The role of vaccination coverage, individual behaviors, and the public health response in the control of measles epidemics: an agent-based simulation for California

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.

Agent-based model of the impact of higher influenza vaccine efficacy on seasonal influenza burden

Introduction

Current influenza vaccines have limited effectiveness. COVID-19 vaccines using mRNA technology have demonstrated very high efficacy, suggesting that mRNA vaccines could be more effective for influenza. Several such influenza vaccines are in development. FRED, an agent-based modeling platform, was used to estimate the impact of more effective influenza vaccines on seasonal influenza burden.

Methods

Simulations were performed using an agent-based model of influenza that included varying levels of vaccination efficacy (40-95 % effective). In some simulations, level of infectiousness and/or length of infectious period in agents with breakthrough infections was also decreased. Impact of increased and decreased levels of vaccine uptake were also modeled. Outcomes included number of symptomatic influenza cases estimated for the US.

Results

Highly effective vaccines significantly reduced estimated influenza cases in the model. When vaccine efficacy was increased from 40 % to a maximum of 95 %, estimated influenza cases in the US decreased by 43 % to > 99 %. The base simulation (40 % efficacy) resulted in ∼ 28 million total yearly cases in the US, while the most effective vaccine modeled (95 % efficacy) decreased estimated cases to ∼ 22,000.

Discussion

Highly effective vaccines could dramatically reduce influenza burden. Model estimates suggest that even modest increases in vaccine efficacy could dramatically reduce seasonal influenza disease burden.

Contact tracing & super-spreaders in the branching-process model

In recent years, it became clear that super-spreader events play an important role, particularly in the spread of airborne infections. We investigate a novel model for super-spreader events, not based on a heterogeneous contact graph but on a random contact rate: Many individuals become infected synchronously in single contact events. We use the branching-process approach for contact tracing to analyze the impact of super-spreader events on the effect of contact tracing. Here we neglect a tracing delay. Roughly speaking, we find that contact tracing is more efficient in the presence of super-spreaders if the fraction of symptomatics is small, the tracing probability is high, or the latency period is distinctively larger than the incubation period. In other cases, the effect of contact tracing can be decreased by super-spreaders. Numerical analysis with parameters suited for SARS-CoV-2 indicates that super-spreaders do not decrease the effect of contact tracing crucially in case of that infection.

Trends, barriers and enablers to measles immunisation coverage in Saskatchewan, Canada: A mixed methods study

Many social, cultural, and systemic challenges affect the uptake of measles immunisation services. Prior studies have looked at the caregivers' perspectives, but little is known about the perspectives of the health care providers on the barriers of measles immunisation services in Canada. This study examined measles immunisation coverage trends across the regional health authorities in Saskatchewan and explored the barriers and enablers to measles immunisation coverage from providers' perspectives. The study adopted an explanatory sequential mixed method. We utilized the entire population of 16,582 children under two years of age available in the Saskatchewan Immunisation Management System (SIMS) registry for 2002 and 2013 in aggregate format and interviewed 18 key informants in pre-determined two-stages in 2016 and 2017. The quantitative analysis was done with Joinpoint regression modelling, while the qualitative interview data was analyzed using hybrid inductive and deductive thematic approaches. There was a 16.89%-point increase in measles immunisation coverage in the province from 56.32% to 73.21% between 2002 and 2013. There was also a persistently higher coverage among the affluent (66.95% - 82.37%) than the most deprived individuals (45.79% - 62.60%) in the study period. The annual rate of coverage change was marginally higher among the most deprived (16.81%; and average annual percentage change (AAPC) 2.0, 95% CI 1.7-2.2) than among the affluent group (15.42% and AAPC 3.0; 95% CI 2.0-4.0). While access-related issues, caregivers' fears, hesitancy, anti-vaccination challenges, and resource limitations were barriers to immunisation, improving community engagement, service delivery flexibility, targeted social responses and increasing media role were found useful to address the uptake of measles and other vaccine-preventable diseases immunisation. There is low coverage and inequity in measles immunisation uptake in Saskatchewan from social and institutional barriers. Even though there is evidence of disparity reduction among the different groups, the barriers to increasing measles immunisation coverage have implications for the health of the socio-economically deprived groups, the healthcare system and other vaccination programs. There is a need to improve policy framework for community engagement, targeted programs, and public health discourse.

COVID's collateral damage: likelihood of measles resurgence in the United States

BACKGROUND

Lockdowns imposed throughout the US to control the COVID-19 pandemic led to a decline in all routine immunizations rates, including the MMR (measles, mumps, rubella) vaccine. It is feared that post-lockdown, these reduced MMR rates will lead to a resurgence of measles.

METHODS

To measure the potential impact of reduced MMR vaccination rates on measles outbreak, this research examines several counterfactual scenarios in pre-COVID-19 and post-COVID-19 era. An agent-based modeling framework is used to simulate the spread of measles on a synthetic yet realistic social network of Virginia. The change in vulnerability of various communities to measles due to reduced MMR rate is analyzed.

RESULTS

Results show that a decrease in vaccination rate [Formula: see text] has a highly non-linear effect on the number of measles cases and this effect grows exponentially beyond a threshold [Formula: see text]. At low vaccination rates, faster isolation of cases and higher compliance to home-isolation are not enough to control the outbreak. The overall impact on urban and rural counties is proportional to their population size but the younger children, African Americans and American Indians are disproportionately infected and hence are more vulnerable to the reduction in the vaccination rate.

CONCLUSIONS

At low vaccination rates, broader interventions are needed to control the outbreak. Identifying the cause of the decline in vaccination rates (e.g., low income) can help design targeted interventions which can dampen the disproportional impact on more vulnerable populations and reduce disparities in health. Per capita burden of the potential measles resurgence is equivalent in the rural and the urban communities and hence proportionally equitable public health resources should be allocated to rural regions.

Measles epidemic in Southern Vietnam: an age-stratified spatio-temporal model for infectious disease counts

Measles resurged in Vietnam between 2018 and 2020, especially in the Southern region. The proportion of children with measles infection showed quite some variation at the provincial level. We applied a spatio-temporal endemic-epidemic modelling framework for age-stratified infectious disease counts using measles surveillance data collected in Southern Vietnam between 1 January 2018 and 30 June 2020. We found that disease transmission within age groups was greatest in young children aged 0-4 years whereas a relatively high between-group transmission was observed in older age groups (5-14 years, 15-24 years and 25+ years groups). At the provincial level, spatial transmission followed an age-dependent distance decay with measles spread mainly depending on local and neighbouring transmission. Our study helped to clarify the measles transmission dynamics in a more detailed fashion with respect to age strata, time and space. Findings from this study may help determine proper strategies in measles outbreak control including promotion of age-targeted intervention programmes in specific areas.

National rates and disparities in childhood vaccination and vaccine-preventable disease during the COVID-19 pandemic: English sentinel network retrospective database study

OBJECTIVES

To describe rates and variation in uptake of pneumococcal and measles, mumps and rubella (MMR) vaccines in children and associated change in vaccine-preventable diseases (VPDs) across the first and second waves of the COVID-19 pandemic.

METHODS

Retrospective database study of all children aged <19 registered with a general practice in the Oxford Royal College of General Practitioners Research and Surveillance Centre English national sentinel surveillance network between 2 November 2015 and 18 July 2021.

