Fine-scale spatial clustering of measles nonvaccination that increases outbreak potential is obscured by aggregated reporting data

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.

Non-detection of emerging and re-emerging pathogens in wastewater surveillance to confirm absence of transmission risk: A case study of polio in New York

Infectious disease surveillance systems, including wastewater surveillance, can alert communities to the threat of emerging pathogens. We need methods to infer understanding of transmission dynamics from non-detection. We estimate a sensitivity of detection of poliovirus in wastewater to inform the sensitivity of wastewater surveillance for poliovirus using both a clinical epidemiology and fecal shedding approach. We then apply freedom from disease to estimate the sensitivity of the wastewater surveillance network. Estimated sensitivity to detect a single poliovirus infection was low, <11% at most wastewater treatment plants and <3% in most counties. However, the maximum threshold for the number of infections when polio is not detected in wastewater was much lower. Prospective wastewater surveillance can confirm the absence of a polio threat and be escalated in the case of poliovirus detection. These methods can be applied to any emerging or re-emerging pathogen, and improve understanding from wastewater surveillance.

Spatial scales of COVID-19 transmission in Mexico

During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing nonpharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here, we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases at the municipality level in Mexico to investigate how behavioral changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March-June 2020). We find that the epidemic dynamics in Mexico were initially driven by exports of COVID-19 cases from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronized. Our results provide dynamic insights into how to use network science and epidemiological modeling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.

Analysis of the yearly transition function in measles disease modeling

Globally, there were an estimated 9.8 million measles cases and 207 500 measles deaths in 2019. As the effort to eliminate measles around the world continues, modeling remains a valuable tool for public health decision-makers and program implementers. This study presents a novel approach to the use of a yearly transition function that formulates mathematically the vaccine schedules for different age groups while accounting for the effects of the age of vaccination, the timing of vaccination, and disease seasonality on the yearly number of measles cases in a country. The methodology presented adds to an existing modeling framework and expands its analysis, making its utilization more adjustable for the user and contributing to its conceptual clarity. This article also adjusts for the temporal interaction between vaccination and exposure to disease, applying adjustments to estimated yearly counts of cases and the number of vaccines administered that increase population immunity. These new model features provide the ability to forecast and compare the effects of different vaccination timing scenarios and seasonality of transmission on the expected disease incidence. Although the work presented is applied to the example of measles, it has potential relevance to modeling other vaccine-preventable diseases.

Detecting high-risk neighborhoods and socioeconomic determinants for common oral diseases in Germany

BACKGROUND

Ideally, health services and interventions to improve dental health should be tailored to local target populations. But this is not the standard. Little is known about risk clusters in dental health care and their evaluation based on small-scale, spatial data, particularly among under-represented groups in health surveys. Our study aims to investigate the incidence rates of major oral diseases among privately insured and self-paying individuals in Germany, explore the spatial clustering of these diseases, and evaluate the influence of social determinants on oral disease risk clusters using advanced data analysis techniques, i.e. machine learning.

METHODS

A retrospective cohort study was performed to calculate the age- and sex-standardized incidence rate of oral diseases in a study population of privately insured and self-pay patients in Germany who received dental treatment between 2016 and 2021. This was based on anonymized claims data from BFS health finance, Bertelsmann, Dortmund, Germany. The disease history of individuals was recorded and aggregated at the ZIP code 5 level (n = 8871).

RESULTS

Statistically significant, spatially compact clusters and relative risks (RR) of incidence rates were identified. By linking disease and socioeconomic databases on the ZIP-5 level, local risk models for each disease were estimated based on spatial-neighborhood variables using different machine learning models. We found that dental diseases were spatially clustered among privately insured and self-payer patients in Germany. Incidence rates within clusters were significantly elevated compared to incidence rates outside clusters. The relative risks (RR) for a new dental disease in primary risk clusters were min = 1.3 (irreversible pulpitis; 95%-CI = 1.3-1.3) and max = 2.7 (periodontitis; 95%-CI = 2.6-2.8), depending on the disease. Despite some similarity in the importance of variables from machine learning models across different clusters, each cluster is unique and must be treated as such when addressing oral public health threats.

CONCLUSIONS

Our study analyzed the incidence of major oral diseases in Germany and employed spatial methods to identify and characterize high-risk clusters for targeted interventions. We found that private claims data, combined with a network-based, data-driven approach, can effectively pinpoint areas and factors relevant to oral healthcare, including socioeconomic determinants like income and occupational status. The methodology presented here enables the identification of disease clusters of greatest demand, which would allow implementing more targeted approaches and improve access to quality care where they can have the most impact.

