Social connections with COVID-19-affected areas increase compliance with mobility restrictions

Collective Information Seeking During a Health Crisis : Predictors of Google Trends During COVID-19

This article approaches collective health information seeking from computational method by investigating patterns of Google Trends data in the United States during the early stages of the COVID-19 pandemic. We analyzed factors that prompted a community's curiosity, and information that communities were most curious about. The results of our cross-sectional and time-series-based analyses reveal a few salient findings: (1) Republican leaning states searched less frequently, and while states with more cases searched more, partisan lean is a more significant predictor; (2) States with greater level of poverty searched less frequently; (3) Leadership on the national level significantly influenced people's searching behavior; (4) Communities were most interested in "local risk" information as well as quantifiable information. We show in this work that established individual information seeking theoretical predictors (risk) can predict online collective information demand and information seeking subcategories with important contributions from collective conditions (leadership). Health communication practitioners can design health messages and choose media channels more purposefully according to what people are most interested in searching.

Why do democracies respond differently to COVID-19? A comparison of the United States and South Korea

Background

COVID-19 has posed severe challenges to crisis management in democracies. Different democracies respond to the crisis differently. This article proposes an analytical framework to explain why democracies respond differently to the public health crisis and how different contextual factors affect crisis response in democracies.

Methods

By comparing COVID-19 responses in the US and South Korea, this article conducts a comparative case study with a most similar system design. The two countries have been selected as cases because they are both developed democracies with a robust healthcare system. However, different contextual factors in the two countries have created different crisis responses by shaping different crisis leadership and political and social solidarity. This study collected data from different sources, including government documents, official websites, leaders' speeches, research reports, academic articles and news media. We tried to enhance the reliability of the data by comparing different data sources.

Results

We found that individual, institutional and cultural dimensions of contextual factors can influence different crisis responses of democratic countries by shaping crisis leadership and political and social solidarity. On the individual and institutional dimensions, leadership style and governance structure shape crisis leadership (sense making, decision making and coordinating, and meaning making), which in turn influences crisis management. On the cultural dimension, political and social solidarity measured by political polarization and social cooperation are shaped by cultural and social norms.

Conclusion

Our findings indicate that democracies require strong crisis leadership and a high degree of political and social solidarity to tackle public health crises. A centralized and coordinated system, as well as a political elite leadership style shaped by rich crisis response experience, expertise and high sensitivity to crises are conducive to crisis management. Fostering a cultural and social norm that facilitates state-society collaboration can promote crisis management. These findings provide valuable insights for decision-makers to effectively respond to future pandemics.

The anatomy of a population-scale social network

Large-scale human social network structure is typically inferred from digital trace samples of online social media platforms or mobile communication data. Instead, here we investigate the social network structure of a complete population, where people are connected by high-quality links sourced from administrative registers of family, household, work, school, and next-door neighbors. We examine this multilayer social opportunity structure through three common concepts in network analysis: degree, closure, and distance. Findings present how particular network layers contribute to presumably universal scale-free and small-world properties of networks. Furthermore, we suggest a novel measure of excess closure and apply this in a life-course perspective to show how the social opportunity structure of individuals varies along age, socio-economic status, and education level.

Shifting Mobility Behaviors in Unprecedented Times: A Multigroup MIMIC Model Investigating Intentions to Use On-Demand Ride Services During the COVID-19 Pandemic

The spread of COVID-19 has been a major disruptive force in people's everyday lives and mobility behavior. The demand for on-demand ride services, such as taxis and ridehailing, has been specifically affected given both restrictions in service operations and users' concerns about virus transmission in shared vehicles. In the early months of the pandemic, demand for these modes decreased by as much as 80%. This study examines intentions to use on-demand ride services in the early lockdown stage of the pandemic in the United States, a period of unprecedented mobility reductions, changing household routines and transforming travel behaviors. Using data from a survey disseminated in June 2020 to 700 U.S. respondents, we use multigroup MIMIC (Multiple Indicator Multiple Cause) models to investigate the stated shift in intentions to use on-demand modes of travel. By using group-based segmentation we control for variation in ridership intentions according to personal, household, attitudinal factors, and pandemic experiences. The results point to a reduction across the board in the likelihood of using on-demand mobility associated with a significant COVID-19 effect. Beyond this general decrease, several groups are found to have more positive intentions, including younger adults, urban residents, graduate-degree holders, and people of Hispanic, Latino, Asian, and Pacific Islander ethnicities/races. The attitudinal effect of "tech-savviness" drives higher user intentions, revealing indirect effects of gender, education, and age. Multigroup analysis provides further evidence of potential COVID-triggered shifts in on-demand ridership intentions. The most significant drops in likelihood are observed for younger respondents (below 45), Black compared with all other racial/ethnic status, and for past users of on-demand mobility. This latter result is somewhat surprising, as riders who are younger and more experienced with on-demand travel are more likely to have been users in the past, but also more likely to reduce use during the pandemic. To conclude, we discuss the need to investigate pandemic experiences, risk attitudes, and circumstances to understand evolving mobility behavior and specific service model impacts.

