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

Peer threat evaluations shape one's own threat perceptions and feelings of distress

We are continuously exposed to what others think and feel about content online. How do others' evaluations shared in this medium influence our own beliefs and emotional responses? In two pre-registered studies, we investigated the social transmission of threat and safety evaluations in a paradigm that mimicked online social media platforms. In Study 1 (N = 103), participants viewed images and indicated how distressed they made them feel. Participants then categorised these images as threatening or safe for others to see, while seeing how "previous participants" ostensibly categorised them (these values were actually manipulated across images). We found that participants incorporated both peers' categorisations of the images and their own distress ratings when categorizing images as threatening or safe. Study 2 (N = 115) replicated these findings and further demonstrated that peers' categorisations shifted how distressed these images made them feel. Taken together, our results indicate that people integrate their own and others' experiences when exposed to emotional content and that social information can influence both our perceptions of things as threatening or safe, as well as our own emotional responses to them. Our findings provide replicable experimental evidence that social information is a powerful conduit for the transmission of affective evaluations and experiences.

Enhancing mass vaccination programs with queueing theory and spatial optimization

Background

Mass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or queues, which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake.

Methods

We developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass dog rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those used in a previous vaccination campaign, as well as to sites obtained from a queue-naïve version of the same algorithm.

Results

Sites placed by the queue-conscious algorithm resulted in 9-32% less attrition and 11-12% higher vaccination coverage compared to previously used sites and 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naïve algorithm. Compared to the queue-naïve algorithm, the queue-conscious algorithm placed more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption.

Conclusion

One should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.

Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach

Individuals' perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. In this study, we propose an approach to integrate survey data on contact patterns and disease perception into a data-driven compartmental model, by hypothesizing that perceived severity is a determinant of behavioral change. We explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals' behaviors vary based on their perceived severity of the disease. Results indicate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, in a way depending on the interplay between two contrasting effects. On the one hand, longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Differences in behavior across different population groups may impact strongly the epidemiological curves, with a transition from a scenario with two successive epidemic peaks to one with only one (higher) peak and overall more numerous severe outcomes and deaths. The specific modeling choices for how perceived severity modulates behavior parameters do not strongly impact the model's outcomes. Moreover, the study of several simplified models indicate that the observed phenomenology depends on the combination of data describing age-stratified contact patterns and of the feedback loop between disease perception and behavior, while it is robust with respect to the lack of precise information on the distribution of perceived severity in the population. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and gives hints on the type of data that further extensive data collection should target to enhance model accuracy and relevance.

Enhancing Mass Vaccination Programs with Queueing Theory and Spatial Optimization

Background

Mass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or queues, which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake.

Methods

We developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass canine rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those obtained from a queue-naive version of the same algorithm.

Results

Sites placed by the queue-conscious algorithm resulted in 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naïve algorithm. Compared to the queue-naïve algorithm, the queue-conscious algorithm favored placing more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption.

Conclusion

One should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.

Vaccination homophily in ego contact networks during the COVID-19 pandemic

Vaccine hesitancy is an inevitable risk for societies as it contributes to outbreaks of diseases. Prior research suggests that vaccination decisions of individuals tend to spread within social networks, resulting in a tendency to vaccination homophily. The clustering of individuals resistant to vaccination can substantially make the threshold necessary to achieve herd immunity harder to reach. In this study, we examined the extent of vaccination homophily among social contacts and its association with vaccine uptake during the COVID-19 pandemic in Hungary using a contact diary approach in two cross-sectional surveys. The results indicate strong clustering among both vaccinated and unvaccinated groups. The most powerful predictor of vaccine uptake was the perceived vaccination rate within the egos' social contact network. Vaccination homophily and the role of the interpersonal contact network in vaccine uptake were particularly pronounced in the networks of close relationships, including family, kinship, and strong social ties of the ego. Our findings have important implications for understanding COVID-19 spread dynamics by showing that the strong clustering of unvaccinated individuals posed a great risk in preventing the spread of the disease.

COVID-19 vaccine refusal as unfair free-riding

Contributions to COVID-19 vaccination programmes promise valuable collective goods. They can support public and individual health by creating herd immunity and taking the pressure off overwhelmed public health services; support freedom of movement by enabling governments to remove restrictive lockdown policies; and improve economic and social well-being by allowing businesses, schools, and other essential public services to re-open. The vaccinated can contribute to the production of these goods. The unvaccinated, who benefit from, but who do not contribute to these goods can be morally criticised as free-riders. In this paper defends the claim that in the case of COVID-19, the unvaccinated are unfair free-riders. I defend the claim against two objections. First, that they are not unfair free-riders because they lack the subjective attitudes and intentions of free-riders; second, that although the unvaccinated may be free-riders, their free-riding is not unfair.

