Positive network assortativity of influenza vaccination at a high school: implications for outbreak risk and herd immunity

Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study

BACKGROUND

Most studies assessing the impact of online media and social media use on COVID-19 vaccine hesitancy predominantly rely on survey data, which often fail to capture the clustering of health opinions and behaviors within real-world networks. In contrast, research using social network analysis aims to uncover the diverse communities and discourse themes related to vaccine support and hesitancy within social media platforms. Despite these advancements, there is a gap in the literature on how a person's social circle affects vaccine acceptance, wherein an important part of social influence stems from offline interactions.

OBJECTIVE

We aimed to examine how online media consumption influences vaccination decisions within real-world social networks by analyzing unique quantitative network data collected from Romania, an Eastern European state and member of the European Union.

METHODS

We conducted 83 face-to-face interviews with participants from a living lab in Lerești, a small rural community in Romania, using a personal network analysis framework. This approach involved gathering data on both the respondents and individuals within their social circles (referred to as alters). After excluding cases with missing data, our analysis proceeded with 73% (61/83) of the complete personal networks. To examine the hierarchical structure of alters nested within ego networks, we used a mixed multilevel logistic regression model with random intercepts. The model aimed to predict vaccination status among alters, with the focal independent variable being the respondents' preferred source of health and prevention information. This variable was categorized into 3 types: traditional media, online media (including social media), and a combination of both, with traditional media as the reference category.

RESULTS

In this study, we analyzed 61 personal networks, encompassing between 15 and 25 alters each, totaling 1280 alters with valid data across all variables of interest. Our primary findings indicate that alters within personal networks, whose respondents rely solely on online media for health information, exhibit lower vaccination rates (odds ratio [OR] 0.37, 95% CI 0.15-0.92; P=.03). Conversely, the transition from exclusive traditional media use to a combination of both traditional and online media does not significantly impact vaccination rate odds (OR 0.75, 95% CI 0.32-1.78; P=.52). In addition, our analysis revealed that alters in personal networks of respondents who received the vaccine are more likely to have received the vaccine themselves (OR 3.75, 95% CI 1.79-7.85; P<.001).

CONCLUSIONS

Real-world networks combine diverse human interactions and attributes along with consequences on health opinions and behaviors. As individuals' vaccination status is influenced by how their social alters use online media and vaccination behavior, further insights are needed to create tailored communication campaigns and interventions regarding vaccination in areas with low levels of digital health literacy and vaccination rates, as Romania exposes.

Evaluating the impact of school-based influenza vaccination programme on absenteeism and outbreaks at schools in Hong Kong: a retrospective cohort study protocol

INTRODUCTION

Seasonal influenza causes annual school breaks and student absenteeism in Hong Kong schools and kindergartens. This proposal aims to conduct a retrospective cohort study to evaluate the impact of a school-based influenza vaccination (SIV) programme on absenteeism and outbreaks at schools in Hong Kong.

METHODS

The study will compare schools that implemented the SIV programme with schools that did not. The data will be sourced from school records, encompassing absenteeism records, outbreak reports, and vaccination rates. We will recruit 1000 students from 381 schools and kindergartens in 18 districts of Hong Kong starting June 2024. The primary outcome measures will include absenteeism rates due to influenza and school influenza outbreaks. Secondary outcomes will consist of vaccination coverage rates and the impact of the SIV programme on hospitalisations due to influenza-like illness. A t-test will be conducted to compare the outcomes between schools with and without the SIV programme.

ETHICS AND DISSEMINATION

The school completed signing the participants' informed consent form before reporting the data to us. Our study has been approved by the Hospital Authority Hong Kong West Cluster IRB Committee (IRB No: UW 17-111) and was a subtopic of the research "The estimated age-group specific influenza vaccine coverage rates in Hong Kong and the impact of the school outreach vaccination program".

TRIAL REGISTRATION

This study will be retrospectively registered.

Assortative mixing of opinions about COVID-19 vaccination in personal networks

Many countries worldwide had difficulties reaching a sufficiently high vaccination uptake during the COVID-19 pandemic. Given this context, we collected data from a panel of 30,000 individuals, which were representative of the population of Romania (a country in Eastern Europe with a low 42.6% vaccination rate) to determine whether people are more likely to be connected to peers displaying similar opinions about COVID-19 vaccination. We extracted 443 personal networks, amounting to 4430 alters. We estimated multilevel logistic regression models with random-ego-level intercepts to predict individual opinions about COVID-19 vaccination. Our evidence indicates positive opinions about the COVID-19 vaccination cluster. Namely, the likelihood of having a positive opinion about COVID-19 vaccination increases when peers have, on average, a more positive attitude than the rest of the nodes in the network (OR 1.31, p < 0.001). We also found that individuals with higher education and age are more likely to hold a positive opinion about COVID-19 vaccination. With the given empirical data, our study cannot reveal whether this assortative mixing of opinions is due to social influence or social selection. However, it may nevertheless have implications for public health interventions, especially in countries that strive to reach higher uptake rates. Understanding opinions about vaccination can act as an early warning system for potential outbreaks, inform predictions about vaccination uptake, or help supply chain management for vaccine distribution.

Catch the tweet to fight the flu: Using Twitter to promote flu shots on a college campus

Objective: Over the 2018-2019 flu season we conducted a randomized controlled trial examining the efficacy of a Twitter campaign on vaccination rates. Concurrently we investigated potential interactions between digital social network structure and vaccination status. Participants: Undergratuates at a large midwestern public university were randomly assigned to an intervention (n = 353) or control (n = 349) group. Methods: Vaccination data were collected via monthly surveys. Participant Twitter data were collected through the public-facing Twitter API. Intervention impact was assessed with logistic regression. Standard network science tools examined vaccination coverage over online social networks. Results: The campaign had no effect on vaccination outcome. Receiving a flu shot the prior year had a positive impact on participant vaccination. Evidence of an interaction between digital social network structure and vaccination status was detected. Conclusions: Social media campaigns may not be sufficient for increasing vaccination rates. There may be potential for social media campaigns that leverage network structure.