Impact of the COVID-19 pandemic on the well-being of preschoolers: A parental guide

Unexpected changes brought about by the coronavirus disease 2019 (COVID-19) have affected humans worldwide. This review attempts to address major parental concerns about the development of preschool-aged children during the pandemic from the perspectives of neuropsychology, consultation, and motor development for preschoolers aged 2–5 years.

Methods

A total of 273 articles including original data, review articles, national and regional perspectives, government websites, and commentaries were considered in this review, of which 117 manuscripts were excluded because they were unrelated to children, adolescents, or COVID -19 pandemic/upper respiratory infections. A total of 156 manuscripts were included after reading the abstract and entire article.

Results

Telehealth could be an effective tool for addressing cognitive and emotional challenges that arise during the pandemic. Online consultations are highlighted for nutritional guidelines and to overcome problems that parents face when caring for children in difficult times. Outdoor activities using sanitisers, proper cleanliness, and following standard operating procedures are recommended. Parental preoccupation with media should be avoided. Interpretation: Many preschoolers show delays in reaching their developmental milestones, and the pandemic has increased parents' concerns, as access to practitioners is limited. Therefore, parents should be encouraged to undergo neuropsychological consultations whenever necessary. This study emphasises important strategies to ensure that children's development is minimally affected while staying in the confined environment of their homes. This study serves as a new guide for parents, as they raise young children in the new normal. Parents should undergo basic yearly physical, neuropsychological, nutritional, and speech checkups.

Factors Influencing COVID-19 Prevention Behaviors

COVID-19 remains a public health emergency with prevention guidelines and mitigation strategies being constantly updated to curb the rapid spread of the disease. Despite proven successes of recommended preventive behaviors, there is low uptake of wearing a mask, washing of hands, and social distancing in the United States (US). The purpose of this study was to examine factors that influence COVID-19 preventive behaviors. We used data from the nationally representative COVID-19 Household Impact Survey (n = 19,815) conducted in the US from April to June 2020. Chi-square (χ2) test and bivariate analyses were performed to compare study participants who used all COVID-19 related preventive behaviors and those who did not, and multivariate logistic regressions to determine associations across demographic and social characteristics. Of the 19,815 participants, 79.2% of participants reported practicing the aforementioned COVID-19 preventive behaviors. Further, non-Hispanic white, Spanish speaking, living in urban areas, of older age (60+), being female, having an education above an undergraduate, those with income levels $100K or more, living in the urban northeast region that trust and communicate frequently with family and neighbors were more likely to use all three preventive behaviors. Findings suggest a need for continued provision of information on prevention and vaccination importance, but expand efforts to target adopters of these behaviors and encourage them to share their uptake and adherence efforts. This type of horizontal communication where information is shared within trusted social networks can shape social norms that influence the uptake of COVID-19 preventive behaviors and slowly curb communal spread.

Education and COVID-19 excess mortality

We study the role of education during the COVID-19 epidemic in Italy. We compare excess mortality in 2020 and 2021 compared to the pre-pandemic mortality between municipalities with different shares of educated residents. We find that education initially played a strong protective role, which however quickly faded out. After pondering several alternative explanations, we tentatively interpret this finding as the outcome of the interplay between education, information and public health communication, whose availability and coherence varied along the epidemic.

Predictors of Self-Reported Compliance with COVID-19 Preventive Guidelines: A Quantile Regression Model

The research used an online, convenience cross-sectional sample of adults aged ≥18 years old recruited from Shahroud County, Northeast of Iran. We measured the contribution of multiple determinants for association with behavioral compliance, at the time of the COVID-19 pandemic. The compliance score measured with this questionnaire can be within a range of 5 and 100. Compliance was bounded between 19 and 80 that has been distributed J-shape, so quantile logistic regression model has been fitted for that. Variables related to people's knowledge, including self-reported knowledge and following the news related to COVID-19, were the two main factors that accompanied behavioral compliance at all of its levels in the period of pandemic.

