled with SARS-COV2 outbreaks at the beginning of 2021, many citizens found themselves in yet another period of increasing travel restrictions, if not a strict lockdown. At the same time there was concern that further restrictions would prove to be less effective due to a range of reasons including increasing pandemic fatigue or the lack of appropriate supports. In this study we investigate whether restrictions remained effective as a way to limit non-essential travel in order to curb virus transmission. We do this by analysing adherence during periods of increasing and decreasing restrictions in 125 countries during three different 4-month phases, early (March-June 2020), middle (July-October 2020), and late (November 2020-February 2021) over the course of the first year of the pandemic, and prior to significant population-wide vaccination. We use the strength of the relationship between restriction levels and the level of personal mobility associated with non-essential travel in order to determine the degree of adherence to the restrictions imposed. We show that there is evidence of a significant decrease in adherence to restrictions during the middle and late phases of the pandemic, compared with the early phase. Our analysis further suggests that this decrease in adherence is due to changes in mobility rather than changes in restrictions. We conclude, therefore, that restrictions have become less effective at curbing non-essential travel, which may alter the cost-benefit analysis of restrictions and lockdowns, thus highlighting the need for governments to reconsider large-scale restrictions as a containment strategy in the future, in favour of more focused or flexible mitigation approaches.

A causal learning framework for the analysis and interpretation of COVID-19 clinical data

We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph showing the causal connections among the analyzed features. The workflow consists in a multi-step approach that goes from identifying the main causes of patient's outcome through BSL, to the realization of a tool suitable for clinical practice, based on a Binary Decision Tree (BDT), to recognize patients at high-risk with information available already at hospital admission time. We evaluate our approach on a feature-rich dataset of Coronavirus disease (COVID-19), showing that the proposed framework provides a schematic overview of the multi-factorial processes that jointly contribute to the outcome. We compare our findings with current literature on COVID-19, showing that this approach allows to re-discover established cause-effect relationships about the disease. Further, our approach yields to a highly interpretable tool correctly predicting the outcome of 85% of subjects based exclusively on 3 features: age, a previous history of chronic obstructive pulmonary disease and the PaO2/FiO2 ratio at the time of arrival to the hospital. The inclusion of additional information from 4 routine blood tests (Creatinine, Glucose, pO2 and Sodium) increases predictive accuracy to 94.5%.

The effect of the Ontario stay-at-home order on Covid-19 third wave infections including vaccination considerations: An interrupted time series analysis

The Covid-19 global pandemic that began in March 2020 was not fully mitigated through governmental Non-Pharmaceutical Interventions (NPIs) and continued to infect people and take lives through 2021. Since many countries were affected by the second, third, and fourth waves of Covid-19, governments extended and strengthened NPIs, but these actions led to citizen protests and fatigue. In this study, we investigate the effect of a lockdown policy on Covid-19 third wave implemented by the province of Ontario, Canada, on April 3rd 2021, followed by a stay-at-home order on April 7th 2021 while free Covid-19 testing and vaccination were in progress. Herein, the effect of both NPIs and vaccination are considered simultaneously. We used the prevalence of Covid-19 cases, tests, and administered vaccines data reported publicly by the Government of Ontario on their website. Because mobility changes can reflect the behaviors and adherence of residents with a stay-at-home order, Covid-19 community mobility data for Ontario provided by Google was also considered. A statistical method called interrupted time series was used to analyze the data. The results indicated that, although vaccinations helped to control the Covid-19 infection rate during this time, the stay-at-home order caused a rate reduction by decreasing the trend of the Covid-19 prevalence by 13 (±0.8962) persons per million daily and the level by 33 (±7.6854) persons per million. Furthermore, the stay-at-home order resulted in approximately a 37% reduction in Covid-19 prevalence one week after the intervention's effective date. Therefore, Ontario's strict lockdown policy, including several NPIs, mitigated the Covid-19 surge during the third wave. The results show that even when vaccination is in progress, strict NPIs such as lockdown is required to control Covid-19 waves, and early re-openings should be avoided. These results may also be useful for other countries that have implemented delayed vaccination schedules.

