The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study

BackgroundPrevious studies on the impact of social distancing on COVID-19 mortality in the United States have predominantly examined this relationship at the national level and have not separated COVID-19 deaths in nursing homes from total COVID-19 deaths. This approach may obscure differences in social distancing behaviors by county in addition to the actual effectiveness of social distancing in preventing COVID-19 deaths.ObjectiveThis study aimed to determine the influence of county-level social distancing behavior on COVID-19 mortality (deaths per 100,000 people) across US counties over the period of the implementation of stay-at-home orders in most US states (March-May 2020).MethodsUsing social distancing data from tracked mobile phones in all US counties, we estimated the relationship between social distancing (average proportion of mobile phone usage outside of home between March and May 2020) and COVID-19 mortality (when the state in which the county is located reported its first confirmed case of COVID-19 and up to May 31, 2020) with a mixed-effects negative binomial model while distinguishing COVID-19 deaths in nursing homes from total COVID-19 deaths and accounting for social distancing– and COVID-19–related factors (including the period between the report of the first confirmed case of COVID-19 and May 31, 2020; population density; social vulnerability; and hospital resource availability). Results from the mixed-effects negative binomial model were then used to generate marginal effects at the mean, which helped separate the influence of social distancing on COVID-19 deaths from other covariates while calculating COVID-19 deaths per 100,000 people.ResultsWe observed that a 1% increase in average mobile phone usage outside of home between March and May 2020 led to a significant increase in COVID-19 mortality by a factor of 1.18 (P<.001), while every 1% increase in the average proportion of mobile phone usage outside of home in February 2020 was found to significantly decrease COVID-19 mortality by a factor of 0.90 (P<.001).ConclusionsAs stay-at-home orders have been lifted in many US states, continued adherence to other social distancing measures, such as avoiding large gatherings and maintaining physical distance in public, are key to preventing additional COVID-19 deaths in counties across the country.     

United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study

BackgroundThe emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends.ObjectiveWe aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States.MethodsWe retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data.ResultsWe observed a nonsignificant weak correlation (ρ= –0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models—for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: ρ=0.643; 2019-2020: ρ=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: ρ=0.746; 2019-2020: ρ=0.707).ConclusionsRelevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool.     

Declining influenza vaccination rates in an underserved pediatric primary care center during the COVID-19 pandemic

BackgroundInfluenza vaccination rates are decreasing in the United States. Disinformation surrounding COVID-related public health protections and SARS-CoV-2 vaccine roll-out may have unintended consequences impacting pediatric influenza vaccination. We assessed influenza vaccination rates before and during the COVID-19 pandemic in one pediatric primary care center, serving a minoritized population.MethodsA cross-sectional study assessed influenza vaccination rates for children aged 6 months to 12 years over the following influenza seasons (September-May): 1) 2018–19 and 2019–20 (pre-pandemic), and 2) 2020–21 and 2021–22 (intra-pandemic). Demographics and responses to social risk questionnaires were extracted from electronic health records. Total tetanus vaccinations across influenza seasons served as approximations of general vaccination rates. Generalized linear regression models with robust standard errors evaluated differences in demographics, social risks, and influenza vaccination rates by season. Multivariable logistic regression with robust standard errors evaluated associations between influenza season, demographics, social risks, and influenza vaccination.ResultsMost patients were young (mean age ～ 6 years), non-Hispanic Black (～80%), and publicly insured (～90%). Forty-two percent of patients eligible to receive the influenza vaccine who were seen in 2019–20 influenza season received the influenza vaccine, compared to 30% in 2021–22. Influenza and tetanus vaccination rates decreased during the COVID-19 pandemic (p < 0.01). The 2020–21 and 2021–22 influenza seasons, older age, Black race, and self-pay were associated with decreased influenza vaccine administration (p < 0.05).ConclusionsInfluenza vaccination rates within one pediatric primary care center decreased during the COVID-19 pandemic and have not rebounded, particularly for older children, those identifying as Black, and those without insurance.     