Standardized local assortativity in networks and systemic risk in financial markets

The study of assortativity allows us to understand the heterogeneity of networks and the implication of network resilience. While a global measure has been predominantly used to characterize this network feature, there has been little research to suggest a local coefficient to account for the presence of local (dis)assortative patterns in diversely mixed networks. We build on existing literature and extend the concept of assortativity with the proposal of a standardized scale-independent local coefficient to observe the assortative characteristics of each entity in networks that would otherwise be smoothed out with a global measure. This coefficient provides a lens through which the granular level of details can be observed, as well as capturing possible pattern (dis)formation in dynamic networks. We demonstrate how the standardized local assortative coefficient discovers the presence of (dis)assortative hubs in static networks on a granular level, and how it tracks systemic risk in dynamic financial networks.

Vaccine hesitancy comes in waves: Longitudinal evidence on willingness to vaccinate against COVID-19 from seven European countries

AIM

This paper investigates the prevalence and determinants of three main states of people's willingness to be vaccinated (WTBV) against COVID-19 - willing, unwilling and hesitant - and the occurrence and predictors of shifts between these states over time. Understanding the dynamics of vaccine intentions is crucial for developing targeted campaigns to increase uptake and emergency response preparedness.

STUDY DESIGN

A panel survey consisting of 9 quarterly waves of data collected between April 2020 and January 2022. Baseline data included 24 952 adults from Germany, UK, Denmark, the Netherlands, France, Portugal, and Italy recruited from online panels to construct census-matched nationally representative samples.

METHODS AND MEASURES

Self-reported COVID-19 vaccine intention was the main outcome. Multinomial logit random effects models were used to analyze the relationships of interest. All results reported as relative risk ratios (RRR).

RESULTS

Hesitancy to get vaccinated was the most unstable vaccine intention, with on average 42% of ever hesitant respondents remaining in this state through future waves, followed by the 'unwilling' (53%) and 'willing (82%). Following COVID-19 news, trust in information from the government, GPs and the WHO, risk preferences, risk perceptions, and confidence in vaccines (or lack thereof) predicted vaccination intention reversals. Risk preferences acted both as an impediment and as a facilitator for the vaccine uptake depending on the initial vaccine intention.

CONCLUSIONS AND RELEVANCE

This study revealed the dynamic nature of COVID-19 vaccine intentions and its predictors in 7 European countries. The findings provide insights to policymakers for designing more effective communication strategies, particularly targeted at hesitant and unwilling to vaccinate population groups, to increase vaccine uptake for future public health emergencies.

A cultural evolutionary model of the interaction between parental beliefs and behaviors, with applications to vaccine hesitancy

Health perceptions and health-related behaviors can change at the population level as cultures evolve. In the last decade, despite the proven efficacy of vaccines, the developed world has seen a resurgence of vaccine-preventable diseases (VPDs) such as measles, pertussis, and polio. Vaccine hesitancy, an individual attitude influenced by historical, political, and socio-cultural forces, is believed to be a primary factor responsible for decreasing vaccine coverage, thereby increasing the risk and occurrence of VPD outbreaks. Behavior change models have been increasingly employed to understand disease dynamics and intervention effectiveness. However, since health behaviors are culturally influenced, it is valuable to examine them within a cultural evolution context. Here, using a mathematical modeling framework, we explore the effects of cultural evolution on vaccine hesitancy and vaccination behavior. With this model, we shed light on facets of cultural evolution (vertical transmission, community influences, homophily, etc.) that promote the spread of vaccine hesitancy, ultimately affecting levels of vaccination coverage and VPD outbreak risk in a population. In addition, we present our model as a generalizable framework for exploring cultural evolution when humans' beliefs influence, but do not strictly dictate, their behaviors. This model offers a means of exploring how parents' potentially conflicting beliefs and cultural traits could affect their children's health and fitness. We show that vaccine confidence and vaccine-conferred benefits can both be driving forces of vaccine coverage. We also demonstrate that an assortative preference among vaccine-hesitant individuals can lead to increased vaccine hesitancy and lower vaccine coverage.

Network segregation and the propagation of misinformation

How does the ideological segregation of online networks impact the spread of misinformation? Past studies have found that homophily generally increases diffusion, suggesting that partisan news, whether true or false, will spread farther in ideologically segregated networks. We argue that network segregation disproportionately aids messages that are otherwise too implausible to diffuse, thus favoring false over true news. To test this argument, we seeded true and false informational messages in experimental networks in which subjects were either ideologically integrated or segregated, yielding 512 controlled propagation histories in 16 independent information systems. Experimental results reveal that the fraction of false information circulating was systematically greater in ideologically segregated networks. Agent-based models show robustness of this finding across different network topologies and sizes. We conclude that partisan sorting undermines the veracity of information circulating on the Internet by increasing exposure to content that would otherwise not manage to diffuse.

Herd immunity and epidemic size in networks with vaccination homophily

We study how the herd immunity threshold and the expected epidemic size depend on homophily with respect to vaccine adoption. We find that the presence of homophily considerably increases the critical vaccine coverage needed for herd immunity and that strong homophily can push the threshold entirely out of reach. The epidemic size monotonically increases as a function of homophily strength for a perfect vaccine, while it is maximized at a nontrivial level of homophily when the vaccine efficacy is limited. Our results highlight the importance of vaccination homophily in epidemic modeling.

The Mediating Effect of Religiousness in the Relationship Between Psychological Resilience and Fear of COVID-19 in Turkey

The purpose of this study was to examine if religiousness has a mediation influence on the link between psychological resilience and fear of COVID-19. Data were collected from 372 participants by using the convenience sampling method. There is a positive significant relationship between psychological resilience and religiousness, a negative significant relationship between religiousness and fear of COVID-19, a negative significant relationship between psychological resilience and a fear of COVID-19. This study was tested with structural equation modeling and bootstrapping was applied. Significant relationships were found between psychological resilience, fear of COVID-19 and religiousness. In addition, it was found that religiousness had a mediating effect on the relationship between psychological resilience and fear of COVID-19. These results suggest that the inverse relationship between psychological resilience and fear of COVID-19 is at least partly explained by level of religiousness.

Inferring the reproduction number using the renewal equation in heterogeneous epidemics

Real-time estimation of the reproduction number has become the focus of modelling groups around the world as the SARS-CoV-2 pandemic unfolds. One of the most widely adopted means of inference of the reproduction number is via the renewal equation, which uses the incidence of infection and the generation time distribution. In this paper, we derive a multi-type equivalent to the renewal equation to estimate a reproduction number which accounts for heterogeneity in transmissibility including through asymptomatic transmission, symptomatic isolation and vaccination. We demonstrate how use of the renewal equation that misses these heterogeneities can result in biased estimates of the reproduction number. While the bias is small with symptomatic isolation, it can be much larger with asymptomatic transmission or transmission from vaccinated individuals if these groups exhibit substantially different generation time distributions to unvaccinated symptomatic transmitters, whose generation time distribution is often well defined. The bias in estimate becomes larger with greater population size or transmissibility of the poorly characterized group. We apply our methodology to Ebola in West Africa in 2014 and the SARS-CoV-2 in the UK in 2020-2021.