Acceptance of a Future Gonorrhea Vaccine in a Post-Coronavirus Disease 2019 World: Impact of Type of Recommendation and Changing Levels of Trust in Health Institutions and Authorities

Widespread uptake of a future gonorrhea vaccine could decrease the burden of disease and limit the spread of antibiotic resistance. However, gonorrhea vaccination will occur in the backdrop of the roll-out of the coronavirus disease 2019 (COVID-19) vaccine, which could have influenced parental perceptions about other, non-COVID-19 vaccines. In an internet-based cross-sectional survey, 74% of parents would get a gonorrhea vaccine for their child, and this was higher among those whose trust in pharmaceutical companies increased since the start of the COVID-19 pandemic. About 60% of adults aged 18 to 45 would receive a vaccine for themselves.

The impact of the COVID-19 pandemic on parental vaccine hesitancy: A cross-sectional survey

BACKGROUND

It is unclear how hesitancy towards pediatric vaccines has changed quantitatively since the onset of the COVID-19 pandemic, and if changes are more readily apparent in clusters of low COVID-19 vaccination. In this study, we assess how clusters of low COVID-19 vaccination correlate with changing parental beliefs about childhood vaccines.

METHODS

A cross-sectional, opt-in, internet-based survey of parents resident in the U.S. was conducted during August-September 2022. Our survey measured changes in beliefs about childhood vaccine safety, importance, and effectiveness since the start of COVID-19. We also measured parents' perceived vaccination rates in the community, assessing its relationship with changing vaccination perceptions using Rao-Scott chi-square tests, and multinomial logistic regression models.

RESULTS

Among 310 parents of children 0-17 years old, 11 % (95 % CI: 7 %, 15 %) believed that childhood vaccines are less safe, 12 % (95 % CI: 8 %, 17 %) less important, and 13 % (95 % CI: 9 %, 18 %) less effective since the start of the COVID-19 pandemic. About 9 % (95 % CI: 5 %, 12 %) stated COVID-19 vaccination coverage was low in their community. Among those who stated COVID-19 vaccination coverage was low, 38 % reported believing childhood vaccines were less effective (vs 12 % of those who stated vaccination coverage was high). This corresponds to 4.34 times greater odds of believing childhood vaccines were less effective since the start of the pandemic (95 % CI: 1.38, 13.73) in those who believe COVID-19 vaccination coverage to be low in their community vs high.

CONCLUSION

Our study demonstrates that parental perceptions about childhood vaccines have been affected by the COVID-19 pandemic through geographic and social clustering of non-vaccination. Beliefs about the COVID-19 vaccine have spillover with beliefs about childhood vaccines, and more negative beliefs may be clustering in areas with low vaccination coverage, which could predispose the area to outbreaks of vaccine-preventable disease.

Mapping the timeliness of routine childhood vaccination in The Gambia: A spatial modelling study

INTRODUCTION

Timeliness of routine vaccination shapes childhood infection risk and thus is an important public health metric. Estimates of indicators of the timeliness of vaccination are usually produced at the national or regional level, which may conceal epidemiologically relevant local heterogeneities and makeitdifficultto identify pockets of vulnerabilities that could benefit from targeted interventions. Here, we demonstrate the utility of geospatial modelling techniques in generating high-resolution maps of the prevalence of delayed childhood vaccination in The Gambia. To guide local immunisation policy and prioritize key interventions, we also identified the districts with a combination of high estimated prevalence and a significant population of affected infants.

METHODS

We used the birth dose of the hepatitis-B vaccine (HepB0), third-dose of the pentavalent vaccine (PENTA3), and the first dose of measles-containing vaccine (MCV1) as examples to map delayed vaccination nationally at a resolution of 1 × 1-km2 pixel. We utilized cluster-level childhood vaccination data from The Gambia 2019-20 Demographic and Health Survey. We adopted a fully Bayesian geostatistical model incorporating publicly available geospatial covariates to aid predictive accuracy. The model was implemented using the integrated nested Laplace approximation-stochastic partial differential equation (INLA-SPDE) approach.

RESULTS

We found significant subnational heterogeneity in delayed HepB0, PENTA3 and MCV1 vaccinations. Specificdistricts in the central and eastern regions of The Gambia consistentlyexhibited the highest prevalence of delayed vaccination, while the coastal districts showed alower prevalence forallthree vaccines. We also found that districts in the eastern, central, as well as in coastal parts of The Gambia had a combination of high estimated prevalence of delayed HepB0, PENTA3 and MCV1 and a significant population of affected infants.

CONCLUSIONS

Our approach provides decision-makers with a valuable tool to better understand local patterns of untimely childhood vaccination and identify districts where strengthening vaccine delivery systems could have the greatest impact.

