Critical dynamics in population vaccinating behavior

Vaccine refusal can lead to renewed outbreaks of previously eliminated diseases and even delay global eradication. Vaccinating decisions exemplify a complex, coupled system where vaccinating behavior and disease dynamics influence one another. Such systems often exhibit critical phenomena—special dynamics close to a tipping point leading to a new dynamical regime. For instance, critical slowing down (declining rate of recovery from small perturbations) may emerge as a tipping point is approached. Here, we collected and geocoded tweets about measles–mumps–rubella vaccine and classified their sentiment using machine-learning algorithms. We also extracted data on measles-related Google searches. We find critical slowing down in the data at the level of California and the United States in the years before and after the 2014–2015 Disneyland, California measles outbreak. Critical slowing down starts growing appreciably several years before the Disneyland outbreak as vaccine uptake declines and the population approaches the tipping point. However, due to the adaptive nature of coupled behavior–disease systems, the population responds to the outbreak by moving away from the tipping point, causing “critical speeding up” whereby resilience to perturbations increases. A mathematical model of measles transmission and vaccine sentiment predicts the same qualitative patterns in the neighborhood of a tipping point to greatly reduced vaccine uptake and large epidemics. These results support the hypothesis that population vaccinating behavior near the disease elimination threshold is a critical phenomenon. Developing new analytical tools to detect these patterns in digital social data might help us identify populations at heightened risk of widespread vaccine refusal.

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Transient indicators of tipping points in infectious diseases

Journal of The Royal Society Interface ◽

10.1098/rsif.2020.0094 ◽

2020 ◽

Vol 17 (170) ◽

pp. 20200094

Author(s):

Suzanne M. O’Regan ◽

Eamon B. O’Dea ◽

Pejman Rohani ◽

John M. Drake

Keyword(s):

Infectious Diseases ◽

Vaccine Uptake ◽

Critical Slowing Down ◽

Critical Threshold ◽

Continuous Systems ◽

Dominant Eigenvalue ◽

Tipping Points ◽

Critical Transition ◽

Slowing Down ◽

Critical Transitions

The majority of known early warning indicators of critical transitions rely on asymptotic resilience and critical slowing down. In continuous systems, critical slowing down is mathematically described by a decrease in magnitude of the dominant eigenvalue of the Jacobian matrix on the approach to a critical transition. Here, we show that measures of transient dynamics, specifically, reactivity and the maximum of the amplification envelope, also change systematically as a bifurcation is approached in an important class of models for epidemics of infectious diseases. Furthermore, we introduce indicators designed to detect trends in these measures and find that they reliably classify time series of case notifications simulated from stochastic models according to levels of vaccine uptake. Greater attention should be focused on the potential for systems to exhibit transient amplification of perturbations as a critical threshold is approached, and should be considered when searching for generic leading indicators of tipping points. Awareness of this phenomenon will enrich understanding of the dynamics of complex systems on the verge of a critical transition.

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COVID-19 Vaccine Intentions in the United States, December 2020 to March 2021

10.1101/2021.05.16.21257290 ◽

2021 ◽

Author(s):

Mark É Czeisler ◽

Shantha M.W. Rajaratnam ◽

Mark E Howard ◽

Charles A Czeisler

Keyword(s):