RESULTS

Coverage of booster dose of pneumococcal vaccine decreased from 94.5% (95% CI 94.3% to 94.7%) at its height on International Organization for Standardization (ISO) week 47 (2020) to 93.6% (95% CI 93.4% to 93.8%) by the end of the study. Coverage of second dose of MMR decreased from 85.0% (95% CI 84.7% to 85.3%) at its height on ISO week 37 (2020) to 84.1% (95% CI 83.8% to 84.4%) by the end of the study. The break point in trends for MMR was at ISO week 34 (2020) (95% CI weeks 32-37 (2020)), while for pneumococcal vaccine the break point was later at ISO week 3 (2021) (95% CI week 53 (2020) to week 8 (2021)). Vaccination coverage for children of white ethnicity was less likely to decrease than other ethnicities. Rates of consultation for VPDs fell and remained low since August 2020.

CONCLUSION

Childhood vaccination rates started to fall ahead of the onset of the second wave; this fall is accentuating ethnic, socioeconomic and geographical disparities in vaccine uptake and risks widening health disparities. Social distancing and school closures may have contributed to lower rates of associated VPDs, but there may be increased risk as these measures are removed.

Non-Pharmaceutical Interventions as Controls to mitigate the spread of epidemics: An analysis using a spatiotemporal PDE model and COVID-19 data

We investigate the spatiotemporal dynamics and control of an epidemic using a partial differential equation (PDE) based Susceptible-Latent-Infected-Recovered (SLIR) model. We first validate the model using empirical COVID-19 data corresponding to a period of 45 days from the state of Ohio, United States. Upon optimizing the model parameters in the learning phase of the analysis using actual infection data from a period of the first 30 days, we then find that the model output closely tracks the actual data for the next 15 days. Next, we introduce a control input into the model to represent the Non-Pharmaceutical Intervention of social distancing. Implementing the control using two distinct schemes, we find that in both cases the control input is able to significantly mitigate the infection spread. In addition to opening a novel pathway towards the characterization, analysis and implementation of Non-Pharmaceutical Interventions across multiple geographical scales using Control frameworks, our results highlight the importance of first-principles based PDE models in understanding the spatiotemporal dynamics of epidemics triggered by novel pathogens.

Impact of Low Rates of Influenza on Next-Season Influenza Infections

INTRODUCTION

Interventions to curb the spread of COVID-19 during the 2020-2021 influenza season essentially eliminated influenza during that season. Given waning antibody titers over time, future residual population immunity against influenza will be reduced. The implication for the subsequent 2021-2022 influenza season is unknown.

METHODS

An agent-based model of influenza implemented in the Framework for Reconstructing Epidemiological Dynamics simulation platform was used to estimate cases and hospitalizations over 2 successive influenza seasons. The impact of reduced residual immunity owing to protective measures in the first season was estimated over varying levels of similarity (cross-immunity) between influenza strains over the seasons.

RESULTS

When cross-immunity between first- and second-season strains was low, a decreased first season had limited impact on second-season cases. High levels of cross-immunity resulted in a greater impact on the second season. This impact was modified by the transmissibility of strains in the 2 seasons. The model estimated a possible increase of 13.52%-46.95% in cases relative to that in a normal season when strains have the same transmissibility and 40%-50% cross-immunity in a season after a very low one.

CONCLUSIONS

Given the light 2020-2021 influenza season, cases may increase by as much as 50% in 2021-2022, although the increase could be much less, depending on cross-immunity from past infection and transmissibility of strains. Enhanced vaccine coverage or continued interventions to reduce transmission could reduce this high season. Young children may have a higher risk in 2021-2022 owing to limited exposure to infection in the previous year.

Planning as Inference in Epidemiological Dynamics Models

In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of inference tasks undertaken include computing the posterior distribution over controllable, via direct policy-making choices, simulation model parameters that give rise to acceptable disease progression outcomes. Among other things, we illustrate the use of a probabilistic programming language that automates inference in existing simulators. Neither the full capabilities of this tool for automating inference nor its utility for planning is widely disseminated at the current time. Timely gains in understanding about how such simulation-based models and inference automation tools applied in support of policy-making could lead to less economically damaging policy prescriptions, particularly during the current COVID-19 pandemic.

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.

Cost-effectiveness and public health impact of RTS,S/AS01 E malaria vaccine in Malawi, using a Markov static model

Background: The RTS,S/AS01 _E malaria vaccine is being assessed in Malawi, Ghana and Kenya as part of a large-scale pilot implementation programme. Even if impactful, its incorporation into immunisation programmes will depend on demonstrating cost-effectiveness. We analysed the cost-effectiveness and public health impact of the RTS,S/AS01 _E malaria vaccine use in Malawi.

Methods: We calculated the Incremental Cost Effectiveness Ratio (ICER) per disability-adjusted life year (DALY) averted by vaccination and compared it to Malawi’s mean per capita Gross Domestic Product. We used a previously validated Markov model, which simulated malaria progression in a 2017 Malawian birth cohort for 15 years. We used a 46% vaccine efficacy, 75% vaccine coverage, USD5 estimated cost per vaccine dose, published local treatment costs for clinical malaria and Malawi specific malaria indicators for interventions such as bed net and antimalarial use. We took a healthcare provider, household and societal perspective. Costs were discounted at 3% per year, no discounting was applied to DALYs. For public health impact, we calculated the DALYs, and malaria events averted.

Results: The ICER/DALY averted was USD115 and USD109 for the health system perspective and societal perspective respectively, lower than GDP per capita of USD398.6 for Malawi. Sensitivity analyses exploring the impact of variation in vaccine costs, vaccine coverage rate and coverage of four doses showed vaccine implementation would be cost-effective across a wide range of different outcomes. RTS,S/AS01 was predicted to avert a median of 93,940 (range 20,490–126,540) clinical cases and 394 (127–708) deaths for the three-dose schedule, or 116,480 (31,450–160,410) clinical cases and 484 (189–859) deaths for the four-dose schedule, per 100 000 fully vaccinated children.

Conclusions: We predict the introduction of the RTS,S/AS01 vaccine in the Malawian expanded programme of immunisation (EPI) likely to be highly cost effective.

Pragmatic cluster randomised control trial using Vaxcards as an age-appropriate tool to incentivise and educate school students about vaccination

OBJECTIVE

This trial aimed to determine if return rates of consent forms for vaccination could be improved when Vaxcards were offered as an incentive to school children.

SETTING

Nineteen schools in South East Melbourne participated.

INTERVENTIONS

Students in the experimental arm received a pack of Vaxcards when they returned their government consent form.

OUTCOME MEASURES

Return of 'yes' consent forms for vaccination as part of a local government council vaccine programme was the primary outcome of this trial. Return rates were compared between the intervention and control schools and with historical return rates.

RESULTS

Secondary school students (N=3087) from 19 schools participated. Compared with historical returns, a small global reduction in 'yes' responses to consent forms of -4.21% in human papilloma virus consent 'yes' responses and -4.69% for diphtheria, tetanus and pertussis was observed across all schools. No difference between the experimental and control groups was observed.

CONCLUSIONS

Low 'yes' consent rates and reduction in consent rates between 2018 and 2019 for all groups are concerning. This finding highlights the need for behaviour change interventions across all groups to increase vaccine confidence. Lack of effect of incentivisation with Vaxcards in this study may have been due to the timing of receiving the cards (after the decision to vaccinate had been made, not before) and the limited intensity of the intervention. Optimising the timing and the intensity of exposure to Vaxcards could improve the outcome.