Using social contact data to improve the overall effect estimate of a cluster-randomized influenza vaccination program in Senegal

This study estimates the overall effect of two influenza vaccination programs consecutively administered in a cluster-randomized trial in western Senegal over the course of two influenza seasons from 2009-2011. We apply cutting-edge methodology combining social contact data with infection data to reduce bias in estimation arising from contamination between clusters. Our time-varying estimates reveal a reduction in seasonal influenza from the intervention and a nonsignificant increase in H1N1 pandemic influenza. We estimate an additive change in overall cumulative incidence (which was 6.13% in the control arm) of -0.68 percentage points during Year 1 of the study (95% CI: -2.53, 1.18). When H1N1 pandemic infections were excluded from analysis, the estimated change was -1.45 percentage points and was significant (95% CI, -2.81, -0.08). Because cross-cluster contamination was low (0-3% of contacts for most villages), an estimator assuming no contamination was only slightly attenuated (-0.65 percentage points). These findings are encouraging for studies carefully designed to minimize spillover. Further work is needed to estimate contamination - and its effect on estimation - in a variety of settings.

Effect of homophily and correlation of beliefs on COVID-19 and general infectious disease outbreaks

Contact between people with similar opinions and characteristics occurs at a higher rate than among other people, a phenomenon known as homophily. The presence of clusters of unvaccinated people has been associated with increased incidence of infectious disease outbreaks despite high population-wide vaccination rates. The epidemiological consequences of homophily regarding other beliefs as well as correlations among beliefs or circumstances are poorly understood, however. Here, we use a simple compartmental disease model as well as a more complex COVID-19 model to study how homophily and correlation of beliefs and circumstances in a social interaction network affect the probability of disease outbreak and COVID-19-related mortality. We find that the current social context, characterized by the presence of homophily and correlations between who vaccinates, who engages in risk reduction, and individual risk status, corresponds to a situation with substantially worse disease burden than in the absence of heterogeneities. In the presence of an effective vaccine, the effects of homophily and correlation of beliefs and circumstances become stronger. Further, the optimal vaccination strategy depends on the degree of homophily regarding vaccination status as well as the relative level of risk mitigation high- and low-risk individuals practice. The developed methods are broadly applicable to any investigation in which node attributes in a graph might reasonably be expected to cluster or exhibit correlations.

Assortativity and Bias in Epidemiologic Studies of Contagious Outcomes: A Simulated Example in the Context of Vaccination

Assortativity is the tendency of individuals connected in a network to share traits and behaviors. Through simulations, we demonstrated the potential for bias resulting from assortativity by vaccination, where vaccinated individuals are more likely to be connected with other vaccinated individuals. We simulated outbreaks of a hypothetical infectious disease and vaccine in a randomly generated network and a contact network of university students living on campus. We varied protection of the vaccine to the individual, transmission potential of vaccinated-but-infected individuals, and assortativity by vaccination. We compared a traditional approach, which ignores the structural features of a network, with simple approaches which summarized information from the network. The traditional approach resulted in biased estimates of the unit-treatment effect when there was assortativity by vaccination. Several different approaches that included summary measures from the network reduced bias and improved confidence interval coverage. Through simulations, we showed the pitfalls of ignoring assortativity by vaccination. While our example is described in terms of vaccines, our results apply more widely to exposures for contagious outcomes. Assortativity should be considered when evaluating exposures for contagious outcomes.

Model-driven mitigation measures for reopening schools during the COVID-19 pandemic

Reopening schools is an urgent priority as the COVID-19 pandemic drags on. To explore the risks associated with returning to in-person learning and the value of mitigation measures, we developed stochastic, network-based models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in primary and secondary schools. We find that a number of mitigation measures, alone or in concert, may reduce risk to acceptable levels. Student cohorting, in which students are divided into two separate populations that attend in-person classes on alternating schedules, can reduce both the likelihood and the size of outbreaks. Proactive testing of teachers and staff can help catch introductions early, before they spread widely through the school. In secondary schools, where the students are more susceptible to infection and have different patterns of social interaction, control is more difficult. Especially in these settings, planners should also consider testing students once or twice weekly. Vaccinating teachers and staff protects these individuals and may have a protective effect on students as well. Other mitigations, including mask wearing, social distancing, and increased ventilation, remain a crucial component of any reopening plan.

Influenza Vaccination and Non-Pharmaceutical Measure Effectiveness for Preventing Influenza Outbreaks in Schools: A Surveillance-Based Evaluation in Beijing

Although schools are known to play a major role in the spread of influenza virus, few studies have evaluated the effectiveness of vaccination and non-pharmaceutical measures for preventing influenza outbreaks in schools. We investigated all febrile illness outbreaks in primary and secondary schools in Beijing reported between August 2018 and July 2019. We obtained epidemiological information on febrile illness outbreaks and oral pharyngeal swabs from students in the outbreaks to test for influenza virus. We surveyed schools that did not report febrile illness outbreaks. We developed multi-level models to identify and evaluate factors associated with serious influenza outbreaks and explored the association of vaccine coverage and outbreaks using multi-stage regression models. We identified a total of 748 febrile illness outbreaks involving 8176 students in Beijing; 462 outbreaks were caused by influenza virus. Adjusted regression modeling showed that large class size (odds ratio (OR) = 2.38) and the number of days from identification of the first case to initiation of an intervention (OR = 1.17) were statistically significant and positively associated with serious outbreaks, and that high vaccination coverage (relative risk (RR) = 0.50) was statistically significant and negatively associated with outbreaks. Multi-stage regression modeling showed that RR decreased fastest when vaccination coverage was 45% to 51%. We conclude that high influenza vaccination coverage can prevent influenza outbreaks in schools and that rapid identification of febrile children and early initiation of non-pharmaceutical measures can reduce outbreak size.

