ABSTRACT
BackgroundCOVID-19 vaccination rates among children have stalled, while new coronavirus strains continue to emerge. To improve child vaccination rates, policymakers must better understand parental preferences and reasons for COVID-19 vaccination among their children. Methods and FindingsCross-sectional surveys were administered online to 30,174 US parents with at least one child of COVID-19 vaccine eligible age (5-17 years) between January 1 and May 9, 2022. Participants self-reported willingness to vaccinate their child and reasons for hesitancy, and answered additional questions about demographics, pandemic related behavior, and vaccination status. Willingness to vaccinate a child for COVID-19 was strongly associated with parental vaccination status (multivariate odds ratio 97.9, 95% confidence interval 86.9-111.0). The majority of fully vaccinated (86%) and unvaccinated (84%) parents reported concordant vaccination preferences for their eligible child. Age and education had differing relationships by vaccination status, with higher age and education positively associated with willingness among vaccinated parents. Among all parents hesitant to vaccinate their children, the two most frequently reported reasons were possible side effects (47%) and that vaccines are too new (44%). Among hesitant parents, parental vaccination status was inversely associated with reported lack of trust in government (p<.001) and scientists (p<.001). Cluster analysis identified three groups of hesitant parents based on their reasons for hesitance to vaccinate, with distinct concerns that may be obscured when analyzed in aggregate. ConclusionFactors associated with willingness to vaccinate children and reasons for hesitancy may inform targeted approaches to increase vaccination.
ABSTRACT
Asymptomatic individuals carrying SARS-CoV-2 can transmit the virus and contribute to outbreaks of COVID-19, but it is not yet clear how the proportion of asymptomatic infections varies by age and geographic location. Here we use detailed surveillance data gathered during COVID-19 resurgences in six cities of China at the beginning of 2021 to investigate this question. Data were collected by multiple rounds of city-wide PCR test with detailed contact tracing, where each patient was monitored for symptoms through the whole course of infection. We find that the proportion of asymptomatic infections declines with age (coefficient =-0.006, P<0.01), falling from 56% in age group 0-9 years to 12% in age group >60 years. Using an age-stratified compartment model, we show that this age-dependent asymptomatic pattern together with the age distribution of overall cases can explain most of the geographic differences in reported asymptomatic proportions. Combined with demography and contact matrices from other countries worldwide, we estimate that a maximum of 22%-55% of SARS-CoV-2 infections would come from asymptomatic cases in an uncontrolled epidemic based on asymptomatic proportions in China. Our analysis suggests that flare-ups of COVID-19 are likely if only adults are vaccinated and that surveillance and possibly control measures among children will be still needed in the future to contain epidemic resurgence.
ABSTRACT
Simultaneously tracking the global COVID-19 impact across multiple populations is challenging due to regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide reliable and standardized data streams to support monitoring and decision-making world-wide, in real time, and with limited local resources. The University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, invites daily cross-sectional samples from the social media platforms active users to participate in the survey since launch April 23, 2020. COVID-19 indicators through December 20, 2020, from N=31,142,582 responses representing N=114 countries, weighted for nonresponse and adjusted to basic demographics, were benchmarked with government data. COVID-19-related signals showed similar concordance with reported benchmark case and test positivity. Bonferroni significance and minimal Spearman correlation strength thresholds were met in the majority. Light Gradient Boost machine learning trained on national and pooled global data verified known symptom indicators, and predicted COVID-19 trends similar to other signals. Risk mitigation behavior trends are correlated with, but sometimes lag, risk perception trends. In regions with strained health infrastructure, but active social media users, we show it is possible to define suitable COVID-19 impact trajectories. This syndromic surveillance public health tool is the largest global health survey to date, and, with brief participant engagement, can provide meaningful, timely insights into the COVID-19 pandemic and response in regions under-represented in epidemiological analyses. Significance StatementThe University of Maryland Global COVID Trends and Impact Survey (UMD-CTIS), launched April 23, 2020, is the largest remote global health monitoring system. This study includes about 30 million UMD-CTIS responses over 34 weeks (through December 2020) from N=114 countries with survey-weights to adjust for nonresponse and demographics. Using limited self-reported data, sampled daily from an international cohort of Facebook users, we demonstrate validity and utility for COVID-19 impacts trends, even in regions with scant or delayed government data. We predict COVID-19 cases in the absence of testing, and characterize perceived COVID-19 risk versus risk-lowering measures. The UMD-CTIS has the potential to support existing monitoring systems for the COVID-19 pandemic, as well as other new, as-yet-undefined global health threats.
