No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic

An analysis of the spatio-temporal behavior of COVID-19 patients using activity trajectory data

During the global pandemic, COVID-19 patients' activity trajectories and actions emerge as revelatory conduits elucidating their spatiotemporal behavior and transmission dynamics. This study analyzes COVID-19 patients' behavior in Nanjing and Yangzhou, China, by using patient activity trajectory data in conjunction with complex network theory. The main findings are as follows: (1) The evaluation of the activity network structure of patients revealed that "residential areas" and "vegetable markets" had the highest betweenness centrality, indicating that these are the primary nodes of COVID-19 transmission. (2) The power-law distribution of the degree distribution of nodes for different facility types revealed that residential areas, vegetable markets, and shopping malls had the most scale-free characteristics, indicating that a large number of patients visited these three facility types at a few access points. (3) Community detection showed that patient visitation sites in Nanjing and Yangzhou were divided into five or six communities, with the largest community containing the outbreak origin and several residential areas surrounding it. (4) Patients had fewer activities across administrative regions but more activities across the life circle when the pandemic broke out in the suburbs, and more activities across administrative regions but fewer activities across the life circle when the pandemic broke out in the central city. Based on these findings, this paper makes recommendations for future pandemic preparedness in an effort to achieve effective pandemic control and reduce the damage caused by pandemics. Overall, this study provides insights into understanding the transmission patterns of COVID-19 and may inform future pandemic control strategies.

Media exposure, threat processing, and mitigation behaviors in Gulf Coast residents facing the co-occurring threats of COVID-19 and hurricanes

The 2020 hurricane season threatened millions of Americans concurrently grappling with COVID-19. Processes guiding individual-level mitigation for these conceptually distinct threats, one novel and chronic (COVID-19), the other familiar and episodic (hurricanes), are unknown. Theories of health protective behaviors suggest that inputs from external stimuli (e.g., traditional and social media) lead to threat processing, including perceived efficacy (self- and response) and perceived threat (susceptibility and severity), guiding mitigation behavior. We surveyed a representative sample of Florida and Texas residents (N = 1846) between April 14, 2020 and April 27, 2020; many had previous hurricane exposure; all were previously assessed between September 8, 2017 and September 11, 2017. Using preregistered analyses, two generalized structural equation models tested direct and indirect effects of media exposure (traditional media, social media) on self-reported (1) COVID-19 mitigation (handwashing, mask-wearing, social distancing) and (2) hurricane mitigation (preparation behaviors), as mediated through perceived efficacy (self- and response) and perceived threat (susceptibility and severity). Self-efficacy and response efficacy were associated with social distancing (p = .002), handwashing, mask-wearing, and hurricane preparation (ps < 0.001). Perceived susceptibility was positively associated with social distancing (p = 0.017) and hurricane preparation (p < 0.001). Perceived severity was positively associated with social distancing (p < 0.001). Traditional media exhibited indirect effects on COVID-19 mitigation through increased response efficacy (ps < 0.05), and to a lesser extent self-efficacy (p < 0.05), and on hurricane preparation through increased self-efficacy and response efficacy and perceived susceptibility (ps < 0.05). Social media did not exhibit indirect effects on COVID-19 or hurricane mitigation. Communications targeting efficacy and susceptibility may encourage mitigation behavior; research should explore how social media campaigns can more effectively target threat processing, guiding protective actions.

Uneven impacts of COVID-19 on residents' utilization of urban parks: A case study of Guangzhou, China

As COVID-19 increased people's dependency on urban parks for physical and psychological well-being, it also has uncertain impacts on park utilization. Understanding these impacts and how the pandemic has contributed to them is an issue that warrants urgent attention. We used multi-source spatio-temporal data to examine urban park use before and during COVID-19 in Guangzhou, China, and constructed a set of regression models to evaluate the associated factors. We found that COVID-19 has significantly reduced the overall utilization of urban parks while also exacerbating spatial unevenness. This was due to residents' limited movement distance, and the diminished role of urban transportation affecting the efficient citywide use of parks. Meanwhile, residents' increased demand for nearby parks amplified the importance of community parks, which exacerbated the consequences caused by the uneven distribution of park resources. We propose that city administrators improve the efficiency of existing parks and prioritize the adequate placement of community parks at urban fringes to improve access. Furthermore, cities with similar layouts as Guangzhou should plan for urban parks from a multi-perspective and consider the sub-city level differences to address unevenness during the current pandemic and in the future.

A qualitative descriptive study of the impact of the COVID-19 pandemic on staff in a Canadian intensive care unit

PURPOSE

We sought to explore the lived experiences of a professionally diverse sample of healthcare workers (HCWs) in a single intensive care unit (ICU) serving a large and generalizable Canadian population. We aimed to understand how working during the COVID-19 pandemic affected their professional and personal lives, including their perceptions of institutional support, to inform interventions to ameliorate impacts of the COVID-19 and future pandemics.

METHODS

In this qualitative descriptive study, 23 ICU HCWs, identified using convenience purposive sampling, took part in individual semistructured interviews between July and November 2020, shortly after the first wave of the pandemic in Ontario. We used inductive thematic analysis to identify major themes.

RESULTS

We identified five major themes related to the COVID-19 pandemic: 1) communication and informational needs (e.g., challenges communicating policy changes); 2) adjusting to restricted visitation (e.g., spending less time interacting with patients); 3) staffing and workplace supports (e.g., importance of positive team dynamics); 4) permeability of professional and personal lives (e.g., balancing shift work and childcare); and 5) a dynamic COVID-19 landscape (e.g., coping with constant change). The COVID-19 pandemic contributed to HCWs in the ICU experiencing varied negative repercussions on their work environment, including staffing and institutional support, which carried into their personal lives.

CONCLUSION

Healthcare workers in the ICU perceived that the COVID-19 pandemic had negative repercussions on their work environment, including staffing and institutional support, as well as their professional and personal lives. Understanding both the negative and positive experiences of all ICU HCWs working during the COVID-19 pandemic is critical to future pandemic preparedness. Their perspectives will help to inform the development of mental health and wellbeing interventions to support staff during the COVID-19 pandemic and beyond.

A Retrospective Database Analysis of Before and After Social Distancing in Relation to Pediatric Infection Rate and Healthcare Services Usage During the Coronavirus Disease 2019 Pandemic

BACKGROUND

Social distancing policy was introduced in Israel in 2020 to reduce the spread of coronavirus disease 2019 (COVID-19). The aim of this study was to analyze the effect of social distancing on other infections in children, by comparing disease rate and healthcare utilization before and after social distancing.

METHODS

This was a before-and-after study. Within this retrospective database analysis of parallel periods in 2019 (periods 1 and 2) and 2020 (periods 3 [prelockdown period] and 4 [lockdown period]) we included all pediatric population registered in the electronic medical records of the Maccabi Healthcare Services, Israel, looking at the occurrence of non-COVID-19 infections, antibiotic purchasing, physician visits, ambulatory emergency care center visits, emergency department visits, and hospitalizations.