Clustering of susceptible individuals within households can drive measles outbreaks: an individual-based model exploration

When estimating important measures such as the herd immunity threshold, and the corresponding efforts required to eliminate measles, it is often assumed that susceptible individuals are uniformly distributed throughout populations. However, unvaccinated individuals may be clustered in a variety of ways, including by geographic location, by age, in schools, or in households. Here, we investigate to which extent different levels of within-household clustering of susceptible individuals may impact the risk and persistence of measles outbreaks. To this end, we apply an individual-based model, Stride, to a population of 600,000 individuals, using data from Flanders, Belgium. We construct a metric to estimate the level of within-household susceptibility clustering in the population. Furthermore, we compare realistic scenarios regarding the distribution of susceptible individuals within households in terms of their impact on epidemiological measures for outbreak risk and persistence. We find that higher levels of within-household clustering of susceptible individuals increase the risk, size and persistence of measles outbreaks. Ignoring within-household clustering thus leads to underestimations of required measles elimination and outbreak mitigation efforts.

Modelling anti-vaccine sentiment as a cultural pathogen

Culturally transmitted traits that have deleterious effects on health-related traits can be regarded as cultural pathogens. A cultural pathogen can produce coupled dynamics with its associated health-related traits, so that understanding the dynamics of a health-related trait benefits from consideration of the dynamics of the associated cultural pathogen. Here, we treat anti-vaccine sentiment as a cultural pathogen, modelling its 'infection' dynamics with the infection dynamics of the associated vaccine-preventable disease. In a coupled susceptible-infected-resistant (SIR) model, consisting of an SIR model for the anti-vaccine sentiment and an interacting SIR model for the infectious disease, we explore the effect of anti-vaccine sentiment on disease dynamics. We find that disease endemism is contingent on the presence of the sentiment, and that presence of sentiment can enable diseases to become endemic when they would otherwise have disappeared. Furthermore, the sentiment dynamics can create situations in which the disease suddenly returns after a long period of dormancy. We study the effect of assortative sentiment-based interactions on the dynamics of sentiment and disease, identifying a tradeoff whereby assortative meeting aids the spread of a disease but hinders the spread of sentiment. Our results can contribute to finding strategies that reduce the impact of a cultural pathogen on disease, illuminating the value of cultural evolutionary modelling in the analysis of disease dynamics.

Influenza vaccine effectiveness estimates against influenza A(H3N2) and A(H1N1) pdm09 among children during school-based outbreaks in the 2016-2017 season in Beijing, China

Background: Since 2007, trivalent inactivated influenza vaccine (TIV) has been provided free-of-charge to primary, middle school and high school students in Beijing. However, there have been few school-based studies on influenza vaccine effectiveness (VE). In this report, we estimated influenza VE against laboratory-confirmed influenza illness among school children in Beijing, China during the 2016-2017 influenza season.Methods: The VE of 2016-2017 TIV against laboratory-confirmed influenza virus infection among school-age children was assessed through a case-control design. Conditional logistic regression was conducted on matched case-control sets to estimate VE. The effect of prior vaccination on current VE was also examined.Results: All 176 samples tested positive for influenza A virus with the positive rate of 55.5%. The average coverage rate of 2016-2017 TIV among students across the 37 schools was 30.6%. The fully adjusted VE of 2016-2017 TIV against laboratory-confirmed influenza was 69% (95% CI: 51 to 81): 60% (95% CI: -15 to 86) for influenza A(H1N1)pdm09 and 73% (95% CI: 52 to 84) for influenza A(H3N2). The overall VE for receipt of 2015-2016 vaccination only, 2016-2017 vaccination only, and vaccinations in both seasons was 46% (95% CI: -5 to 72), 77% (95% CI: 58 to 87), and 57% (95%CI: 17 to 78), respectively.Conclusions: Our study during school outbreaks found that VE of 2016-2017 TIV was moderate against influenza A(H3N2) as well as A(H1N1)pdm09 viruses.

A Rational Explanation of Limited FMD Vaccine Uptake in Endemic Regions

Vaccination for foot-and-mouth (FMD) disease remains low in parts of Africa despite the existence of vaccines. In East Africa, the presence of multiple virus serotypes and sub-types makes matching a vaccine with the circulating virus type in the field, or providing a high potency vaccine, a challenge. In this paper we use game theory to show that the resulting vaccine uncertainty associated with these vaccination conditions in an endemic setting help explain the low vaccine uptake. We evaluate vaccination for FMD in the context of East Africa due to FMD being endemic in the region, the diversity of FMD virus types, and barriers to implementing other disease control measures, such as controlled movements. We incorporate these conditions into a vaccination game setting and compare the payoffs to those of a traditional vaccination game for seasonal influenza and commercial livestock vaccination in a developed country context. In showing that vaccination provides households with a lower payoff than not vaccinating, our simple game theoretical explanation supports existing evidence calling for improved vaccine quality and efforts to enhance surveillance to provide early information on disease status.

Transmission of and susceptibility to seasonal influenza in Switzerland from 2003 to 2015

Understanding the seasonal patterns of influenza transmission is critical to help plan public health measures for the management and control of epidemics. Mathematical models of infectious disease transmission have been widely used to quantify the transmissibility of and susceptibility to past influenza seasons in many countries. The objective of this study was to obtain a detailed picture of the transmission dynamics of seasonal influenza in Switzerland from 2003 to 2015. To this end, we developed a compartmental influenza transmission model taking into account social mixing between different age groups and seasonal forcing. We applied a Bayesian approach using Markov chain Monte Carlo (MCMC) methods to fit the model to the reported incidence of influenza-like-illness (ILI) and virological data from Sentinella, the Swiss Sentinel Surveillance Network. The maximal basic reproduction number, R₀, ranged from 1.46 to 1.81 (median). Median estimates of susceptibility to influenza ranged from 29% to 98% for different age groups, and typically decreased with age. We also found a decline in ascertainability of influenza cases with age. Our study illustrates how influenza surveillance data from Switzerland can be integrated into a Bayesian modeling framework in order to assess age-specific transmission of and susceptibility to influenza.

Observational study to assess the effects of social networks on the seasonal influenza vaccine uptake by early career doctors

OBJECTIVES

To evaluate the effect of social network influences on seasonal influenza vaccination uptake by healthcare workers.

DESIGN

Cross-sectional, observational study.

SETTING

A large secondary care NHS Trust which includes four hospital sites in Greater Manchester.

PARTICIPANTS

Foundation doctors (FDs) working at the Pennine Acute Hospitals NHS Trust during the study period. Data collection took place during compulsory weekly teaching sessions, and there were no exclusions. Of the 200 eligible FDs, 138 (70%) provided complete data.