Post Infection ◽

Political Ideology ◽

The United States ◽

Vaccine Uptake ◽

Vaccine Hesitancy ◽

Efficacy Data ◽

Post Vaccination ◽

Safety And Efficacy ◽

Vaccine Refusal ◽

The Us

Importance SARS-CoV-2 containment is estimated to require attainment of high (>80%) post-infection and post-vaccination population immunity. Objective To assess COVID-19 vaccine intentions among US adults and their children, and reasons for vaccine hesitancy among potential refusers. Design Internet-based surveys were administered cross-sectionally to US adults during December 2020 and February to March 2021 (March-2021). Setting Surveys were administered through Qualtrics using demographic quota sampling. Participants A large, demographically diverse sample of 10,444 US adults (response rate, 63.9%). Main Outcomes and Measures COVID-19 vaccine uptake, intentions, and reasons for potential refusal. Adults living with or caring for children aged 2 to 18 years were asked about their intent to have their children vaccinated. Multivariable weighted logistic regression models were used to estimate adjusted odds ratios for vaccine refusal. Results Of 5256 March-2021 respondents, 3467 (66.0%) reported they would definitely or most likely obtain a COVID-19 vaccine as soon as possible (ASAP Obtainers), and an additional 478 (9.1%) reported they were waiting for more safety and efficacy data before obtaining the vaccine. Intentions for children and willingness to receive a booster shot largely matched personal COVID-19 vaccination intentions. Vaccine refusal (ie, neither ASAP Obtainers nor waiting for more safety and efficacy data) was most strongly associated with not having obtained an influenza vaccine in 2020 (adjusted odds ratio, 4.11 [95% CI, 3.05-5.54]), less frequent mask usage (eg, rarely or never versus always or often, 3.92 [2.52-6.10]) or social gathering avoidance (eg, rarely or never versus always or often, 2.65 [1.95-3.60]), younger age (eg, aged 18-24 versus over 65 years, 3.88 [2.02-7.46]), and more conservative political ideology (eg, very conservative versus very liberal, 3.58 [2.16-5.94]); all P<.001. Conclusions and Relevance Three-quarters of March-2021 respondents in our large, demographically diverse sample of US adults reported they would likely obtain a COVID-19 vaccine, and 60% of adults living with or caring for children plan to have them vaccinated as soon as possible. With an estimated 27% of the US population having been infected with SARS-CoV-2, once vaccines are available to children and they have been vaccinated, combined post-infection and post-vaccination immunity will approach 80% of the US population in 2021, even without further infections.

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Spatial Correlation as an Early Warning Signal of Regime Shifts in a Multiplex Disease-Behaviour Network

10.1101/195388 ◽

2017 ◽

Author(s):

Peter C. Jentsch ◽

Madhur Anand ◽

Chris T. Bauch

Keyword(s):

Spatial Correlation ◽

Early Warning ◽

Regime Shift ◽

Tipping Point ◽

Regime Shifts ◽

Critical Slowing Down ◽

Warning Signals ◽

Early Warning Signals ◽

Network Layer ◽

Slowing Down

AbstractEarly warning signals of sudden regime shifts are a widely studied phenomenon for their ability to quantify a system’s proximity to a tipping point to a new and contrasting dynamical regime. However, this effect has been little studied in the context of the complex interactions between disease dynamics and vaccinating behaviour. Our objective was to determine whether critical slowing down (CSD) occurs in a multiplex network that captures opinion propagation on one network layer and disease spread on a second network layer. We parameterized a network simulation model to represent a hypothetical self-limiting, acute, vaccine-preventable infection with shortlived natural immunity. We tested five different network types: random, lattice, small-world, scale-free, and an empirically derived network. For the first four network types, the model exhibits a regime shift as perceived vaccine risk moves beyond a tipping point from full vaccine acceptance and disease elimination to full vaccine refusal and disease endemicity. This regime shift is preceded by an increase in the spatial correlation in non-vaccinator opinions beginning well before the bifurcation point, indicating CSD. The early warning signals occur across a wide range of parameter values. However, the more gradual transition exhibited in the empirically-derived network underscores the need for further research before it can be determined whether trends in spatial correlation in real-world social networks represent critical slowing down. The potential upside of having this monitoring ability suggests that this is a worthwhile area for further research.

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Assessing the distribution and drivers of vaccine hesitancy using medical claims data

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v9i1.7590 ◽

2017 ◽

Vol 9 (1) ◽

Cited By ~ 2

Author(s):

Sandra Goldlust ◽

Elizabeth Lee ◽

Shweta Bansal

Keyword(s):