RESULTS

A total of 776 828 children were included from 2019, and 777 729 from 2020. We found a lower infection rate in 2020 versus 2019. We did not find a difference in infection rate between periods 1 and 2, while there was a significant difference between periods 3 and 4. We found a significant difference between periods 2 and 4, with a higher RR than for the comparison between periods 1 and 3. There was a modest decrease in ambulatory emergency care center visits in 2020, and lower increases in emergency department visits and hospital admissions. We found decreases in antibiotic purchasing between periods 1 and 3 and between periods 2 and 4, more pronounced in 2020 than in 2019.

CONCLUSIONS

Analysis of findings before and after social distancing and masking showed reduced prevalence of non-COVID-19 pediatric infections and reduced consumption of healthcare services and antibiotics related with the lockdown period.

Using passive Wi-Fi for community crowd sensing during the COVID-19 pandemic

Sensing passersby and detecting crowded locations is a growing area of research and development in the last decades. The COVID-19 pandemic compelled authorities and public and private institutions to monitor access and occupancy of crowded spaces. This work addresses the detection of crowds in points of interest (POI) by using a territory grid analysis categorizing POIs by the services available in each location and comparing data gathered from a community passive Wi-Fi infrastructure against mobile cellular tower association data from telecom companies. In Madeira islands (Portugal), we used data from the telecom provider NOS for the timespan of 4 months as ground truth and found a strong correlation with sparse passive Wi-Fi. An official regional mobile application shows the occupancy data to end-users based on the territory categorization and the passive Wi-Fi infrastructure in POIs. Occupancy data shows historical hourly trends of each location, and the real-time occupation, helping visitors and locals plan their commutes better to avoid crowded spaces.

Prognosis of COVID-19 patients using lab tests: A data mining approach

Background

The rapid prevalence of coronavirus disease 2019 (COVID-19) has caused a pandemic worldwide and affected the lives of millions. The potential fatality of the disease has led to global public health concerns. Apart from clinical practice, artificial intelligence (AI) has provided a new model for the early diagnosis and prediction of disease based on machine learning (ML) algorithms. In this study, we aimed to make a prediction model for the prognosis of COVID-19 patients using data mining techniques.

Methods

In this study, a data set was obtained from the intelligent management system repository of 19 hospitals at Shahid Beheshti University of Medical Sciences in Iran. All patients admitted had shown positive polymerase chain reaction (PCR) test results. They were hospitalized between February 19 and May 12 in 2020, which were investigated in this study. The extracted data set has 8621 data instances. The data include demographic information and results of 16 laboratory tests. In the first stage, preprocessing was performed on the data. Then, among 15 laboratory tests, four of them were selected. The models were created based on seven data mining algorithms, and finally, the performances of the models were compared with each other.

Results

Based on our results, the Random Forest (RF) and Gradient Boosted Trees models were known as the most efficient methods, with the highest accuracy percentage of 86.45% and 84.80%, respectively. In contrast, the Decision Tree exhibited the least accuracy (75.43%) among the seven models.

Conclusion

Data mining methods have the potential to be used for predicting outcomes of COVID-19 patients with the use of lab tests and demographic features. After validating these methods, they could be implemented in clinical decision support systems for better management and providing care to severe COVID-19 patients.

A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement

Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patients.

The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis

The objective of this systematic review with PRISMA guidelines is to discover how population movement information has epidemiological implications for the spread of COVID-19. In November 2022, the Web of Science and Scopus databases were searched for relevant reports for the review. The inclusion criteria are: (1) the study uses data from Apple Mobility Trends Reports, (2) the context of the study is about COVID-19 mobility patterns, and (3) the report is published in a peer-reviewed venue in the form of an article or conference paper in English. The review included 35 studies in the period of 2020-2022. The main strategy used for data extraction in this review is a matrix proposal to present each study from a perspective of research objective and outcome, study context, country, time span, and conducted research method. We conclude by pointing out that these data are not often used in studies and it is better to study a single country instead of doing multiple-country research. We propose topic classifications for the context of the studies as transmission rate, transport policy, air quality, re-increased activities, economic activities, and financial markets.

COVID-19 and organisational resilience in Brazil's water sector

The COVID-19 pandemic required a wide range of adaptations to the way that water sector operated globally. This paper looks into the impact of the COVID-19 pandemic on Brazilian water sector and evaluates the water sector's organisational resilience from the lens of water professionals. This study uses British Standard (BS 65000:2014)'s Resilience Maturity Scale method to evaluate organisational resilience in water sector under two defined scenarios of before and during the pandemic. For this purpose, the self-assessment framework developed by Southern Water in the United Kingdom (based on BS 65000:2014), comprising of the core resilience elements of Direction, Awareness, Alignment, Learning, Strengthening, and Assurance, are used for evaluations. A qualitative-quantitative surveying method is used for data collection. A total of 14 responses to the whole questionnaire were received from May 2021 to August 2021, each representing one water company in Brazil (four local companies and ten state-owned ones). The analyses identified COVID-19 as a threat multiplier particularly to already existing financial challenges due to the pre-existing threats in water sector. Bad debt and the COVID-19 emergency measures are recognised as the main challenges by 21 % and 14 % of the survey respondents. The state-owned and local companies scored an almost similar maturity level 3, 35 % and 34 % respectively, while the local companies scored much lower at maturity level 4 i.e., 26 % as opposed to 47 % in state-owned sector. This indicates that COVID-19 has a greater impact on local companies and the needs to increase preparedness. This study replicates an international experience to raise awareness on water sector's resiliency in Brazil and how it can be improved to withstand future external shocks. It sheds light on how and what existing challenges can be exacerbated facing a global shock and proposes opportunities for improvement of resilience maturity in water sector in Brazil.

Promoting Social Distancing and COVID-19 Vaccine Intentions to Mothers: Randomized Comparison of Information Sources in Social Media Messages

Background

Social media disseminated information and spread misinformation during the COVID-19 pandemic that affected prevention measures, including social distancing and vaccine acceptance.

Objective

In this study, we aimed to test the effect of a series of social media posts promoting COVID-19 nonpharmaceutical interventions (NPIs) and vaccine intentions and compare effects among 3 common types of information sources: government agency, near-peer parents, and news media.

Methods

A sample of mothers of teen daughters (N=303) recruited from a prior trial were enrolled in a 3 (information source) × 4 (assessment period) randomized factorial trial from January to March 2021 to evaluate the effects of information sources in a social media campaign addressing NPIs (ie, social distancing), COVID-19 vaccinations, media literacy, and mother–daughter communication about COVID-19. Mothers received 1 social media post per day in 3 randomly assigned Facebook private groups, Monday-Friday, covering all 4 topics each week, plus 1 additional post on a positive nonpandemic topic to promote engagement. Posts in the 3 groups had the same messages but differed by links to information from government agencies, near-peer parents, or news media in the post. Mothers reported on social distancing behavior and COVID-19 vaccine intentions for self and daughter, theoretic mediators, and covariates in baseline and 3-, 6-, and 9-week postrandomization assessments. Views, reactions, and comments related to each post were counted to measure engagement with the messages.