Human Close Contact Behavior-Based Interventions for COVID-19 Transmission

COVID-19 has threatened human lives. Countries have implemented various interventions such as vaccination, mask-wearing, body temperature screening, and isolation. However, the effectiveness of single and combined interventions has not yet been accurately analyzed. In this study, an improved SEIR model considering both real human indoor close contact behaviors and susceptibility to COVID-19 was established. Taking Hong Kong as an example, a quantitative assessment of the relationship between the efficiency of single and combined interventions and implementation time and intensity was carried out. The results showed that the infection risk (one-hour close contact with an infected person) of COVID-19 of students, workers, and non-workers/non-students was 3.1%, 8.7%, and 13.6%, respectively. Workplace closures were more effective among built environment interventions. If mask-wearing was mandatorily required in schools, workplaces, supermarkets, shopping centers, and public transport, COVID-19 could not be totally restricted. Workers should be prioritized for vaccination, followed by non-workers/non-students and students. Among all interventions, reducing close contact rate and increasing vaccination rate were better interventions. There was no COVID-19 outbreak (basic reproduction number R0 = 1) if the close contact reduction rate was 59.9% or the vaccination rate reached 89.5%. The results may provide scientific support for COVID-19 prevention and control.

Evaluating scenarios for school reopening under COVID19

BACKGROUND

Thousands of school systems have struggled with the decisions about how to deliver education safely and effectively amid the COVID19 pandemic. This study evaluates the public health impact of various school reopening scenarios (when, and how to return to in-person instruction) on the spread of COVID19.

METHODS

An agent-based simulation model was adapted and used to project the impact of various school reopening strategies on the number of infections, hospitalizations, and deaths in the state of Georgia during the study period, i.e., February 18^th-November 24^th, 2020. The tested strategies include (i) schools closed, i.e., all students receive online instruction, (ii) alternating school day, i.e., half of the students receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iii) alternating school day for children, i.e., half of the children (ages 0-9) receive in-person instruction on Mondays and Wednesdays and the other half on Tuesdays and Thursdays, (iv) children only, i.e., only children receive in-person instruction, (v) regular, i.e., all students return to in-person instruction. We also tested the impact of universal masking in schools.

RESULTS

Across all scenarios, the number of COVID19-related deaths ranged from approximately 8.8 to 9.9 thousand, the number of cumulative infections ranged from 1.76 to 1.96 million for adults and 625 to 771 thousand for children and youth, and the number of COVID19-related hospitalizations ranged from approximately 71 to 80 thousand during the study period. Compared to schools reopening August 10 with a regular reopening strategy, the percentage of the population infected reduced by 13%, 11%, 9%, and 6% in the schools closed, alternating school day for children, children only, and alternating school day reopening strategies, respectively. Universal masking in schools for all students further reduced outcome measures.

CONCLUSIONS

Reopening schools following a regular reopening strategy would lead to higher deaths, hospitalizations, and infections. Hybrid in-person and online reopening strategies, especially if offered as an option to families and teachers who prefer to opt-in, provide a good balance in reducing the infection spread compared to the regular reopening strategy, while ensuring access to in-person education.

A media surveillance analysis of COVID-19 workplace outbreaks in Canada and the United States

A media surveillance analysis was conducted to identify COVID-19 workplace outbreaks and associated transmission risk for new and emerging occupations. We identified 1,111 unique COVID-19 workplace outbreaks using the Factiva database. Occupations identified in the media articles were coded to the 2016 National Occupational Classification (V1.3) and were compared and contrasted with the same occupation in the Vancouver School of Economics (VSE) COVID Risk/Reward Assessment Tool by risk rating. After nurse aides, orderlies, and patient service associates ( n = 109, very high risk), industrial butchers and meat cutters, and poultry preparers and related workers had the most workplace outbreaks reported in the media ( n = 79) but were rated as medium risk for COVID-19 transmission in the VSE COVID Risk Tool. Outbreaks were also reported among material handlers ( n = 61) and general farm workers ( n = 28), but these occupations were rated medium–low risk and low risk, respectively. Food and beverage services ( n = 72) and cashiers ( n = 60) were identified as high-risk occupations in the VSE COVID Risk Tool. Differences between the media results and the risk tool point to key determinants of health that compound the risk of COVID-19 exposure in the workplace for some occupations and highlight the importance of collecting occupation data during a pandemic.

Lockdowns lose one third of their impact on mobility in a month

As the novel coronavirus (COVID‐19) pandemic spread across the world over the past year, many countries imposed lockdowns in the form of stay at home requirements on their citizens to mitigate its spread. We analyze mobility data from 93 countries implementing lockdowns to investigate their immediate impact on mobility and the subsequent evolution of mobility. We find that at the start of a lockdown, median mobility is reduced to 36% below the baseline, and by another 18% in the subsequent 2 weeks. 70 countries had lockdowns lasting beyond 4 weeks and showed a significant reduction in mobility compared to that prior to the lockdown. Mobility was at its minimum 18 days into the lockdown for the median country. Comparing this minimum mobility to the average mobility 2 weeks before the lockdown, we observe a median reduction of 50 percentage points, evidencing that lockdowns reduce mobility. For 59 of these 70 countries, lockdowns lasted at least 4 weeks after reaching minimum mobility and most observed a significant rebound in mobility during the lockdown period. For the median country, 30.1% of the mobility reduction achieved is lost within 4 weeks, and lockdowns lose all their impact on mobility in 112.1 days. Overall, our findings show that while lockdowns significantly reduce mobility, this impact is also subject to fatigue as the lockdown period extends longer. The magnitude of mobility reductions achieved and fatigues reported in this research can help policy makers anticipate the likely impact of their lockdown policies.

