Characterizing the Spatiotemporal Heterogeneity of the COVID-19 Vaccination Landscape

Reducing spatial heterogeneity in coverage improves the effectiveness of dog vaccination campaigns against rabies

Vaccination programs are the mainstay of control for many infectious diseases. Heterogeneous coverage is hypothesised to reduce vaccination programme effectiveness, but this impact has not been quantified in real systems. We address this gap using fine-scale data from two decades of rabies contact tracing and dog vaccination campaigns in Serengeti district, Tanzania. We also aimed to identify drivers of continued circulation of rabies in the district despite annual vaccination campaigns. Using generalised linear mixed models, we find that current focal (village-level) dog rabies incidence decreases with increasing recent focal vaccination coverage. However, current focal incidence depends most on recent incidence, both focally and in the wider district, consistent with high population connectivity. Removing the masking effects of prior non-focal incidence shows that, for the same average prior non-focal (wider-district) vaccination coverage, increased heterogeneity in coverage among the non-focal villages leads to increased focal incidence. These effects led to outbreaks following years when vaccination campaigns missed many villages, whereas when heterogeneity in coverage was reduced, incidence declined to low levels (<0.4 cases/1,000 dogs annually and no human deaths) and short vaccination lapses thereafter did not lead to resurgence. Through transmission-tree reconstruction, we inferred frequent incursions into the district each year (mean of 7). Inferred incursions substantially increased as a percentage of all cases in recent years, reaching 50% in 2022, suggesting regional connectivity is driving residual transmission. Overall, we empirically demonstrate how population connectivity and spatial heterogeneity in vaccination can impact disease outcomes, highlighting the importance of fine-scale monitoring in managing vaccination programs.

Characterizing US contact patterns relevant to respiratory transmission from a pandemic to baseline: Analysis of a large cross-sectional survey

Background

Contact plays a critical role in infectious disease transmission. Characterizing heterogeneity in contact patterns across individuals, time, and space is necessary to inform accurate estimates of transmission risk, particularly to explain superspreading, predict age differences in vulnerability, and inform social distancing policies. Current respiratory disease models often rely on data from the 2008 POLYMOD study conducted in Europe, which is now outdated and potentially unrepresentative of behavior in the US. We seek to understand the variation in contact patterns across time, spatial scales, and demographic and social classifications, and what social behavior looks like at baseline in the absence of an ongoing pandemic.

Methods

We analyze spatiotemporal non-household contact patterns across 10.7 million survey responses from June 2020 - April 2021 post-stratified on age and gender to correct for sample representation. To characterize spatiotemporal heterogeneity in respiratory contact patterns at the county-week scale, we use generalized additive models. In the absence of non-pandemic US contact data, we employ a regression approach to estimate baseline contact and address this gap.

Findings

Although contact patterns varied over time during the pandemic, contact is relatively stable after controlling for disease. We find that the mean number of non-household contacts is spatially heterogeneous regardless of disease. There is additional heterogeneity across age, gender, race/ethnicity, and contact setting, with mean contact decreasing with age and lower in women. The contacts of White individuals and contacts at work or social events change the most under increased national incidence.

Interpretation

We develop the first county-level estimates of non-pandemic contact rates for the US that can fill critical gaps in parameterizing future disease models. Our results identify that spatiotemporal, demographic, and social heterogeneity in contact patterns is highly structured, informing the risk landscape of respiratory infectious disease transmission in the US.

Funding

Research reported in this publication was supported by the National Institutes of Health under award number R01GM123007 and R35GM153478 (SB).

The geography of COVID-19 vaccine completion by age in North Carolina, U.S

BACKGROUND

Geographic variation in COVID-19 vaccination can create areas at higher risk of infection, complications, and death, exacerbating health inequalities. This ecological study examined geographic patterns of COVID-19 vaccine completion, using age and sociodemographic characteristics as possible explanatory mechanisms.

METHODS AND FINDINGS

Using 2020-2022 data from the North Carolina COVID-19 Vaccination Management System and U.S. Census Bureau American Community Survey, at the Zip code-level, we evaluated completion of the primary COVID-19 vaccine series across age groups. We examined geographic clustering of age-specific completion by Zip code and evaluated similarity of the age-specific geographic patterns. Using unadjusted and adjusted spatial autoregressive models, we examined associations between sociodemographic characteristics and age-specific vaccine completion. COVID-19 vaccine completion was moderately geographically clustered in younger groups, with lower clustering in older groups. Urban areas had clusters of higher vaccine completion. Younger and middle-aged groups were the most similar in completion geographically, while the oldest group was most dissimilar to other age groups. Higher income was associated with higher completion in adjusted models across all age groups, while a higher percent of Black residents was associated with higher completion for some groups.