TRIAL REGISTRATION NUMBER

ACTRN12618001753246.

Agent-Based Simulation Framework for Epidemic Forecasting during Hajj Seasons in Saudi Arabia

The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.

Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection

Capturing the coupled dynamics between individual behavioural decisions that affect disease transmission and the epidemiology of outbreaks is critical to pandemic mitigation strategy. We develop a multiplex network approach to model how adherence to health-protective behaviours that impact COVID-19 spread are shaped by perceived risks and resulting community norms. We focus on three synergistic dynamics governing individual behavioural choices: (i) social construction of concern, (ii) awareness of disease incidence, and (iii) reassurance by lack of disease. We show why policies enacted early or broadly can cause communities to become reassured and therefore unwilling to maintain or adopt actions. Public health policies for which success relies on collective action should therefore exploit the behaviourally receptive phase; the period between the generation of sufficient concern to foster adoption of novel actions and the relaxation of adherence driven by reassurance fostered by avoidance of negative outcomes over time.

Trends in measles incidence and measles vaccination coverage in Nigeria, 2008-2018

INTRODUCTION

All WHO regions have set measles elimination objective for 2020. To address the specific needs of achieving measles elimination, Nigeria is using a strategy focusing on improving vaccination coverage with the first routine dose of (monovalent) measles (MCV1) at 9 months, providing measles vaccine through supplemental immunization activities (children 9-59 months), and intensified measles case-based surveillance system.

METHODS

We reviewed measles immunization coverage from population-based surveys conducted in 2010, 2013 and 2017-18. Additionally, we analyzed measles case-based surveillance reports from 2008-2018 to determine annual, regional and age-specific incidence rates.

FINDINGS

Survey results indicated low MCV1 coverage (54.0% in 2018); with lower coverage in the North (mean 45.5%). Of the 153,097 confirmed cases reported over the studied period, 85.5% (130,871) were from the North. Moreover, 70.8% (108,310) of the confirmed cases were unvaccinated. Annual measles incidence varied from a high of 320.39 per 1,000,000 population in 2013 to a low of 9.80 per 1,000,000 in 2009. The incidence rate is higher among the 9-11 months (524.0 per million) and 12-59 months (376.0 per million). Between 2008 and 2018, the incidence rate had showed geographical variation, with higher incidence in the North (70.6 per million) compare to the South (17.8 per million).

CONCLUSION

The aim of this study was to provide a descriptive analysis of measles vaccine coverage and incidence in Nigeria from 2008 to 2018 to assess country progress towards measles elimination. Although the total numbers of confirmed measles cases had decreased over the time period, measles routine coverage remains sub-optimal, and the incidence rates are critically high. The high burden of measles in the North highlight the need for region-specific interventions. The measles program relies heavily on polio resources. As the polio program winds down, strong commitments will be required to achieve elimination goals.

COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

Resources management impact on neonatal services performance in the United Kingdom: A system dynamics modelling approach

Demand for neonatal care in the United Kingdom (UK) has increased in recent years. This care is provided by neonatal services, which are chronically saturated due to years of budget austerity in the UK. The aim of this paper is to investigate the possible impact of increasing resources to these services to improve their operational performance and alleviate the pressure they are facing. To achieve this aim, a system dynamics (SD) simulation model was built and validated in a UK neonatal unit. The SD model was used initially to evaluate the impact of increasing resources on the unit performance and the results showed that this policy will have a limited effect on performance. The model was then extended to predict the effect of reducing the length of stay (LoS) in conjunction with increasing resources. These joint interventions will have a positive impact on the unit performance if LoS is reduced for all care categories and resources are slightly increased. Results' implications and SD's modelling usefulness to guide decision making in complex health settings are discussed.

Contact tracing - Old models and new challenges

Contact tracing is an effective method to control emerging infectious diseases. Since the 1980's, modellers are developing a consistent theory for contact tracing, with the aim to find effective and efficient implementations, and to assess the effects of contact tracing on the spread of an infectious disease. Despite the progress made in the area, there remain important open questions. In addition, technological developments, especially in the field of molecular biology (genetic sequencing of pathogens) and modern communication (digital contact tracing), have posed new challenges for the modelling community. In the present paper, we discuss modelling approaches for contact tracing and identify some of the current challenges for the field.

Preferences for a COVID-19 vaccine in Australia

In absence of a COVID-19 vaccine, testing, contact tracing and social restrictions are among the most powerful strategies adopted around the world to slow down the spread of the pandemic. Citizens of most countries are suffering major physical, psychological and economic distress. At this stage, a safe and effective COVID-19 vaccine is the most sustainable option to manage the current pandemic. However, vaccine hesitancy by even a small subset of the population can undermine the success of this strategy.

The objective of this research is to investigate the vaccine characteristics that matter the most to Australian citizens and to explore the potential uptake of a COVID-19 vaccine in Australia. Through a stated preference experiment, preferences towards a COVID-19 vaccine of 2136 residents of the Australian states and territories were collected and analysed via a latent class model.

Results show that preferences for mild adverse cases, mode of administration, location of administration, price and effectiveness are heterogeneous. Conversely, preferences for immediacy and severe reactions are homogeneous, with respondents preferring a shorter period until vaccine is available and lower instances of severe side effects. The expected uptake of the vaccine is estimated under three different scenarios, with the value of 86% obtained for an average scenario. By calculating individual preferences, the willingness to pay is estimated for immediacy, effectiveness, mild and severe side effects.

Cost-effectiveness and public health impact of RTS,S/AS01E malaria vaccine in Malawi, using a Markov static model

Background: The RTS,S/AS01E malaria vaccine is being assessed in Malawi, Ghana and Kenya as part of a large-scale pilot implementation programme. Even if impactful, its incorporation into immunisation programmes will depend on demonstrating cost-effectiveness. We analysed the cost-effectiveness and public health impact of the RTS,S/AS01E malaria vaccine use in Malawi. Methods: We calculated the Incremental Cost Effectiveness Ratio (ICER) per disability-adjusted life year (DALY) averted by vaccination and compared it to Malawi’s mean per capita Gross Domestic Product. We used a previously validated Markov model, which simulated malaria progression in a 2017 Malawian birth cohort for 15 years. We used a 46% vaccine efficacy, 75% vaccine coverage, USD5 estimated cost per vaccine dose, published local treatment costs for clinical malaria and Malawi specific malaria indicators for interventions such as bed net and antimalarial use. We took a healthcare provider, household and societal perspective. Costs were discounted at 3% per year, no discounting was applied to DALYs. For public health impact, we calculated the DALYs, and malaria events averted. Results: The ICER/DALY averted was USD115 and USD109 for the health system perspective and societal perspective respectively, lower than GDP per capita of USD398.6 for Malawi. Sensitivity analyses exploring the impact of variation in vaccine costs, vaccine coverage rate and coverage of four doses showed vaccine implementation would be cost-effective across a wide range of different outcomes. RTS,S/AS01 was predicted to avert a median of 93,940 (range 20,490–126,540) clinical cases and 394 (127–708) deaths for the three-dose schedule, or 116,480 (31,450–160,410) clinical cases and 484 (189–859) deaths for the four-dose schedule, per 100 000 fully vaccinated children. Conclusions: We predict the introduction of the RTS,S/AS01 vaccine in the Malawian expanded programme of immunisation (EPI) likely to be highly cost effective.