Response to the COVID-19 Outbreak in Urban Settings in China

The COVID-19 outbreak in China was devastating and spread throughout the country before being contained. Stringent physical distancing recommendations and shelter-in-place were first introduced in the hardest-hit provinces, and by March, these recommendations were uniform throughout the country. In the presence of an evolving and deadly pandemic, we sought to investigate the impact of this pandemic on individual well-being and prevention practices among Chinese urban residents. From March 2–11, 2020, 4607 individuals were recruited from 11 provinces with varying numbers of COVID-19 cases using the social networking app WeChat to complete a brief, anonymous, online survey. The analytical sample was restricted to 2551 urban residents. Standardized scales measured generalized anxiety disorder (GAD), the primary outcome. Multiple logistic regression was conducted to identify correlates of GAD alongside assessment of community practices in response to the COVID-19 pandemic. We found that during the COVID-19 pandemic, the recommended public health practices significantly (p < 0.001) increased, including wearing facial mask, practicing physical distancing, handwashing, decreased public spitting, and going outside in urban communities. Overall, 40.3% of participants met screening criteria for GAD and 49.3%, 62.6%, and 55.4% reported that their work, social life, and family life were interrupted by anxious feelings, respectively. Independent correlates of having anxiety symptoms included being a healthcare provider (aOR = 1.58, p < 0.01), living in regions with a higher density of COVID-19 cases (aOR = 2.13, p < 0.01), having completed college (aOR = 1.38, p = 0.03), meeting screening criteria for depression (aOR = 6.03, p < 0.01), and poorer perceived health status (aOR = 1.54, p < 0.01). COVID-19 had a profound impact on the health of urban dwellers throughout China. Not only did they markedly increase their self- and community-protective behaviors, but they also experienced high levels of anxiety associated with a heightened vulnerability like depression, having poor perceived health, and the potential of increased exposure to COVID-19 such as living closer to the epicenter of the pandemic.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11524-020-00498-8.     

Early influenza vaccination rates decline in children during the COVID-19 pandemic

BackgroundThis investigation sought to determine whether early season rates of pediatric influenza vaccination changed in a season when there was a concurrent COVID-19 pandemic.MethodsThis study used cohort and cross sectional data from an academic primary care division in Southcentral Pennsylvania that serves approximately 17,500 patients across 4 practice sites. Early season (prior to November 1) vaccination rates in 2018, 2019 and 2020 were recorded for children, age 6 months to 17 years. To explore the impact of COVID-19 on vaccination, we fit a model with a logit link (estimated via generalized estimating equations to account for clustering by patient over time) on calendar year, adjusted for race, ethnicity, age, and insurance type. We examined interaction effects of demographic covariates with calendar year.ResultsEarly vaccination rates were lower in 2020 (29.7%) compared with 2018 and 2019 (34.2% and 33.3%). After adjusting for covariates and accounting for clustering over time, the odds of early vaccination in 2020 were 19% lower compared to 2018 (OR 0.81, 95% CI: 0.78–0.85). In 2020, children with private insurance were more likely to receive early vaccination than in 2018 (OR 1.51, 95% CI: 1.04–1.15), whereas children with public insurance were less likely to receive early vaccination in 2020 than in 2018 (OR 0.62, 95% CI: 1.38–1.65).ConclusionsEarly influenza vaccination rates declined in a year with a concurrent COVID-19 pandemic. Modeling that accounts for individual trends and demographic variables identified specific populations with lower odds of early vaccination in 2020. Additional research is needed to investigate whether the COVID-19 pandemic impacted parental intent to obtain the influenza vaccine, or introduced barriers to healthcare access.     

Italians Do It … Less. COVID-19 Lockdown Impact on Sexual Activity: Evidence From a Large Representative Sample of Italian Adults

BackgroundTo explore how sexual activity was impacted by coronavirus disease 2019 (COVID-19) lockdown measures in the general adult population.MethodsA cross-sectional survey was conducted among 6,003 Italian adults aged 18–74 years who were representative of the Italian general population. Study subjects were recruited at the time of the nationwide stay-at-home order (from April 27 to May 3, 2020). We identified characteristics associated with decreased frequency of sex during lockdown, differentiating between cohabiting and non-cohabiting subjects.ResultsOver one-third (35.3%) of Italians reported to have changed their sexual activity during lockdown (8.4% increased and 26.9% decreased). When focusing on cohabitants (N = 3,949, 65.8%), decreased sexual activity (20.7%) was more frequently reported by men (22.3%; compared to women, multivariable odds ratio 1.23; 95% confidence interval, 1.05–1.44), younger subjects (P for trend <0.001), more educated subjects (P for trend = 0.004), subjects living in smaller houses (P for trend = 0.003), and those reporting longer time spent outdoors before the lockdown (P for trend <0.001).ConclusionsCOVID-19 lockdown drastically altered people’s day-to-day life and is likely to have impacted lifestyle habits and behavioral risk factors, including sexual attitudes and practice. This is the first national population-level study exploring changes in sexual life in this COVID-19 era. As we report sexual practice to have been affected by lockdown restrictions, we suggest that the mental health, social, and other determinants of these changes are to be explored beyond imposed social distancing.Key words: COVID-19, lockdown restrictions, sexual activity     