CONCLUSIONS

COVID-19 vaccination completion is more variable among younger age groups in North Carolina, and it is higher in urban areas with higher income. Higher completion in areas with more Black residents may reflect the success of racial equity efforts in the state. The findings show a need to reach younger populations and lower income areas that were not prioritized during early vaccination distribution.

Comprehensive Clustering Analysis and Profiling of COVID-19 Vaccine Hesitancy and Related Factors across U.S. Counties: Insights for Future Pandemic Responses

This study employs comprehensive clustering analysis to examine COVID-19 vaccine hesitancy and related socio-demographic factors across U.S. counties, using the collected and curated data from Johns Hopkins University. Utilizing K-Means and hierarchical clustering, we identify five distinct clusters characterized by varying levels of vaccine hesitancy, MMR vaccination coverage, population demographics, and political affiliations. Principal Component Analysis (PCA) was conducted to reduce dimensionality, and key variables were selected based on their contribution to cumulative explained variance. Our analysis reveals significant geographic and demographic patterns in vaccine hesitancy, providing valuable insights for public health strategies and future pandemic responses. Geospatial analysis highlights the distribution of clusters across the United States, indicating areas with high and low vaccine hesitancy. In addition, multiple regression analyses within each cluster identify key predictors of vaccine hesitancy in corresponding U.S. county clusters, emphasizing the importance of socio-economic and demographic factors. The findings underscore the need for targeted public health interventions and tailored communication strategies to address vaccine hesitancy across the United States and, potentially, across the globe.

Socio-demographic determinants of COVID-19 vaccine uptake in Ontario: Exploring differences across the Health Region model

The COVID-19 pandemic continues to be a worldwide public health concern. Although vaccines against this disease were rapidly developed, vaccination uptake has not been equal across all the segments of the population, particularly in the case of underrepresented groups. However, there are also differences in vaccination across geographical areas, which might be important to consider in the development of future public health vaccination policies. In this study, we examined the relationship between vaccination status (having received the first dose of a COVID-19 vaccine), socio-economic strata, and the Health Regions for individuals in Ontario, Canada. Our results show that between October of 2021 and January of 2022, individuals from underrepresented communities were three times less likely to be vaccinated than White/Caucasian individuals across the province of Ontario, and that in some cases, within these groups, individuals in low-income brackets had significantly higher odds of vaccination when compared to their peers in high income brackets. Finally, we identified significantly lower odds of vaccination in the Central, East and West Health Regions of Ontario within certain underrepresented groups. This study shows that there is an ongoing need to better understand and address differences in vaccination uptake across diverse segments of the population of Ontario that the pandemic has largely impacted.

A population level study on the determinants of COVID-19 vaccination rates at the U.S. county level

Multiple COVID-19 vaccines were proven to be safe and effective in curbing severe illness, but despite vaccine availability, vaccination rates were relatively low in the United States (U.S.). To better understand factors associated with low COVID-19 vaccine uptake in the U.S., our study provides a comprehensive, data-driven population-level statistical analysis at the county level. We find that political affiliation, as determined by the proportion of votes received by the Republican candidate in the 2020 presidential election, has the strongest association with our response variable, the percent of the population that received no COVID-19 vaccine. The next strongest association was median household income, which has a negative association. The percentage of Black people and the average number of vehicles per household are positively associated with the percent unvaccinated. In contrast, COVID-19 infection rate, percentage of Latinx people, postsecondary education percentage, median age, and prior non-COVID-19 childhood vaccination coverage are negatively associated with percent unvaccinated. Unlike previous studies, we do not find significant relationships between cable TV news viewership or Twitter misinformation variables with COVID-19 vaccine uptake. These results shed light on some factors that may impact vaccination choice in the U.S. and can be used to target specific populations for educational outreach and vaccine campaign strategies in efforts to increase vaccination uptake.

Spatiotemporal variability in socioeconomic inequalities in COVID-19 vaccination in Catalonia, Spain

OBJECTIVES

Socioeconomic inequalities have played a significant role in the unequal coverage of the COVID-19 vaccine. The objectives of this study were to (1) assess the socioeconomic inequalities in COVID-19 vaccination coverage in Catalonia, Spain; (2) analyse the spatial variation over time of these inequalities; and (3) assess variations in time and space in the effect of vaccination on inequalities in COVID-19 outcomes.