Critical spatial clusters for vaccine preventable diseases

The standard public health intervention for controlling the spread of highly contagious diseases, such as measles, is to vaccinate a large fraction of the population. However, it has been shown that in some parts of the United States, even though the average vaccination rate is high, geographical clusters of undervaccinated populations are emerging. Given that public health resources for response are limited, identifying and rank-ordering critical clusters can help prioritize and allocate scarce resources for surveillance and quick intervention. We quantify the criticality of a cluster as the additional number of infections caused if the immunization rate in a cluster reduces. This notion of criticality has not been studied before, and, based on clusters identified in prior research, we show that the current underimmunization rate in the cluster, and its criticality are not correlated. We apply our methods to a population model for the state of Minnesota, where we find undervaccinated clusters with significantly higher criticality than those obtained by other natural heuristics.

Risk of disease and willingness to vaccinate in the United States: A population-based survey

BACKGROUND

Vaccination complacency occurs when perceived risks of vaccine-preventable diseases are sufficiently low so that vaccination is no longer perceived as a necessary precaution. Disease outbreaks can once again increase perceptions of risk, thereby decrease vaccine complacency, and in turn decrease vaccine hesitancy. It is not well understood, however, how change in perceived risk translates into change in vaccine hesitancy. We advance the concept of vaccine propensity, which relates a change in willingness to vaccinate with a change in perceived risk of infection-holding fixed other considerations such as vaccine confidence and convenience.

METHODS AND FINDINGS

We used an original survey instrument that presents 7 vaccine-preventable "new" diseases to gather demographically diverse sample data from the United States in 2018 (N = 2,411). Our survey was conducted online between January 25, 2018, and February 2, 2018, and was structured in 3 parts. First, we collected information concerning the places participants live and visit in a typical week. Second, participants were presented with one of 7 hypothetical disease outbreaks and asked how they would respond. Third, we collected sociodemographic information. The survey was designed to match population parameters in the US on 5 major dimensions: age, sex, income, race, and census region. We also were able to closely match education. The aggregate demographic details for study participants were a mean age of 43.80 years, 47% male and 53% female, 38.5% with a college degree, and 24% nonwhite. We found an overall change of at least 30% in proportion willing to vaccinate as risk of infection increases. When considering morbidity information, the proportion willing to vaccinate went from 0.476 (0.449-0.503) at 0 local cases of disease to 0.871 (0.852-0.888) at 100 local cases (upper and lower 95% confidence intervals). When considering mortality information, the proportion went from 0.526 (0.494-0.557) at 0 local cases of disease to 0.916 (0.897-0.931) at 100 local cases. In addition, we ffound that the risk of mortality invokes a larger proportion willing to vaccinate than mere morbidity (P = 0.0002), that older populations are more willing than younger (P<0.0001), that the highest income bracket (>$90,000) is more willing than all others (P = 0.0001), that men are more willing than women (P = 0.0011), and that the proportion willing to vaccinate is related to both ideology and the level of risk (P = 0.004). Limitations of this study include that it does not consider how other factors (such as social influence) interact with local case counts in people's vaccine decision-making, it cannot determine whether different degrees of severity in morbidity or mortality failed to be statistically significant because of survey design or because participants use heuristically driven decision-making that glosses over degrees, and the study does not capture the part of the US that is not online.

CONCLUSIONS

In this study, we found that different degrees of risk (in terms of local cases of disease) correspond with different proportions of populations willing to vaccinate. We also identified several sociodemographic aspects of vaccine propensity. Understanding how vaccine propensity is affected by sociodemographic factors is invaluable for predicting where outbreaks are more likely to occur and their expected size, even with the resulting cascade of changing vaccination rates and the respective feedback on potential outbreaks.

Estimation of effects of contact tracing and mask adoption on COVID-19 transmission in San Francisco: a modeling study

The current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco's shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, -20.1%-81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.

Integrating Social Dynamics Into Modeling Cigarette and E-Cigarette Use

Background. The use of electronic cigarettes (e-cigarette) offers potential to facilitate cigarette smoking cessation, yet potentially increases risk of cigarette smoking initiation. This relationship has been primarily modeled in mathematical ways that often do not represent real-world complexities, which could inform decisions regarding local prevention programs or policies. Aims. To develop a model of cigarette and e-cigarette use that combines current research on tobacco use and incorporates real-world geographic and demographic data. Method. We used a platform for developing agent-based models with demographic information representative of the population in Pennsylvania. We developed three models of cigarette and e-cigarette use. The primary outcome for each was the total number of users for cigarette, e-cigarette, and total nicotine. The first model applied current cigarette and e-cigarette data, the second tested the effect of implementing a program of e-cigarette education and policies, and the third considered a social contagion factor, where local schools functioned as a transmission vector. Results. The baseline and social contagion models found an overall decline in cigarette use but an increase in e-cigarette and total nicotine use. The education/policies model had declines in all categories. Sensitivity analysis suggested the importance of nuanced e-cigarette/cigarette interactions when modeling tobacco use. Discussion. Public health campaigns that focus on reducing youth e-cigarette usage can have a large effect. Social contagion should be strongly considered when studying e-cigarette spread. Conclusion. Targeted public health campaigns focused on reducing school prevalence of e-cigarette use may be particularly valuable.

Factors affecting the vaccination choices of pregnant women for their children: a systematic review of the literature

In recent years, an increase in vaccine hesitancy has led to a decrease in vaccination coverage in several countries. We conducted a systematic review of studies that assessed knowledge of and attitudes toward pediatric vaccinations, and the vaccination choices and their determinants among pregnant women. A total of 6,277 records were retrieved, and 16 full texts were included in the narrative synthesis. The published literature on the topic shows that, overall, pregnant women believe that vaccines are important for the protection of their children and the community, but various concerns and misunderstandings persist around vaccine safety and efficacy, which reduce the trust of expectant mothers in immunization. Nevertheless, such attitudes and choices vary depending on the vaccine being considered and the corresponding determinants should therefore be studied in the context of each specific vaccination. Further research on this topic is needed, particularly in non-western countries.

An Agent-Based Infectious Disease Model of Rubella Outbreaks

This study proposes a simulation model of rubella. SIR (Susceptible, Infected, Recovered) model has been widely used to analyse infectious diseases such as influenza, smallpox, bioterrorism, to name a few. On the other hand, agent-based model begins to spread in recent years. The model enables to represent the behaviour of each person on the computer. It also reveals the spread of infection by simulation of the contact process among people in the model. The study designs a model based on smallpox and Ebola fever model in which several health policies are decided such as vaccination, the gender-specific workplace and so on. The infectious simulation of rubella, which has not yet vaccinated completely for men in Japan, is implemented in the model. As results of experiments using the model, it has been found that preventive vaccine to all the men is crucial factors to prevent the spread in women.