Are all underimmunized measles clusters equally critical?

This research develops a novel system science approach to examine the potential risk of outbreaks caused by geographical clustering of underimmunized individuals for an infectious disease like measles. We use an activity-based population network model and school immunization records to identify underimmunized clusters of zip codes in the Commonwealth of Virginia. Although Virginia has high vaccine coverage for measles at the state level, finer-scale investigation at the zip code level finds three statistically significant underimmunized clusters. This research examines why some underimmunized geographical clusters are more critical in causing outbreaks and how their criticality changes with a possible drop in overall vaccination coverage. Results show that different clusters can cause vastly different outbreaks in a region, depending on their size, location, immunization rate and network characteristics. Among the three underimmunized clusters, we find one to be critical and the other two to be benign in terms of an outbreak risk. However, when the vaccine coverage among children drops by just 5% (or 0.8% overall in the population), one of the benign clusters becomes highly critical. This work also examines the demographic and network properties of these clusters to identify factors that are responsible for affecting the criticality of the clusters. Although this work focuses on measles, the methodology is generic and can be applied to study other infectious diseases.

Invited Commentary: To Make Long-Term Gains Against Infection Inequity, Infectious Disease Epidemiology Needs to Develop a More Sociological Imagination

In a recent article in the Journal, Noppert et al. (Am J Epidemiol. 2023;192(3):475-482) articulated in detail the mechanisms connecting high-level "fundamental social causes" of health inequity to inequitable infectious disease outcomes, including infection, severe disease, and death. In this commentary, we argue that while intensive focus on intervening mechanisms is welcome and necessary, it cannot occur in isolation from examination of the way that fundamental social causes-including racism, socioeconomic inequity, and social stigma-sustain infection inequities even when intervening mechanisms are addressed. We build on the taxonomy of intervening mechanisms laid out by Noppert et al. to create a road map for strengthening the connection between fundamental cause theory and infectious disease epidemiology and discuss its implications for future research and intervention.

Are all underimmunized measles clusters equally critical?

Disruptions in routine immunizations due to the COVID-19 pandemic have been a cause of significant concern for health organizations worldwide. This research develops a system science approach to examine the potential risk of geographical clustering of underimmunized individuals for an infectious disease like measles. We use an activity-based population network model and school immunization records to identify underimmunized clusters of zip codes in the Commonwealth of Virginia. Although Virginia has high vaccine coverage at the state level for measles, finer-scale investigation at the zip code level finds three statistically significant underimmunized clusters. To estimate the criticality of these clusters, a stochastic agent-based network epidemic model is used. Results show that different clusters can cause vastly different outbreaks in the region, depending on their size, location, and network characteristics. This research aims to understand why some underimmunized geographical clusters do not cause a large outbreak while others do. A detailed network analysis shows that it is not the average degree of the cluster or the percentage of underimmunized individuals in the cluster but the average eigenvector centrality of the cluster that is important in determining its potential risk.

Spatial distribution and determinants of childhood vaccination refusal in the United States

Parental refusal and delay of childhood vaccination has increased in recent years in the United States. This phenomenon challenges maintenance of herd immunity and increases the risk of outbreaks of vaccine-preventable diseases. We examine US county-level vaccine refusal for patients under five years of age collected during the period 2012-2015 from an administrative healthcare dataset. We model these data with a Bayesian zero-inflated negative binomial regression model to capture social and political processes that are associated with vaccine refusal, as well as factors that affect our measurement of vaccine refusal. Our work highlights fine-scale socio-demographic characteristics associated with vaccine refusal nationally, finds that spatial clustering in refusal can be explained by such factors, and has the potential to aid in the development of targeted public health strategies for optimizing vaccine uptake.

The global landscape of smallpox vaccination history and implications for current and future orthopoxvirus susceptibility: a modelling study

BACKGROUND

More than four decades after the eradication of smallpox, the ongoing 2022 monkeypox outbreak and increasing transmission events of other orthopoxviruses necessitate a greater understanding of the global distribution of susceptibility to orthopoxviruses. We aimed to characterise the current global landscape of smallpox vaccination history and orthopoxvirus susceptibility.

METHODS

We characterised the global landscape of smallpox vaccination at a subnational scale by integrating data on current demography with historical smallpox vaccination programme features (coverage and cessation dates) from eradication documents and published literature. We analysed this landscape to identify the factors that were most associated with geographical heterogeneity in current vaccination coverage. We considered how smallpox vaccination history might translate into age-specific susceptibility profiles for orthopoxviruses under different vaccination effectiveness scenarios.