Exploring the spatial disparity of home-dwelling time patterns in the USA during the COVID-19 pandemic via Bayesian inference

In this study, we aim to reveal hidden patterns and confounders associated with policy implementation and adherence by investigating the home-dwelling stages from a data-driven perspective via Bayesian inference with weakly informative priors and by examining how home-dwelling stages in the USA varied geographically, using fine-grained, spatial-explicit home-dwelling time records from a multi-scale perspective. At the U.S. national level, two changepoints are identified, with the former corresponding to March 22, 2020 (9 days after the White House declared the National Emergency on March 13) and the latter corresponding to May 17, 2020. Inspections at U.S. state and county level reveal notable spatial disparity in home-dwelling stage-related variables. A pilot study in the Atlanta Metropolitan area at the Census Tract level reveals that the self-quarantine duration and increase in home-dwelling time are strongly correlated with the median household income, echoing existing efforts that document the economic inequity exposed by the U.S. stay-at-home orders. To our best knowledge, our work marks a pioneering effort to explore multi-scale home-dwelling patterns in the USA from a purely data-driven perspective and in a statistically robust manner.

Using aggregated mobile phone location data to compare the realised foodscapes of different socio-economic groups

The foodscape (the built food environment) is considered one of the driving factors of the higher burden of obesity and chronic disease observed in low socio-economic status (SES) groups. Traditional data collection methods struggle to accurately capture actual access and exposure to the foodscape (realised foodscape). We assess the use of anonymised mobile phone location data (location data) in foodscape studies by applying them to a case study in Perth, Western Australia to test the hypothesis that lower SES groups have poorer realised foodscapes than high SES groups. Kernel density estimation was used to calculate realised foodscapes of different SES groups and home foodscape typologies, which were compared to home foodscapes of the different groups. The location data enabled us to measure realised foodscapes of multiple groups over an extended period and at the city scale. Low SES groups had poor availability of food outlets, including unhealthy outlets, in their home and realised foodscapes and may be more susceptible to a poor home foodscape because of low mobility.

The Effects of Non-pharmaceutical Interventions on COVID-19 Mortality: A Generalized Synthetic Control Approach Across 169 Countries

Importance

Governments have introduced non-pharmaceutical interventions (NPIs) in response to the pandemic outbreak of Coronavirus disease (COVID-19). While NPIs aim at preventing fatalities related to COVID-19, the previous literature on their efficacy has focused on infections and on data of the first half of 2020. Still, findings of early NPI studies may be subject to underreporting and missing timeliness of reporting of cases. Moreover, the low variation in treatment timing during the first wave makes identification of robust treatment effects difficult.

Objective

We enhance the literature on the effectiveness of NPIs with respect to the period, the number of countries, and the analytical approach.

Design, Setting, and Participants

To circumvent problems of reporting and treatment variation, we analyse data on daily confirmed COVID-19-related deaths per capita from Our World in Data, and on 10 different NPIs from the Oxford COVID-19 Government Response Tracker (OxCGRT) for 169 countries from 1st July 2020 to 1st September 2021. To identify the causal effects of introducing NPIs on COVID-19-related fatalities, we apply the generalized synthetic control (GSC) method to each NPI, while controlling for the remaining NPIs, weather conditions, vaccinations, and NPI-residualized COVID-19 cases. This mitigates the influence of selection into treatment and allows to model flexible post-treatment trajectories.

Results

We do not find substantial and consistent COVID-19-related fatality-reducing effects of any NPI under investigation. We see a tentative change in the trend of COVID-19-related deaths around 30 days after strict stay-at-home rules and to a slighter extent after workplace closings have been implemented. As a proof of concept, our model is able to identify a fatality-reducing effect of COVID-19 vaccinations. Furthermore, our results are robust with respect to various crucial sensitivity checks.

Conclusion

Our results demonstrate that many implemented NPIs may not have exerted a significant COVID-19-related fatality-reducing effect. However, NPIs might have contributed to mitigate COVID-19-related fatalities by preventing exponential growth in deaths. Moreover, vaccinations were effective in reducing COVID-19-related deaths.

How do mobility restrictions and social distancing during COVID-19 affect oil price?

This paper provides an analysis of the effect of the COVID-19 outbreak on the crude oil price. Using a newly developed air mobility index and Apple's driving trends index, we assess the effect of human mobility restrictions and social distancing during the COVID-19 pandemic on the crude oil price. We apply a quantile regression model, which evaluates different quantiles of the crude oil price. We also conduct an extreme value modeling, which examines the lower tail of the crude oil price distribution. We find that both the air mobility index and driving trends index significantly influence lower and upper quantiles of the WTI crude oil price. The extreme value models suggest that there is a potential risk of a negative crude oil price for a sudden extreme fall of air mobility.