United States ◽

Claims Data ◽

The United States ◽

Vaccine Uptake ◽

Vaccine Hesitancy ◽

Modeling Framework ◽

Medical Claims ◽

Vaccine Refusal ◽

The Social ◽

Disparate Data

ObjectiveThe purpose of this study was to investigate the use of large-scalemedical claims data for local surveillance of under-immunizationfor childhood infections in the United States, to develop a statisticalframework for integrating disparate data sources on surveillance ofvaccination behavior, and to identify the determinants of vaccinehesitancy behavior.IntroductionIn the United States, surveillance of vaccine uptake for childhoodinfections is limited in scope and spatial resolution. The NationalImmunization Survey (NIS) - the gold standard tool for monitoringvaccine uptake among children aged 19-35 months - is typicallyconstrained to producing coarse state-level estimates.1In recent years,vaccine hesitancy (i.e., a desire to delay or refuse vaccination, despiteavailability of vaccination services)2has resurged in the United States,challenging the maintenance of herd immunity. In December 2014,foreign importation of the measles virus to Disney theme parks inOrange County, California resulted in an outbreak of 111 measlescases, 45% of which were among unvaccinated individuals.3Digitalhealth data offer new opportunities to study the social determinantsof vaccine hesitancy in the United States and identify finer spatialresolution clusters of under-immunization using data with greaterclinical accuracy and rationale for hesitancy.4MethodsOur U.S. medical claims data comprised monthly reports ofdiagnosis codes for under-immunization and vaccine refusal(Figure 1). These claims were aggregated to five-digit zip-codes bypatient age-group from 2012 to 2015. Spatial generalized linear mixedmodels were used to generate county-level maps for surveillanceof under-immunization and to identify the determinants of vaccinehesitancy, such as income, education, household size, religious grouprepresentation, and healthcare access. We developed a Bayesianmodeling framework that separates the observation of vaccinehesitancy in our data from true underlying rates of vaccine hesitancyin the community. Our model structure also enabled us to borrowinformation from neighboring counties, which improves predictionof vaccine hesitancy in areas with missing or minimal data. Estimatesof the posterior distributions of model parameters were generated viaMarkov chain Monte Carlo (MCMC) methods.ResultsOur modeling framework enabled the production of county-levelmaps of under-immunization and vaccine refusal in the UnitedStates between 2012-2015, the identification of geographic clustersof under-immunization, and the quantification of the associationbetween various epidemiological factors and vaccination status.In addition, we found that our model structure enabled us to accountfor spatial variation in reporting vaccine hesitancy, which improvedour estimation.ConclusionsOur work demonstrate the utility of using large-scale medicalclaims data to improve surveillance systems for vaccine uptake andto assess the social and ecological determinants of vaccine hesitancy.We describe a flexible, hierarchical modeling framework forintegrating disparate data sources, particularly for data collectedthrough different measurement processes or at different spatial scales.Our findings will enhance our understanding of the causes of under-immunization, inform the design of vaccination policy, and aid inthe development of targeted public health strategies for optimizingvaccine uptake.Figure 1. Instances of vaccine refusal (per 100,000 population) for UnitedStates counties in 2014 as observed in medical claims data.

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Study of critical slowing-down in SU(2) Landau gauge fixing

Nuclear Physics B - Proceedings Supplements ◽

10.1016/s0920-5632(96)00789-x ◽

1997 ◽

Vol 53 (1-3) ◽

pp. 811-814 ◽

Cited By ~ 19

Author(s):

Attilio Cucchieri ◽

Tereza Mendes

Keyword(s):

Landau Gauge ◽

Critical Slowing Down ◽

Slowing Down ◽

Gauge Fixing

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Personal Watercraft Incident Court Decisions: The Plaintiff’s Odds?

Sustainability ◽

10.3390/su13095096 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5096

Author(s):

Eui-Yul Choi ◽

Woo Jeong Cho

Keyword(s):

Motor Vehicle ◽

The United States ◽

Insurance Companies ◽

Court Decisions ◽

Regression Analyses ◽

Demographic Information ◽

Slowing Down ◽

Personal Watercraft ◽

Successful Adults ◽

Significant Factors

A personal watercraft (PWC) is a vessel that uses an inboard motor powering a water jet pump as a source of power and is operated by a person sitting, standing, or kneeling. Maneuvering a PWC is different from operating a motor vehicle or boat. An obstacle cannot be avoided by slowing down and turning the watercraft; throttle power is required to turn or maneuver the PWC. The watercraft stops only by drifting or turning sharply. The study examined sixty court decisions published in LexisNexis databases of the United States over the last decade. Cases included individuals injured while operating a PWC as a driver, passenger, or as a result of contact with a watercraft. A content analysis identified items to be used in the study. Crosstab and logistic regression analyses were used to identify demographic information and the characteristics of those who succeeded in a court of law. One-third of the cases were successful; adults, males, and the party who sustained a severe injury were more successful in a court of law with the exception of the statistically significant factors (high risk maneuvers and sharp turns). Among the additional results, we should be aware that insurance companies may not pay; additionally, it is unwise to loan a PWC to a female who has no experience.

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