FINDINGS

We found substantial global spatial heterogeneity in the landscape of smallpox vaccination, with vaccination coverage estimated to range from 7% to 60% within admin-1 regions (ie, regions one administrative level below country) globally, with negligible uncertainty (99·6% of regions have an SD less than 5%). We identified that geographical variation in vaccination coverage was driven mostly by differences in subnational demography. Additionally, we found that susceptibility for orthopoxviruses was highly age specific based on age at cessation and age-specific coverage; however, the age profile was consistent across vaccine effectiveness values.

INTERPRETATION

The legacy of smallpox eradication can be observed in the current landscape of smallpox vaccine protection. The strength and longevity of smallpox vaccination campaigns globally, combined with current demographic heterogeneity, have shaped the epidemiological landscape today, revealing substantial geographical variation in orthopoxvirus susceptibility. This study alerts public health decision makers to non-endemic regions that might be at greatest risk in the case of widespread and sustained transmission in the 2022 monkeypox outbreak and highlights the importance of demography and fine-scale spatial dynamics in predicting future public health risks from orthopoxviruses.

FUNDING

US National Institutes of Health and US National Science Foundation.

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.

TransCode: Uncovering COVID-19 transmission patterns via deep learning

BACKGROUND

The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning.

METHODS

We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available.

RESULTS

The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.

CONCLUSIONS

The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.

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 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 U.S. While numerous studies have investigated 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 U.S. through different phases of the pandemic.

Objective

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

Methods

We analyze spatiotemporal masking patterns in over eight million behavioral survey responses from across the United States starting in September 2020 through May 2021. We adjust for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debias 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 evaluate whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data.

Results

We find that county-level masking behavior is spatially heterogeneous along an urban-rural gradient, with mask-wearing peaking in winter 2021 and declining sharply through May 2021. Our results identify 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 validate our bias-correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of small sample size and representation. Self-reported behavior estimates are especially prone to social desirability and non-response biases and our findings demonstrate 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.

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.

COVID-19 vaccination and mask wearing behaviors in the United States, August 2020 - June 2021

BACKGROUND

During the rollout of COVID-19 vaccination, many states relaxed mask wearing guidance for those vaccinated. The aim of this study was to examine the association between vaccination status and mask wearing behaviors.

METHODS

Seven waves of surveys (n = 6721) were conducted between August 2020 and June 2021. Participants were asked about initiation of COVID-19 vaccination and mask wearing behavior at work/school or a grocery store. Odds ratios (ORs) and 95% confidence intervals (CIs) from logistic regression were used to estimate associations between vaccination status and mask wearing at work/school and at the grocery store.

RESULTS

Between April and June 2021, mask wearing at work or school declined among both those vaccinated (74% to 49%) and unvaccinated (46% to 35%). There was a similar decline for mask wearing at grocery stores. The odds of wearing a mask were 2.35 times higher at work/school (95% CI: 1.82, 3.04) and 1.65 times at a grocery store (95% CI: 1.29, 2.11) among the vaccinated compared to unvaccinated.

CONCLUSION

Mask wearing decreased after mask guidelines were relaxed, with consistently lower mask wearing among the unvaccinated, indicating a reluctance among the unvaccinated to adopt COVID-19 risk reduction behaviors.

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.

Characterizing the Spatiotemporal Heterogeneity of the COVID-19 Vaccination Landscape

As variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have emerged throughout 2021-2022, the need to maximize vaccination coverage across the United States to minimize severe outcomes of coronavirus disease 2019 (COVID-19) has been critical. Maximizing vaccination requires that we track vaccination patterns to measure the progress of the vaccination campaign and target locations that may be undervaccinated. To improve efforts to track and characterize COVID-19 vaccination progress in the United States, we integrated Centers for Disease Control and Prevention and state-provided vaccination data, identifying and rectifying discrepancies between these data sources. We found that COVID-19 vaccination coverage in the United States exhibits significant spatial heterogeneity at the county level, and we statistically identified spatial clusters of undervaccination, all with foci in the southern United States. We also identified vaccination progress at the county level as variable through summer 2021; the progress of vaccination in many counties stalled in June 2021, and few had recovered by July, with transmission of the SARS-CoV-2 delta variant rapidly rising. Using a comparison with a mechanistic growth model fitted to our integrated data, we classified vaccination dynamics across time at the county scale. Our findings underline the importance of curating accurate, fine-scale vaccination data and the continued need for widespread vaccination in the United States, especially with the continued emergence of highly transmissible SARS-CoV-2 variants.