ABSTRACT
Mask-wearing has been a controversial measure to control the COVID-19 pandemic. While masks are known to substantially reduce disease transmission in healthcare settings [1-3], studies in community settings report inconsistent results [4-6]. Investigating the inconsistency within epidemiological studies, we find that a commonly used proxy, government mask mandates, does not correlate with large increases in mask-wearing in our window of analysis. We thus analyse the effect of mask-wearing on transmission instead, drawing on several datasets covering 92 regions on 6 continents, including the largest survey of individual-level wearing behaviour (n=20 million) [7]. Using a hierarchical Bayesian model, we estimate the effect of both mask-wearing and mask-mandates on transmission by linking wearing levels (or mandates) to reported cases in each region, adjusting for mobility and non-pharmaceutical interventions. We assess the robustness of our results in 123 experiments spanning 22 sensitivity analyses. Across these analyses, we find that an entire population wearing masks in public leads to a median reduction in the reproduction number R of 25.8%, with 95% of the medians between 22.2% and 30.9%. In our window of analysis, the median reduction in R associated with the wearing level observed in each region was 20.4% [2.0%, 23.3%]1. We do not find evidence that mandating mask-wearing reduces transmission. Our results suggest that mask-wearing is strongly affected by factors other than mandates. We establish the effectiveness of mass mask-wearing, and highlight that wearing data, not mandate data, are necessary to infer this effect.
ABSTRACT
SARS-CoV-2 vaccine distribution is at risk of further propagating the inequities of COVID-19, which in the United States (US) has disproportionately impacted the elderly, people of color, and the medically vulnerable. We identify vaccine deserts - US Census tracts with localized, geographic barriers to vaccine-associated herd immunity - using a comprehensive supply database (VaccineFinder) and an empirically parameterized model of spatial access to essential resources. Incorporating high-resolution COVID-19 burden and time-willing-to-travel for vaccination, we show that early (February - March 2021) vaccine allocation disadvantaged rural and medically vulnerable populations. Data-driven vaccine distribution to vaccine deserts may improve immunization in the hesitant and control SARS-CoV-2.
ABSTRACT
BackgroundSymptomatic testing programmes are crucial to the COVID-19 pandemic response. We sought to examine United Kingdom (UK) testing rates amongst individuals with test-qualifying symptoms, and factors associated with not testing. MethodsWe analysed a cohort of untested symptomatic app users (N=1,237), nested in the Zoe COVID Symptom Study (Zoe, N= 4,394,948); and symptomatic survey respondents who wanted, but did not have a test (N=1,956), drawn from the University of Maryland-Facebook Covid-19 Symptom Survey (UMD-Facebook, N=775,746). FindingsThe proportion tested among individuals with incident test-qualifying symptoms rose from [~]20% to [~]75% from April to December 2020 in Zoe. Testing was lower with one vs more symptoms (73.0% vs 85.0%), or short vs long symptom duration (72.6% vs 87.8%). 40.4% of survey respondents did not identify all three test-qualifying symptoms. Symptom identification decreased for every decade older (OR=0.908 [95% CI 0.883-0.933]). Amongst symptomatic UMD-Facebook respondents who wanted but did not have a test, not knowing where to go was the most cited factor (32.4%); this increased for each decade older (OR=1.207 [1.129-1.292]) and for every 4-years fewer in education (OR=0.685 [0.599-0.783]). InterpretationDespite current UK messaging on COVID-19 testing, there is a knowledge gap about when and where to test, and this may be contributing to the [~]25% testing gap. Risk factors, including older age and less education, highlight potential opportunities to tailor public health messages. FundingZoe Global Limited, Department of Health, Wellcome Trust, EPSRC, NIHR, MRC, Alzheimers Society, Facebook Sponsored Research Agreement. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSTo assess current evidence on test uptake in symptomatic testing programmes, and the reasons for not testing, we searched PubMed from database inception for research using the keywords (COVID-19) AND (testing) AND ((access) OR (uptake)). We did not find any work reporting on levels of test uptake amongst symptomatic individuals. We found three papers investigating geographic barriers to testing. We found one US based survey reporting on knowledge barriers to testing, and one UK based survey reporting on barriers in the period March - August 2020. Neither of these studies were able to combine testing behaviour with prospectively collected symptom reports from the users surveyed. Added value of this studyThrough prospective collection of symptom and test reports, we were able to estimate testing uptake amongst individuals with test-qualifying symptoms in the UK. Our results indicate that whilst testing has improved since the start of the pandemic, there remains a considerable testing gap. Investigating this gap we find that individuals with just one test-qualifying symptom or short symptom duration are less likely to get tested. We also find knowledge barriers to testing: a substantial proportion of individuals do not know which symptoms qualify them for a COVID-19 test, and do not know where to seek testing. We find a larger knowledge gap in individuals with older age and fewer years of education. Implications of all the available evidenceDespite the UK having a simple set of symptom-based testing criteria, with tests made freely available through nationalised healthcare, a quarter of individuals with qualifying symptoms do not get tested. Our findings suggest testing uptake may be limited by individuals not acting on mild or transient symptoms, not recognising the testing criteria, and not knowing where to get tested. Improved messaging may help address this testing gap, with opportunities to target individuals of older age or fewer years of education. Messaging may prove even more valuable in countries with more fragmented testing infrastructure or more nuanced testing criteria, where knowledge barriers are likely to be greater.
ABSTRACT
BackgroundLimitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing COVID-19 pandemic globally. Syndromic surveillance of COVID-19 is an important public health tool that can help detect outbreaks, mobilize a rapid response, and thereby reduce morbidity and mortality. The primary objective of this study was to determine whether syndromic surveillance through self-reported COVID-19 symptoms could be a timely proxy for laboratory-confirmed case trends in the Canadian province of Ontario. MethodsWe retrospectively analyzed self-reported symptoms data collected using an online tool - Outbreaks Near Me (ONM) - from April 20th to Oct 11th, 2020 in Ontario, Canada. We estimated the correlation coefficient between the weekly proportion of respondents reporting a COVID-like illness (CLI) to both the weekly number of PCR-confirmed COVID-19 cases and the percent positivity in the same period for the same week and with a one-week lag. ResultsThere were 314,686 responses from 188,783 unique respondents to the ONM symptom survey. Respondents were more likely to be female and be in the 40-59 age demographic compared to the Ontario general population. There was a strong positive correlation between the weekly number of reported cases in Ontario and the percent of respondents reporting CLI each week (r = 0.89, p <0.01) and with a one-week lag (r = 0.89, p <0.01). InterpretationWe demonstrate a strong positive and significant correlation (r = 0.89, p <0.01) between percent of self-reported COVID-like illness and the subsequent weeks COVID-19 cases reported, highlighting that a rise in CLI may precede official statistics by at least 1 week. This demonstrates the utility of syndromic surveillance in predicting near-future disease activity. Digital surveillance systems are low-cost tools that may help measure the burden of COVID-19 in a community if there is under-detection of cases through conventional laboratory diagnostic testing. This additional information can be used to guide a healthcare response and policy decisions.
ABSTRACT
BackgroundSyndromic surveillance systems for COVID-19 are being increasingly used to track and predict outbreaks of confirmed cases. Seasonal circulating respiratory viruses share syndromic overlap with COVID-19, and it is unknown how they will impact the performance of syndromic surveillance tools. Here we investigated the role of non-SARS-CoV-2 respiratory virus test positivity on COVID-19 two independent syndromic surveillance systems in Ontario, Canada. MethodsWe compared the weekly number of reported COVID-19 cases reported in the province of Ontario against two syndromic surveillance metrics: 1) the proportion of respondents with a self-reported COVID-like illness (CLI) from COVID Near You (CNY) and 2) the proportion of emergency department visits for upper respiratory conditions from the Acute Care Enhanced Surveillance (ACES) system. Separately, we plotted the percent positivity for other seasonal respiratory viruses over the same time period and reported Pearsons correlation coefficients before and after the uncoupling of syndromic tools to COVID-19 cases. ResultsThere were strong positive correlations of both CLI and ED visits for upper respiratory causes with COVID-19 cases up to and including a rise in entero/rhinovirus (r = 0.86 and 0.87, respectively). There was a strong negative correlation of both CLI and ED visits for upper respiratory causes with COVID-19 cases (r = -0.85 and -0.91, respectively) during a fall in entero/rhinovirus. InterpretationTwo methods of syndromic surveillance showed strong positive correlations with COVID-19 confirmed case counts before and during a rise in circulating entero/rhinovirus. However, as positivity for enterovirus/rhinovirus fell in late September 2020, syndromic signals became uncoupled from COVID-19 cases and instead tracked the fall in entero/rhinovirus. This finding provides proof-of-principle that regional transmission of seasonal respiratory viruses may complicate the interpretation of COVID-19 surveillance data. It is imperative that surveillance systems incorporate other respiratory virus testing data in order to more accurately track and forecast COVID-19 disease activity.