The SIPHER Consortium: Introducing the new UK hub for systems science in public health and health economic research

The conditions in which we are born, grow, live, work and age are key drivers of health and inequalities in life chances. To maximise health and wellbeing across the whole population, we need well-coordinated action across government sectors, in areas including economic, education, welfare, labour market and housing policy. Current research struggles to offer effective decision support on the cross-sector strategic alignment of policies, and to generate evidence that gives budget holders the confidence to change the way major investment decisions are made. This open letter introduces a new research initiative in this space. The SIPHER ( Systems Science in Public Health and Health Economics Research) Consortium brings together a multi-disciplinary group of scientists from across six universities, three government partners at local, regional and national level, and ten practice partner organisations. The Consortium’s vision is a shift from health policy to healthy public policy, where the wellbeing impacts of policies are a core consideration across government sectors. Researchers and policy makers will jointly tackle fundamental questions about: a) the complex causal relationships between upstream policies and wellbeing, economic and equality outcomes; b) the multi-sectoral appraisal of costs and benefits of alternative investment options; c) public values and preferences for different outcomes, and how necessary trade-offs can be negotiated; and d) creating the conditions for intelligence-led adaptive policy design that maximises progress against economic, social and health goals. Whilst our methods will be adaptable across policy topics and jurisdictions, we will initially focus on four policy areas: Inclusive Economic Growth, Adverse Childhood Experiences, Mental Wellbeing and Housing.

Measles transmission during a large outbreak in California

A large measles outbreak in 2014–2015, linked to Disneyland theme parks, attracted international attention, and led to changes in California vaccine policy. We use dates of symptom onset and known epidemic links for California cases in this outbreak to estimate time-varying transmission in the outbreak, and to estimate generation membership of cases probabilistically. We find that transmission declined significantly during the course of the outbreak (p = 0.012), despite also finding that estimates of transmission rate by day or by generation can overestimate temporal decline. We additionally find that the outbreak size and duration alone are sufficient in this case to distinguish temporal decline from time-invariant transmission (p = 0.014). As use of a single large outbreak can lead to underestimates of immunity, however, we urge caution in interpretation of quantities estimated from this outbreak alone. Further research is needed to distinguish causes of temporal decline in transmission rates.

Genotype-Specific Measles Transmissibility: A Branching Process Analysis

Background

Substantial heterogeneity in measles outbreak sizes may be due to genotype-specific transmissibility. Using a branching process analysis, we characterize differences in measles transmission by estimating the association between genotype and the reproduction number R among postelimination California measles cases during 2000-2015 (400 cases, 165 outbreaks).

Methods

Assuming a negative binomial secondary case distribution, we fit a branching process model to the distribution of outbreak sizes using maximum likelihood and estimated the reproduction number R for a multigenotype model.

Results

Genotype B3 is found to be significantly more transmissible than other genotypes (P = .01) with an R of 0.64 (95% confidence interval [CI], .48-.71), while the R for all other genotypes combined is 0.43 (95% CI, .28-.54). This result is robust to excluding the 2014-2015 outbreak linked to Disneyland theme parks (referred to as "outbreak A" for conciseness and clarity) (P = .04) and modeling genotype as a random effect (P = .004 including outbreak A and P = .02 excluding outbreak A). This result was not accounted for by season of introduction, age of index case, or vaccination of the index case. The R for outbreaks with a school-aged index case is 0.69 (95% CI, .52-.78), while the R for outbreaks with a non-school-aged index case is 0.28 (95% CI, .19-.35), but this cannot account for differences between genotypes.

Conclusions

Variability in measles transmissibility may have important implications for measles control; the vaccination threshold required for elimination may not be the same for all genotypes or age groups.

Forecasted Size of Measles Outbreaks Associated With Vaccination Exemptions for Schoolchildren

IMPORTANCE

Vaccine exemptions, which allow unvaccinated children to attend school, have increased by a factor of 28 since 2003 in Texas. Geographic clustering of unvaccinated children facilitates the spread of measles introductions, but the potential size of outbreaks is unclear.

OBJECTIVE

To forecast the range of measles outbreak sizes in each metropolitan area of Texas at 2018 and future reduced school vaccination rates.

DESIGN, SETTING, AND PARTICIPANTS

An agent-based decision analytical model using a synthetic population of Texas, derived from the 2010 US Census, was used to simulate measles transmission in the Texas population. Real schools were represented in the simulations, and the 2018 vaccination rate of each real school was applied to a simulated hypothetical equivalent. Single cases of measles were introduced, daily activities and interactions were modeled for each population member, and the number of infections over the course of 9 months was counted for 1000 simulated runs in each Texas metropolitan area.

INTERVENTIONS

To determine the outcomes of further decreases in vaccination coverage, additional simulations were performed with vaccination rates reduced by 1% to 10% in schools with populations that are currently undervaccinated.

MAIN OUTCOMES AND MEASURES

Expected distributions of outbreak sizes in each metropolitan area of Texas at 2018 and reduced vaccination rates.

RESULTS

At 2018 vaccination rates, the median number of cases in large metropolitan areas was typically small, ranging from 1 to 3 cases, which is consistent with outbreaks in Texas 2006 to 2017. However, the upper limit of the distribution of plausible outbreaks (the 95th percentile, associated with 1 in 20 measles introductions) exceeded 400 cases in both the Austin and Dallas metropolitan areas, similar to the largest US outbreaks since measles was eliminated in 2000. Decreases in vaccination rates in schools with undervaccinated populations in 2018 were associated with exponential increases in the potential size of outbreaks: a 5% decrease in vaccination rate was associated with a 40% to 4000% increase in potential outbreak size, depending on the metropolitan area. A mean (SD) of 64% (11%) of cases occurred in students for whom a vaccine had been refused, but a mean (SD) of 36% (11%) occurred in others (ie, bystanders).

CONCLUSIONS AND RELEVANCE

This study suggests that vaccination rates in some Texas schools are currently low enough to allow large measles outbreaks. Further decreases are associated with dramatic increases in the probability of large outbreaks. Limiting vaccine exemptions could be associated with a decrease in the risk of large measles outbreaks.

Knowledge and attitudes on pediatric vaccinations and intention to vaccinate in a sample of pregnant women from the City of Rome

BACKGROUND

In recent years, pediatric immunization rates in Italy have decreased well below the recommended thresholds, largely due to an increase in scepticism about the efficacy and safety of vaccines. We aimed to identify the degree of such scepticism, and the factors driving it, among a sample of pregnant women in the City of Rome.

METHODS

We conducted a cross-sectional survey on a sample of pregnant women attending antenatal classes (CANs) in Rome through distribution of a self-administered questionnaire. Multiple logistic regression models were built to analyze the determinants of knowledge, attitudes and intention to vaccinate in this population.

RESULTS

A total of 458 pregnant women attending CANs in 36 family health centers and two hospitals in Rome answered the survey. Mean age was 32.9 (±5.0) years, and over 90% of women were in their first pregnancy. More than 26% of respondents showed a good level of knowledge of the safety and efficacy of vaccines, but there were high rates of uncertainty or agreement with some of the most common anti-vaccination sentiments. Only 75% of women were sure about vaccinating their children with the hexavalent vaccine, and 64.3% with MMR. A good level of knowledge was the strongest predictor of positive attitudes towards vaccination (OR 11.61, 95% CI 6.43-20.96), which, in turn, influenced the intention to vaccinate for most vaccines with the perception of the benefit of immunization for protection against disease.

CONCLUSIONS

Scepticism about the safety, efficacy and importance of vaccines is associated to pregnant women's hesitancy to vaccinate their children, suggesting the need to develop strategies to increase vaccine acceptance in the antenatal period. The capacity of health care professionals, particularly midwives, to correctly deliver information to future parents should be strengthened in order to reduce the spread of misinformation and fear of vaccine safety.