Buhat CA, Lutero DS, Quindala KM, Rabajante JF. Local government responses for COVID-19 management in the Philippines

BACKGROUND

Responses of subnational government units are crucial in the containment of the spread of pathogens in a country. To mitigate the impact of the COVID-19 pandemic, the Philippine national government through its Inter-Agency Task Force on Emerging Infectious Diseases outlined different quarantine measures wherein each level has a corresponding degree of rigidity from keeping only the essential businesses open to allowing all establishments to operate at a certain capacity. Other measures also involve prohibiting individuals at a certain age bracket from going outside of their homes. The local government units (LGUs)-municipalities and provinces-can adopt any of these measures depending on the extent of the pandemic in their locality. The purpose is to keep the number of infections and mortality at bay while minimizing the economic impact of the pandemic. Some LGUs have demonstrated a remarkable response to the COVID-19 pandemic. The purpose of this study is to identify notable non-pharmaceutical interventions of these outlying LGUs in the country using quantitative methods.

METHODS

Data were taken from public databases such as Philippine Department of Health, Philippine Statistics Authority Census, and Google Community Mobility Reports. These are normalized using Z-transform. For each locality, infection and mortality data (dataset Y) were compared to the economic, health, and demographic data (dataset X) using Euclidean metric d=(x-y)2, where x∈X and y∈Y. If a data pair (x,y) exceeds, by two standard deviations, the mean of the Euclidean metric values between the sets X and Y, the pair is assumed to be a 'good' outlier.

RESULTS

Our results showed that cluster of cities and provinces in Central Luzon (Region III), CALABARZON (Region IV-A), the National Capital Region (NCR), and Central Visayas (Region VII) are the 'good' outliers with respect to factors such as working population, population density, ICU beds, doctors on quarantine, number of frontliners and gross regional domestic product. Among metropolitan cities, Davao was a 'good' outlier with respect to demographic factors.

CONCLUSIONS

Strict border control, early implementation of lockdowns, establishment of quarantine facilities, effective communication to the public, and monitoring efforts were the defining factors that helped these LGUs curtail the harm that was brought by the pandemic. If these policies are to be standardized, it would help any country's preparedness for future health emergencies.

Systematic review of empirical studies comparing the effectiveness of non-pharmaceutical interventions against COVID-19

OBJECTIVES

To evaluate which non-pharmaceutical interventions (NPIs) have been more and less effective in controlling the COVID-19 pandemic.

METHODS

We performed a systematic review of published and unpublished empirical studies, either observational or interventional, analysing the comparative effectiveness of NPIs against the COVID-19 pandemic. We searched Embase/Medline and medRxiv to identify the relevant literature.

RESULTS

We identified 34 studies. During the first wave of the COVID-19 pandemic, school closing was the most effective NPI, followed by workplace closing, business and venue closing and public event bans. Public information campaigns and mask wearing requirements were also effective in controlling the pandemic while being less disruptive for the population than other NPIs. There was no evidence on the effectiveness of public transport closure, testing and contact tracing strategies and quarantining or isolation of individuals. Early implementation was associated with a higher effectiveness in reducing COVID-19 cases and deaths, while general stringency of the NPIs was not.

CONCLUSIONS

In this systematic review, we found that school closing, followed by workplace closing, business and venue closing and public event bans were the most effective NPIs in controlling the spread of COVID-19. An early response and a combination of specific social distancing measures are effective at reducing COVID-19 cases and deaths. Continuous monitoring of NPIs effectiveness is needed in order to adapt decision making.

Perception of the Movement Control Order during the COVID-19 Pandemic: A Qualitative Study in Malaysia

Malaysia implemented its first Movement Control Order (MCO) during the early phase of the COVID-19 pandemic to slow the transmission of the virus. This study aimed to explore the public perception of the MCO implementation and people’s experiences during this period. The study employed qualitative explorative in-depth interviews conducted with 23 Malaysian adults from various demographic backgrounds. Thematic analysis was performed using NVivo 12. Three main themes were identified: a period of information surge, heterogeneous emotional response, and attempts to adapt. During the MCO, the participants obtained information from multiple platforms. They suggested the need for clear and repeated instructions to avoid confusion and misinformation. They also acknowledged the importance of the MCO in breaking the chain of transmission and safeguarding high-risk groups; however, they also expressed that stricter enforcement from the authorities was warranted. The changes in the participants’ work–life routines, lack of physical interaction, and uncertainty about their health and the economy due to the MCO negatively impacted their psychological states. Despite these challenges, the participants attempted to adapt to life under the MCO in different ways. The findings imply that during a crisis, the public tends to seek clear and reliable information, experience emotional turmoil, and adapt to changes. The MCO implementation can be improved through an effective communication strategy and efforts to battle misinformation.