Leveraging a national biorepository in Zambia to assess measles and rubella immunity gaps across age and space

High-quality, representative serological surveys allow direct estimates of immunity profiles to inform vaccination strategies but can be costly and logistically challenging. Leveraging residual serum samples is one way to increase their feasibility. We subsampled 9854 residual sera from a 2016 national HIV survey in Zambia and tested these specimens for anti-measles and anti-rubella virus IgG antibodies using indirect enzyme immunoassays. We demonstrate innovative methods for sampling residual sera and analyzing seroprevalence data, as well as the value of seroprevalence estimates to understand and control measles and rubella. National measles and rubella seroprevalence for individuals younger than 50 years was 82.8% (95% CI 81.6, 83.9%) and 74.9% (95% CI 73.7, 76.0%), respectively. Despite a successful childhood vaccination program, measles immunity gaps persisted across age groups and districts, indicating the need for additional activities to complement routine immunization. Prior to vaccine introduction, we estimated a rubella burden of 96 congenital rubella syndrome cases per 100,000 live births. Residual samples from large-scale surveys can reduce the cost and challenges of conducting serosurveys, and multiple pathogens can be tested. Procedures to access quality specimens, ensure ethical approvals, and link sociodemographic data can improve the timeliness and value of results.

COVID-19 Vaccine Rollouts and the Reproduction of Urban Spatial Inequality: Disparities Within Large US Cities in March and April 2021 by Racial/Ethnic and Socioeconomic Composition

Rollouts of COVID-19 vaccines in the USA were opportunities to redress disparities that surfaced during the pandemic. Initial eligibility criteria, however, neglected geographic, racial/ethnic, and socioeconomic considerations. Marginalized populations may have faced barriers to then-scarce vaccines, reinforcing disparities. Inequalities may have subsided as eligibility expanded. Using spatial modeling, we investigate how strongly local vaccination levels were associated with socioeconomic and racial/ethnic composition as authorities first extended vaccine eligibility to all adults. We harmonize administrative, demographic, and geospatial data across postal codes in eight large US cities over 3 weeks in Spring 2021. We find that, although vaccines were free regardless of health insurance coverage, local vaccination levels in March and April were negatively associated with poverty, enrollment in means-tested public health insurance (e.g., Medicaid), and the uninsured population. By April, vaccination levels in Black and Hispanic communities were only beginning to reach those of Asian and White communities in March. Increases in vaccination were smaller in socioeconomically disadvantaged Black and Hispanic communities than in more affluent, Asian, and White communities. Our findings suggest vaccine rollouts contributed to cumulative disadvantage. Populations that were left most vulnerable to COVID-19 benefited least from early expansions in vaccine availability in large US cities.

The Long-Term Success of Mandatory Vaccination Laws After Implementing the First Vaccination Campaign in 19th Century Rural Finland

In high-income countries, childhood infections are on the rise, a phenomenon attributed in part to persistent hesitancy toward vaccines. To combat vaccine hesitancy, several countries recently made vaccinating children mandatory, but the effect of such vaccination laws on vaccination coverage remains debated, and the long-term consequences are unknown. Here we quantified the consequences of vaccination laws on vaccination coverage, monitoring for a period of 63 years (1837-1899) rural Finland's first vaccination campaign against the highly lethal childhood infection smallpox. We found that annual vaccination campaigns were focused on children up to 1 year old and that their vaccination coverage was low and declined over time until the implementation of the vaccination law, which stopped the declining trend and was associated with an abrupt coverage increase, of 20%, to cover >80% of all children. Our results indicate that vaccination laws can have a long-term beneficial effect of increasing the vaccination coverage and will help public health practitioners to make informed decisions on how to act against vaccine hesitancy and optimize the impact of vaccination programs.

Static Socio-Ecological COVID-19 Vulnerability Index and Vaccine Hesitancy Index for England

BACKGROUND

Population characteristics can be used to infer vulnerability of communities to COVID-19, or to the likelihood of high levels of vaccine hesitancy. Communities harder hit by the virus, or at risk of being so, stand to benefit from greater resource allocation than their population size alone would suggest. This study reports a simple but efficacious method of ranking small areas of England by relative characteristics that are linked with COVID-19 vulnerability and vaccine hesitancy.

METHODS

Publicly available data on a range of characteristics previously linked with either poor COVID-19 outcomes or vaccine hesitancy were collated for all Middle Super Output Areas of England (MSOA, n=6790, excluding Isles of Scilly), scaled and combined into two numeric indices. Multivariable linear regression was used to build a parsimonious model of vulnerability (static socio-ecological vulnerability index, SEVI) in 60% of MSOAs, and retained variables were used to construct two simple indices. Assuming a monotonic relationship between indices and outcomes, Spearman correlation coefficients were calculated between the SEVI and cumulative COVID-19 case rates at MSOA level in the remaining 40% of MSOAs over periods both during and out with national lockdowns. Similarly, a novel vaccine hesitancy index (VHI) was constructed using population characteristics aligned with factors identified by an Office for National Statistics (ONS) survey analysis. The relationship between the VHI and vaccine coverage in people aged 12+years (as of 2021-06-24) was determined using Spearman correlation. The indices were split into quintiles, and MSOAs within the highest vulnerability and vaccine hesitancy quintiles were mapped.