Nursing home quality, COVID-19 deaths, and excess mortality

The COVID-19 pandemic in the US has been particularly devastating for nursing home residents. A key question is how have some nursing homes been able to effectively protect their residents, while others have not? Using data on the universe of US nursing homes, we examine whether establishment quality is predictive of COVID-19 mortality. Higher-quality nursing homes, as measured by CMS overall five-star rating, have substantially lower COVID-19 mortality through September of 2020. Quality does not predict the ability to prevent any COVID-19 resident or staff cases, but higher-quality establishments prevent the spread of resident infections conditional on having one. Preventing COVID-19 cases and deaths may come at some cost, as high-quality homes have substantially higher non-COVID deaths. The positive correlation between establishment quality and non-COVID mortality is strong enough that high-quality homes also have more total deaths than their low-quality counterparts and this relationship has grown with time. As of late April 2021, five-star homes have experienced 8.4 percent more total deaths than one-star homes.

Estimating the effect of mobility on SARS-CoV-2 transmission during the first and second wave of the COVID-19 epidemic, Switzerland, March to December 2020

IntroductionHuman mobility was considerably reduced during the COVID-19 pandemic. To support disease surveillance, it is important to understand the effect of mobility on transmission.AimWe compared the role of mobility during the first and second COVID-19 wave in Switzerland by studying the link between daily travel distances and the effective reproduction number (Rt) of SARS-CoV-2.MethodsWe used aggregated mobile phone data from a representative panel survey of the Swiss population to measure human mobility. We estimated the effects of reductions in daily travel distance on Rt via a regression model. We compared mobility effects between the first (2 March-7 April 2020) and second wave (1 October-10 December 2020).ResultsDaily travel distances decreased by 73% in the first and by 44% in the second wave (relative to February 2020). For a 1% reduction in average daily travel distance, Rt was estimated to decline by 0.73% (95% credible interval (CrI): 0.34-1.03) in the first wave and by 1.04% (95% CrI: 0.66-1.42) in the second wave. The estimated mobility effects were similar in both waves for all modes of transport, travel purposes and sociodemographic subgroups but differed for movement radius.ConclusionMobility was associated with SARS-CoV-2 Rt during the first two epidemic waves in Switzerland. The relative effect of mobility was similar in both waves, but smaller mobility reductions in the second wave corresponded to smaller overall reductions in Rt. Mobility data from mobile phones have a continued potential to support real-time surveillance of COVID-19.

An Instagram Hashtag Fostering Science Education of Vulnerable Groups during the Pandemic

Scientific literature presents young people as a vulnerable group at risk of poverty and social exclusion. One of the elements that have the most significant impact on reducing their vulnerability is promoting education. Little is known about how social networks can promote the education of young people. To address this, the present study aims to analyse how social networks, specifically Instagram, which is one of the most used by young people, has promoted, among other aspects, the scientific education of young people during the COVID-19 pandemic. This study analyses 5000 education-related Instagram posts made during the COVID-19 pandemic (March 2021) European research project ALLINTERACT. We have analysed those posts that show, on the one hand, how citizens benefit from scientific research and, on the other hand, citizens’ awareness of the impact of scientific research. Through the analysis of the posts, it has been observed how Instagram has been a social network that has provided information and scientific advances in various branches of knowledge, created knowledge networks, and provided a channel for information about the pandemic. Through the analysis of the 5000 posts, it is evident how Instagram has provided spaces for scientific learning, fostering access to scientific education for young people.

Mental Health and Time Management Behavior among Students During COVID-19 Pandemic: Towards Persuasive Technology Design

The study explored the impact of the COVID-19 pandemic on students’ mental health in higher education while capturing their perceptions and attitudes towards time management. The aim was to examine relationships between stress, anxiety, and specific time management-related factors. Considering possible differences between genders and degree levels, we developed five structural equation models (SEMs) to delineate these relationships. Results of a large-scale study of 502 participants show that students suffered from stress and two types of COVID-19-related anxiety: disease and consequences. Students’ preference for organization was the only factor that significantly promoted their perceived control over time, which contributes to reducing stress, hence anxiety. However, female students reported higher stress and anxiety levels than male students. Graduate students reported higher anxiety levels related to the consequences of the pandemic compared to undergrads. To promote students’ preference for organization, we map the three categories of organization to corresponding persuasive strategies which could be used in the design of persuasive interventions. This creates an opportunity for developing technological interventions to improve students’ perceived control over time, thus reduce stress and anxiety.

JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

How limitations in energy access, poverty, and socioeconomic disparities compromise health interventions for outbreaks in urban settings

Low-income households (LIHs) have experienced increased poverty and inaccess to healthcare services during the COVID-19 pandemic, limiting their ability to adhere to health-protective behaviors. We use an epidemiological model to show how a households' inability to adopt social distancing, owing to constraints in utility and healthcare expenditure, can drastically impact the course of disease outbreaks in five urban U.S. counties. LIHs suffer greater burdens of disease and death than higher income households, while functioning as a consistent source of virus exposure for the entire community due to socioeconomic barriers to following public health guidelines. These impacts worsened when social distancing policy could not be imposed. Health interventions combining social distancing and LIH resource protection strategies (e.g., utility and healthcare access) were the most effective in limiting virus spread for all income levels. Policies need to address the multidimensionality of energy, housing, and healthcare access for future disaster management.

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Energy and socioeconomic constraints and public health interventions are discussed

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Utility and health costs constrain a county's ability to enact public health policies

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Securing household utilities is essential to low-income households' health and safety

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Affordable energy and healthcare for vulnerable communities are a critical policy issue

Utility of Facebook's Social Connectedness Index in Modeling COVID-19 Spread: Exponential Random Graph Modeling Study

BACKGROUND

The COVID-19 (the disease caused by the SARS-CoV-2 virus) pandemic has underscored the need for additional data, tools, and methods that can be used to combat emerging and existing public health concerns. Since March 2020, there has been substantial interest in using social media data to both understand and intervene in the pandemic. Researchers from many disciplines have recently found a relationship between COVID-19 and a new data set from Facebook called the Social Connectedness Index (SCI).

OBJECTIVE

Building off this work, we seek to use the SCI to examine how social similarity of Missouri counties could explain similarities of COVID-19 cases over time. Additionally, we aim to add to the body of literature on the utility of the SCI by using a novel modeling technique.

METHODS

In September 2020, we conducted this cross-sectional study using publicly available data to test the association between the SCI and COVID-19 spread in Missouri using exponential random graph models, which model relational data, and the outcome variable must be binary, representing the presence or absence of a relationship. In our model, this was the presence or absence of a highly correlated COVID-19 case count trajectory between two given counties in Missouri. Covariates included each county's total population, percent rurality, and distance between each county pair.

RESULTS

We found that all covariates were significantly associated with two counties having highly correlated COVID-19 case count trajectories. As the log of a county's total population increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 66% (odds ratio [OR] 1.66, 95% CI 1.43-1.92). As the percent of a county classified as rural increased, the odds of two counties having highly correlated COVID-19 case count trajectories increased by 1% (OR 1.01, 95% CI 1.00-1.01). As the distance (in miles) between two counties increased, the odds of two counties having highly correlated COVID-19 case count trajectories decreased by 43% (OR 0.57, 95% CI 0.43-0.77). Lastly, as the log of the SCI between two Missouri counties increased, the odds of those two counties having highly correlated COVID-19 case count trajectories significantly increased by 17% (OR 1.17, 95% CI 1.09-1.26).

CONCLUSIONS

These results could suggest that two counties with a greater likelihood of sharing Facebook friendships means residents of those counties have a higher likelihood of sharing similar belief systems, in particular as they relate to COVID-19 and public health practices. Another possibility is that the SCI is picking up travel or movement data among county residents. This suggests the SCI is capturing a unique phenomenon relevant to COVID-19 and that it may be worth adding to other COVID-19 models. Additional research is needed to better understand what the SCI is capturing practically and what it means for public health policies and prevention practices.

Anticipation of COVID-19 vaccines reduces willingness to socially distance

We investigate how the anticipation of COVID-19 vaccines affects voluntary social distancing. In a large-scale preregistered survey experiment with a representative sample, we study whether providing information about the safety, effectiveness, and availability of COVID-19 vaccines affects the willingness to comply with public health guidelines. We find that vaccine information reduces peoples' voluntary social distancing, adherence to hygiene guidelines, and their willingness to stay at home. Getting positive information on COVID-19 vaccines induces people to believe in a swifter return to normal life. The results indicate an important behavioral drawback of successful vaccine development: An increased focus on vaccines can lower compliance with public health guidelines and accelerate the spread of infectious disease. The results imply that, as vaccinations roll out and the end of a pandemic feels closer, policies aimed at increasing social distancing will be less effective, and stricter policies might be required.

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.