Epidemiological characteristics and trends of a Nationwide measles outbreak in Mongolia, 2015-2016

Background

Mongolia was one of the four countries that received a measles-elimination certificate from the World Health Organization Regional Office for the Western Pacific in 2014. Following the outbreaks in many countries including China, a large measles outbreak occurred in Mongolia in 2015. This study reports 2015–2016 measles outbreak incidence, mortality, and complications, according to time, geographical distribution, and host characteristics.

Methods

The epidemiological characteristics and trends of measles outbreak were analyzed using the Mongolian national surveillance data reported to the Center for Health Development, Ministry of Health, from January 2015 to December 2016.

Results

In total, 23,464 cases of measles including eight deaths were reported in 2015, and 30,273 cases of measles including 132 deaths were reported in 2016, which peaked in June 2015 and March 2016, respectively. Majority of the cases were reported from Ulaanbaatar (35,397, 65.9%). The highest attack rates were 241 per 10,000 population in Darkhan-Uul aimag, and 263 per 10,000 population in Ulaanbaatar. Measles-related death, nosocomial infection, and complications were most frequent among children aged < 1 year.

Conclusions

Following no reports of measles since 2011, a large nationwide outbreak occurred in Mongolia, despite the high vaccination coverage in the past. The highest incidence rate was reported in Ulaanbaatar city, and Umnugovi aimag in 2015 and Darkhan-Uul aimag in 2016. The most affected age group were aged < 1 year and those aged 15–24 years. Mortality cases were prominent among children aged < 1 year who were not eligible for vaccination. A systematic vaccination strategy is required to prevent another measles outbreak.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-6511-0) contains supplementary material, which is available to authorized users.

Discovery of under immunized spatial clusters using network scan statistics

BACKGROUND

Clusters of under-vaccinated children are emerging in a number of states in the United States due to rising rates of vaccine hesitancy and refusal. As the measles outbreaks in California and other states in 2015 and in Minnesota in 2017 showed, such clusters can pose a significant public health risk. Prior methods have used publicly-available school immunization data for analysis (except for a few, which use private healthcare patient records). School immunization data has limited demographic information-as a result, such analyses are not able to provide demographic characteristics of significant clusters. Further, the resolution of the clusters identified by prior methods is limited since they are typically restricted to disks or well-rounded shapes.

METHODS

We use realistic population models for Minnesota (MN) and Washington (WA) state, which provide a model of activities for all individuals in the population. We combine this with school level immunization data for these two states, to estimate vaccine coverage at the level of census block groups. A scan statistic method defined on networks is used for finding significant clusters of under-immunized block groups, without any restrictions on shape. Further we provide the demographic characteristics of these clusters.

RESULTS

We find 2 significant under-vaccinated clusters in MN and 3 in WA. These are very irregular in shape, in contrast to the circular disks reported in prior work, which rely on the SatScan approach. Some of the clusters found by our method are not contained in those computed using SatScan, a state-of-the-art software tool used in similar studies in other states.

CONCLUSIONS

The emergence of under-immunized clusters is a growing concern for public health agencies because they can act as reservoirs of infection and increase the risk of infection into the wider population. Higher resolution clusters computed using our network based approach and population models provide new insights on the structure and characteristics of such clusters and enable targeted interventions.

Infection prevention behaviour and infectious disease modelling: a review of the literature and recommendations for the future

Background

Given the importance of person to person transmission in the spread of infectious diseases, it is critically important to ensure that human behaviour with respect to infection prevention is appropriately represented within infectious disease models. This paper presents a large scale scoping review regarding the incorporation of infection prevention behaviour in infectious disease models. The outcomes of this review are contextualised within the psychological literature concerning health behaviour and behaviour change, resulting in a series of key recommendations for the incorporation of human behaviour in future infectious disease models.

Methods

The search strategy focused on terms relating to behaviour, infectious disease and mathematical modelling. The selection criteria were developed iteratively to focus on original research articles that present an infectious disease model with human-human spread, in which individuals’ self-protective health behaviour varied endogenously within the model. Data extracted included: the behaviour that is modelled; how this behaviour is modelled; any theoretical background for the modelling of behaviour, and; any behavioural data used to parameterise the models.

Results

Forty-two papers from an initial total of 2987 were retained for inclusion in the final review. All of these papers were published between 2002 and 2015. Many of the included papers employed a multiple, linked models to incorporate infection prevention behaviour. Both cognitive constructs (e.g., perceived risk) and, to a lesser extent, social constructs (e.g., social norms) were identified in the included papers. However, only five papers made explicit reference to psychological health behaviour change theories. Finally, just under half of the included papers incorporated behavioural data in their modelling.

Conclusions

By contextualising the review outcomes within the psychological literature on health behaviour and behaviour change, three key recommendations for future behavioural modelling are made. First, modellers should consult with the psychological literature on health behaviour/ behaviour change when developing new models. Second, modellers interested in exploring the relationship between behaviour and disease spread should draw on social psychological literature to increase the complexity of the social world represented within infectious disease models. Finally, greater use of context-specific behavioural data (e.g., survey data, observational data) is recommended to parameterise models.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5223-1) contains supplementary material, which is available to authorized users.

Spatial clustering of measles vaccination coverage among children in sub-Saharan Africa

Background

During the past two decades, vaccination programs have greatly reduced global morbidity and mortality due to measles, but recently this progress has stalled. Even in countries that report high vaccination coverage rates, transmission has continued, particularly in spatially clustered subpopulations with low vaccination coverage.

Methods

We examined the spatial heterogeneity of measles vaccination coverage among children aged 12–23 months in ten Sub-Saharan African countries. We used the Anselin Local Moran’s I to estimate clustering of vaccination coverage based on data from Demographic and Health Surveys conducted between 2008 and 2013. We also examined the role of sociodemographic factors to explain clustering of low vaccination.

Results

We detected 477 spatial clusters with low vaccination coverage, many of which were located in countries with relatively high nationwide vaccination coverage rates such as Zambia and Malawi. We also found clusters in border areas with transient populations. Clustering of low vaccination coverage was related to low health education and limited access to healthcare.

Conclusions

Systematically monitoring clustered populations with low vaccination coverage can inform supplemental immunization activities and strengthen elimination programs. Metrics of spatial heterogeneity should be used routinely to determine the success of immunization programs and the risk of disease persistence.

Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015)

Background

Individual-based models (IBMs) are useful to simulate events subject to stochasticity and/or heterogeneity, and have become well established to model the potential (re)emergence of pathogens (e.g., pandemic influenza, bioterrorism). Individual heterogeneity at the host and pathogen level is increasingly documented to influence transmission of endemic diseases and it is well understood that the final stages of elimination strategies for vaccine-preventable childhood diseases (e.g., polio, measles) are subject to stochasticity. Even so it appears IBMs for both these phenomena are not well established. We review a decade of IBM publications aiming to obtain insights in their advantages, pitfalls and rationale for use and to make recommendations facilitating knowledge transfer within and across disciplines.

Methods

We systematically identified publications in Web of Science and PubMed from 2006-2015 based on title/abstract/keywords screening (and full-text if necessary) to retrieve topics, modeling purposes and general specifications. We extracted detailed modeling features from papers on established vaccine-preventable childhood diseases based on full-text screening.