STUDY DESIGN

A mixed longitudinal ecological study design was used.

METHODS

Catalonia is divided in to 373 Basic Health Areas. Weekly data from these Basic Health Areas were obtained from the last week of December 2020 until the first week of March of 2022. A joint spatio-temporal model was used with the dependent variables of vaccination and COVID-19 outcomes, which were estimated using a Bayesian approach. The study controlled for observed confounders, unobserved heterogeneity, and spatial and temporal dependencies. The study allowed the effect of the explanatory variables on the dependent variables to vary in space and in time.

RESULTS

Areas with lower socioeconomic level were those with the lowest vaccination rates and the highest risk of COVID-19 outcomes. In general, individuals in areas that were located in the upper two quartiles of average net income per person and in the lower two quartiles of unemployment rate (i.e., the least economically disadvantaged) had a higher propensity to be vaccinated than those in the most economically disadvantaged areas. In the same sense, the greater the percentage of the population aged ≥65 years, the higher the propensity to be vaccinated, while areas located in the two upper quartiles of population density and areas with a high percentage of poor housing had a lower propensity to be vaccinated. Higher vaccination rates reduced the risk of COVID-19 outcomes, while COVID-19 outcomes did not influence the propensity to be vaccinated. The effects of the explanatory variables were not the same in all areas or between the different waves of the pandemic, and clusters of excess risk of low vaccination in the most disadvantaged areas were detected.

CONCLUSIONS

COVID-19 vaccination inequalities in the most disadvantaged areas could be a result of structural barriers, such as the lack of access to information about the vaccination process, and/or logistical challenges, such as the lack of transportation, limited Internet access or difficulty in scheduling appointments. Public health strategies should be developed to mitigate these barriers and reduce vaccination inequalities.

Modeling impact of vaccination on COVID-19 dynamics in St. Louis

The region of St. Louis, Missouri, has displayed a high level of heterogeneity in COVID-19 cases, hospitalization, and vaccination coverage. We investigate how human mobility, vaccination, and time-varying transmission rates influenced SARS-CoV-2 transmission in five counties in the St. Louis area. A COVID-19 model with a system of ordinary differential equations was developed to illustrate the dynamics with a fully vaccinated class. Using the weekly number of vaccinations, cases, and hospitalization data from five counties in the greater St. Louis area in 2021, parameter estimation for the model was completed. The transmission coefficients for each county changed four times in that year to fit the model and the changing behaviour. We predicted the changes in disease spread under scenarios with increased vaccination coverage. SafeGraph local movement data were used to connect the forces of infection across various counties.

Changes in Population Immunity Against Infection and Severe Disease From Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variants in the United States Between December 2021 and November 2022

BACKGROUND

Although a substantial fraction of the US population was infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during December 2021-February 2022, the subsequent evolution of population immunity reflects the competing influences of waning protection over time and acquisition or restoration of immunity through additional infections and vaccinations.

METHODS

Using a Bayesian evidence synthesis model of reported coronavirus disease 2019 (COVID-19) data (diagnoses, hospitalizations), vaccinations, and waning patterns for vaccine- and infection-acquired immunity, we estimate population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States, by location (national, state, county) and week.

RESULTS

By 9 November 2022, 97% (95%-99%) of the US population were estimated to have prior immunological exposure to SARS-CoV-2. Between 1 December 2021 and 9 November 2022, protection against a new Omicron infection rose from 22% (21%-23%) to 63% (51%-75%) nationally, and protection against an Omicron infection leading to severe disease increased from 61% (59%-64%) to 89% (83%-92%). Increasing first booster uptake to 55% in all states (current US coverage: 34%) and second booster uptake to 22% (current US coverage: 11%) would increase protection against infection by 4.5 percentage points (2.4-7.2) and protection against severe disease by 1.1 percentage points (1.0-1.5).

CONCLUSIONS

Effective protection against SARS-CoV-2 infection and severe disease in November 2022 was substantially higher than in December 2021. Despite this high level of protection, a more transmissible or immune evading (sub)variant, changes in behavior, or ongoing waning of immunity could lead to a new SARS-CoV-2 wave.