Exploring preventive factors against insufficient antibody positivity rate for foot-and-mouth disease in pig farms in South Korea: a preliminary ecological study

BACKGROUND

Foot-and-mouth disease (FMD) is a highly contagious viral disease in livestock that has tremendous economic impact nationally. After multiple FMD outbreaks, the South Korean government implemented a vaccination policy for efficient disease control. However, during active surveillance by quarantine authorities, pig farms have reported an insufficient antibody positivity rate to FMD.

OBJECTIVE

In this study, the spatial and temporal trends of insufficiency among pig farms were analyzed, and the effect of the number of government veterinary officers was explored as a potential preventive factor.

METHODS

Various data were acquired, including national-level surveillance data for antibody insufficiency from the Korea Animal Health Integrated System, the number of veterinary officers, and the number of local pig farms. Temporal and geographical descriptive analyses were conducted to overview spatial and temporal trends. Additionally, logistic regression models were employed to investigate the association between the number of officers per pig farm with antibody insufficiency. Spatial cluster analysis was conducted to detect spatial clusters.

RESULTS

The results showed that the incidence of insufficiency tended to decrease in recent years (odds ratio [OR], 0.803; 95% confidence interval [95% CIs], 0.721-0.893), and regions with a higher density of governmental veterinary officers (OR, 0.942; 95% CIs, 0.918-0.965) were associated with a lower incidence.

CONCLUSIONS

This study implies that previously conducted national interventions would be effective, and the quality of government-provided veterinary care could play an important role in addressing the insufficient positivity rate of antibodies.

Geospatially clustered low COVID-19 vaccine rates among adolescents in socially vulnerable US counties

COVID-19 vaccinations are widely available across the United States (U.S.), yet little is known about the spatial clustering of COVID-19 vaccinations. This study aimed to test for geospatial clustering of COVID-19 vaccine rates among adolescents aged 12-17 across the U.S. counties and to compare these clustering patterns by sociodemographic characteristics. County-level data on COVID-19 vaccinations and sociodemographic characteristics were obtained from the COVID-19 Community Profile Report up to April 14, 2022. A total of 3,108 counties were included in the analysis. Global Moran's I statistic and Anselin Local Moran's analysis were used, and clustering patterns were compared to sociodemographic variables using t-tests. Counties with low COVID-19 vaccinated clusters were more likely, when compared to unclustered counties, to have higher numbers of individuals in poverty and uninsured individuals, and higher values of Social Vulnerability Index (SVI) and COVID-19 Community Vulnerability Index (CCVI). While high COVID-19 vaccinated clusters, compared to neighboring counties, had lower numbers of Black population, individuals in poverty, and uninsured individuals, and lower values of SVI and CCVI, but a higher number of Hispanic population. This study emphasizes the importance of addressing systemic barriers, such as poverty and lack of health insurance, which were found to be associated with low COVID-19 vaccination coverage.

Institutional trust is a distinct construct related to vaccine hesitancy and refusal

BACKGROUND

Vaccine hesitancy is driven by a heterogeneous and changing set of psychological, social and historical phenomena, requiring multidisciplinary approaches to its study and intervention. Past research has brought to light instances of both interpersonal and institutional trust playing an important role in vaccine uptake. However, no comprehensive study to date has specifically assessed the relative importance of these two categories of trust as they relate to vaccine behaviors and attitudes.

METHODS

In this paper, we examine the relationship between interpersonal and institutional trust and four measures related to COVID-19 vaccine hesitancy and one measure related to general vaccine hesitancy. We hypothesize that, across measures, individuals with vaccine hesitant attitudes and behaviors have lower trust-especially in institutions-than those who are not hesitant. We test this hypothesis in a sample of 1541 Canadians.

RESULTS

A deficit in both interpersonal and institutional trust was associated with higher levels of vaccine hesitant attitudes and behaviors. However, institutional trust was significantly lower than interpersonal trust in those with high hesitancy scores, suggesting that the two types of trust can be thought of as distinct constructs in the context of vaccine hesitancy.

CONCLUSIONS

Based on our findings, we suggest that diminished institutional trust plays a crucial role in vaccine hesitancy. We propose that this may contribute to a tendency to instead place trust in interpersonally propagated belief systems, which may be more strongly misaligned with mainstream evidence and thus support vaccine hesitancy attitudes. We offer strategies rooted in these observations for creating public health messages designed to enhance vaccine uptake.