ABSTRACT
IntroductionCloth face coverings and surgical masks have become commonplace across the United States in response to the SARS-CoV-2 epidemic. While evidence suggests masks help curb the spread of respiratory pathogens, research is limited. Face masks have quickly become a topic of public debate as government mandates have started requiring their use. Here we investigate the association between self-reported mask wearing, social distancing and community SARS-CoV-2 transmission in the United States, as well as the effect of statewide mandates on mask uptake. MethodsSerial cross-sectional surveys were administered June 3 through July 27, 2020 via web platform. Surveys queried individuals likelihood to wear a face mask to the grocery store or with family and friends. Responses (N=378,207) were aggregated by week and state and combined with measures of the instantaneous reproductive number (Rt), social distancing proxies, respondent demographics and other potential sources of confounding. We fit multivariate logistic regression models to estimate the association between mask wearing and community transmission control (Rt <1) for each state and week. Multiple sensitivity analyses were considered to corroborate findings across mask wearing definitions, Rt estimators and data sources. Additionally, mask wearing in 12 states was evaluated two weeks before and after statewide mandates. ResultsWe find an upward trend in mask usage across the U.S., although uptake varies by geography and demographic groups. A multivariate logistic model controlling for social distancing and other variables found a 10% increase in mask wearing was associated with a 3.53 (95% CI: 2.03, 6.43) odds of transmission control (Rt <1). We also find that communities with high mask wearing and social distancing have the highest predicted probability of a controlled epidemic. These positive associations were maintained across sensitivity analyses. Segmented regression analysis of mask wearing found no statistical change following mandates, however the positive trend of increased mask wearing over time was preserved. ConclusionWidespread utilization of face masks combined with social distancing increases the odds of SARS-CoV-2 transmission control. Mask wearing rose separately from government mask mandates, suggesting supplemental public health interventions are needed to maximize mask adoption and disrupt the spread of SARS-CoV-2, especially as social distancing measures are relaxed.
ABSTRACT
BackgroundThe United States CDC has reported that racial and ethnic disparities in the COVID-19 pandemic may in part be due to socioeconomic disadvantages that require individuals to continue to work outside their home and a lack of paid sick leave.1 However, data-driven analyses of the socioeconomic determinants of COVID-19 burden are still needed. Using data from New York City (NYC), we aimed to determine how socioeconomic factors impact human mobility and COVID-19 burden. Methods/SummaryNew York City has a large amount of heterogeneity in socioeconomic status (SES) and demographics among neighborhoods. We used this heterogeneity to conduct a cross-sectional spatial analysis of the associations between human mobility (i.e., subway ridership), sociodemographic factors, and COVID-19 incidence as of April 26, 2020. We also conducted a secondary analysis of NYC boroughs (which are equivalent to counties in the city) to assess the relationship between the decline in subway use and the time it took for each borough to end the exponential growth period of COVID-19 cases. FindingsAreas with lower median income, a greater percentage of individuals who identify as non-white and/or Hispanic/Latino, a greater percentage of essential workers, and a greater percentage of healthcare workers had more subway use during the pandemic. When adjusted for the percent of essential workers, these association do not remain; this suggests essential work is what drives subway use in lower SES zip codes and communities of color. Increased subway use was associated with a higher rate of COVID-19 cases per 100,000 population when adjusted for testing effort (aRR = 1.11; 95% CI: 1.03 - 1.19), but this association was weaker once we adjusted for median income (aRR = 1.06; 95% CI: 1.00 - 1.12). All sociodemographic variables were significantly associated with the rate of positive cases per 100,000 population when adjusting for testing effort (except percent uninsured) and adjusting for both income and testing effort. The risk factor with the strongest association with COVID-19 was the percent of individuals in essential work (aRR = 1.59, 95% CI: 1.36 - 1.86). We found that subway use declined prior to any executive order, and there was an estimated 28-day lag between the onset of reduced subway use and the end of the exponential growth period of SARS-CoV-2 within New York City boroughs. InterpretationOur results suggest that the ability to stay home during the pandemic has been constrained by SES and work circumstances. Poorer neighborhoods are not afforded the same reductions in mobility as their richer counterparts. Furthermore, lower SES neighborhoods have higher disease burdens, which may be due to inequities in ability to shelter-in-place, and/or due to the plethora of other existing health disparities that increase vulnerability to COVID-19. Furthermore, the extended lag time between the dramatic fall in subway ridership and the end of the exponential growth phase for COVID-19 cases is important for future policy, because it demonstrates that if there is a resurgence, and stay-at-home orders are re-issued, then cities can expect to wait a month before reported cases will plateau.
ABSTRACT
BackgroundSyndromic surveillance through web or phone-based polling has been used to track the course of infectious diseases worldwide. Our study objective was to describe the characteristics, symptoms, and self-reported testing rates of respondents in three different COVID-19 symptom surveys in Canada. MethodsData sources consisted of two distinct Canada-wide web-based surveys, and phone polling in Ontario. All three sources contained self-reported information on COVID-19 symptoms and testing. In addition to describing respondent characteristics, we examined symptom frequency and the testing rate among the symptomatic, as well as rates of symptoms and testing across respondent groups. ResultsWe found that 1.6% of respondents experienced a symptom on the day of their survey, 15% of Ontario households had a symptom in the previous week, and 44% of Canada-wide respondents had a symptom in the previous month over March-April 2020. Across the three surveys, SARS-CoV-2-testing was reported in 2-9% of symptomatic responses. Women, younger and middle-aged adults (versus older adults) and Indigenous/First nations/Inuit/Metis were more likely to report at least one symptom, and visible minorities were more likely to report the combination of fever with cough or shortness of breath. InterpretationThe low rate of testing among those reporting symptoms suggests significant opportunity to expand testing among community-dwelling residents of Canada. Syndromic surveillance data can supplement public health reports and provide much-needed context to gauge the adequacy of current SARS-CoV-2 testing rates.
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ImportanceAccess to testing is key to a successful response to the COVID-19 pandemic. ObjectiveTo determine the geographic accessibility to SARS-CoV-2 testing sites in the United States, as quantified by travel time. DesignCross-sectional analysis of SARS-CoV-2 testing sites as of April 7, 2020 in relation to travel time. SettingUnited States COVID-19 pandemic. ParticipantsThe United States, including the 48 contiguous states and the District of Columbia. ExposuresPopulation density, percent minority, percent uninsured, and median income by county from the 2018 American Community Survey demographic data. Main OutcomeSARS-CoV-2 testing sites identified in two national databases (Carbon Health and CodersAgainstCovid), geocoded by address. Median county 1 km2 gridded friction surface of travel times, as a measure of geographic accessibility to SARS-CoV-2 testing sites. Results6,236 unique SARS-CoV-2 testing sites in 3,108 United States counties were identified. Thirty percent of the U.S. population live in a county (N = 1,920) with a median travel time over 20 minutes. This was geographically heterogeneous; 86% of the Mountain division population versus 5% of the Middle Atlantic population lived in counties with median travel times over 20 min. Generalized Linear Models showed population density, percent minority, percent uninsured and median income were predictors of median travel time to testing sites. For example, higher percent uninsured was associated with longer travel time ({beta} = 0.41 min/percent, 95% confidence interval 0.3-0.53, p = 1.2x10-12), adjusting for population density. Conclusions and RelevanceGeographic accessibility to SARS-Cov-2 testing sites is reduced in counties with lower population density and higher percent of minority and uninsured, which are also risk factors for worse healthcare access and outcomes. Geographic barriers to SARS-Cov-2 testing may exacerbate health inequalities and bias county-specific transmission estimates. Geographic accessibility should be considered when planning the location of future testing sites and interpreting epidemiological data. Key PointsO_LISARS-CoV-2 testing sites are distributed unevenly in the US geography and population. C_LIO_LIMedian county-level travel time to SARS-CoV-2 testing sites is longer in less densely populated areas, and in areas with a higher percentage of minority or uninsured populations. C_LIO_LIImproved geographic accessibility to testing sites is imperative to manage the COVID-19 pandemic in the United States. C_LI
ABSTRACT
The COVID-19 pandemic is straining public health systems worldwide and major non-pharmaceutical interventions have been implemented to slow its spread1-4. During the initial phase of the outbreak the spread was primarily determined by human mobility5,6. Yet empirical evidence on the effect of key geographic factors on local epidemic spread is lacking7. We analyse highly-resolved spatial variables for cities in China together with case count data in order to investigate the role of climate, urbanization, and variation in interventions across China. Here we show that the epidemic intensity of COVID-19 is strongly shaped by crowding, such that epidemics in dense cities are more spread out through time, and denser cities have larger total incidence. Observed differences in epidemic intensity are well captured by a metapopulation model of COVID-19 that explicitly accounts for spatial hierarchies. Densely-populated cities worldwide may experience more prolonged epidemics. Whilst stringent interventions can shorten the time length of these local epidemics, although these may be difficult to implement in many affected settings.