Internal migration networks and mortality in home communities: Evidence from Italy during the Covid-19 pandemic

Do internal migration networks benefit or harm their home communities in case of a communicable disease? Looking at the spread of Covid in Italy and using pre-determined province-to-province migration, excess mortality and mobile phone tracking data, we document that provinces with a greater share of migrants in outbreak areas show greater compliance with self-isolation measures (information mechanism), but also a greater population inflow from outbreak areas (carrier mechanism). For a subset of localities, the net effect on mortality is negative. However, for the average locality, the effect is positive and large, suggesting that the role of migrants as information providers is trumped by their role as virus carriers. The effect is quantitatively important and could be incorporated in epidemiological models forecasting the spread of communicable diseases.

Evaluation of the Strategies to Control COVID-19 Pandemic in Four European Countries

On March 11, 2020, the World Health Organization (WHO) has officially declared the novel coronavirus outbreak a pandemic. The national governments deployed a series of severe control measures and a set of public health policies in order to stop the spread of COVID-19 pandemic. The aim of this study is to investigate the correlation between specific interventions and incident cases during the second wave in multiple and specific countries. The observational study was based on data from the Oxford COVID-19 Government Response Tracker (OxCGRT) source retrieved from October 1st, 2020 to January 10, 2021. Thirteen specific indicators related to measures adopted were considered. Four European countries were taken into account: Italy, German, Spain and UK. A vector autoregression (VAR) model and the Granger Causality test were performed to allow for an assessment of any possible effect induced by each control measure against the overall pandemic growth. Wald test was conducted to compute p-values. No correlation between the applied measures and incident cases in the four countries was shown by the Granger causality test. Only closings of workplaces (C2) and limits on private gatherings showed (C4) a significant correlation with incident cases in UK and restrictions on internal movement between cities/regions in Germany. The Granger causality also tested that C2 and C4 forecasted the decrease of incident cases after a time lag of 6–30 days in UK and Germany, respectively. Policy makers must analyze the context in which policies are set because of effectiveness of interventions can be influenced by local context and, especially, by socio-economic and demographic characteristics, and make a proper communication to support the resilience of the population capable of guaranteeing adherence to the interventions implemented.

Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data

INTRODUCTION

To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.

METHODS

We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.

RESULTS

Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.

CONCLUSION

Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

The Impact of Mobility Restriction Strategies in the Control of the COVID-19 Pandemic: Modelling the Relation between COVID-19 Health and Community Mobility Data

BACKGROUND

Non-pharmaceutical interventions (NPIs), particularly mobility restrictions, are mainstay measures for the COVID-19 pandemic worldwide. We evaluated the effects of Oman's mobility restriction strategies to highlight their efficacy in controlling the pandemic.

METHODS

Accessible national data of daily admissions and deaths were collected from 1 April 2020 to 22 May 2021. Google Community Mobility Report (CMR) data were downloaded for the same period. Among six CMR categories, three were used and reduced to one index-the community mobility index (CMI). We used a generalised linear model with a negative binomial distribution combined with a non-linear distributed lag model to investigate the short-term effects of CMI on the number of admitted PCR-confirmed COVID-19 cases and deaths, controlling for public holidays, day of the week, and Eid/Ramadan days.

RESULTS

We demonstrated the feasibility of using CMRs in the evaluation and monitoring of different NPIs, particularly those related to movement restriction. The best movement restriction strategy was a curfew from 7 p.m. to 5 a.m. (level 3 of CMI = 8), which had a total reduction of 35% (95% confidence interval (CI); 25-44%) in new COVID-19 admissions in the following two weeks, and a fatality reduction in the following four weeks by 52% (95% CI; 11-75%).

CONCLUSION

Evening lockdown significantly affected the course of the pandemic in Oman which lines up with similar studies throughout the world.

Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk

As the COVID-19 pandemic continues to ravage the world, it is critical to assess the COVID-19 risk timely on multi-scale. To implement it and evaluate the public health policies, we develop a machine learning assisted framework to predict epidemic dynamics from the reported infection data. It contains a county-level spatio-temporal epidemiological model, which combines spatial cellular automata (CA) with time sensitive-undiagnosed-infected-removed (SUIR) model, and is compatible with the existing risk prediction models. The CA-SUIR model shows the multi-scale risk to the public and reveals the transmission modes of coronavirus in different scenarios. Through transfer learning, this new toolbox is used to predict the prevalence of multi-scale COVID-19 in all 412 counties in Germany. A t-day-ahead risk forecast as well as assessment of the non-pharmaceutical intervention policies is presented. We analyzed the situation at Christmas of 2020, and found that the most serious death toll could be 34.5. However, effective policy could control it below 21thousand, which provides a quantitative basis for evaluating the public policies implemented by the government. Such intervening evaluation process would help to improve public health policies and restart the economy appropriately in pandemics.