The role of race and scientific trust on support for COVID-19 social distancing measures in the United States

Pundits and academics across disciplines note that the human toll brought forth by the novel coronavirus (COVID-19) pandemic in the United States (U.S.) is fundamentally unequal for communities of color. Standing literature on public health posits that one of the chief predictors of racial disparity in health outcomes is a lack of institutional trust among minority communities. Furthermore, in our own county-level analysis from the U.S., we find that counties with higher percentages of Black and Hispanic residents have had vastly higher cumulative deaths from COVID-19. In light of this standing literature and our own analysis, it is critical to better understand how to mitigate or prevent these unequal outcomes for any future pandemic or public health emergency. Therefore, we assess the claim that raising institutional trust, primarily scientific trust, is key to mitigating these racial inequities. Leveraging a new, pre-pandemic measure of scientific trust, we find that trust in science, unlike trust in politicians or the media, significantly raises support for COVID-19 social distancing policies across racial lines. Our findings suggest that increasing scientific trust is essential to garnering support for public health policies that lessen the severity of the current, and potentially a future, pandemic.

The impact of non-pharmaceutical interventions on COVID-19 epidemic growth in the 37 OECD member states

We estimated the impact of a comprehensive set of non-pharmeceutical interventions on the COVID-19 epidemic growth rate across the 37 member states of the Organisation for Economic Co-operation and Development during the early phase of the COVID-19 pandemic and between October and December 2020. For this task, we conducted a data-driven, longitudinal analysis using a multilevel modelling approach with both maximum likelihood and Bayesian estimation. We found that during the early phase of the epidemic: implementing restrictions on gatherings of more than 100 people, between 11 and 100 people, and 10 people or less was associated with a respective average reduction of 2.58%, 2.78% and 2.81% in the daily growth rate in weekly confirmed cases; requiring closing for some sectors or for all but essential workplaces with an average reduction of 1.51% and 1.78%; requiring closing of some school levels or all school levels with an average reduction of 1.12% or 1.65%; recommending mask wearing with an average reduction of 0.45%, requiring mask wearing country-wide in specific public spaces or in specific geographical areas within the country with an average reduction of 0.44%, requiring mask-wearing country-wide in all public places or all public places where social distancing is not possible with an average reduction of 0.96%; and number of tests per thousand population with an average reduction of 0.02% per unit increase. Between October and December 2020 work closing requirements and testing policy were significant predictors of the epidemic growth rate. These findings provide evidence to support policy decision-making regarding which NPIs to implement to control the spread of the COVID-19 pandemic.

The Impact of the Social Distancing Policy on COVID-19 Incidence Cases and Deaths in Iran from February 2020 to January 2021: Insights from an Interrupted Time Series Analysis

Background: In December 2019, a viral outbreak occurred in China, and rapidly spread out worldwide. Due to the lack of immediately available vaccines and effective drugs, many policy- and decision-makers have focused on non-pharmacological methods, including social distancing. This study was aimed at assessing the effects of the implementation of this policy in Iran, one of the countries most affected by COVID-19. We conducted a quasi-experimental study, utilizing the interrupted time series analysis (ITSA) approach. Methods: We collected daily data between February 20, 2020 and January 29, 2021, through governmental websites from 954 public hospitals and healthcare settings. The Iranian government launched the social distancing policy on March 27, 2020. Statistical analyses, including ITSA, were carried out with R software Version 3.6.1 (London, UK). Results: During the study period, 1,398,835 confirmed incidence cases and 57,734 deaths occurred. We found a decrease of -179.93 (95% CI: -380.11 to -20.25, P-value=0.078) confirmed incidence cases following the implementation of the social distancing policy, corresponding to a daily decrease in the trend of -31.17 (95% CI: -46.95 to -15.40, P-value=0.08). Moreover, we found a decrease of -28.28 (95% CI: -43.55 to -13.01, P-value=0.05) deaths, corresponding to a daily decrease in the trend of -4.52 (95% CI: -5.25 to -3.78, P-value=0.003). Conclusion: The growth rate of confirmed incidence cases and deaths from COVID-19 in Iran has decreased from March 27, 2020 to January 29, 2021, after the implementation of social distancing. By implementing this policy in all countries, the burden of COVID-19 may be mitigated.