Excess Deaths during Influenza and Coronavirus Disease and Infection-Fatality Rate for Severe Acute Respiratory Syndrome Coronavirus 2, the Netherlands

Since the 2009 influenza pandemic, the Netherlands has used a weekly death monitoring system to estimate deaths in excess of expectations. We present estimates of excess deaths during the ongoing coronavirus disease (COVID-19) epidemic and 10 previous influenza epidemics. Excess deaths per influenza epidemic averaged 4,000. The estimated 9,554 excess deaths (41% in excess) during the COVID-19 epidemic weeks 12–19 of 2020 appeared comparable to the 9,373 excess deaths (18%) during the severe influenza epidemic of 2017–18. However, these deaths occurred in a shorter time, had a higher peak, and were mitigated by nonpharmaceutical control measures. Excess deaths were 1.8-fold higher than reported laboratory-confirmed COVID-19 deaths (5,449). Based on excess deaths and preliminary results from seroepidemiologic studies, we estimated the infection-fatality rate to be 1%. Monitoring of excess deaths is crucial for timely estimates of disease burden for influenza and COVID-19. Our data complement laboratory-confirmed COVID-19 death reports and enable comparisons between epidemics.     

COVID-19 outbreak and risk factors for infection in a taekwondo gym in the Republic of Korea

ObjectivesRelatively few studies have assessed risk factors for coronavirus disease 2019 (COVID-19) in public facilities used by children and adolescents. This study presents an analysis of a COVID-19 outbreak that occurred in a taekwondo gym in Korea, predominantly among children and adolescents, with the aim of providing insights on managing COVID-19 outbreaks in similar facilities.MethodsAll 108 taekwondo gym students and staff received COVID-19 tests. A survey and closed-circuit television analyses were used to identify risk factors. A univariate analysis was conducted, followed by multivariate logistic regression analysis with backward elimination for variables with a significance level <0.10 in the univariate analysis.Results COVID-19 was confirmed in 30 of 108 subjects at the taekwondo gym (attack rate, 27.8%). The outbreak started in an adult class student. This student transmitted the virus to the staff, who consequently transmitted the virus to adolescent students. In the univariate analysis, the relative risk for younger age (≤9 years) was 2.14 (95% confidence interval [CI], 1.01–4.54; p=0.054), and that for food consumption inside the gym was 2.12 (95% CI, 1.04–4.30; p=0.048). In the multivariate logistic regression analysis, the odds ratio for younger age was 2.96 (95% CI, 1.07–8.20; p=0.036), and that for food consumption inside the gym was 3.00 (95% CI, 1.10–8.17; p=0.032).Conclusion Food consumption inside the facility and young age were significant risk factors for COVID-19 transmission in this taekwondo gym. Food consumption should be prohibited in sports facilities, and infection prevention education for young students is also required.     

Silver lining of COVID-19: Heightened global interest in pneumococcal and influenza vaccines,an infodemiology study