Electricity consumption of Singaporean households reveals proactive community response to COVID-19 progression

Understanding how populations' daily behaviors change during the COVID-19 pandemic is critical to evaluating and adapting public health interventions. Here, we use residential electricity-consumption data to unravel behavioral changes within peoples' homes in this period. Based on smart energy-meter data from 10,246 households in Singapore, we find strong positive correlations between the progression of the pandemic in the city-state and the residential electricity consumption. In particular, we find that the daily new COVID-19 cases constitute the most dominant influencing factor on the electricity demand in the early stages of the pandemic, before a lockdown. However, this influence wanes once the lockdown is implemented, signifying that residents have settled into their new lifestyles under lockdown. These observations point to a proactive response from Singaporean residents-who increasingly stayed in or performed more activities at home during the evenings, despite there being no government mandates-a finding that surprisingly extends across all demographics. Overall, our study enables policymakers to close the loop by utilizing residential electricity usage as a measure of community response during unprecedented and disruptive events, such as a pandemic.

Overcoming the discourse of science mistrust: how science education can be used to develop competent consumers and communicators of science information

Science educators can provide learning experiences that challenge notions of mistrust in science, and provide students with the science skills necessary to obtain, evaluate, and communicate credible scientific information. As the COVID-19 pandemic persists, the American public continues to be inundated with messages reinforcing the importance of social distancing, hand-washing and the effectiveness of masks to slow the spread of the coronavirus. While most citizens diligently responded to these recommendations with compliance, there remained subcultures of the American public determined to resist these recommendations and engage in a discourse rooted in a mistrust of science. This discourse of science mistrust was perpetuated through the use of social media, as well as the modeling behaviors of government leaders, particularly as social media posts and news coverage were primary methods of social interaction during mandated stay-at-home orders. The discourse of science mistrust perpetuated during the COVID-19 pandemic reveals that a concerted effort is required by all science educators to aid with overcoming this discourse. Overcoming science mistrust begins in the science classroom by implementing pedagogical opportunities for science students to obtain, evaluate, and communicate scientific information. Allowing students to obtain and evaluate information are a critical skill to develop in the science classroom as science educators aim to produce competent consumers of scientific information. Furthermore, science students ought to also have experience with the skills associated with communicating scientific information. Communicating scientific information is a critical skill for science students to develop as it is through the effective communication of credible scientific information that the discourse of science mistrust can be overcome. Providing these learning opportunities to science students empower students to effectively evaluate social media and news coverage associated with the COVID-19 pandemic, and promote a future, wherein citizens are able to read, interpret, and critically consume scientific information to overcome discourses of science mistrust.

Associations between changes in population mobility in response to the COVID-19 pandemic and socioeconomic factors at the city level in China and country level worldwide: a retrospective, observational study

Background

Until broad vaccination coverage is reached and effective therapeutics are available, controlling population mobility (ie, changes in the spatial location of a population that affect the spread and distribution of pathogens) is one of the major interventions used to reduce transmission of SARS-CoV-2. However, population mobility differs across locations, which could reduce the effectiveness of pandemic control measures. Here we assess the extent to which socioeconomic factors are associated with reductions in population mobility during the COVID-19 pandemic, at both the city level in China and at the country level worldwide.

Methods

In this retrospective, observational study, we obtained anonymised daily mobile phone location data for 358 Chinese cities from Baidu, and for 121 countries from Google COVID-19 Community Mobility Reports. We assessed the intra-city movement intensity, inflow intensity, and outflow intensity of each Chinese city between Jan 25 (when the national emergency response was implemented) and Feb 18, 2020 (when population mobility was lowest) and compared these data to the corresponding lunar calendar period from the previous year (Feb 5 to March 1, 2019). Chinese cities were classified into four socioeconomic index (SEI) groups (high SEI, high–middle SEI, middle SEI, and low SEI) and the association between socioeconomic factors and changes in population mobility were assessed using univariate and multivariable linear regression. At the country level, we compared six types of mobility (residential, transit stations, workplaces, retail and recreation, parks, and groceries and pharmacies) 35 days after the implementation of the national emergency response in each country and compared these to data from the same day of the week in the baseline period (Jan 3 to Feb 6, 2020). We assessed associations between changes in the six types of mobility and the country's sociodemographic index using univariate and multivariable linear regression.