FINDINGS

The SEVI showed a moderate to strong relationship with case rates in the validation dataset across the whole study period, and for every intervening period studied except early in the pandemic when testing was highly selective. The SEVI was more strongly correlated with case rates than any of its domains (rs 0·59 95% CI 0.57-0.62) and outperformed an existing MSOA-level vulnerability index. The VHI was significantly negatively correlated with COVID-19 vaccine coverage in the validation data at the time of writing (rs -0·43 95% CI -0·46 to -0·41). London had the largest number and proportion of MSOAs in quintile 5 (most vulnerable/hesitant) of SEVI and VHI concurrently.

INTERPRETATION

The indices presented offer an efficacious way of identifying geographical disparities in COVID-19 risk, thus helping focus resources according to need.

FUNDING

Funder: Integrated Covid Hub North East.

AWARD NUMBER

n/a.

GRANT RECIPIENT

Fiona Matthews.

Large university with high COVID-19 incidence is not associated with excess cases in non-student population

Large US colleges and universities that re-opened campuses in the fall of 2020 and the spring of 2021 experienced high per capita rates of COVID-19. Returns to campus were controversial because they posed a potential risk to surrounding communities. A large university in Pennsylvania that returned to in-person instruction for Fall 2020 and Spring 2021 semesters reported high incidence of COVID-19 among students. However, the co-located non-student resident population in the county experienced fewer COVID-19 cases per capita than reported in neighboring counties. Activity patterns from mobile devices indicate that the non-student resident population near the university restricted their movements during the pandemic more than residents of neighboring counties. Respiratory virus prevention and management in student and non-student populations requires different, specifically targeted strategies.

Spatial Clustering of Vaccine Exemptions on the Risk of a Measles Outbreak

OBJECTIVES

Areas of increased school-entry vaccination exemptions play a key role in epidemics of vaccine-preventable diseases in the United States. California eliminated nonmedical exemptions in 2016, which increased overall vaccine coverage but also rates of medical exemptions. We examine how spatial clustering of exemptions contributed to measles outbreak potential pre- and postpolicy change.

METHODS

We modeled measles transmission in an empirically calibrated hypothetical population of youth aged 0 to 17 years in California and compared outbreak sizes under the observed spatial clustering of exemptions in schools pre- and postpolicy change with counterfactual scenarios of no postpolicy change increase in medical exemptions, no clustering of exemptions, and lower population immunization levels.

RESULTS

The elimination of nonmedical exemptions significantly reduced both average and maximal outbreak sizes, although increases in medical exemptions resulted in more than twice as many infections, on average, than if medical exemptions were maintained at prepolicy change levels. Spatial clustering of nonmedical exemptions provided some initial protection against random introduction of measles infections; however, it ultimately allowed outbreaks with thousands more infections than when exemptions were randomly distributed. The large-scale outbreaks produced by exemption clusters could not be reproduced when exemptions were distributed randomly until population vaccination was lowered by >6 percentage points.

CONCLUSIONS

Despite the high overall vaccinate rate, the spatial clustering of exemptions in schools was sufficient to threaten local herd immunity and reduce protection from measles outbreaks. Policies strengthening vaccine requirements may be less effective if alternative forms of exemptions (eg, medical) are concentrated in existing low-immunization areas.

Context-specific emergence and growth of the SARS-CoV-2 Delta variant

The Delta variant of concern of SARS-CoV-2 has spread globally causing large outbreaks and resurgences of COVID-19 cases 1-3 . The emergence of Delta in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions 4,5 . Here we analyse 52,992 Delta genomes from England in combination with 93,649 global genomes to reconstruct the emergence of Delta, and quantify its introduction to and regional dissemination across England, in the context of changing travel and social restrictions. Through analysis of human movement, contact tracing, and virus genomic data, we find that the focus of geographic expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced >1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers from India reduced onward transmission from importations; however the transmission chains that later dominated the Delta wave in England had been already seeded before restrictions were introduced. In England, increasing inter-regional travel drove Delta's nationwide dissemination, with some cities receiving >2,000 observable lineage introductions from other regions. Subsequently, increased levels of local population mixing, not the number of importations, was associated with faster relative growth of Delta. Among US states, we find that regions that previously experienced large waves also had faster Delta growth rates, and a model including interactions between immunity and human behaviour could accurately predict the rise of Delta there. Delta's invasion dynamics depended on fine scale spatial heterogeneity in immunity and contact patterns and our findings will inform optimal spatial interventions to reduce transmission of current and future VOCs such as Omicron.

Context-specific emergence and growth of the SARS-CoV-2 Delta variant

The Delta variant of concern of SARS-CoV-2 has spread globally causing large outbreaks and resurgences of COVID-19 cases. The emergence of Delta in the UK occurred on the background of a heterogeneous landscape of immunity and relaxation of non-pharmaceutical interventions. Here we analyse 52,992 Delta genomes from England in combination with 93,649 global genomes to reconstruct the emergence of Delta, and quantify its introduction to and regional dissemination across England, in the context of changing travel and social restrictions. Through analysis of human movement, contact tracing, and virus genomic data, we find that the focus of geographic expansion of Delta shifted from India to a more global pattern in early May 2021. In England, Delta lineages were introduced >1,000 times and spread nationally as non-pharmaceutical interventions were relaxed. We find that hotel quarantine for travellers from India reduced onward transmission from importations; however the transmission chains that later dominated the Delta wave in England had been already seeded before restrictions were introduced. In England, increasing inter-regional travel drove Delta's nationwide dissemination, with some cities receiving >2,000 observable lineage introductions from other regions. Subsequently, increased levels of local population mixing, not the number of importations, was associated with faster relative growth of Delta. Among US states, we find that regions that previously experienced large waves also had faster Delta growth rates, and a model including interactions between immunity and human behaviour could accurately predict the rise of Delta there. Delta’s invasion dynamics depended on fine scale spatial heterogeneity in immunity and contact patterns and our findings will inform optimal spatial interventions to reduce transmission of current and future VOCs such as Omicron.

Estimating Vaccine Efficacy Against Transmission via Effect on Viral Load

Determining policies to end the SARS-CoV-2 pandemic will require an understanding of the efficacy and effectiveness (hereafter, efficacy) of vaccines. Beyond the efficacy against severe disease and symptomatic and asymptomatic infection, understanding vaccine efficacy against virus transmission, including efficacy against transmission of different viral variants, will help model epidemic trajectory and determine appropriate control measures. Recent studies have proposed using random virologic testing in individual randomized controlled trials to improve estimation of vaccine efficacy against infection. We propose to further use the viral load measures from these tests to estimate efficacy against transmission. This estimation requires a model of the relationship between viral load and transmissibility and assumptions about the vaccine effect on transmission and the progress of the epidemic. We describe these key assumptions, potential violations of them, and solutions that can be implemented to mitigate these violations. Assessing these assumptions and implementing this random sampling, with viral load measures, will enable better estimation of the crucial measure of vaccine efficacy against transmission.

Differential Effect of Vaccine Effectiveness and Safety on COVID-19 Vaccine Acceptance across Socioeconomic Groups in an International Sample

Controlling the spread of SARS-CoV-2 will require high vaccination coverage, but acceptance of the vaccine could be impacted by perceptions of vaccine safety and effectiveness. The aim of this study was to characterize how vaccine safety and effectiveness impact acceptance of a vaccine, and whether this impact varied over time or across socioeconomic and demographic groups. Repeated cross-sectional surveys of an opt-in internet sample were conducted in 2020 in the US, mainland China, Taiwan, Malaysia, Indonesia, and India. Individuals were randomized into receiving information about a hypothetical COVID-19 vaccine with different safety and effectiveness profiles (risk of fever 5% vs. 20% and vaccine effectiveness 50% vs. 95%). We examined the effect of the vaccine profile on vaccine acceptance in a logistic regression model, and included interaction terms between vaccine profile and socioeconomic/demographic variables to examine the differences in sensitivity to the vaccine profile. In total, 12,915 participants were enrolled in the six-country study, including the US (4054), China (2797), Taiwan (1278), Malaysia (1497), Indonesia (1527), and India (1762). Across time and countries, respondents had stronger preferences for a safer and more effective vaccine. For example, in the US in November 2020, acceptance was 3.10 times higher for a 95% effective vaccine with a 5% risk of fever, vs a vaccine 50% effective, with a 20% risk of fever (95% CI: 2.07, 4.63). Across all countries, there was an increase in the effect of the vaccine profile over time (p < 0.0001), with stronger preferences for a more effective and safer vaccine in November 2020 compared to August 2020. Sensitivity to the vaccine profile was also stronger in August compared to November 2020, in younger age groups, among those with lower income; and in those that are vaccine hesitant. Uptake of COVID-19 vaccines could vary in a country based upon effectiveness and availability. Effective communication tools will need to be developed for certain sensitive groups, including young adults, those with lower income, and those more vaccine hesitant.

Evaluating Michigan's Administrative Rule Change on Nonmedical Vaccine Exemptions

OBJECTIVES

Vaccine hesitancy is a growing threat to health in the United States. Facing the fourth highest vaccine exemption rate in the United States in 2014, Michigan changed its state Administrative Rules, effective January 1, 2015, requiring parents to attend an in-person vaccine education session at their local health department before obtaining a nonmedical exemption (NME). In this article, we evaluate the longer-term impact of this policy change on the rate, spatial distribution, and sociodemographic predictors of NMEs in Michigan.

METHODS

Using school-level kindergarten vaccination data from Michigan from 2011 to 2018, we evaluated sociodemographic predictors of NMEs before and after this Administrative Rule change using Bayesian binomial regression. We measured the persistence and location of school district-level geographic clustering using local indicators of spatial association.

RESULTS

Immediately after the rule change, rates of NMEs fell by 32%. However, NME rates rebounded in subsequent years, increasing by 26% by 2018, although income disparities in NME rates decreased after the rule change. Philosophical, religious, and medical vaccine exemptions exhibited distinct geographic patterns across the state, which largely persisted after 2015, illustrating that NME clusters remain a concern despite this rule change.

CONCLUSIONS

Although Michigan's Administrative Rule change caused a short-term decline in NME rates, NME rates have risen dramatically in the following 4 years since the policy was implemented. Michigan's administrative effort to require parental education at the local health department before receiving an exemption did not cause a sustained reduction in the rate or spatial distribution of NMEs.

Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence

Understanding the causes and consequences of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is crucial to pandemic control yet difficult to achieve because they arise in the context of variable human behavior and immunity. We investigated the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data. We identified a multistage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7's increased intrinsic transmissibility. We further explored how B.1.1.7 spread was shaped by nonpharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates.

Ignoring spatial heterogeneity in drivers of SARS-CoV-2 transmission in the US will impede sustained elimination

To dissect the transmission dynamics of SARS-CoV-2 in the United States, we integrate parallel streams of high-resolution data on contact, mobility, seasonality, vaccination and seroprevalence within a metapopulation network. We find the COVID-19 pandemic in the US is characterized by a geographically localized mosaic of transmission along an urban-rural gradient, with many outbreaks sustained by between-county transmission. We detect a dynamic tension between the spatial scale of public health interventions and population susceptibility as pre-pandemic contact is resumed. Further, we identify regions rendered particularly at risk from invasion by variants of concern due to spatial connectivity. These findings emphasize the public health importance of accounting for the hierarchy of spatial scales in transmission and the heterogeneous impacts of mobility on the landscape of contagion risk.

The missing season: The impacts of the COVID-19 pandemic on influenza

Throughout the COVID-19 pandemic, many have worried that the additional burden of seasonal influenza would create a devastating scenario, resulting in overwhelmed healthcare capacities and further loss of life. However, many were pleasantly surprised: the 2020 Southern Hemisphere and 2020-2021 Northern Hemisphere influenza seasons were entirely suppressed. The potential causes and impacts of this drastic public health shift are highly uncertain, but provide lessons about future control of respiratory diseases, especially for the upcoming influenza season.

Measuring Multiple Dimensions and Indices of Nonvaccination Clustering in Michigan, 2008-2018

Michigan experienced a significant measles outbreak in 2019 amidst rising rates of nonmedical vaccine exemptions (NMEs) and low vaccination coverage compared with the rest of the United States. There is a critical need to better understand the landscape of nonvaccination in Michigan to assess the risk of vaccine-preventable disease outbreaks in the state, yet there is no agreed-upon best practice for characterizing spatial clustering of nonvaccination, and numerous clustering metrics are available in the statistical, geographical, and epidemiologic literature. We used school-level data to characterize the spatiotemporal landscape of vaccine exemptions in Michigan for the period 2008-2018 using Moran's I, the isolation index, the modified aggregation index, and the Theil index at 4 spatial scales. We also used nonvaccination thresholds of 5%, 10%, and 20% to assess the bias incurred when aggregating vaccination data. We found that aggregating school-level data to levels commonly used for public reporting can lead to large biases in identifying the number and location of at-risk students and that different clustering metrics yielded variable interpretations of the nonvaccination landscape in Michigan. This study shows the importance of choosing clustering metrics with their mechanistic interpretations in mind, be it large- or fine-scale heterogeneity or between- and within-group contributions to spatial variation.