Quantifying the impact of SARS-CoV-2 temporal vaccination trends and disparities on disease control

SARS-CoV-2 vaccines have been distributed at unprecedented speed. Still, little is known about temporal vaccination trends, their association with socioeconomic inequality, and their consequences for disease control. Using data from 161 countries/territories and 58 states, we examined vaccination rates across high and low socioeconomic status (SES), showing that disparities in coverage exist at national and subnational levels. We also identified two distinct vaccination trends: a rapid initial rollout, quickly reaching a plateau, or sigmoidal and slow to begin. Informed by these patterns, we implemented an SES-stratified mechanistic model, finding profound differences in mortality and incidence across these two vaccination types. Timing of initial rollout affects disease outcomes more substantially than final coverage or degree of SES disparity. Unexpectedly, timing is not associated with wealth inequality or GDP per capita. While socioeconomic disparity should be addressed, accelerating initial rollout for all over focusing on increasing coverage is an accessible intervention that could minimize the burden of disease across socioeconomic groups.

Neighborhood-level COVID vaccination and booster disparities: A population-level analysis across California

Objectives

To describe vaccine and booster uptake by neighborhood-level factors in California.

Methods

We examined trends in COVID-19 vaccination up to September 21, 2021, and boosters up to March 29, 2022 using data from the California Department of Public Health. Quasi-Poisson regression was used to model the association between neighborhood-level factors and fully vaccinated and boosted among ZIP codes. Sub-analyses on booster rates were compared among the 10 census regions.

Results

In a minimally adjusted model, a higher proportion of Black residents was associated with lower vaccination (HR = 0.97; 95%CI: 0.96-0.98). However, in a fully adjusted model, proportion of Black, Hispanic/Latinx, and Asian residents were associated with higher vaccination rates (HR = 1.02; 95%CI: 1.01-1.03 for all). The strongest predictor of low vaccine coverage was disability (HR = 0.89; 95%CI: 0.86-0.91). Similar trends persisted for booster doses. Factors associated with booster coverage varied by region.

Conclusions

Examining neighborhood-level factors associated with COVID-19 vaccination and booster rates uncovered significant variation within the large and geographically and demographically diverse state of California. Equity-based approaches to vaccination must ensure a robust consideration of multiple social determinants of health.

Estimating county-level flu vaccination in the United States

In the United States, influenza vaccines are an important part of public health efforts to blunt the effects of seasonal influenza epidemics. This in turn emphasizes the importance of understanding the spatial distribution of influenza vaccination coverage. Despite this, high quality data at a fine spatial scale and spanning a multitude of recent flu seasons are not readily available. To address this gap, we develop county-level counts of vaccination across five recent, consecutive flu seasons and fit a series of regression models to these data that account for bias. We find that the spatial distribution of our bias-corrected vaccination coverage estimates is generally consistent from season to season, with the highest coverage in the Northeast and Midwest but is spatially heterogeneous within states. We also observe a negative relationship between a county's vaccination coverage and social vulnerability. Our findings stress the importance of quantifying flu vaccination coverage at a fine spatial scale, as relying on state or region-level estimates misses key heterogeneities.

Spatiotemporal Trends in Self-Reported Mask-Wearing Behavior in the United States: Analysis of a Large Cross-sectional Survey

BACKGROUND

Face mask wearing has been identified as an effective strategy to prevent the transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance, potentially generating heterogeneities in the local trajectories of COVID-19 in the United States. Although numerous studies have investigated the patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask wearing at fine spatial scales across the United States through different phases of the pandemic.

OBJECTIVE

Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the United States. This information is critical to further assess the effectiveness of masking, evaluate the drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges.

METHODS

We analyzed spatiotemporal masking patterns in over 8 million behavioral survey responses from across the United States, starting in September 2020 through May 2021. We adjusted for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debiased self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county level. Lastly, we evaluated whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.

RESULTS

We found that county-level masking behavior was spatially heterogeneous along an urban-rural gradient, with mask wearing peaking in winter 2021 and declining sharply through May 2021. Our results identified regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask wearing may be influenced by national guidance and disease prevalence. We validated our bias correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of a small sample size and representation. Self-reported behavior estimates were especially prone to social desirability and nonresponse biases, and our findings demonstrated that these biases can be reduced if individuals are asked to report on community rather than self behaviors.

CONCLUSIONS

Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.

Population Immunity to Pre-Omicron and Omicron Severe Acute Respiratory Syndrome Coronavirus 2 Variants in US States and Counties Through 1 December 2021

BACKGROUND

Both severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) vaccination contribute to population-level immunity against SARS-CoV-2. This study estimated the immunological exposure and effective protection against future SARS-CoV-2 infection in each US state and county over 2020-2021 and how this changed with the introduction of the Omicron variant.