Findings

The reduction in intra-city movement intensity in China was stronger in cities with a higher SEI than in those with a lower SEI (r=–0·47, p<0·0001). However, reductions in inter-city movement flow (both inflow and outflow intensity) were not associated with SEI and were only associated with government control measures. In the country-level analysis, countries with higher sociodemographic and Universal Health Coverage indexes had greater reductions in population mobility (ie, in transit stations, workplaces, and retail and recreation) following national emergency declarations than those with lower sociodemographic and Universal Health Coverage indexes. A higher sociodemographic index showed a greater reduction in mobility in transit stations (r=–0·27, p=0·0028), workplaces (r=–0·34, p=0·0002), and areas retail and recreation (rxs=–0·30, p=0·0012) than those with a lower sociodemographic index.

Interpretation

Although COVID-19 outbreaks are more frequently reported in larger cities, our analysis shows that future policies should prioritise the reduction of risks in areas with a low socioeconomic level—eg, by providing financial assistance and improving public health messaging. However, our study design only allows us to assess associations, and a long-term study is needed to decipher causality.

Funding

Chinese Ministry of Science and Technology, Research Council of Norway, Beijing Municipal Science & Technology Commission, Beijing Natural Science Foundation, Beijing Advanced Innovation Program for Land Surface Science, National Natural Science Foundation of China, China Association for Science and Technology.

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travel bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.

Behavior of Traffic Congestion and Public Transport in Eight Large Cities in Latin America during the COVID-19 Pandemic

This comparative study analyzes the impact of the COVID-19 pandemic on motorized mobility in eight large cities of five Latin American countries. Public institutions and private organizations have made public data available for a better understanding of the contagion process of the pandemic, its impact, and the effectiveness of the implemented health control measures. In this research, data from the IDB Invest Dashboard were used for traffic congestion as well as data from the Moovit© public transport platform. For the daily cases of COVID-19 contagion, those published by Johns Hopkins Hospital University were used. The analysis period corresponds from 9 March to 30 September 2020, approximately seven months. For each city, a descriptive statistical analysis of the loss and subsequent recovery of motorized mobility was carried out, evaluated in terms of traffic congestion and urban transport through the corresponding regression models. The recovery of traffic congestion occurs earlier and faster than that of urban transport since the latter depends on the control measures imposed in each city. Public transportation does not appear to have been a determining factor in the spread of the pandemic in Latin American cities.

Effectiveness of Face Coverings in Mitigating the COVID-19 Pandemic in the United States

The COVID-19 pandemic has been one of the biggest public health challenges of the 21st century. Many prevalent measures have been taken to prevent its spread and protect the public. However, the use of face coverings as an effective preventive measure remains contentious. The goal of the current study is to evaluate the effectiveness of face coverings as a protective measure. We examined the effectiveness of face coverings between 1 April and 31 December 2020. This was accomplished by analyzing trends of daily new COVID-19 cases, cumulative confirmed cases, and cases per 100,000 people in different U.S. states, including the District of Columbia. The results indicated a sharp change in trends after face covering mandates. For the 32 states with face covering mandates, 63% and 66% exhibited a downward trend in confirmed cases within 21 and 28 days of implementation, respectively. We estimated that face covering mandates in the 32 states prevented approximately 78,571 and 109,703 cases within 21- and 28-day periods post face covering mandate, respectively. A statistically significant (p = 0.001) negative correlation (-0.54) was observed between the rate of cases and days since the adoption of a face covering mandate. We concluded that the use of face coverings can provide necessary protection if they are properly used.

Precise Transmission for COVID-19 Information: Based on China's Experience

Information on coronavirus disease 2019 (COVID-19) has been a significant focus for the global public since the outbreak of the disease. In response, data visualization has become the main form of media used to inform the public about the global pandemic's progress. This paper studies the example of China, the main country affected by the virus in the early stage of the pandemic, to explain the problems regarding the differences in time, knowledge, and technology for information transmission. This paper also tries to explain the causes behind the dissemination of rumors, misjudgment of the public, and the difficulties of perception regarding pandemic information based on the three aspects of information collection, processing, and presentation. We argue that comprehensive information transmission with direct and clear visual presentation could help the public better understand the development of the pandemic, relieve social panic, and help authorities promptly adjust public health policies to absorb the social and economic impacts of the pandemic. Based on a case study, we propose that hierarchical presentation, comprehensive descriptions, and accurate visualizations of pandemic data can effectively improve information transmission, thus providing helpful references for authorities and organizations to improve the effectiveness of pandemic information transmission.

Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data

INTRODUCTION

To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and results in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies.

METHODS

We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions.

RESULTS

Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels.

CONCLUSION

Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.