PRIMARY OUTCOME MEASURES

Self-reported seasonal influenza vaccination status.

RESULTS

Among participants, 100 (72%) reported that they had received a seasonal influenza vaccination. Statistical modelling demonstrated that having a higher proportion of vaccinated neighbours increased an individual's likelihood of being vaccinated. The coefficient for γ, the social network parameter, was 0.965 (95% CI: 0.248 to 1.682; odds: 2.625 (95% CI: 1.281 to 5.376)), that is, a diffusion effect. Adjusting for year group, geographical area and sex did not account for this effect.

CONCLUSIONS

This population exhibited higher than expected vaccination coverage levels-providing protection both in the workplace and for vulnerable patients. The modelling approach allowed covariate effects to be incorporated into social network analysis which gave us a better understanding of the network structure. These techniques have a range of applications in understanding the role of social networks on health behaviours.

Networks of face-to-face social contacts in Niakhar, Senegal

We present the first analysis of face-to-face contact network data from Niakhar, Senegal. Participants in a cluster-randomized influenza vaccine trial were interviewed about their contact patterns when they reported symptoms during their weekly household surveillance visit. We employ a negative binomial model to estimate effects of covariates on contact degree. We estimate the mean contact degree for asymptomatic Niakhar residents to be 16.5 (95% C.I. 14.3, 18.7) in the morning and 14.8 in the afternoon (95% C.I. 12.7, 16.9). We estimate that symptomatic people make 10% fewer contacts than asymptomatic people (95% C.I. 5%, 16%; p = 0.006), and those aged 0-5 make 33% fewer contacts than adults (95% C.I. 29%, 37%; p < 0.001). By explicitly modelling the partial rounding pattern observed in our data, we make inference for both the underlying (true) distribution of contacts as well as for the reported distribution. We created an estimator for homophily by compound (household) membership and estimate that 48% of contacts by symptomatic people are made to their own compound members in the morning (95% CI, 45%, 52%) and 60% in the afternoon/evening (95% CI, 56%, 64%). We did not find a significant effect of symptom status on compound homophily. We compare our findings to those from other countries and make design recommendations for future surveys.

Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings

Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC-and ESBL-KP-carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics only, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI network's ability to support observed incident-colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species. CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination.

An agent-based model of dengue virus transmission shows how uncertainty about breakthrough infections influences vaccination impact projections

Prophylactic vaccination is a powerful tool for reducing the burden of infectious diseases, due to a combination of direct protection of vaccinees and indirect protection of others via herd immunity. Computational models play an important role in devising strategies for vaccination by making projections of its impacts on public health. Such projections are subject to uncertainty about numerous factors, however. For example, many vaccine efficacy trials focus on measuring protection against disease rather than protection against infection, leaving the extent of breakthrough infections (i.e., disease ameliorated but infection unimpeded) among vaccinees unknown. Our goal in this study was to quantify the extent to which uncertainty about breakthrough infections results in uncertainty about vaccination impact, with a focus on vaccines for dengue. To realistically account for the many forms of heterogeneity in dengue virus (DENV) transmission, which could have implications for the dynamics of indirect protection, we used a stochastic, agent-based model for DENV transmission informed by more than a decade of empirical studies in the city of Iquitos, Peru. Following 20 years of routine vaccination of nine-year-old children at 80% coverage, projections of the proportion of disease episodes averted varied by a factor of 1.76 (95% CI: 1.54-2.06) across the range of uncertainty about breakthrough infections. This was equivalent to the range of vaccination impact projected across a range of uncertainty about vaccine efficacy of 0.268 (95% CI: 0.210-0.329). Until uncertainty about breakthrough infections can be addressed empirically, our results demonstrate the importance of accounting for it in models of vaccination impact.

Assessing the Dynamics and Control of Droplet- and Aerosol-Transmitted Influenza Using an Indoor Positioning System

There is increasing evidence that aerosol transmission is a major contributor to the spread of influenza. Despite this, virtually all studies assessing the dynamics and control of influenza assume that it is transmitted solely through direct contact and large droplets, requiring close physical proximity. Here, we use wireless sensors to measure simultaneously both the location and close proximity contacts in the population of a US high school. This dataset, highly resolved in space and time, allows us to model both droplet and aerosol transmission either in isolation or in combination. In particular, it allows us to computationally quantify the potential effectiveness of overlooked mitigation strategies such as improved ventilation that are available in the case of aerosol transmission. Our model suggests that recommendation-abiding ventilation could be as effective in mitigating outbreaks as vaccinating approximately half of the population. In simulations using empirical transmission levels observed in households, we find that bringing ventilation to recommended levels had the same mitigating effect as a vaccination coverage of 50% to 60%. Ventilation is an easy-to-implement strategy that has the potential to support vaccination efforts for effective control of influenza spread.

The importance of dog population contact network structures in rabies transmission

Canine rabies transmission was interrupted in N'Djaména, Chad, following two mass vaccination campaigns. However, after nine months cases resurged with re-establishment of endemic rabies transmission to pre-intervention levels. Previous analyses investigated district level spatial heterogeneity of vaccination coverage, and dog density; and importation, identifying the latter as the primary factor for rabies resurgence. Here we assess the impact of individual level heterogeneity on outbreak probability, effectiveness of vaccination campaigns and likely time to resurgence after a campaign. Geo-located contact sensors recorded the location and contacts of 237 domestic dogs in N'Djaména over a period of 3.5 days. The contact network data showed that urban dogs are socially related to larger communities and constrained by the urban architecture. We developed a network generation algorithm that extrapolates this empirical contact network to networks of large dog populations and applied it to simulate rabies transmission in N'Djaména. The model predictions aligned well with the rabies incidence data. Using the model we demonstrated, that major outbreaks are prevented when at least 70% of dogs are vaccinated. The probability of a minor outbreak also decreased with increasing vaccination coverage, but reached zero only when coverage was near total. Our results suggest that endemic rabies in N'Djaména may be explained by a series of importations with subsequent minor outbreaks. We show that highly connected dogs hold a critical role in transmission and that targeted vaccination of such dogs would lead to more efficient vaccination campaigns.