METHODS

We used a Bayesian model to synthesize estimates of daily SARS-CoV-2 infections, vaccination data and estimates of the relative rates of vaccination conditional on infection status to estimate the fraction of the population with (1) immunological exposure to SARS-CoV-2 (ever infected with SARS-CoV-2 and/or received ≥1 doses of a COVID-19 vaccine), (2) effective protection against infection, and (3) effective protection against severe disease, for each US state and county from 1 January 2020 to 1 December 2021.

RESULTS

The estimated percentage of the US population with a history of SARS-CoV-2 infection or vaccination as of 1 December 2021 was 88.2% (95% credible interval [CrI], 83.6%-93.5%). Accounting for waning and immune escape, effective protection against the Omicron variant on 1 December 2021 was 21.8% (95% CrI, 20.7%-23.4%) nationally and ranged between 14.4% (13.2%-15.8%; West Virginia) and 26.4% (25.3%-27.8%; Colorado). Effective protection against severe disease from Omicron was 61.2% (95% CrI, 59.1%-64.0%) nationally and ranged between 53.0% (47.3%-60.0%; Vermont) and 65.8% (64.9%-66.7%; Colorado).

CONCLUSIONS

While more than four-fifths of the US population had prior immunological exposure to SARS-CoV-2 via vaccination or infection on 1 December 2021, only a fifth of the population was estimated to have effective protection against infection with the immune-evading Omicron variant.

Geographic Heterogeneity in Behavioral and Social Drivers of COVID-19 Vaccination

INTRODUCTION

Little is known about how the drivers of COVID-19 vaccination vary across the U.S. To inform vaccination outreach efforts, this study explores geographic variation in correlates of COVID-19 nonvaccination among adults.

METHODS

Participants were a nationally representative sample of U.S. adults identified through random-digit dialing for the National Immunization Survey-Adult COVID Module. Analyses examined the geographic and temporal landscape of constructs in the Behavioral and Social Drivers of Vaccination Framework among unvaccinated respondents from May 2021 to December 2021 (n=531,798) and sociodemographic and geographic disparities and Behavioral and Social Drivers of Vaccination predictors of COVID-19 nonvaccination from October 2021 to December 2021 (n=187,756).

RESULTS

National coverage with at least 1 dose of COVID-19 vaccine was 79.3% by December 2021, with substantial geographic heterogeneity. Regions with the largest proportion of unvaccinated persons who would probably get a COVID-19 vaccine or were unsure resided in the Southeast and Midwest (Health and Human Services Regions 4 and 5). Both regions had similar temporal trends regarding concerns about COVID-19 and confidence in vaccine importance, although the Southeast had especially low confidence in vaccine safety in December 2021, lowest in Florida (5.5%) and highest in North Carolina (18.0%). The strongest Behavioral and Social Drivers of Vaccination correlate of not receiving a COVID-19 vaccination was lower confidence in COVID-19 vaccine importance (adjusted prevalence ratio=5.19, 95% CI=4.93, 5.47; strongest in the Northeast, Southwest, and Mountain West and weakest in the Southeast and Midwest). Other Behavioral and Social Drivers of Vaccination correlates also varied by region.

CONCLUSIONS

Contributors to nonvaccination showed substantial geographic heterogeneity. Strategies to improve COVID-19 vaccination uptake may need to be tailored regionally.

Changes in population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States between December 2021 and November 2022

Importance

While a substantial fraction of the US population was infected with SARS-CoV-2 during December 2021 - February 2022, the subsequent evolution of population immunity against SARS-CoV-2 Omicron variants reflects the competing influences of waning protection over time and acquisition or restoration of immunity through additional infections and vaccinations.

Objective

To estimate changes in population immunity against infection and severe disease due to circulating SARS-CoV-2 Omicron variants in the United States from December 2021 to November 2022, and to quantify the protection against a potential 2022-2023 winter SARS-CoV-2 wave.

Design setting participants

Bayesian evidence synthesis of reported COVID-19 data (diagnoses, hospitalizations), vaccinations, and waning patterns for vaccine- and infection-acquired immunity, using a mathematical model of COVID-19 natural history.

Main Outcomes and Measures

Population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States, by location (national, state, county) and week.