Results

We identified 698 papers, which applied an IBM for infectious disease transmission, and listed these in a reference database, describing their general characteristics. The diversity of disease-topics and overall publication frequency have increased over time (38 to 115 annual publications from 2006 to 2015). The inclusion of intervention strategies (8 to 52) and economic consequences (1 to 20) are increasing, to the detriment of purely theoretical explorations. Unfortunately, terminology used to describe IBMs is inconsistent and ambiguous. We retrieved 24 studies on a vaccine-preventable childhood disease (covering 7 different diseases), with publication frequency increasing from the first such study published in 2008. IBMs have been useful to explore heterogeneous between- and within-host interactions, but combined applications are still sparse. The amount of missing information on model characteristics and study design is remarkable.

Conclusions

IBMs are suited to combine heterogeneous within- and between-host interactions, which offers many opportunities, especially to analyze targeted interventions for endemic infections. We advocate the exchange of (open-source) platforms and stress the need for consistent “branding”. Using (existing) conventions and reporting protocols would stimulate cross-fertilization between research groups and fields, and ultimately policy making in decades to come.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-017-2699-8) contains supplementary material, which is available to authorized users.

Products of Compartmental Models in Epidemiology

We show that many structured epidemic models may be described using a straightforward product structure in this paper. Such products, derived from products of directed graphs, may represent useful refinements including geographic and demographic structure, age structure, gender, risk groups, or immunity status. Extension to multistrain dynamics, that is, pathogen heterogeneity, is also shown to be feasible in this framework. Systematic use of such products may aid in model development and exploration, can yield insight, and could form the basis of a systematic approach to numerical structural sensitivity analysis.

Uses of Agent-Based Modeling for Health Communication: the TELL ME Case Study

Government communication is an important management tool during a public health crisis, but understanding its impact is difficult. Strategies may be adjusted in reaction to developments on the ground and it is challenging to evaluate the impact of communication separately from other crisis management activities. Agent-based modeling is a well-established research tool in social science to respond to similar challenges. However, there have been few such models in public health. We use the example of the TELL ME agent-based model to consider ways in which a non-predictive policy model can assist policy makers. This model concerns individuals' protective behaviors in response to an epidemic, and the communication that influences such behavior. Drawing on findings from stakeholder workshops and the results of the model itself, we suggest such a model can be useful: (i) as a teaching tool, (ii) to test theory, and (iii) to inform data collection. We also plot a path for development of similar models that could assist with communication planning for epidemics.

Missed Opportunities for Measles, Mumps, Rubella Vaccination Among Departing U.S. Adult Travelers Receiving Pretravel Health Consultations

Background

Measles outbreaks continue to occur in the United States and are mostly due to infections in returning travelers.

Objective

To describe how providers assessed the measles immunity status of departing U.S. adult travelers seeking pretravel consultation and to assess reasons given for nonvaccination among those considered eligible to receive the measles, mumps, rubella (MMR) vaccine.

Design

Observational study in U.S. pretravel clinics.

Setting

24 sites associated with Global TravEpiNet (GTEN), a Centers for Disease Control and Prevention-funded consortium.

Patients

Adults (born in or after 1957) attending pretravel consultations at GTEN sites (2009 to 2014).

Measurements

Structured questionnaire completed by traveler and provider during pretravel consultation.

Results

40 810 adult travelers were included; providers considered 6612 (16%) to be eligible for MMR vaccine at the time of pretravel consultation. Of the MMR-eligible, 3477 (53%) were not vaccinated at the visit; of these, 1689 (48%) were not vaccinated because of traveler refusal, 966 (28%) because of provider decision, and 822 (24%) because of health systems barriers. Most MMR-eligible travelers who were not vaccinated were evaluated in the South (2262 travelers [65%]) or at nonacademic centers (1777 travelers [51%]). Nonvaccination due to traveler refusal was most frequent in the South (1432 travelers [63%]) and in nonacademic centers (1178 travelers [66%]).

Limitation

These estimates could underrepresent the opportunities for MMR vaccination because providers accepted verbal histories of disease and vaccination as evidence of immunity.

Conclusion

Of U.S. adult travelers who presented for pretravel consultation at GTEN sites, 16% met criteria for MMR vaccination according to the provider's assessment, but fewer than half of these travelers were vaccinated. An increase in MMR vaccination of eligible U.S. adult travelers could reduce the likelihood of importation and transmission of measles virus.

Primary Funding Source

Centers for Disease Control and Prevention, National Institutes of Health, and the Steve and Deborah Gorlin MGH Research Scholars Award.

Impact of Nonmedical Vaccine Exemption Policies on the Health and Economic Burden of Measles

OBJECTIVE

Despite relatively high national vaccination coverage for measles, geographic vaccination variation exists resulting in clusters of susceptibility. A portion of this geographic variation can be explained by differences in state policies related to nonmedical vaccine exemptions. The objective of this analysis was to determine the magnitude, likelihood, and cost of a measles outbreak under different nonmedical vaccine exemption policies.

METHODS

An agent-based transmission model simulated the likelihood and magnitude of a measles outbreak under different nonmedical vaccine exemption policies, previously categorized as easy, medium, or difficult. The model accounted for measles herd immunity, infectiousness of the pathogen, vaccine efficacy, duration of incubation and communicable periods, acquired natural immunity, and the rate of recovery. Public health contact tracing was also modeled. Model outcomes, including the number of secondary cases, hospitalizations, and deaths, were monetized to determine the economic burden of the simulated outbreaks.

RESULTS

A state with easy nonmedical vaccine exemption policies is 140% and 190% more likely to experience a measles outbreak compared with states with medium or difficult policies, respectively. The magnitude of these outbreaks can be reduced by half by strengthening exemption policies. These declines are associated with significant cost reductions to public health, the health care system, and the individual.

CONCLUSIONS

Strengthening nonmedical vaccine exemption policies is 1 mechanism to increase vaccination coverage to reduce the health and economic effect of a measles outbreak. States exploring options for decreasing their vulnerability to outbreaks of vaccine-preventable diseases should consider more stringent requirements for nonmedical vaccine exemptions.

Crude childhood vaccination coverage in West Africa: Trends and predictors of completeness

Background: Africa has the lowest childhood vaccination coverage worldwide. If the full benefits of childhood vaccination programmes are to be enjoyed in sub-Saharan Africa, all countries need to improve on vaccine delivery to achieve and sustain high coverage. In this paper, we review trends in vaccination coverage, dropouts between vaccine doses and explored the country-specific predictors of complete vaccination in West Africa.  Methods: We utilized datasets from the Demographic and Health Surveys Program, available for Benin, Burkina Faso, The Gambia, Ghana, Guinea, Cote d'Ivoire, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone and Togo, to obtain coverage for Bacillus Calmette-Guerin, polio, measles, and diphtheria, pertussis and tetanus (DPT) vaccines in children aged 12 - 23 months. We also calculated the DPT1-to-DPT3 and DPT1-to-measles dropouts, and proportions of the fully immunised child (FIC). Factors predictive of FIC were explored using Chi-squared tests and multivariable logistic regression.  Results: Overall, there was a trend of increasing vaccination coverage. The proportion of FIC varied significantly by country (range 24.1-81.4%, mean 49%). DPT1-to-DPT3 dropout was high (range 5.1% -33.9%, mean 16.3%). Similarly, DPT1-measles dropout exceeded 10% in all but four countries. Although no single risk factor was consistently associated with FIC across these countries, maternal education, delivery in a health facility, possessing a vaccine card and a recent post delivery visit to a health facility were the key predictors of complete vaccination.  Conclusions: The low numbers of fully immunised children and high dropout between vaccine doses highlights weaknesses and the need to strengthen the healthcare and routine immunization delivery systems in this region. Country-specific correlates of complete vaccination should be explored further to identify interventions required to increase vaccination coverage. Despite the promise of an increasing trend in vaccination coverage in West African countries, more effort is required to attain and maintain global vaccination coverage targets.