Spatial and temporal risk as drivers for adoption of foot and mouth disease vaccination

Identifying the drivers of vaccine adoption decisions under varying levels of perceived disease risk and benefit provides insight into what can limit or enhance vaccination uptake. To address the relationship of perceived benefit relative to temporal and spatial risk, we surveyed 432 pastoralist households in northern Tanzania on vaccination for foot-and-mouth disease (FMD). Unlike human health vaccination decisions where beliefs regarding adverse, personal health effects factor heavily into perceived risk, decisions for animal vaccination focus disproportionately on dynamic risks to animal productivity. We extended a commonly used stated preference survey methodology, willingness to pay, to elicit responses for a routine vaccination strategy applied biannually and an emergency strategy applied in reaction to spatially variable, hypothetical outbreaks. Our results show that households place a higher value on vaccination as perceived risk and household capacity to cope with resource constraints increase, but that the episodic and unpredictable spatial and temporal spread of FMD contributes to increased levels of uncertainty regarding the benefit of vaccination. In addition, concerns regarding the performance of the vaccine underlie decisions for both routine and emergency vaccination, indicating a need for within community messaging and documentation of the household and population level benefits of FMD vaccination.

Influenza illness averted by influenza vaccination among school year children in Beijing, 2013-2016

BACKGROUND

The benefit of school-based influenza vaccination policy has not been fully addressed in Beijing.

OBJECTIVES

To evaluate the benefit of school-based influenza vaccination policy launched in Beijing.

METHODS

Using existing surveillance and immunization data, we developed a dynamic transmission model to assess the impact of influenza vaccination in school-going children. The outcome was defined as the averted number of medically attended influenza illnesses and the prevented disease fraction to all children aged 5-14 years for the 2013/14, 2014/15, and 2015/16 seasons.

RESULTS

We estimated that during the three consecutive influenza seasons, the averted number of medically attended influenza illnesses among children aged 5-14 years was around 104 000 (95% CI: 101 000-106 000), 23 000 (95% CI: 22 000-23 000), and 21 000 (95% CI: 21 000-22 000), respectively. Corresponding prevented fractions to all children aged 5-14 years were 76.3%, 38.5%, and 43.9%.

CONCLUSIONS

In Beijing, school-based vaccinations reduced a substantial number of medically attended influenza illnesses despite seasonal variation in the prevented fraction. This is strong supportive evidence for the continuation of school-based vaccination programs to reduce the influenza burden in this age group.

Measuring dynamic social contacts in a rehabilitation hospital: effect of wards, patient and staff characteristics

Understanding transmission routes of hospital-acquired infections (HAI) is key to improve their control. In this context, describing and analyzing dynamic inter-individual contact patterns in hospitals is essential. In this study, we used wearable sensors to detect Close Proximity Interactions (CPIs) among patients and hospital staff in a 200-bed long-term care facility over 4 months. First, the dynamic CPI data was described in terms of contact frequency and duration per individual status or activity and per ward. Second, we investigated the individual factors associated with high contact frequency or duration using generalized linear mixed-effect models to account for inter-ward heterogeneity. Hospital porters and physicians had the highest daily number of distinct contacts, making them more likely to disseminate HAI among individuals. Conversely, contact duration was highest between patients, with potential implications in terms of HAI acquisition risk. Contact patterns differed among hospital wards, reflecting varying care patterns depending on reason for hospitalization, with more frequent contacts in neurologic wards and fewer, longer contacts in geriatric wards. This study is the first to report proximity-sensing data informing on inter-individual contacts in long-term care settings. Our results should help better understand HAI spread, parameterize future mathematical models, and propose efficient control strategies.

An Optimized Hemagglutination Inhibition (HI) Assay to Quantify Influenza-specific Antibody Titers

Antibody titers are commonly used as surrogate markers for serological protection against influenza and other pathogens. Detailed knowledge of antibody production pre- and post-vaccination is required to understand vaccine-induced immunity. This article describes a reliable point-by-point protocol to determine influenza-specific antibody titers. The first protocol describes a method to specify the antigen amounts required for hemagglutination, which standardizes the concentrations for subsequent usage in the second protocol (hemagglutination assay, HA assay). The second protocol describes the quantification of influenza-specific antibody titers against different viral strains by using a serial dilution of human serum or cell culture supernatants (hemagglutination inhibition assay, HI assay). As an applied example, we show the antibody response of a healthy cohort, which received a trivalent inactivated influenza vaccine. Additionally, the cross-reactivity between the different influenza viruses is shown and methods to minimize cross-reactivity by using different types of animal red blood cells (RBCs) are explained. The discussion highlights advantages and disadvantages of the presented assays and how the determination of influenza-specific antibody titers can improve the understanding of vaccine-related immunity.

The Timing of Pertussis Cases in Unvaccinated Children in an Outbreak Year: Oregon 2012

OBJECTIVE

To assess whether, during a 2012 pertussis outbreak, unvaccinated and poorly vaccinated cases occurred earlier on a community level.

STUDY DESIGN

Pediatric pertussis among children 2 months to 10 years of age in the Oregon Sentinel Surveillance region during an epidemic starting at the beginning of 2012 were stratified by immunization status, age, zip code, and calendar date of disease onset. Differences in median onset as days between fully or mostly vaccinated, poorly vaccinated, and unvaccinated cases were examined overall and within local zip code areas. Disease clusters also were examined using SatScan analysis.

RESULTS

Overall, 351 pertussis cases occurred among children aged 2 months to 10 years of age residing in 72 distinct zipcodes. Among unvaccinated or poorly vaccinated cases, their median date of onset was at calendar day 117 (April 26, 2012), whereas for those who were fully or mostly vaccinated the median date of onset was 41 days later, at day 158 (June 6, 2012). Within each local zip code area, the unvaccinated cases were 3.2 times more likely than vaccinated cases to have earlier median dates of onset (95% CI 2.9-3.6).

CONCLUSION

In this outbreak, pertussis cases among unvaccinated children represented an earlier spread of disease across local areas. Controlling outbreaks may require attention to the composition and location of the unvaccinated.

Influenza Vaccine Effectiveness in Preventing Influenza Illness Among Children During School-based Outbreaks in the 2014-2015 Season in Beijing, China

BACKGROUND

Little is known about vaccine effectiveness (VE) against nonmedically attended A(H3N2) influenza illness during 2014-2015 when the vaccine component appeared to be a poor match with circulating strains.