Results

Nearly all mothers (n=298, 98.3%) remained in the Facebook private groups throughout the 9-week trial period, and follow-up rates were high (n=276, 91.1%, completed the 3-week posttest; n=273, 90.1%, completed the 6-week posttest; n=275, 90.8%, completed the 9-week posttest; and n=244, 80.5%, completed all assessments). In intent-to-treat analyses, social distancing behavior by mothers (b=–0.10, 95% CI –0.12 to –0.08, P<.001) and daughters (b=–0.10, 95% CI –0.18 to –0.03, P<.001) decreased over time but vaccine intentions increased (mothers: b=0.34, 95% CI 0.19-0.49, P<.001; daughters: b=0.17, 95% CI 0.04-0.29, P=.01). Decrease in social distancing by daughters was greater in the near-peer source group (b=–0.04, 95% CI –0.07 to 0.00, P=.03) and lesser in the government agency group (b=0.05, 95% CI 0.02-0.09, P=.003). The higher perceived credibility of the assigned information source increased social distancing (mothers: b=0.29, 95% CI 0.09-0.49, P<.01; daughters: b=0.31, 95% CI 0.11-0.51, P<.01) and vaccine intentions (mothers: b=4.18, 95% CI 1.83-6.53, P<.001; daughters: b=3.36, 95% CI 1.67-5.04, P<.001). Mothers’ intentions to vaccinate self may have increased when they considered the near-peer source to be not credible (b=–0.50, 95% CI –0.99 to –0.01, P=.05).

Conclusions

Decreasing case counts, relaxation of government restrictions, and vaccine distribution during the study may explain the decreased social distancing and increased vaccine intentions. When promoting COVID-19 prevention, campaign planners may be more effective when selecting information sources that audiences consider credible, as no source was more credible in general.

Trial Registration

ClinicalTrials.gov NCT02835807; https://clinicaltrials.gov/ct2/show/NCT02835807

The adverse effect of the COVID-19 pandemic on health service usage among patients with type 2 diabetes in North Karelia, Finland

AIMS

The COVID-19 pandemic has challenged health systems and their capacity to deliver essential health services while responding to COVID-19. This study examines the pandemic's impact on health service usage among patients with type 2 diabetes in the North Karelia region, in Finland.

METHODS

This retrospective cohort study used electronic health records of 11,458 type 2 diabetes patients, comprising all primary and specialised care contacts in 2019 and 2020. We analysed diabetes and dental healthcare contacts to primary care nurses, doctors and dentists and all emergency visits in specialised care. We compared healthcare usage in three different periods in 2020 (pre-lockdown [1 January-15 March], lockdown [16 March-31 May], post-lockdown [1 June-31 December]) with the equivalent period in 2019.

RESULTS

During the lockdown period, the number of diabetes-related contacts decreased significantly but quickly increased again to nearly the same level as in 2019. Overall, healthcare usage was lower in the pandemic year, with proportionally 9% fewer contacts per person (mean 2.08 vs 2.29) and a proportionally 9% lower proportion of patients making any contact (59.9% vs 65.8%). The proportion of remote consultations was similar in both years in the pre-lockdown period (56.3-59.5%) but then increased to 88.0% during the 2020 lockdown. Patterns were similar when analysed by age group and gender. Emergency visits went down significantly at the beginning of the lockdown period, but a "rebound effect" was observed, so after the lockdown, the number of emergency visits in 2020 exceeded the numbers of the previous year.

CONCLUSION

Despite the COVID-19 pandemic, diabetes care was continuous, and even elderly patients aged ≥70 years accessed the health services. The delivery of many essential services was facilitated by processes that strongly relied on telemedicine already before the pandemic.

A global experiment on motivating social distancing during the COVID-19 pandemic

Communicating in ways that motivate engagement in social distancing remains a critical global public health priority during the COVID-19 pandemic. This study tested motivational qualities of messages about social distancing (those that promoted choice and agency vs. those that were forceful and shaming) in 25,718 people in 89 countries. The autonomy-supportive message decreased feelings of defying social distancing recommendations relative to the controlling message, and the controlling message increased controlled motivation, a less effective form of motivation, relative to no message. Message type did not impact intentions to socially distance, but people’s existing motivations were related to intentions. Findings were generalizable across a geographically diverse sample and may inform public health communication strategies in this and future global health emergencies.

Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This cross-country, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one’s core values) or behavioral intentions. Results supported hypothesized associations between people’s existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges.

How national leaders keep 'us' safe: A longitudinal, four-nation study exploring the role of identity leadership as a predictor of adherence to COVID-19 non-pharmaceutical interventions

OBJECTIVES

To investigate whether citizens' adherence to health-protective non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic is predicted by identity leadership, wherein leaders are perceived to create a sense of shared national identity.

DESIGN

Observational two-wave study. Hypotheses testing was conducted with structural equation modelling.

SETTING

Data collection during the COVID-19 pandemic in China, Germany, Israel and the USA in April/May 2020 and four weeks later.

PARTICIPANTS

Adults in China (n=548, 66.6% women), Germany (n=182, 78% women), Israel (n=198, 51.0% women) and the USA (n=108, 58.3% women).

MEASURES

Identity leadership (assessed by the four-item Identity Leadership Inventory Short-Form) at Time 1, perceived shared national identification (PSNI; assessed with four items) and adherence to health-protective NPIs (assessed with 10 items that describe different health-protective interventions; for example, wearing face masks) at Time 2.

RESULTS

Identity leadership was positively associated with PSNI (95% CI 0.11 to 0.30, p<0.001) in all countries. This, in turn, was related to more adherence to health-protective NPIs in all countries (95% CI 0.03 to 0.36, 0.001≤p≤0.017) except Israel (95% CI -0.03 to 0.27, p=0.119). In Germany, the more people saw Chancellor Merkel as engaging in identity leadership, the more they adhered to health-protective NPIs (95% CI 0.04 to 0.18, p=0.002). In the USA, in contrast, the more people perceived President Trump as engaging in identity leadership, the less they adhered to health-protective NPIs (95% CI -0.17 to -0.04, p=0.002).

CONCLUSIONS

National leaders can make a difference by promoting a sense of shared identity among their citizens because people are more inclined to follow health-protective NPIs to the extent that they feel part of a united 'us'. However, the content of identity leadership (perceptions of what it means to be a nation's citizen) is essential, because this can also encourage people to disregard such recommendations.

Application of the protection motivation theory to understand determinants of compliance with the measure of banning gathering size >4 in all public areas for controlling COVID-19 in a Hong Kong Chinese adult general population

BACKGROUND

Restriction of gathering size in all public areas is a newly and commonly exercised governmental social distancing policy during the COVID-19 pandemic. Its effectiveness depends on the general public's compliance. This study applied the Protection Motivation Theory (PMT) to investigate determinants (i.e., perceived severity, perceived susceptibility, perceived response efficacy, and perceived self-efficacy) of compliance with the social distancing policy of banning gathering of >4 people in all public areas (BG4PA) in the Hong Kong general adult population.

METHODS

300 participants were interviewed through a population-based telephone survey during April 21-28, 2020.

RESULTS

The compliance rate of BG4PA was high (78%). Adjusted for the background factors, multiple linear regression analysis found that perceived response efficacy and perceived self-efficacy were significantly and positively associated with compliance with BG4PA (p<0.05), while the associations between perceived severity/perceived susceptibility and compliance were of marginal significance (0.05

CONCLUSIONS

PMT can be applied to understand compliance with BG4PA. Perceived response efficacy and perceived self-efficacy were more influential than perceived severity and perceived susceptibility. Health promotion may focus on improving coping appraisal.