BackgroundHealth-seeking behaviors change during pandemics and may increase with regard to illnesses with symptoms similar to the pandemic. The global reaction to COVID-19 may drive interest in vaccines for other diseases.ObjectivesOur study investigated the correlation between global online interest in COVID-19 and interest in CDC-recommended routine vaccines.Design, settings, measurementsThis infodemiology study used Google Trends data to quantify worldwide interest in COVID-19 and CDC-recommended vaccines using the unit search volume index (SVI), which estimates volume of online search activity relative to highest volume of searches within a specified period. SVIs from December 30, 2019 to March 30, 2020 were collected for “coronavirus (Virus)” and compared with SVIs of search terms related to CDC-recommended adult vaccines. To account for seasonal variation, we compared SVIs from December 30, 2019 to March 30, 2020 with SVIs from the same months in 2015 to 2019. We performed country-level analyses in ten COVID-19 hotspots and ten countries with low disease burden.ResultsThere were significant positive correlations between SVIs for “coronavirus (Virus)” and search terms for pneumococcal (R = 0.89, p < 0.0001) and influenza vaccines (R = 0.93, p < 0.0001) in 2020, which were greater than SVIs for the same terms in 2015–2019 (p = 0.005, p < 0.0001, respectively). Eight in ten COVID-19 hotspots demonstrated significant positive correlations between SVIs for coronavirus and search terms for pneumococcal and influenza vaccines.LimitationsSVIs estimate relative changes in online interest and do not represent the interest of people with no Internet access.ConclusionA peak in worldwide interest in pneumococcal and influenza vaccines coincided with the COVID-19 pandemic in February and March 2020. Trends are likely not seasonal in origin and may be driven by COVID-19 hotspots. Global events may change public perception about the importance of vaccines. Our findings may herald higher demand for pneumonia and influenza vaccines in the upcoming season.     

The Effectiveness of Community-based Social Distancing for Mitigating the Spread of the COVID-19 Pandemic in Turkey

ObjectivesThe objective of this study was to demonstrate the effects of community-based social distancing interventions after the first coronavirus disease 2019 (COVID-19) case in Turkey on the course of the pandemic and to determine the number of prevented cases.MethodsIn this ecological study, the interventions implemented in response to the first COVID-19 cases in Turkey were evaluated and the effect of the interventions was demonstrated by calculating the effective reproduction number (R_t) of severe acute respiratory syndrome coro navirus 2 (SARS-CoV-2) when people complied with community-based social distancing rules.ResultsGoogle mobility scores decreased by an average of 36.33±22.41 points (range, 2.60 to 84.80) and a median of 43.80 points (interquartile range [IQR], 24.90 to 50.25). The interventions caused the calculated R_t to decrease to 1.88 (95% confidence interval, 1.87 to 1.89). The median growth rate was 19.90% (IQR, 10.90 to 53.90). A positive correlation was found between Google mobility data and R_t (r=0.783; p<0.001). The expected number of cases if the growth rate had not changed was predicted according to Google mobility categories, and it was estimated to be 1 381 922 in total. Thus, community-based interventions were estimated to have prevented 1 299 593 people from being infected.ConclusionsCommunity-based social distancing interventions significantly decreased the R_t of COVID-19 by reducing human mobility, and thereby prevented many people from becoming infected. Another important result of this study is that it shows health policy-makers that data on human mobility in the community obtained via mobile phones can be a guide for measures to be taken.     

Lineage-matched versus mismatched influenza B vaccine effectiveness following seasons of marginal influenza B circulation

BackgroundSeveral countries have recently transitioned from the trivalent inactivated influenza vaccine (TIV) to the quadrivalent inactivated influenza vaccine (QIV) in order to outweigh influenza B vaccine-mismatch. However, few studies thus far evaluated its benefits versus the TIV in a systematic manner. Our objective was to compare the QIV VE with lineage-mismatched TIV VE.MethodsWe estimated the 2015–2016, 2017–2018, 2019–2020 end-of season influenza B VE against laboratory-confirmed influenza-like illness (ILI) among community patients, using the test-negative design. VE was estimated for pre-determined age groups and for moving age intervals of 15 years.ResultsSince 2011–2012 season, alternate seasons in Israel were dominated by influenza B circulation. Compared with the lineage-mismatched TIV used during the 2015–2016 and 2017–2018 seasons, the 2019–2020 QIV showed the highest all-ages VE, with VE estimates of 56.9 (95% CI 30.1 to 73.4), 16.5 (95% CI –22.5 to 43.1) and ?25.8 (95% CI ?85.3 to 14.6) for the 2019–2020, 2017–2018 and 2015–2016 seasons, respectively. The 2019–2020 VE point estimated were the highest for the 0.5–4, 5–17 and 18–44 years age groups and for more 15-year age intervals as compared to the other seasons.ConclusionsOur results support the rapid transition from the TIV to the QIV.     