METHODS

Forty-three eligible school influenza outbreaks in Beijing, China, from November 1, 2014, to December 31, 2014, were included in this study. The VE of 2014-2015 trivalent inactivated influenza vaccine (IIV3) was assessed in preventing laboratory-confirmed influenza among school-age children through a case-control design, using asymptomatic controls. Influenza vaccination was documented from a vaccination registry. VE was estimated adjusting for age group, sex, rural versus urban area, body mass index, chronic conditions, onset week and schools through a mixed effects logistic regression model.

RESULTS

The average coverage rate of 2014-2015 IIV3 among students across the 43 schools was 47.6%. The fully adjusted VE of 2014-2015 IIV3 against laboratory-confirmed influenza was 38% [95% confidence interval (CI): 12%-57%]. Receipt of previous season's (2013-2014) IIV3 significantly modified VE of the 2014-2015 IIV3; children who received both 2013-2014 and 2014-2015 vaccinations had VE of 29% (95% CI: -8% to 53%), whereas VE for children who received 2014-2015 IIV3 only was 54% (95% CI: 8%-77%).

CONCLUSIONS

VE for 2014-2015 IIV3 against A(H3N2) illness identified in schools was modest. Children who did not receive the prior season's vaccine with a homologous A(H3N2) component may have enjoyed greater protection than repeated vaccinees.

Livestock vaccinations translate into increased human capital and school attendance by girls

To fulfill the United Nation's Sustainable Development Goals (SDGs), it is useful to understand whether and how specific agricultural interventions improve human health, educational opportunity, and food security. In sub-Saharan Africa, 75% of the population is engaged in small-scale farming, and 80% of these households keep livestock, which represent a critical asset and provide protection against economic shock. For the 50 million pastoralists, livestock play an even greater role. Livestock productivity for pastoralist households is constrained by multiple factors, including infectious disease. East Coast fever, a tick-borne protozoal disease, is the leading cause of calf mortality in large regions of eastern and Southern Africa. We examined pastoralist decisions to adopt vaccination against East Coast fever and the economic outcomes of adoption. Our estimation strategy provides an integrated model of adoption and impact that includes direct effects of vaccination on livestock health and productivity outcomes, as well as indirect effects on household expenditures, such as child education, food, and health care. On the basis of a cross-sectional study of Kenyan pastoralist households, we found that vaccination provides significant net income benefits from reduction in livestock mortality, increased milk production, and savings by reducing antibiotic and acaricide treatments. Households directed the increased income resulting from East Coast fever vaccination into childhood education and food purchase. These indirect effects of livestock vaccination provide a positive impact on rural, livestock-dependent families, contributing to poverty alleviation at the household level and more broadly to achieving SDGs.

The social network around influenza vaccination in health care workers: a cross-sectional study

Background

Influenza vaccination coverage remains low among health care workers (HCWs) in many health facilities. This study describes the social network defined by HCWs’ conversations around an influenza vaccination campaign in order to describe the role played by vaccination behavior and other HCW characteristics in the configuration of the links among subjects.

Methods

This study used cross-sectional data from 235 HCWs interviewed after the 2010/2011 influenza vaccination campaign at the Hospital Clinic of Barcelona (HCB), Spain. The study asked: “Who did you talk to or share some activity with respect to the seasonal vaccination campaign?” Variables studied included sociodemographic characteristics and reported conversations among HCWs during the influenza campaign. Exponential random graph models (ERGM) were used to assess the role of shared characteristics (homophily) and individual characteristics in the social network around the influenza vaccination campaign.

Results

Links were more likely between HCWs who shared the same professional category (OR 3.13, 95% CI = 2.61–3.75), sex (OR 1.34, 95% CI = 1.09–1.62), age (OR 0.7, 95% CI = 0.63–0.78 per decade of difference), and department (OR 11.35, 95% CI = 8.17–15.64), but not between HCWs who shared the same vaccination behavior (OR 1.02, 95% CI = 0.86–1.22). Older (OR 1.26, 95% CI = 1.14–1.39 per extra decade of HCW) and vaccinated (OR 1.32, 95% CI = 1.09–1.62) HCWs were more likely to be named.

Conclusions

This study finds that there is no homophily by vaccination status in whom HCWs speak to or interact with about a workplace vaccination promotion campaign. This result highlights the relevance of social network analysis in the planning of health promotion interventions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13012-016-0522-3) contains supplementary material, which is available to authorized users.

Quantifying Protection Against Influenza Virus Infection Measured by Hemagglutination-inhibition Assays in Vaccine Trials

Supplemental Digital Content is available in the text.

Correlations between hemagglutination-inhibition titers (hereafter “titers”) and protection against infection have been identified in historical studies. However, limited information is available about the dynamics of how titer influences protection.

Separating temporal and topological effects in walk-based network centrality

The recently introduced concept of dynamic communicability is a valuable tool for ranking the importance of nodes in a temporal network. Two metrics, broadcast score and receive score, were introduced to measure the centrality of a node with respect to a model of contagion based on time-respecting walks. This article examines the temporal and structural factors influencing these metrics by considering a versatile stochastic temporal network model. We analytically derive formulas to accurately predict the expectation of the broadcast and receive scores when one or more columns in a temporal edge-list are shuffled. These methods are then applied to two publicly available data sets and we quantify how much the centrality of each individual depends on structural or temporal influences. From our analysis, we highlight two practical contributions: a way to control for temporal variation when computing dynamic communicability and the conclusion that the broadcast and receive scores can, under a range of circumstances, be replaced by the row and column sums of the matrix exponential of a weighted adjacency matrix given by the data.

Estimates of Social Contact in a Middle School Based on Self-Report and Wireless Sensor Data

Estimates of contact among children, used for infectious disease transmission models and understanding social patterns, historically rely on self-report logs. Recently, wireless sensor technology has enabled objective measurement of proximal contact and comparison of data from the two methods. These are mostly small-scale studies, and knowledge gaps remain in understanding contact and mixing patterns and also in the advantages and disadvantages of data collection methods. We collected contact data from a middle school, with 7th and 8th grades, for one day using self-report contact logs and wireless sensors. The data were linked for students with unique initials, gender, and grade within the school. This paper presents the results of a comparison of two approaches to characterize school contact networks, wireless proximity sensors and self-report logs. Accounting for incomplete capture and lack of participation, we estimate that "sensor-detectable", proximal contacts longer than 20 seconds during lunch and class-time occurred at 2 fold higher frequency than "self-reportable" talk/touch contacts. Overall, 55% of estimated talk-touch contacts were also sensor-detectable whereas only 15% of estimated sensor-detectable contacts were also talk-touch. Contacts detected by sensors and also in self-report logs had longer mean duration than contacts detected only by sensors (6.3 vs 2.4 minutes). During both lunch and class-time, sensor-detectable contacts demonstrated substantially less gender and grade assortativity than talk-touch contacts. Hallway contacts, which were ascertainable only by proximity sensors, were characterized by extremely high degree and short duration. We conclude that the use of wireless sensors and self-report logs provide complementary insight on in-school mixing patterns and contact frequency.