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Key Words Collapse

MESH Headings

• Adult
• COVID-19/epidemiology
• COVID-19/prevention & control
• Cross-Sectional Studies
• Hong Kong/epidemiology
• Humans
• Motivation
• Pandemics/prevention & control
• Surveys and Questionnaires

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Grants

• Internal funding of Center for Health Behaviours Research, the Chinese University of Hong Kong

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Affiliation(s)

Yanqiu Yu Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
Mason M. C. Lau Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
Joseph T. F. Lau Centre for Health Behaviours Research, Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China School of Psychiatry, Wenzhou Medical University, Wenzhou, China

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Lockdowns, Community Mobility Patterns, and COVID-19: A Retrospective Analysis of Data from 16 Countries

Objectives

During the coronavirus disease 2019 (COVID-19) pandemic, countries around the world framed specific laws and imposed varying degrees of lockdowns to ensure the maintenance of physical distancing. Understanding changes in temporal and spatial mobility patterns may provide insights into the dynamics of this infectious disease. Therefore, we assessed the efficacy of lockdown measures in 16 countries worldwide by analyzing the relationship between community mobility patterns and the doubling time of COVID-19.

Methods

We performed a retrospective record-based analysis of population-level data on the doubling time for COVID-19 and community mobility. The doubling time for COVID-19 was calculated based on the laboratory-confirmed cases reported daily over the study period (from February 15 to May 2, 2020). Principal component analysis (PCA) of six mobility pattern-related variables was conducted. To explain the magnitude of the effect of mobility on the doubling time, a finite linear distributed lag model was fitted. The k-means clustering approach was employed to identify countries with similar patterns in the significant co-efficient of the mobility index, with the optimal number of clusters derived using Elbow’s method.

Results

The countries analyzed had reduced mobility in commercial and social places. Reduced mobility had a significant and favorable association with the doubling time of COVID-19—specifically, the greater the mobility reduction, the longer the time taken for the COVID-19 cases to double.

Conclusions

COVID-19 lockdowns achieved the immediate objective of mobility reduction in countries with a high burden of cases.

Association of stay-at-home orders and COVID-19 incidence and mortality in rural and urban United States: a population-based study

OBJECTIVE

We examined the association between stay-at-home order implementation and the incidence of COVID-19 infections and deaths in rural versus urban counties of the United States.

DESIGN

We used an interrupted time-series analysis using a mixed effects zero-inflated Poisson model with random intercept by county and standardised by population to examine the associations between stay-at-home orders and county-level counts of daily new COVID-19 cases and deaths in rural versus urban counties between 22 January 2020 and 10 June 2020. We secondarily examined the association between stay-at-home orders and mobility in rural versus urban counties using Google Community Mobility Reports.

INTERVENTIONS

Issuance of stay-at-home orders.

PRIMARY AND SECONDARY OUTCOME MEASURES

Co-primary outcomes were COVID-19 daily incidence of cases (14-day lagged) and mortality (26-day lagged). Secondary outcome was mobility.

RESULTS

Stay-at-home orders were implemented later (median 30 March 2020 vs 28 March 2020) and were shorter in duration (median 35 vs 54 days) in rural compared with urban counties. Indoor mobility was, on average, 2.6%-6.9% higher in rural than urban counties both during and after stay-at-home orders. Compared with the baseline (pre-stay-at-home) period, the number of new COVID-19 cases increased under stay-at-home by incidence risk ratio (IRR) 1.60 (95% CI, 1.57 to 1.64) in rural and 1.36 (95% CI, 1.30 to 1.42) in urban counties, while the number of new COVID-19 deaths increased by IRR 14.21 (95% CI, 11.02 to 18.34) in rural and IRR 2.93 in urban counties (95% CI, 1.82 to 4.73). For each day under stay-at-home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981 to 0.982) in rural and 0.952 (95% CI, 0.951 to 0.953) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.977 (95% CI, 0.976 to 0.977) in rural counties and 0.935 (95% CI, 0.933 to 0.936) in urban counties. Each day after stay-at-home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994 to 0.995) in rural and 0.997 (95% CI, 0.995 to 0.999) in urban counties compared with prior to stay-at-home, while number of new deaths changed by a factor of 0.969 (95% CI, 0.968 to 0.970) in rural counties and 0.928 (95% CI, 0.926 to 0.929) in urban counties.

CONCLUSION

Stay-at-home orders decreased mobility, slowed the spread of COVID-19 and mitigated COVID-19 mortality, but did so less effectively in rural than in urban counties. This necessitates a critical re-evaluation of how stay-at-home orders are designed, communicated and implemented in rural areas.

High Acceptance of COVID-19 Tracing Technologies in Taiwan: A Nationally Representative Survey Analysis

Taiwan has been a world leader in controlling the spread of SARS-CoV-2 during the COVID-19 pandemic. Recently, the Taiwan Government launched its COVID-19 tracing app, 'Taiwan Social Distancing App'; however, the effectiveness of this tracing app depends on its acceptance and uptake among the general population. We measured the acceptance of three hypothetical tracing technologies (telecommunication network tracing, a government app, and the Apple and Google Bluetooth exposure notification system) in four nationally representative Taiwanese samples. Using Bayesian methods, we found a high acceptance of all three tracking technologies, with acceptance increasing with the inclusion of additional privacy measures. Modeling revealed that acceptance increased with the perceived technology benefits, trust in the providers' intent, data security and privacy measures, the level of ongoing control, and one's level of education. Acceptance decreased with data sensitivity perceptions and a perceived low policy compliance by others among the general public. We consider the policy implications of these results for Taiwan during the COVID-19 pandemic and in the future.

Simulating crowding of urban green areas to manage access during lockdowns

During the COVID-19 emergency, cities around the world introduced measures to guarantee physical distancing that restricted access to urban parks and green areas, with potentially negative effects on citizens' health and wellbeing. This study aims at providing insights to manage access to urban green space in physical distancing times, when the risk of crowding should be avoided. Using the city of Trento (Italy) as a case study, the study simulates policy scenarios corresponding to different restrictions and assesses their effects on green space access and crowding. Policy scenarios are obtained by combining different distances that people are allowed to travel, different types of green areas available for public use (only urban parks or parks and schoolyards), and different target populations (all residents or only people with no private gardens). The results unveil the trade-off between access and crowding of green areas, and can be used to suggest policy interventions and regulations that can be adopted in an emergency. Particularly, the study shows that: i) The relationship between distance threshold and the percentage of people with access to green areas is non-linear, and this should be carefully considered when proposing travel restrictions; ii) Changing the maximum travel distance does not produce major effects on the number of crowded green areas, hence additional or alternative measures need to be adopted; iii) Off-the-shelf measures, such as opening schoolyards, are beneficial and can be implemented rapidly in an emergency. Finally, the study reveals "hotspots" of green space deprivation/overcrowding in the city that should be addressed by urban planning to ensure that green space continues to benefit citizens also during emergency conditions.

Effectiveness of wireless emergency alerts for social distancing against COVID-19 in Korea

This study aimed to evaluate the effectiveness of wireless emergency alerts (WEAs) on social distancing policy. The Republic of Korea has been providing information to the public through WEAs using mobile phones. This study used five data sets: WEA messages, news articles including the keyword “COVID-19,” the number of confirmed COVID-19 patients, public foot traffic data, and the government’s social distancing level. The WEAs were classified into two topics—“warning” and “guidance”—using a random forest model. The results of the correlation analysis and further detailed analysis confirmed that the “warning” WEA topic and number of news articles significantly affected public foot traffic. However, the “guidance” topic was not significantly associated with public foot traffic. In general, the Korean government’s WEAs were effective at encouraging the public to follow social distance recommendations during the COVID-19 pandemic. In particular, the “warning” WEA topic, by providing information about the relative risk directly concerning the recipients, was significantly more effective than the “guidance” topic.