Estimating the potential impact and diagnostic requirements for SARS-CoV-2 test-and-treat programs

Oral antivirals have the potential to reduce the public health burden of COVID-19. However, now that we have exited the emergency-phase of the COVID-19 pandemic, declining SARS-CoV-2 clinical testing rates (average testing rates = [Formula: see text]10 tests/100,000 people/day in low-and-middle income countries; <100 tests/100,000 people/day in high-income countries; September 2023) make the development of effective test-and-treat programs challenging. We used an agent-based model to investigate how testing rates and strategies affect the use and effectiveness of oral antiviral test-to-treat programs in four country archetypes of different income levels and demographies. We find that in the post-emergency-phase of the pandemic, in countries where low testing rates are driven by limited testing capacity, significant population-level impact of test-and-treat programs can only be achieved by both increasing testing rates and prioritizing individuals with greater risk of severe disease. However, for all countries, significant reductions in severe cases with antivirals are only possible if testing rates were substantially increased with high willingness of people to seek testing. Comparing the potential population-level reductions in severe disease outcomes of test-to-treat programs and vaccination shows that test-and-treat strategies are likely substantially more resource intensive requiring very high levels of testing (≫100 tests/100,000 people/day) and antiviral use suggesting that vaccination should be a higher priority.

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.

Parental compliance and reasons for COVID-19 Vaccination among American children

COVID-19 vaccination rates among children have stalled, while new coronavirus strains continue to emerge. To improve child vaccination rates, policymakers must better understand parental preferences and reasons for COVID-19 vaccination among their children. Cross-sectional surveys were administered online to 30,174 US parents with at least one child of COVID-19 vaccine eligible age (5-17 years) between January 1 and May 9, 2022. Participants self-reported willingness to vaccinate their child and reasons for refusal, and answered additional questions about demographics, pandemic related behavior, and vaccination status. Willingness to vaccinate a child for COVID-19 was strongly associated with parental vaccination status (multivariate odds ratio 97.9, 95% confidence interval 86.9-111.0). The majority of fully vaccinated (86%) and unvaccinated (84%) parents reported concordant vaccination preferences for their eligible child. Age and education had differing relationships by vaccination status, with higher age and education positively associated with willingness among vaccinated parents. Among all parents unwilling to vaccinate their children, the two most frequently reported reasons were possible side effects (47%) and that vaccines are too new (44%). Unvaccinated parents were much more likely to list a lack of trust in government (41% to 21%, p < .001) and a lack of trust in scientists (34% to 19%, p < .001) as reasons for refusal. Cluster analysis identified three groups of unwilling parents based on their reasons for refusal to vaccinate, with distinct concerns that may be obscured when analyzed in aggregate. Factors associated with willingness to vaccinate children and reasons for refusal may inform targeted approaches to increase vaccination.

The shortfalls of vulnerability indexes for public health decision-making in the face of emergent crises: the case of COVID-19 vaccine uptake in Virginia

Equitable and effective vaccine uptake is a key issue in addressing COVID-19. To achieve this, we must comprehensively characterize the context-specific socio-behavioral and structural determinants of vaccine uptake. However, to quickly focus public health interventions, state agencies and planners often rely on already existing indexes of "vulnerability." Many such "vulnerability indexes" exist and become benchmarks for targeting interventions in wide ranging scenarios, but they vary considerably in the factors and themes that they cover. Some are even uncritical of the use of the word "vulnerable," which should take on different meanings in different contexts. The objective of this study is to compare four vulnerability indexes produced by private, federal, and state institutions to assess the application of these measures to the needs of the COVID-19 pandemic and other emergent crises. We focus on federal, state, and private industries' vulnerability indexes for the Commonwealth of Virginia. Qualitative comparison is done by considering each index's methodologies to see how and why they defined and measured "vulnerability." We also quantitatively compare them using percent agreement and illustrate the overlaps in localities identified as among the most vulnerable on a choropleth map. Finally, we provide a short case study that explores vaccine uptake in the six localities that were identified by at least three indexes as most vulnerable, and six localities with very low vaccine coverage that were identified by two or fewer indexes as highly vulnerable. By comparing the methodologies and index (dis)agreements, we discuss the appropriateness of using pre-existing vulnerability indexes as a public health decision-making tool for emergent crises, using COVID-19 vaccine uptake as a case study. The inconsistencies reflected by these indexes show both the need for context-specific and time-sensitive data collection in public health and policy response, and a critical critique of measured "vulnerability."

Epidemic time series similarity is related to geographic distance and age structure

Objective

More similar locations may have similar infectious disease dynamics. There is clear overlap in putative causes for epidemic similarity, such as geographic distance, age structure, and population size. We compare the effects of these potential drivers on epidemic similarity compared to a baseline assumption that differences in the basic reproductive number (R 0) will translate to differences in epidemic trajectories.

Methods

Using COVID-19 case counts from United States counties, we explore the importance of geographic distance, population size differences, and age structure dissimilarity on resulting epidemic similarity.

Results

We find clear effects of geographic space, age structure, population size, and R 0 on epidemic similarity, but notably the effect of age structure was stronger than the baseline assumption that differences in R 0 would be most related to epidemic similarity.

Conclusions

Together, this highlights the role of spatial and demographic processes on SARS-CoV2 epidemics in the United States.