The role of heterogeneity in contact timing and duration in network models of influenza spread in schools

Influenza poses a significant health threat to children, and schools may play a critical role in community outbreaks. Mathematical outbreak models require assumptions about contact rates and patterns among students, but the level of temporal granularity required to produce reliable results is unclear. We collected objective contact data from students aged 5–14 at an elementary school and middle school in the state of Utah, USA, and paired those data with a novel, data-based model of influenza transmission in schools. Our simulations produced within-school transmission averages consistent with published estimates. We compared simulated outbreaks over the full resolution dynamic network with simulations on networks with averaged representations of contact timing and duration. For both schools, averaging the timing of contacts over one or two school days caused average outbreak sizes to increase by 1–8%. Averaging both contact timing and pairwise contact durations caused average outbreak sizes to increase by 10% at the middle school and 72% at the elementary school. Averaging contact durations separately across within-class and between-class contacts reduced the increase for the elementary school to 5%. Thus, the effect of ignoring details about contact timing and duration in school contact networks on outbreak size modelling can vary across different schools.

Social Contact Networks and Mixing among Students in K-12 Schools in Pittsburgh, PA

Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools.

Infectious Disease Containment Based on a Wireless Sensor System

Infectious diseases pose a serious threat to public health due to its high infectivity and potentially high mortality. One of the most effective ways to protect people from being infected by these diseases is through vaccination. However, due to various resource constraints, vaccinating all the people in a community is not practical. Therefore, targeted vaccination, which vaccinates a small group of people, is an alternative approach to contain infectious diseases. Since many infectious diseases spread among people by droplet transmission within a certain range, we deploy a wireless sensor system in a high school to collect contacts happened within the disease transmission distance. Based on the collected traces, a graph is constructed to model the disease propagation, and a new metric (called connectivity centrality) is presented to find the important nodes in the constructed graph for disease containment. Connectivity centrality considers both a node's local and global effect to measure its importance in disease propagation. Centrality based algorithms are presented and further enhanced by exploiting the information of the known infected nodes, which can be detected during targeted vaccination. Simulation results show that our algorithms can effectively contain infectious diseases and outperform other schemes under various conditions.

Differences in Pertussis Incidence by Income among Oregon Teens during an Outbreak

That disease and poverty are connected is a cornerstone of public health thought. In the case of pertussis, however, it is possible that the expected relationship to poverty is reversed. Grounds exist for considering that increases in income are associated with increases in pertussis rates, both in terms of real risk through social and network features and through the possibility of greater likelihood of care seeking and detection based on income. Using reported adolescent pertussis cases from a 2012 outbreak in Oregon, pertussis incidence rates were determined for areas grouped by zip code into higher, middle, and lower median household income. Adolescents of ages 13–16 years in higher income areas were 2.6 times (95% CI 1.8–3.8) more likely as all others to have reported pertussis during the 2012 outbreak and 3.1 (95% CI 1.4–6.5) times as likely as those in lower income areas. The higher pertussis rates associated with higher income areas were observed regardless of Tdap rate differences. These results suggest that income may be associated with disease risk, likelihood of diagnosis and reporting, or both. Further evaluation of this finding is warranted.

Coupled disease-behavior dynamics on complex networks: A review

It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

Tracking social contact networks with online respondent-driven detection: who recruits whom?

Background

Transmission of respiratory pathogens in a population depends on the contact network patterns of individuals. To accurately understand and explain epidemic behaviour information on contact networks is required, but only limited empirical data is available. Online respondent-driven detection can provide relevant epidemiological data on numbers of contact persons and dynamics of contacts between pairs of individuals. We aimed to analyse contact networks with respect to sociodemographic and geographical characteristics, vaccine-induced immunity and self-reported symptoms.

Methods

In 2014, volunteers from two large participatory surveillance panels in the Netherlands and Belgium were invited for a survey. Participants were asked to record numbers of contacts at different locations and self-reported influenza-like-illness symptoms, and to invite 4 individuals they had met face to face in the preceding 2 weeks. We calculated correlations between linked individuals to investigate mixing patterns.

Results

In total 1560 individuals completed the survey who reported in total 30591 contact persons; 488 recruiter-recruit pairs were analysed. Recruitment was assortative by age, education, household size, influenza vaccination status and sentiments, indicating that participants tended to recruit contact persons similar to themselves. We also found assortative recruitment by symptoms, reaffirming our objective of sampling contact persons whom a participant may infect or by whom a participant may get infected in case of an outbreak. Recruitment was random by sex and numbers of contact persons. Relationships between pairs were influenced by the spatial distribution of peer recruitment.

Conclusions

Although complex mechanisms influence online peer recruitment, the observed statistical relationships reflected the observed contact network patterns in the general population relevant for the transmission of respiratory pathogens. This provides useful and innovative input for predictive epidemic models relying on network information.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-015-1250-z) contains supplementary material, which is available to authorized users.

Increasing herd immunity with influenza revaccination

Seasonal influenza is a significant public health concern globally. While influenza vaccines are the single most effective intervention to reduce influenza morbidity and mortality, there is considerable debate surrounding the merits and consequences of repeated seasonal vaccination. Here, we describe a two-season influenza epidemic contact network model and use it to demonstrate that increasing the level of continuity in vaccination across seasons reduces the burden on public health. We show that revaccination reduces the influenza attack rate not only because it reduces the overall number of susceptible individuals, but also because it better protects highly connected individuals, who would otherwise make a disproportionately large contribution to influenza transmission. We also demonstrate that our results hold on an empirical contact network, in the presence of assortativity in vaccination status, and are robust for a range of vaccine coverage and efficacy levels. Our work contributes a population-level perspective to debates about the merits of repeated influenza vaccination and advocates for public health policy to incorporate individual vaccine histories.

Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study

Introduction

The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics.

Methods

We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students’ beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network.

Results

Of the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics.

Conclusions

This medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student’s perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers.