Forecasted Size of Measles Outbreaks Associated With Vaccination Exemptions for Schoolchildren

Strategies for Improving Vaccine Communication and Uptake

Vaccines have led to a significant decrease in rates of vaccine-preventable diseases and have made a significant impact on the health of children. However, some parents express concerns about vaccine safety and the necessity of vaccines. The concerns of parents range from hesitancy about some immunizations to refusal of all vaccines. This clinical report provides information about the scope and impact of the problem, the facts surrounding common vaccination concerns, and the latest evidence regarding effective communication techniques for the vaccine conversation. After reading this clinical report, readers can expect to: Understand concepts and underlying determinants of vaccine uptake and vaccine hesitancy.Understand the relationship between vaccine hesitancy and costs of preventable medical care.Recognize and address specific concerns (eg, vaccine safety) with caregivers when hesitancy is present.

The Impact of the Good Behavior Game on Risk for Drug Use Disorder in an Agent-Based Model of Southern Sweden

OBJECTIVE

Drug use disorder (DUD) is a worldwide problem, and strategies to reduce its incidence are central to decreasing its burden. This investigation seeks to provide a proof of concept for the ability of agent-based modeling to predict the impact of the introduction of an effective school-based intervention, the Good Behavior Game (GBG), on reducing DUD in Scania, Sweden, primarily through increasing school achievement.

METHOD

We modified an existing agent-based simulation model of opioid use disorder to represent DUD in Scania County, southern Sweden. The model represents every individual in the population and is calibrated with the linked individual data from multiple sources including demographics, education, medical care, and criminal history. Risks for developing DUD were estimated from the population in Scania. Scenarios estimated the impact of introducing the GBG in schools located in disadvantaged areas.

RESULTS

The model accurately reflected the growth of DUD in Scania over a multiyear period and reproduced the levels of affected individuals in various socioeconomic strata over time. The GBG was estimated to improve school achievement and lower DUD registrations over time in males residing in disadvantaged areas by 10%, reflecting a decrease of 540 cases of DUD. Effects were considerably smaller in females.

CONCLUSIONS

This work provides support for the impact of improving school achievement on long-term risks of developing DUD. It also demonstrated the value of using simulation modeling calibrated with data from a real population to estimate the impact of an intervention applied at a population level.

Impact of school operating scenarios on COVID-19 transmission under vaccination in the U.S.: an agent-based simulation model

At the height of the COVID-19 pandemic, K-12 schools struggled to safely operate under the fast-changing pandemic situation. However, little is known about the impact of different school operating scenarios considering the ongoing efforts of vaccination. In this study, we deployed an agent-based simulation model to mimic disease transmission in a mid-sized community consisting of 10,000 households. A total of eight school operating scenarios were simulated, in decreasing order of restrictiveness regarding COVID-19 mitigation measures. When masks were worn at school, work, and community environments, increasing in-person education from 50% to 100% would result in only 1% increase in cumulative infections. When there were no masks nor contact tracing while schools were 100% in person, the cumulative infection increased by 86% compared to the scenario when both masking and contact tracing were in place. In the sensitivity analysis for vaccination efficacy, we found that higher vaccination efficacy was essential in reducing overall infections. Our findings showed that full in-person education was safe, especially when contact tracing, masking, and widespread vaccination were in place. If no masking nor contact tracing was practiced, the transmission would rose dramatically but eventually slow down due to herd immunity.

Predicting Measles Outbreaks in the United States: Evaluation of Machine Learning Approaches (Preprint)

BACKGROUND

Measles, a highly contagious viral infection, is resurging in the United States, driven by international importation and declining domestic vaccination coverage. Despite this resurgence, measles outbreaks are still rare events that are difficult to predict. Improved methods to predict outbreaks at the county level would facilitate the optimal allocation of public health resources.

OBJECTIVE

We aimed to validate and compare extreme gradient boosting (XGBoost) and logistic regression, 2 supervised learning approaches, to predict the US counties most likely to experience measles cases. We also aimed to assess the performance of hybrid versions of these models that incorporated additional predictors generated by 2 clustering algorithms, hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and unsupervised random forest (uRF).

METHODS

We constructed a supervised machine learning model based on XGBoost and unsupervised models based on HDBSCAN and uRF. The unsupervised models were used to investigate clustering patterns among counties with measles outbreaks; these clustering data were also incorporated into hybrid XGBoost models as additional input variables. The machine learning models were then compared to logistic regression models with and without input from the unsupervised models.

RESULTS

Both HDBSCAN and uRF identified clusters that included a high percentage of counties with measles outbreaks. XGBoost and XGBoost hybrid models outperformed logistic regression and logistic regression hybrid models, with the area under the receiver operating curve values of 0.920-0.926 versus 0.900-0.908, the area under the precision-recall curve values of 0.522-0.532 versus 0.485-0.513, and F₂ scores of 0.595-0.601 versus 0.385-0.426. Logistic regression or logistic regression hybrid models had higher sensitivity than XGBoost or XGBoost hybrid models (0.837-0.857 vs 0.704-0.735) but a lower positive predictive value (0.122-0.141 vs 0.340-0.367) and specificity (0.793-0.821 vs 0.952-0.958). The hybrid versions of the logistic regression and XGBoost models had slightly higher areas under the precision-recall curve, specificity, and positive predictive values than the respective models that did not include any unsupervised features.

CONCLUSIONS

XGBoost provided more accurate predictions of measles cases at the county level compared with logistic regression. The threshold of prediction in this model can be adjusted to align with each county's resources, priorities, and risk for measles. While clustering pattern data from unsupervised machine learning approaches improved some aspects of model performance in this imbalanced data set, the optimal approach for the integration of such approaches with supervised machine learning models requires further investigation.

Social clustering of unvaccinated children in schools in the Netherlands

For the measles-mumps-rubella (MMR) vaccine, the World Health Organization-recommended coverage for herd protection is 95% for measles and 80% for rubella and mumps. However, a national vaccine coverage does not reflect social clustering of unvaccinated children, e.g. in schools of Orthodox Protestant or Anthroposophic identity in The Netherlands. To fully characterise this clustering, we estimated one-dose MMR vaccination coverages at all schools in the Netherlands. By combining postcode catchment areas of schools and school feeder data, each child in the Netherlands was characterised by residential postcode, primary and secondary school (referred to as school career). Postcode-level vaccination data were used to estimate vaccination coverages per school career. These were translated to coverages per school, stratified by school identity. Most schools had vaccine coverages over 99%, but major exceptions were Orthodox Protestant schools (63% in primary and 58% in secondary schools) and Anthroposophic schools (67% and 78%). School-level vaccine coverage estimates reveal strong clustering of unvaccinated children. The school feeder data reveal strongly connected Orthodox Protestant and Anthroposophic communities, but separated from one another. This suggests that even at a national one-dose MMR coverage of 97.5%, thousands of children per cohort are not protected by herd immunity.

A population study of the NYS measles epidemic: Lessons learned

OBJECTIVES

In 2019 the United States experienced the largest outbreak of measles in 27 years, 19 years after the United States declared measles eliminated. The purpose of this paper was to present a population study of a measles outbreak within Orthodox Jewish communities in New York that led to the elimination of religious exemption for school mandated vaccines.

METHODS

Peer reviewed articles, news media, health department, and government resources were used to investigate environmental factors that led to this outbreak. State, county, and city immunization records were accessed to explore measles compliance rates before and after policy change.

RESULTS

Rockland County had low compliance rates compared to the rest of the state, and the elimination of religious exemptions raised compliance rate almost to state level. In all but one affected New York City zip codes, compliance following policy change rose to 97.95%-99.15%.

CONCLUSIONS

Overall, changes in measles compliance rates reflect policy goals, but localized differences imply a need for more customized interventions for each unique community. Public health planning to promote vaccination should be sensitive to the concerns and perceptions of each community in order for health interventions to have a positive effect.

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.

Prevalence of SARS-CoV-2 genes in water reclamation facilities: From influent to anaerobic digester

Several treatment plants were sampled for influent, primary clarifier sludge, return activated sludge (RAS), and anaerobically digested sludge throughout nine weeks during the summer of the COVID-19 pandemic. Primary clarifier sludge had a significantly higher number of SARS-CoV-2 gene copy number per liter (GC/L) than other sludge samples, within a range from 1.0 × 10⁵ to 1.0 × 10⁶ GC/L. Gene copy numbers in raw influent significantly correlated with gene copy numbers in RAS in Silver Creek (p-value = 0.007, R² = 0.681) and East Canyon (p-value = 0.009, R² = 0.775) WRFs; both of which lack primary clarifiers or industrial pretreatment processes. This data indicates that SARS-CoV-2 gene copies tend to partition into primary clarifier sludges, at which point a significant portion of them are removed through sedimentation. Furthermore, it was found that East Canyon WRF gene copy numbers in influent were a significant predictor of daily cases (p-value = 0.0322, R² = 0.561), and gene copy numbers in RAS were a significant predictor of weekly cases (p-value = 0.0597, R² = 0.449). However, gene copy numbers found in primary sludge samples from other plants significantly predicted the number of COVID-19 cases for the following week (t = 2.279) and the week after that (t = 2.122) respectively. These data indicate that SARS-CoV-2 extracted from WRF biosolids may better suit epidemiological monitoring that exhibits a time lag. It also supports the observation that primary sludge removes a significant portion of SARS-CoV-2 marker genes. In its absence, RAS can also be used to predict the number of COVID-19 cases due to direct flow through from influent. This research represents the first of its kind to thoroughly examine SARS-CoV-2 gene copy numbers in biosolids throughout the wastewater treatment process and the relationship between primary, return activated, and anaerobically digested sludge and reported positive COVID-19 cases.

The Use of Digital Technologies to Support Vaccination Programmes in Europe: State of the Art and Best Practices from Experts' Interviews

Digitalisation offers great potential to improve vaccine uptake, supporting the need for effective life-course immunisation services. We conducted semi-structured in-depth interviews with public health experts from 10 Western European countries (Germany, Greece, Italy, Luxembourg, Malta, the Netherlands, Norway, Poland, Portugal, and the United Kingdom) to assess the current level of digitalisation in immunisation programmes and retrieve data on interventions and best practices. Interviews were performed using an ad hoc questionnaire, piloted on a sample of national experts. We report a mixed level of digital technologies deployment within vaccination services across Europe: Some countries are currently developing eHealth strategies, while others have already put in place robust programmes. Institutional websites, educational videos, and electronic immunisation records are the most frequently adopted digital tools. Webinars and dashboards represent valuable resources to train and support healthcare professionals in immunisation services organisation. Text messages, email-based communication, and smartphone apps use is scattered across Europe. The main reported barrier to the implementation of digital-based programmes is the lack of resources and shared standards. Our study offers a comprehensive picture of the European context and shows the need for robust collaboration between states and international institutions to share best practices and inform the planning of digital intervention models with the aim of countering vaccine hesitancy and increasing vaccine uptake.

Current understandings of the impact of mandatory vaccination laws in Europe

Vaccinations are among the most successful preventive tools to protect collective health. In response to alarming vaccines preventable diseases (VPDs) outbreaks resurgence, decreased vaccination coverage and vaccine refusal, several European countries have recently revised their vaccination policies introducing or extending mandatory vaccinations. This review examines the health, political and ethical aspects of mandatory vaccination.The authors first clarify terms and definitions and propose a conceptual framework of mandatory policies. Second, they describe the current status of mandatory childhood immunization programmes in Europe, assessing selected mandatory laws. Third, as the authors conduct a systematic review of the literature (retrieving from Medline 17 relevant records between 2010 and 2020), they take an analytical approach to measure the impact of mandatory vaccination policies on both VPDs control and immunization coverage, but also on population attitudes toward vaccines. 40% of European countries currently have mandatory vaccination policies; however, policies vary widely and, although there is evidence of increased vaccine uptake, their impact on informed adherence to preventive behaviors is scant.Although mandatory vaccination policies might be needed to protect collective health in times of emergency, public health goals of VPD prevention and health promotion should primarily be pursued through health education and population empowerment.