Delays in the diagnosis and treatment of tuberculosis during the COVID-19 outbreak in the Republic of Korea in 2020

ObjectivesWe investigated the impact of the coronavirus disease 2019 (COVID-19) pandemic on tuberculosis (TB) "diagnosis and" management in the Republic of Korea (ROK).Methods This retrospective cross-sectional study used nationwide ROK TB notification data (98,346 cases) from 2017 to 2020. The median time from the onset of TB symptoms to treatment initiation and the compliance rates with the required timing for notification and individual case investigations were measured and compared across periods and regions affected by the COVID-19 epidemic.Results TB diagnosis during the COVID-19 pandemic was delayed. The median time to TB treatment initiation (25 days) in 2020 increased by 3 days compared to that of the previous 3 years (22 days) (p<0.0001). In the outbreak in Seoul, Incheon, and Gyeonggi province during August, the time to TB diagnosis was 4 days longer than in the previous 3 years (p=0.0303). In the outbreak in Daegu and Gyeongbuk province from February to March 2020, the compliance rate with the required timing for individual case investigations was 2.2%p lower than in other areas in 2020 (p=0.0148). For public health centers, the rate was 13%p lower than in other areas (80.3% vs. 93.3%, p=0.0003).Conclusion TB diagnoses during the COVID-19 pandemic in the ROK were delayed nationwide, especially for patients notified by public-private mix TB control hospitals. TB individual case investigations were delayed in regional COVID-19 outbreak areas (Daegu and Gyeongbuk province), especially in public health centers. Developing strategies to address this issue will be helpful for sustainable TB management during future outbreaks.     

Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach

BackgroundDespite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally.ObjectiveSouth Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19.MethodsThe country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers.ResultsThe risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R₀ was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The R_t was estimated in South Korea and in both Seoul and Gyeonggi.ConclusionsA statistical model accounting for imported and locally transmitted cases was employed to estimate R₀ and R_t. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea.     

Identifying Communities at Risk for COVID-19–Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators

BackgroundAlthough it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health.ObjectiveThis study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning.MethodsWe developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19–related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health.ResultsUsing 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19–related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r²=0.87).ConclusionsThe COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19–related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.     

Impacts of Social Distancing During the COVID-19 Outbreaks in Korea: Level 1 Trauma Center Data of Domestic Incidents and Intentional Injury

ObjectivesAs a protective measure to slow down the transmission of coronavirus disease 2019 in Korea, social distancing was implemented from February 29^th, 2020. This study aimed to evaluate the prevalence of domestic incidents and intentional injury during March 2020 when social distancing was in effect.MethodsThere were 12,638 patients who visited the Level 1 trauma center of Chungnam province with injuries from domestic incidents, familial discord, and intentional injury. The prevalence of injuries during March 2020 was compared with the average of the previous 5 years, and the average for every March between 2015 and 2019.ResultsThe prevalence of domestic incidents in March 2020 was significantly higher than the 5-year average, and the average for every March from 2015 to 2019 (p < 0.001). Familial discord (p = 0.002) and intentional injury (p = 0.031) were more frequently observed in March 2020. Adolescents showed a markedly higher level of intentional injury in March 2020 than in both the 5-year average (p = 0.031), and average for every March over the previous 5 years (p = 0.037).ConclusionThe prevalence of domestic incidents and intentional injury were significantly higher during the period of social distancing in Korea. There is a need for social consensus, better policies, and psychological support services, especially if faced with a second or third wave of coronavirus disease.     

Exploring the Utility of Google Mobility Data During the COVID-19 Pandemic in India: Digital Epidemiological Analysis