Does social media provide adequate health education for prevention of COVID-19? A case study of YouTube videos on social distancing and hand-washing

Social media offers an opportune platform for educating the public about the recommended interventions during global health emergencies. This case study evaluated information in the popular social media platform YouTube about two key interventions (namely, 'social distancing' and 'hand washing') recommended during coronavirus disease-2019. Using the keywords 'social distancing' and 'hand washing', 77 and 78 videos, respectively, were selected from YouTube through pre-defined criteria. The understandability, actionability and quality of information in these videos were assessed. Cumulatively, the social distancing videos received >9 million views and the hand-washing videos received >37 million views. Thirteen social distancing videos (16.9%) and 46 hand-washing videos (58.9%) provided understandable, actionable and good-quality information. The non-understandable, non-actionable or poor-quality videos had paradoxically more viewer engagements than the understandable, actionable or good-quality videos, respectively. Most social distancing videos came from news agencies (68.8%). Hand-washing videos were mostly uploaded by health agencies or academic institutes (52.6%). The videos were less likely to be understandable and actionable and to be of good quality when uploaded by sources other than health agencies or academic institutes. The paucity of adequate information and the limited representation of 'authoritative' sources were concerning. Strategies for harnessing social media as an effective medium for public health education are necessary during pandemics.

Human mobility and infection from Covid-19 in the Osaka metropolitan area

Controlling human mobility is thought to be an effective measure to prevent the spread of the COVID-19 pandemic. This study aims to clarify the human mobility types that impacted the number of COVID-19 cases during the medium-term COVID-19 pandemic in the Osaka metropolitan area. The method used in this study was analysis of the statistical relationship between human mobility changes and the total number of COVID-19 cases after two weeks. In conclusion, the results indicate that it is essential to control the human mobility of groceries/pharmacies to between −5 and 5% and that of parks to more than −20%. The most significant finding for urban sustainability is that urban transit was not found to be a source of infection. Hence governments in cities around the world may be able to encourage communities to return to transit mobility, if they are able to follow the kind of hygiene processes conducted in Osaka.

Mental Well-Being during COVID-19: A Cross-Sectional Study of Fly-In Fly-Out Workers in the Mining Industry in Australia

Coronavirus disease 2019 (COVID-19) has devastated the world, and its mental health impact has been recognized in the general population. However, little is known about the mental health impact of COVID-19 on fly-in fly-out (FIFO) workers, who are flown to temporarily stay and work in remote areas, during this pandemic. This study examined the mental well-being of FIFO workers in the mining industry during COVID-19 restrictions in Western Australia. An online survey was conducted between May to November 2020 among (N = 842) FIFO workers who underwent COVID-19 screening at a large mining company in Western Australia. The mental well-being score among workers was higher than population norms. One-way ANOVA with Bonferroni post-hoc tests showed significant differences in mental well-being by age, being placed under travel quarantine, undertaking self-isolation, impact of social distance guidelines, and experience of COVID-19 related symptoms. Multiple linear regression analysis showed workers who were younger, placed under travel quarantine and experienced two or more COVID-19 related symptoms were more likely to have worse mental well-being. Acknowledging the negative emotions and distress experiences among the vulnerable groups could help in providing suitable support to help lessen these negative experiences in FIFO workers.

Where is the Third E in Controlling Coronavirus Disease-19 in Indonesia? A Mini Review

Coronavirus disease (COVID-19) continues to attract global attention and is the most serious health problem today. Indonesia faces a very significant spike in cases in mid-2021. This may be related to inadequate enforcement before cases reach their peak. To review what has happened and what should be done in the future, this paper presents the three Es concept to be applied more consistently in efforts to control COVID-19 in Indonesia.

Rapid Real-time Tracking of Nonpharmaceutical Interventions and Their Association With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Positivity: The Coronavirus Disease 2019 (COVID-19) Pandemic Pulse Study

BACKGROUND

Current mitigation strategies for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on the population-wide adoption of nonpharmaceutical interventions (NPIs). Monitoring the adoption of NPIs and their associations with SARS-CoV-2 infection history can provide key information for public health.

METHODS

We sampled 1030 individuals in Maryland from 17-28 June 2020 to capture sociodemographically and geographically resolved information about NPI adoption and access to SARS-CoV-2 testing, and examine associations with self-reported SARS-CoV-2 positivity.

RESULTS

Overall, 92% reported traveling for essential services and 66% visited friends/family. Use of public transport was reported by 18%. In total, 68% reported strict social distancing indoors and 53% reported strict masking indoors; indoor social distancing was significantly associated with age, and race/ethnicity and income were associated with masking. Overall, 55 participants (5.3%) self-reported ever testing positive for SARS-CoV-2, with strong dose-response relationships between several forms of movement frequency and SARS-CoV-2 positivity. In a multivariable analysis, a history of SARS-CoV-2 infection was negatively associated with strict social distancing (adjusted odds ratio [aOR] for outdoor social distancing, 0.10; 95% confidence interval, .03-.33). Only public transport use (aOR for >7 times vs never, 4.3) and visiting a place of worship (aOR for ≥3 times vs never, 16.0) remained significantly associated with SARS-CoV-2 infection after adjusting for strict social distancing and demographics.

CONCLUSIONS

These results support public health messaging that strict social distancing during most activities can reduce SARS-CoV-2 transmission. Additional considerations are needed for indoor activities with large numbers of persons (places of worship and public transportation), where even NPIs may not be possible or sufficient.

Change in Loneliness Experienced by Older Men and Women Living Alone and With Others at the Onset of the COVID-19 Pandemic

Building on theory suggesting that loneliness is distinct from living arrangements, social isolation, and perceived social support, we examined change in loneliness for older people at the onset of the COVID-19 pandemic. Analyzing 14-years of data with multilevel mixed-effects models, we found higher levels of loneliness among people living alone, people more socially isolated, and people with less perceived support. Gender affected changes in loneliness, controlling for social isolation, perceived support, living arrangements, age, education, income, health, and marital status. Women, whether living alone or with others, experienced increases in loneliness; women living alone reported the greatest increase in loneliness. Men living alone reported high levels of loneliness prior to the pandemic, but only a slight increase over time. These analyses, which demonstrate that loneliness changed at the onset of the pandemic as a function of gender and living arrangement identify older people most likely to benefit from intervention.

Association between air pollution in Lima and the high incidence of COVID-19: findings from a post hoc analysis

BACKGROUND

Coronavirus disease 2019 (COVID-19) originated in the People's Republic of China in December 2019. Thereafter, a global logarithmic expansion of cases occurred. Some countries have a higher rate of infections despite the early implementation of quarantine. Air pollution might be related to high susceptibility to the virus and associated case fatality rates (deaths/cases*100). Lima, Peru, has the second highest incidence of COVID-19 in Latin America and also has one the highest levels of air pollution in the region.

METHODS

This study investigated the association of levels of PM2.5 exposure in previous years (2010-2016) in 24 districts of Lima with cases, deaths and case fatality rates for COVID-19. Multiple linear regression was used to evaluate this association controlled by age, sex, population density and number of food markets per district. The study period was from March 6 to June 12, 2020.

RESULTS

There were 128,700 cases in Lima and 2382 deaths due to COVID-19. The case fatality rate was 1.93%. Previous exposure to PM2.5 (2010-2016) was associated with the number of COVID-19- cases (β = 0.07; 95% CI: 0.034-0.107) and deaths (β = 0.0014; 95% CI: 0.0006-0.0.0023) but not with the case fatality rate.

CONCLUSIONS

After adjusting for age, sex and number of food markets, the higher rates of COVID-19 in Metropolitan Lima are attributable to the increased PM2.5 exposure in the previous years, among other reasons. Reduction in air pollution from a long-term perspective and social distancing are needed to prevent the spread of virus outbreaks.

Effective Communication at Different Phases of COVID-19 Prevention: Roles, Enablers and Barriers

In an age of globalisation and hyperconnectivity, the COVID-19 pandemic has caused unprecedented and sustained impact worldwide. This article discusses issues related to (science) communication at different phases of the COVID-19 epidemic timeline. We consider the role of communication for prevention from the ecological perspective, taking into consideration that many emerging pathogens, including COVID-19, likely arise in part due to anthropogenic changes to natural environments. Communication forms part of the early response setting the scene for public buy-in of public health interventions at the start of an outbreak, as well as to maintain precautions over time. Finally, communication is a key element in increasing acceptance for new tools that require mass uptake to be effective, as seen with roll-out challenges for the COVID-19 vaccines, which faced heightened concerns of efficacy and safety while mired with rampant misinformation. Ultimately, strategies for prevention of viral epidemics such as COVID-19 must include communication strategies at the forefront to reduce the risk of the emergence of new diseases and enhance efforts to control their spread and burden. Despite key themes emerging, what constitutes effective communication strategies for different people and contexts needs to be investigated further.

Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review

The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Temporal clustering of disorder events during the COVID-19 pandemic

The COVID-19 pandemic has unleashed multiple public health, socio-economic, and institutional crises. Measures taken to slow the spread of the virus have fostered significant strain between authorities and citizens, leading to waves of social unrest and anti-government demonstrations. We study the temporal nature of pandemic-related disorder events as tallied by the "COVID-19 Disorder Tracker" initiative by focusing on the three countries with the largest number of incidents, India, Israel, and Mexico. By fitting Poisson and Hawkes processes to the stream of data, we find that disorder events are inter-dependent and self-excite in all three countries. Geographic clustering confirms these features at the subnational level, indicating that nationwide disorders emerge as the convergence of meso-scale patterns of self-excitation. Considerable diversity is observed among countries when computing correlations of events between subnational clusters; these are discussed in the context of specific political, societal and geographic characteristics. Israel, the most territorially compact and where large scale protests were coordinated in response to government lockdowns, displays the largest reactivity and the shortest period of influence following an event, as well as the strongest nationwide synchrony. In Mexico, where complete lockdown orders were never mandated, reactivity and nationwide synchrony are lowest. Our work highlights the need for authorities to promote local information campaigns to ensure that livelihoods and virus containment policies are not perceived as mutually exclusive.

Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study

BACKGROUND

The dynamics of the COVID-19 pandemic vary owing to local population density and policy measures. During decision-making, policymakers consider an estimate of the effective reproduction number Rt, which is the expected number of secondary infections spread by a single infected individual.

OBJECTIVE

We propose a simple method for estimating the time-varying infection rate and the Rt.

METHODS

We used a sliding window approach with a Susceptible-Infectious-Removed (SIR) model. We estimated the infection rate from the reported cases over a 7-day window to obtain a continuous estimation of Rt. A proposed adaptive SIR (aSIR) model was applied to analyze the data at the state and county levels.

RESULTS

The aSIR model showed an excellent fit for the number of reported COVID-19 cases, and the 1-day forecast mean absolute prediction error was <2.6% across all states. However, the 7-day forecast mean absolute prediction error approached 16.2% and strongly overestimated the number of cases when the Rt was rapidly decreasing. The maximal Rt displayed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We found that the aSIR model can rapidly adapt to an increase in the number of tests and an associated increase in the reported cases of infection. Our results also suggest that intensive testing may be an effective method of reducing Rt.

CONCLUSIONS

The aSIR model provides a simple and accurate computational tool for continuous Rt estimation and evaluation of the efficacy of mitigation measures.

Association between air pollution in Lima and the high incidence of COVID-19: findings from a post hoc analysis

Background

Corona virus disease (COVID-19) originated in China in December 2019. Thereafter, a global logarithmic expansion of the cases has occurred. Some countries have a higher rate of infections despite of early implementation of quarantine. Air pollution could be related to the high susceptibility to SARS-CoV-2 and the associated case-fatality rates (deaths/cases*100). Lima, Peru has the second highest incidence of COVID-19 in Latin America, and it is also one of the cities with highest levels of air pollution in the Region.

Methods

This study investigated the association of the levels of PM_2.5 exposure in the previous years (2010-2016) in 24 districts of Lima with the positive-cases, deaths and case-fatality rates of COVID-19. Multiple Linear regression was used to evaluate this association controlled by age, sex, population density and number of food markets per district. The study period was from March 6 to June 12, 2020.

Results

There were in Lima 128,700 SARS-CoV-2 positive cases, and 2,382 deaths due to COVID-19. The case-fatality rate was 1.93%. Previous exposure to PM_2.5 (years 2010-2016) was associated with number of Covid-19 positive-cases (β = 0.07; 95% CI: 0.034-0.107) and deaths (β = 0.0014; 95% CI: 0.0006-0.0.0023), but not with case-fatality rate.

Conclusions

the higher rates of COVID-19 in Metropolitan Lima is attributable, among others, to the increased PM_2.5 exposure in the previous years after adjusting for age, sex and number of food markets. Reduction of air pollution since a long-term perspective, and social distancing are needed to prevent spreads of virus outbreak.

"Association between air pollution in Lima and the high incidence of COVID-19: findings from a post hoc analysis."

Background Corona virus disease (COVID-19) originated in China in December 2019. Thereafter, a global logarithmic expansion of the cases has occurred. Some countries have a higher rate of infections despite of early implementation of quarantine. Air pollution could be related to the high susceptibility to SARS-CoV-2 and the associated case-fatality rates (deaths/cases*100). Lima, Peru has the second highest incidence of COVID-19 in Latin America, and it is also one of the cities with highest levels of air pollution in the Region. Methods This study investigated the association of the levels of PM _2.5 exposure in the previous years (2010-2016) in 24 districts of Lima with the positive-cases, deaths and case-fatality rates of COVID-19. Multiple Linear regression was used to evaluate this association controlled by age, sex, population density and number of food markets per district. The study period was from March 6 to June 12, 2020. Results There were in Lima 128,700 SARS-CoV-2 positive cases, and 2,382 deaths due to COVID-19. The case-fatality rate was 1.93%. Previous exposure to PM _2.5 (years 2010-2016) was associated with number of Covid-19 positive-cases ( β  = 0.07; 95% CI: 0.034-0.107) and deaths ( β  = 0.0014; 95% CI: 0.0006-0.0.0023), but not with case-fatality rate. Conclusions the higher rates of COVID-19 in Metropolitan Lima is attributable, among others, to the increased PM _2.5 exposure in the previous years after adjusting for age, sex and number of food markets. Reduction of air pollution since a long-term perspective, and social distancing are needed to prevent spreads of virus outbreak.

Stay-at-home policy is a case of exception fallacy: an internet-based ecological study

A recent mathematical model has suggested that staying at home did not play a dominant role in reducing COVID-19 transmission. The second wave of cases in Europe, in regions that were considered as COVID-19 controlled, may raise some concerns. Our objective was to assess the association between staying at home (%) and the reduction/increase in the number of deaths due to COVID-19 in several regions in the world. In this ecological study, data from www.google.com/covid19/mobility/ , ourworldindata.org and covid.saude.gov.br were combined. Countries with > 100 deaths and with a Healthcare Access and Quality Index of ≥ 67 were included. Data were preprocessed and analyzed using the difference between number of deaths/million between 2 regions and the difference between the percentage of staying at home. The analysis was performed using linear regression with special attention to residual analysis. After preprocessing the data, 87 regions around the world were included, yielding 3741 pairwise comparisons for linear regression analysis. Only 63 (1.6%) comparisons were significant. With our results, we were not able to explain if COVID-19 mortality is reduced by staying at home in ~ 98% of the comparisons after epidemiological weeks 9 to 34.

Evaluating Social Distancing Measures and Their Association with the Covid-19 Pandemic in South America

Social distancing is a powerful non-pharmaceutical intervention used as a way to slow the spread of the SARS-CoV-2 virus around the world since the end of 2019 in China. Taking that into account, this work aimed to identify variations on population mobility in South America during the pandemic (15 February to 27 October 2020). We used a data-driven approach to create a community mobility index from the Google Covid-19 Community Mobility and relate it to the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT). Two hypotheses were established: countries which have adopted stricter social distancing measures have also a lower level of circulation (H1), and mobility is occurring randomly in space (H2). Considering a transient period, a low capacity of governments to respond to the pandemic with more stringent measures of social distancing was observed at the beginning of the crisis. In turn, considering a steady-state period, the results showed an inverse relationship between the Covid stringency index and the community mobility index for at least three countries (H1 rejected). Regarding the spatial analysis, global and local Moran indices revealed regional mobility patterns for Argentina, Brazil, and Chile (H1 rejected). In Brazil, the absence of coordinated policies between the federal government and states regarding social distancing may have played an important role for several and extensive clusters formation. On the other hand, the results for Argentina and Chile could be signals for the difficulties of governments in keeping their population under control, and for long periods, even under stricter decrees.

The Impact of a Digital Intervention (Happify) on Loneliness During COVID-19: Qualitative Focus Group

BACKGROUND

Loneliness is a growing area of concern, attracting attention as a public health concern due to its association with a variety of psychological and physical health problems. However, interventions targeting loneliness are less common than interventions for other mental health problems, such as depression and anxiety, and existing interventions focus primarily on building social skills and increasing opportunities for social interaction despite research suggesting these techniques are not the most effective. Furthermore, although there is an increasing need for scalable and convenient interventions, digital interventions for loneliness are even less common.

OBJECTIVE

Using a qualitative approach, we explore how adults (18-64 years of age) who express wanting to be more connected to others experience loneliness and react to a digital mental health intervention targeting loneliness.

METHODS

A total of 11 participants were recruited from a pilot randomized controlled trial exploring the impact of a digital mental health intervention, Happify Health, on loneliness among adults aged 18-64 years who indicated wanting to feel more connected to others when signing up for the platform. Participants were invited to participate in a 3-day asynchronous focus group about their experiences with loneliness, with Happify Health, and with social distancing during the COVID-19 pandemic. All 11 participants completed the focus group in May 2020.

RESULTS

Participants' responses were coded using thematic analysis, which led to identifying five themes, each with separate subthemes, that could be applied across the 3-day focus group: loneliness, relationships, social distancing, skill acquisition, and coping. Overall, we observed variability across participants in terms of the source of their loneliness, their perceptions of their social connections, and their motivation to reduce feelings of loneliness; however, participants commonly referred to negative self-perceptions as a cause or consequence of loneliness. Participants also varied in the extent to which they felt social distancing increased or decreased feelings of loneliness. In regard to the intervention, participants showed evidence of adopting skills they used to address their loneliness, particularly mindfulness and gratitude, and then using these skills to shift toward more active coping strategies following the intervention, including during the COVID-19 pandemic.

CONCLUSIONS

The heterogeneity in participants' experiences with loneliness described during this focus group emphasizes the subjective and complex nature of loneliness. This highlights the importance of developing loneliness interventions that use a variety of strategies, including both direct and indirect strategies for reducing loneliness. However, based on our data, a key component to loneliness interventions is incorporating strategies for addressing underlying negative self-perceptions that stem from, but also contribute to, loneliness. This data also provides preliminary evidence that digital platforms may be an effective tool for disseminating loneliness interventions while providing the added benefit of offering a productive distraction when feeling lonely.

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

BACKGROUND

SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic.

OBJECTIVE

The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression.

METHODS

A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI.

RESULTS

The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme.

CONCLUSIONS

In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19

While the COVID-19 pandemic is still ongoing in a majority of countries, a wealth of literature published in reputable journals attempted to model the spread of the disease. A vast majority of these studies dealt with compartmental models such as susceptible-infected-recovered (SIR) model. Although these models are rather simple, intuitive, and insightful, we argue that they do not necessarily provide a good enough fit to the reported data, which are usually reported in the form of daily fatalities and cases during pandemics. This study proposes an alternative analytics approach that relies on diffusion models to predict the number of cases and fatalities in epidemics. After evaluating several of the well-known and widely used diffusion models in business literature, including ADBUDG, Gompertz, and Bass models, we developed and used a modified/improved version of the original Bass diffusion model to address the shortcomings of the ordinary compartmental models such as SIR and demonstrated its applicability on the portrayal of the COVID-19 pandemic incident data. The proposed model differentiates itself from other similar models by fitting the data without the need for preprocessing, requiring no initial conditions and assumptions, not involving in heavy parameterization, and also properly addressing the pressing issues such as undocumented cases, length of infectious or recovery periods.

Preparing for COVID-19 exit strategies

The COVID-19 pandemic has affected 20 million people worldwide with over 732,000 deaths and trillions of dollars of lost economic productivity. It has put many countries into lockdown to contain the virus and save lives. As COVID-19 cases in some countries start to plateau and societies work hard to 'flatten the curve', leaders are being asked to formulate plans for safe and staged 'exit strategies' to reopen society. Each country will decide on their own exit strategy but many plans are considering similar vital healthcare principles including the maintenance of social distancing to prevent ongoing community transmission, testing capacity, protection of the healthcare systems and the health of their care workers. This review aims to provide an overview of essential factors that plans for exit strategy should consider and their effect on the societies' social and healthcare life.

Impact of Social Distancing Due to Coronavirus Disease 2019 on the Changes in Glycosylated Hemoglobin Level in People with Type 2 Diabetes Mellitus

This study investigated the impact of social distancing due to coronavirus disease 2019 (COVID-19) on glycemic control in people with type 2 diabetes mellitus (T2DM). We retrospectively analyzed the change in glycosylated hemoglobin level (ΔHbA1c) in people with T2DM who undertook social distancing because of COVID-19. We compared the ΔHbA1c between COVID-19 and non-COVID-19 cohorts that were enrolled at the same time of year. The ΔHbA1c of the COVID-19 cohort was significantly higher than that of two non-COVID-19 cohorts. Subgroup analysis according to age and baseline HbA1c level showed that social distancing significantly increased the mean HbA1c level of participants of <50 years. The ΔHbA1c of participants of <50 years and with HbA1c <7.0% in the COVID-19 cohort showed larger changes than other subgroups. In adjusted model, adjusted ΔHbA1c levels in the COVID-19 cohort remained significantly higher than those in the two other cohorts. Social distancing negatively impacts blood glucose control in people with T2DM, especially those who are younger and have good blood glucose control.

Social Estrangement and Psychological Distress before and during the COVID-19 Pandemic: Patterns of Change in Canadian Workers

This article argues that the COVID-19 pandemic and associated social distancing measures intended to slow the rate of transmission of the virus resulted in greater subjective isolation and community distrust, in turn adversely impacting psychological distress. To support this argument, we examine data from the Canadian Quality of Work and Economic Life Study, two national surveys of Canadian workers-one from late September 2019 (N = 2,477) and the second from mid-March 2020 (N = 2,446). Analyses show that subjective isolation and community distrust increased between the two surveys, which led to a substantial rise in psychological distress. Increases in subjective isolation were stronger in older respondents, resulting in a greater escalation in psychological distress. These findings support a Durkheimian perspective on the harm to social integration and mental health caused by periods of rapid social change but also illustrate how a life course context can differentiate individual vulnerability to disintegrative social forces.

State heterogeneity in the associations of human mobility with COVID-19 epidemics in the European Union

BACKGROUND

Human mobility was associated with epidemic changes of coronavirus disease 2019 (COVID-19) in the countries, where strict public health interventions reduced human mobility and COVID-19 epidemics. But its association with COVID-19 epidemics in the European Union (EU) is unclear.

METHODS

In this quasi-experimental interrupted time-series study, we modelled trends in human mobility and epidemics of COVID-19 in 27 EU states between January 15 and May 9, 2020. The associations of lockdown-date, and turning points of these trends were assessed.

RESULTS

There were 982,332 laboratory-confirmed COVID-19 cases in the EU states (median 7,896, interquartile 1,689 to 25,702 for individual states) during the study-period. COVID-19 and human mobility had 3 trend-segments, including an upward trend in COVID-19 daily incidence and a downward trend in most human mobilities in the middle segment. Compared with the states farther from Italy, the state-wide lockdown dates were more likely linked to turning points of human mobilities in the states closer to Italy, which were also more likely linked to second turning points of COVID-19 epidemics. Among the examined human mobilities, the second turning points in driving mobility and the first turning points in parks mobility were the best factors that connected lockdown dates and COVID-19 epidemics in the EU states closer to Italy.

CONCLUSIONS

We show state- and mobility-heterogeneity in the associations of public health interventions and human mobility with the changes of COVID-19 epidemics in the EU. These findings may help inform policymakers on the best timing and monitoring-parameters of state-level interventions in the EU.

COVID 19: challenges for virologists in the food industry

The COVID-19 pandemic is not only a challenge for public health and hospitals, but affects many aspects of our societies. This Lilliput minireview deals with problems that the pandemic causes for the food industry, addressing the presence and persistence of SARS-CoV-2 in the food environment, methods of virus inactivation and the protection of the food worker and the consumer. So far food has not been implicated in the transmission of the infection, but social disruptions caused by the pandemic could cause problems with food security.

Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study

BACKGROUND

The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible.

OBJECTIVE

We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (Rt) as compared to social mobility estimates reported from Google and Apple Maps.

METHODS

In this observational study, the estimated Rt was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of "social distancing" or "#socialdistancing" on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between Rt and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (ρ) with significance set to P<.05.

RESULTS

Negative correlations were found between Google search interest for "social distancing" and Rt in the United States (P<.001), and between search interest and state-specific Rt for 9 states with the highest COVID-19 cases (P<.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag "#socialdistancing" and at 6 days for Twitter (P<.001). Significant correlations between Rt and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at -6 and -4 days. Meanwhile, changes in social mobility correlated best with Rt at -2 days and +1 day for workplace and grocery/pharmacy, respectively.

CONCLUSIONS

Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with Rt when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.

Rapid real-time tracking of non-pharmaceutical interventions and their association with SARS-CoV-2 positivity: The COVID-19 Pandemic Pulse Study

BACKGROUND

Current mitigation strategies for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on population-wide adoption of non-pharmaceutical interventions (NPIs). Collecting demographically and geographically resolved data on NPIs and their association with SARS-CoV-2 infection history can provide critical information related to reopening geographies.

METHODS

We sampled 1,030 individuals in Maryland from June 17 - June 28, 2020 to capture socio-demographically and geographically resolved information about NPI adoption, access to SARS-CoV-2 testing, and examine associations with self-reported SARS-CoV-2 positivity.

RESULTS

Median age of the sample was 43 years and 45% were men; Whites and Blacks/African Americans represented 60% and 23%, respectively. Overall, 96% of the sample reported traveling outside their home for non-employment related services: most commonly cited reasons were essential services (92%) and visiting friends/family (66%). Use of public transport was reported by 18% of respondents. 68% reported always social distancing indoors and 53% always wearing masks indoors; indoor social distancing was significantly less common among younger vs. older individuals, and race/ethnicity and income were significantly associated with mask use (p<0.05 for all). 55 participants (5.3%) self-reported ever testing positive for SARS-CoV-2 with strong dose-response relationships between movement frequency and SARS-CoV-2 positivity that were significantly attenuated by social distancing. In multivariable analysis, history of SARS-CoV-2 infection was negatively associated with the practice of social distancing (adjusted Odd Ratio [aOR]: 0.10; 95% Confidence Interval: 0.03 - 0.33); the only travel associated with higher likelihood of SARS-CoV-2 infection was use of public transport (aOR for ≥7 times vs. never: 4.29) and visiting a place of worship (aOR for ≥3 times vs. never: 16.0) after adjusting for social distancing.

CONCLUSIONS

Using a rapid cost-efficient approach, we highlight the role of movement and social distancing on SARS-CoV-2 transmission risk. Continued monitoring of NPI uptake, access to testing, and the subsequent impact on SARS-CoV-2 transmission will be critical for pandemic control and decisions about reopening geographies.

U.S. county level analysis to determine If social distancing slowed the spread of COVID-19

Objective.

To analyze the effectiveness of social distancing in the United States (U.S.).

Methods.

A novel cell-phone ping data was used to quantify the measures of social distancing by all U.S. counties.

Results.

Using a difference-in-difference approach results show that social distancing has been effective in slowing the spread of COVID-19.

Conclusions.

As policymakers face the very difficult question of the necessity and effectiveness of social distancing across the U.S., counties where the policies have been imposed have effectively increased social distancing and have seen slowing the spread of COVID-19. These results might help policymakers to make the public understand the risks and benefits of the lockdown.