Measles (Rubeola): A Case Of Vaccine Hesitancy And Pregnancy

Measles (rubeola) is a highly contagious virus. Vaccination has nearly eradicated measles in the United States, yet sporadic outbreaks persist. Although the live-attenuated measles, mumps, rubella vaccine has not been found to cause fetal harm, pregnancy is considered a contraindication for the vaccine and contracting measles during pregnancy can have serious sequelae. Furthermore, lifelong immunity conferred by childhood vaccination is questionable as the vaccine's protection may wane during the childbearing years. Reluctance to vaccinate, or vaccine hesitancy, may leave a proportion of people of childbearing age unprotected. It is unlikely that many clinicians providing preconception, primary, and perinatal care have had occasion to diagnose measles. Susceptibility to infection combined with clinician inexperience may contribute to missed opportunities to halt the spread of this highly contagious, preventable illness. A case of parents' religion-based vaccine hesitancy complicating the pregnancy of their adult daughter is presented. Guidelines for screening for immunity, identifying measles in the clinical setting, and protocols for mitigating spread are reviewed.

The landscape of childhood vaccine exemptions in the United States

Once-eliminated vaccine-preventable childhood diseases, such as measles, are resurging across the United States. Understanding the spatio-temporal trends in vaccine exemptions is crucial to targeting public health intervention to increase vaccine uptake and anticipating vulnerable populations as cases surge. However, prior available data on childhood disease vaccination is either at too rough a spatial scale for this spatially-heterogeneous issue, or is only available for small geographic regions, making general conclusions infeasible. Here, we have collated school vaccine exemption data across the United States and provide it at the county-level for all years included. We demonstrate the fine-scale spatial heterogeneity in vaccine exemption levels, and show that many counties may fall below the herd immunity threshold. We also show that vaccine exemptions increase over time in most states, and non-medical exemptions are highly prevalent where allowed. Our dataset also highlights the need for greater data sharing and standardized reporting across the United States.

COVID-19 Outbreak Prediction with Machine Learning

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.

Reasons for not receiving the HPV vaccine among eligible adults: Lack of knowledge and of provider recommendations contribute more than safety and insurance concerns

Background

The upward trends of vaccine exemptions in Texas are alarming. While HPV vaccine rates in this State are among the lowest nationwide, factors that contribute to the low HPV vaccination uptake among adults remain unknown. In this study, we examined the main reasons for not receiving HPV vaccination among age‐eligible adults.

Methods

The Texas health screening survey (2018), a multistage area probability design‐based survey of a representative sample of Texas residents, was used to identify 907 eligible adults (age ≥ 18 years) respondents, including 724 women aged ≤ 26 years in 2007 (≤38 years in 2018), and 183 men aged ≤ 21 years in 2011 (≤28 years in 2018). Participants who reported having never received an HPV shot, where asked the main reason for not receiving the vaccine.

Results

Overall, 58.5% (95%CI: 55.1‐62.0) of vaccine eligible adults reported having never received the HPV vaccine. The most commonly reported reasons for not receiving it were: did not know about the vaccine (18.5% (14.9‐22.1)), and provider did not recommend (14.1% (10.9‐17.4)). In contrast, commonly perceived reasons such as: safety concerns (7.2% (4.8‐9.5)), lack of insurance (3.4% (1.7‐5.1), and concerns about increasing sexual activity if vaccinated (0.2% (0.0‐0.5)), were less frequently reported.

Conclusion

Among vaccine‐eligible adults, safety and sexuality concerns do not appear to be the prime factors underlying low HPV vaccination rates. Rather than emphasizing them, educational interventions should aim at improving vaccine's knowledge, and enhancing provider recommendations on the necessity of HPV vaccination.

COVID-19 Pandemic Prediction for Hungary; A Hybrid Machine Learning Approach

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

Mandatory vaccination for infants and children: the Italian experience

Maintaining high vaccination coverage is important in order to protect the individual and the community. Mandatory vaccination is an option in case of declining coverage. Widely used in the USA, it is considered a rather controversial issue in Europe. In Italy, after a decrease of vaccination coverage for the hexavalent and the MPR vaccine under the optimal threshold, a new law, which extended the number of mandatory vaccines from 4 to 10 and reinforced coercive measures, was introduced in July 2017. After 2 years, vaccination coverage increased for all mandatory vaccines and for the other two recommended vaccines (anti-pneumococcal and anti-meningococcal C). Although it is not possible to disentangle the role of other factors contributing to the positive outcome, consistently with the results of studies conducted in the USA, vaccine mandates appeared to be successful in increasing vaccination coverage in Italy. The long-term sustainability of the effect of mandatory vaccination and the potential negative drawbacks of the coercive measures need to be evaluated to generate scientific evidence in public health.