BackgroundAssociation between human mobility and disease transmission has been established for COVID-19, but quantifying the levels of mobility over large geographical areas is difficult. Google has released Community Mobility Reports (CMRs) containing data about the movement of people, collated from mobile devices.ObjectiveThe aim of this study is to explore the use of CMRs to assess the role of mobility in spreading COVID-19 infection in India.MethodsIn this ecological study, we analyzed CMRs to determine human mobility between March and October 2020. The data were compared for the phases before the lockdown (between March 14 and 25, 2020), during lockdown (March 25-June 7, 2020), and after the lockdown (June 8-October 15, 2020) with the reference periods (ie, January 3-February 6, 2020). Another data set depicting the burden of COVID-19 as per various disease severity indicators was derived from a crowdsourced API. The relationship between the two data sets was investigated using the Kendall tau correlation to depict the correlation between mobility and disease severity.ResultsAt the national level, mobility decreased from –38% to –77% for all areas but residential (which showed an increase of 24.6%) during the lockdown compared to the reference period. At the beginning of the unlock phase, the state of Sikkim (minimum cases: 7) with a –60% reduction in mobility depicted more mobility compared to –82% in Maharashtra (maximum cases: 1.59 million). Residential mobility was negatively correlated (–0.05 to –0.91) with all other measures of mobility. The magnitude of the correlations for intramobility indicators was comparatively low for the lockdown phase (correlation ≥0.5 for 12 indicators) compared to the other phases (correlation ≥0.5 for 45 and 18 indicators in the prelockdown and unlock phases, respectively). A high correlation coefficient between epidemiological and mobility indicators was observed for the lockdown and unlock phases compared to the prelockdown phase.ConclusionsMobile-based open-source mobility data can be used to assess the effectiveness of social distancing in mitigating disease spread. CMR data depicted an association between mobility and disease severity, and we suggest using this technique to supplement future COVID-19 surveillance.     

Changes in Dietary Habits and Exercise Pattern of Korean Adolescents from Prior to during the COVID-19 Pandemic

This study aimed to investigate changes in the exercise pattern and dietary habits in adolescents during the COVID-19 pandemic. The 12–18-year-old population in the Korea Youth Risk Behavior Web-Based Survey data of 2019 and 2020 was enrolled. The exercise pattern and dietary habits of 105,600 participants (53,461 in the 2019 group and 52,139 in the 2020 group) were compared. The odds ratios (ORs) for the dietary habits and exercise pattern of the 2020 group compared to the 2019 group were analyzed using multiple logistic regression analysis with complex sampling. The odds of eating fruit, drinking soda, drinking sweet drinks, and consuming fast food were lower in the 2020 group than in the 2019 group (all p < 0.001). The odds of eating breakfast were higher in the 2020 group than in the 2019 group (all p < 0.001). The 2020 group showed lower odds of frequent vigorous and moderate aerobic exercise and higher odds of frequent anaerobic exercise than the 2019 group (all p < 0.001). During the COVID-19 pandemic, adolescents consumed less fruit, soda, and sweet drinks, while they had more breakfast. The frequency of aerobic exercise was lower, while the frequency of anaerobic exercise were higher during the COVID-19 pandemic period.     

Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data,Online News Articles,and Search Queries

BackgroundThe SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.ObjectiveThe main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources.MethodsWe first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis.ResultsOur proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t₁₉₈=–8.722, P<.001; model 2, t₁₉₈=–5.000, P<.001, model 3, t₁₉₈=–1.882, P=.06; model 4, t₁₉₈=–4.644, P<.001; model 5, t₁₉₈=–4.488, P<.001). In Hubei province, our proposed model''s forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t₁₉₈=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks.ConclusionsOur approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies'' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.     

The impact of seasonal influenza vaccination uptake on COVID-19 vaccination attitudes in a rural area in Greece

IntroductionPromoting vaccination for coronavirus disease 2019 (COVID-19), especially for high-risk groups such as the elderly and persons with comorbidities, is important for reducing the incidence of severe disease and death.MethodsRetrospective cross-sectional study of factors associated with COVID-19 vaccination, including previous influenza vaccination, among all persons who received medical services in a rural area in Crete, Greece, between October 2020-May 2021.ResultsAmong 3129 participants, receipt of influenza vaccination in 2020–21 was strongly associated with COVID-19 vaccination, as was influenza vaccination in 2019–20, albeit to a lesser extent. In addition, persons older than 59 years (with exception of those 90 + years old) and those who lived closer to the hospital/health center, were more likely to vaccinate for COVID-19. Persons younger than 40 years of age, females, persons with mental illness or neurologic disease, were also less likely to vaccinate for COVID-19 (all p < 0.001).ConclusionsCOVID-19 vaccination was more likely among those who were vaccinated for influenza before and during the pandemic. Access to healthcare services and specific comorbidities, were important influencers for vaccination, underlying the importance of tailored interventions to enforce vaccination in high-risk groups.