Results

By November 9, 2022, 94% (95% CrI, 79%-99%) of the US population were estimated to have been infected by SARS-CoV-2 at least once. Combined with vaccination, 97% (95%-99%) were estimated to have some prior immunological exposure to SARS-CoV-2. Between December 1, 2021 and November 9, 2022, protection against a new Omicron infection rose from 22% (21%-23%) to 63% (51%-75%) nationally, and protection against an Omicron infection leading to severe disease increased from 61% (59%-64%) to 89% (83%-92%). Increasing first booster uptake to 55% in all states (current US coverage: 34%) and second booster uptake to 22% (current US coverage: 11%) would increase protection against infection by 4.5 percentage points (2.4-7.2) and protection against severe disease by 1.1 percentage points (1.0-1.5).

Conclusions and Relevance

Effective protection against SARS-CoV-2 infection and severe disease in November 2022 was substantially higher than in December 2021. Despite this high level of protection, a more transmissible or immune evading (sub)variant, changes in behavior, or ongoing waning of immunity could lead to a new SARS-CoV-2 wave.

Key points

Question: How did population immunity against SARS-CoV-2 infection and subsequent severe disease change between December 2021, and November 2022?Findings: On November 9, 2022, the protection against a SARS-CoV-2 infection with the Omicron variant was estimated to be 63% (51%-75%) in the US, and the protection against severe disease was 89% (83%-92%).Meaning: As most of the newly acquired immunity has been accumulated in the December 2021-February 2022 Omicron wave, risk of reinfection and subsequent severe disease remains present at the beginning of the 2022-2023 winter, despite high levels of protection.

Spatial clustering in vaccination hesitancy: The role of social influence and social selection

The phenomenon of vaccine hesitancy behavior has gained ground over the last three decades, jeopardizing the maintenance of herd immunity. This behavior tends to cluster spatially, creating pockets of unprotected sub-populations that can be hotspots for outbreak emergence. What remains less understood are the social mechanisms that can give rise to spatial clustering in vaccination behavior, particularly at the landscape scale. We focus on the presence of spatial clustering, and aim to mechanistically understand how different social processes can give rise to this phenomenon. In particular, we propose two hypotheses to explain the presence of spatial clustering: (i) social selection, in which vaccine-hesitant individuals share socio-demographic traits, and clustering of these traits generates spatial clustering in vaccine hesitancy; and (ii) social influence, in which hesitant behavior is contagious and spreads through neighboring societies, leading to hesitant clusters. Adopting a theoretical spatial network approach, we explore the role of these two processes in generating patterns of spatial clustering in vaccination behaviors under a range of spatial structures. We find that both processes are independently capable of generating spatial clustering, and the more spatially structured the social dynamics in a society are, the higher spatial clustering in vaccine-hesitant behavior it realizes. Together, we demonstrate that these processes result in unique spatial configurations of hesitant clusters, and we validate our models of both processes with fine-grain empirical data on vaccine hesitancy, social determinants, and social connectivity in the US. Finally, we propose, and evaluate the effectiveness of two novel intervention strategies to diminish hesitant behavior. Our generative modeling approach informed by unique empirical data provides insights on the role of complex social processes in driving spatial heterogeneity in vaccine hesitancy.

COVID-19 Vaccination Status, Attitudes, and Values among US Adults in September 2021

Background: The Delta COVID-19 variant caused a resurgence in cases and deaths during the summer of 2021, particularly among the unvaccinated, highlighting the need to increase vaccine coverage. We describe a survey conducted in September 2021, in the midst of the Delta variant surge, after the FDA fully approved Pfizer-BioNTech’s vaccine for ages 16+ and issued an emergency use authorization for ages 12−15. Methods and Findings: US adults were surveyed to measure COVID-19 vaccination status, intentions, attitudes, values, and trust in public health authorities. More than three-quarters (77%) reported receiving at least one dose of COVID-19 vaccination. Of the unvaccinated, 6% intended to vaccinate, 40% were unlikely to ever vaccinate, and 55% remained uncertain. Most of the unvaccinated were <45 years old (62%), without a bachelor’s degree (83%), earning less than $85,000 annually (74%), and Republican/Independent (66%). Concerns among the unvaccinated-yet-still-uncertain included the vaccines’ safety (86%), speed of development (86%), and suspicion of government (79%) and pharmaceutical companies (69%). Most (86%) of the unvaccinated reported they would not vaccinate if mandated by their employer. About one third (34%) of the unvaccinated reported facing at least one barrier to vaccination. Conclusion: More than half of unvaccinated adults remained uncertain about COVID-19 vaccination, indicating an opportunity to support their decision making. Public health must increase easy and equitable access to vaccination and renew efforts to provide unvaccinated populations access to information from trusted sources.