The Effect of Contact Investigations and Public Health Interventions in the Control and Prevention of Measles Transmission: A Simulation Study

BACKGROUND

Measles cases continue to occur despite its elimination status in the United States. To control transmission, public health officials confirm the measles diagnosis, identify close contacts of infectious cases, deliver public health interventions (i.e., post-exposure prophylaxis) among those who are eligible, and follow-up with the close contacts to determine overall health outcomes. A stochastic network simulation of measles contact tracing was conducted using existing agent-based modeling software and a synthetic population with high levels of immunity in order to estimate the impact of different interventions in controlling measles transmission.

METHODS AND FINDINGS

The synthetic population was created to simulate California`s population in terms of population demographics, household, workplace, school, and neighborhood characteristics using California Department of Finance 2010 census data. Parameters for the model were obtained from a review of the literature, California measles case surveillance data, and expert opinion. Eight different scenarios defined by the use of three different public health interventions were evaluated: (a) post-exposure measles, mumps, and rubella (MMR) vaccine, (b) post-exposure immune globulin (IG), and (c) voluntary isolation and home quarantine in the presence or absence of public health response delays. Voluntary isolation and home quarantine coupled with one or two other interventions had the greatest reduction in the number of secondary cases infected by the index case and the probability of escape situations (i.e., the outbreak continues after 90 days).

CONCLUSIONS

Interrupting contact patterns via voluntary isolation and home quarantine are particularly important in reducing the number of secondary cases infected by the index case and the probability of uncontrolled outbreaks.

The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey

Background

Public trust in immunization is an increasingly important global health issue. Losses in confidence in vaccines and immunization programmes can lead to vaccine reluctance and refusal, risking disease outbreaks and challenging immunization goals in high- and low-income settings. National and international immunization stakeholders have called for better monitoring of vaccine confidence to identify emerging concerns before they evolve into vaccine confidence crises.

Methods

We perform a large-scale, data-driven study on worldwide attitudes to immunizations. This survey – which we believe represents the largest survey on confidence in immunization to date – examines perceptions of vaccine importance, safety, effectiveness, and religious compatibility among 65,819 individuals across 67 countries. Hierarchical models are employed to probe relationships between individual- and country-level socio-economic factors and vaccine attitudes obtained through the four-question, Likert-scale survey.

Findings

Overall sentiment towards vaccinations is positive across all 67 countries, however there is wide variability between countries and across world regions. Vaccine-safety related sentiment is particularly negative in the European region, which has seven of the ten least confident countries, with 41% of respondents in France and 36% of respondents in Bosnia & Herzegovina reporting that they disagree that vaccines are safe (compared to a global average of 13%). The oldest age group (65+) and Roman Catholics (amongst all faiths surveyed) are associated with positive views on vaccine sentiment, while the Western Pacific region reported the highest level of religious incompatibility with vaccines. Countries with high levels of schooling and good access to health services are associated with lower rates of positive sentiment, pointing to an emerging inverse relationship between vaccine sentiments and socio-economic status.

Conclusions

Regular monitoring of vaccine attitudes – coupled with monitoring of local immunization rates – at the national and sub-national levels can identify populations with declining confidence and acceptance. These populations should be prioritized to further investigate the drivers of negative sentiment and to inform appropriate interventions to prevent adverse public health outcomes.

•

Overall vaccine confidence is positive, though responses differ between countries.

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The European region has the lowest confidence in vaccine safety with France the least confident globally.

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Bangladesh, Ecuador, and Iran reported highest agreement that vaccines are important.

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Azerbaijan, Russia, and Italy reported most skepticism around vaccine importance.

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Education increases confidence in vaccine importance and effectiveness but not safety.

This global survey builds on previous studies of vaccines' perceived importance, safety, effectiveness, and religious compatibility. The worldwide survey investigates attitudes towards vaccines on an unprecedented scale, interviewing 65,819 respondents across 67 countries. This can help inform public health agendas by highlighting national and regional variations in attitudes towards vaccines; for example, that the European region is the least confident region towards vaccine safety. One pattern shared by diverse countries worldwide is a worrying gap between high confidence in vaccine importance yet lower confidence in safety, identifying at-risk countries whose vaccine acceptance may be more precarious than previously thought. Meanwhile, factors such as religion, which past research shows to be crucial in some sub-populations, display no consistent pattern at the global scale, emphasizing the importance for future research of understanding the local drivers of vaccine confidence in more detail.

Is population structure sufficient to generate area-level inequalities in influenza rates? An examination using agent-based models

Background

In New Haven County, CT (NHC), influenza hospitalization rates have been shown to increase with census tract poverty in multiple influenza seasons. Though multiple factors have been hypothesized to cause these inequalities, including population structure, differential vaccine uptake, and differential access to healthcare, the impact of each in generating observed inequalities remains unknown. We can design interventions targeting factors with the greatest explanatory power if we quantify the proportion of observed inequalities that hypothesized factors are able to generate. Here, we ask if population structure is sufficient to generate the observed area-level inequalities in NHC. To our knowledge, this is the first use of simulation models to examine the causes of differential poverty-related influenza rates.

Methods

Using agent-based models with a census-informed, realistic representation of household size, age-structure, population density in NHC census tracts, and contact rates in workplaces, schools, households, and neighborhoods, we measured poverty-related differential influenza attack rates over the course of an epidemic with a 23 % overall clinical attack rate. We examined the role of asthma prevalence rates as well as individual contact rates and infection susceptibility in generating observed area-level influenza inequalities.

Results

Simulated attack rates (AR) among adults increased with census tract poverty level (F = 30.5; P < 0.001) in an epidemic caused by a virus similar to A (H1N1) pdm09. We detected a steeper, earlier influenza rate increase in high-poverty census tracts—a finding that we corroborate with a temporal analysis of NHC surveillance data during the 2009 H1N1 pandemic. The ratio of the simulated adult AR in the highest- to lowest-poverty tracts was 33 % of the ratio observed in surveillance data. Increasing individual contact rates in the neighborhood did not increase simulated area-level inequalities. When we modified individual susceptibility such that it was inversely proportional to household income, inequalities in AR between high- and low-poverty census tracts were comparable to those observed in reality.

Discussion

To our knowledge, this is the first study to use simulations to probe the causes of observed inequalities in influenza disease patterns. Knowledge of the causes and their relative explanatory power will allow us to design interventions that have the greatest impact on reducing inequalities.

Conclusion

Differential exposure due to population structure in our realistic simulation model explains a third of the observed inequality. Differential susceptibility to disease due to prevailing chronic conditions, vaccine uptake, and smoking should be considered in future models in order to quantify the role of additional factors in generating influenza inequalities.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-015-2284-2) contains supplementary material, which is available to authorized users.