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Influenza epidemics annually cause substantial morbidity and mortality. For this reason, vaccination is offered yearly to persons with an elevated risk for complications. Assessments of the impact of vaccination are, however, hampered by year-to-year variation in epidemic size and vaccine effectiveness.We estimate the impact of the current vaccination programme comparing simulations with vaccination to counterfactual simulations without vaccination. The simulations rely on an age- and risk-structured transmission model that tracks the build-up and loss of immunity over successive seasons, and that allows the vaccine match to vary between seasons. The model parameters are estimated with a particle Monte Carlo method and approximate Bayesian computation, using epidemiological data on vaccine effectiveness and epidemic size in the Netherlands over a period of 11 years.The number of infections, hospitalisations and deaths vary greatly between years because waning of immunity and vaccine match may differ every season, which is in line with observed variation in influenza epidemic sizes. At an overall coverage of 21%, vaccination has averted on average 13% (7.2–19%, 95% range) of infections, 24% (16–36%) of hospitalisations, and 35% (16–50%) of deaths. This suggests that vaccination is mainly effective in protecting vaccinees from infection rather than reducing transmission. As the Dutch population continues to grow and age, the vaccination programme is projected (up to 2025) to gain in impact, despite a decreasing infection attack rate. Keywords: Influenza virus, Transmission model, Infection attack rate, Vaccine effectiveness, Morbidity, Mortality

Seasonal influenza causes a high disease burden. Many influenza vaccination programmes target the elderly and persons at high risk of complications. Some countries have recommended or even implemented a paediatric vaccination programme. Such a programme is expected to reduce influenza transmission in the population, offering direct protection to the vaccinated children and indirect protection to the elderly.We study the impact of a child vaccination programme with an age- and risk-structured transmission model, calibrated to data of 11 influenza seasons in the Netherlands. The model tracks the build-up of immunes and susceptibles in each age cohort over time, and it allows for seasonal variation in vaccine match and antigenic drift. Different vaccination strategies are evaluated for three target age groups (2–3, 2–12 and 2–16 year olds) over the full range of vaccination coverages (0–100%).The results show that the paediatric vaccination programme has only a limited impact on the elderly age groups, which account for most influenza morbidity and mortality. This is due to two notable changes in infection dynamics. First, an age shift is observed: influenza infections are reduced in vaccinated children, but are increased in young adults with limited natural immunity after years of vaccination. These young adults assume the role of driving the epidemic. Second, a year with low influenza activity can be followed by a large epidemic due to build-up of susceptibles. This variation of the infection attack rate increases with increasing vaccination coverage.The increased variability in the infection attack rate implies that health care facilities should be prepared for rare but larger peaks in influenza patients. Moreover, vaccinating the group with the highest transmission potential, results in a larger dependency on a secure vaccine supply. These arguments should be taken into account in the decision to introduce mass vaccination of school-aged children against influenza. Keywords: Influenza virus, Transmission model, Infection attack rate, Vaccination, Children

The COVID-19 pandemic led to unprecedented changes in behaviour. To estimate if these persisted, a final round of the CoMix social contact survey was conducted in four countries at a time when all societal restrictions had been lifted for several months. We conducted a survey on a nationally representative sample in the UK, Netherlands (NL), Belgium (BE), and Switzerland (CH). Participants were asked about their contacts and behaviours on the previous day. We calculated contact matrices and compared the contact levels to a pre-pandemic baseline to estimate R 0 . Data collection occurred from 17 November to 7 December 2022. 7477 participants were recruited. Some were asked to undertake the survey on behalf of their children. Only 14.4 % of all participants reported wearing a facemask on the previous day. Self-reported vaccination rates in adults were similar for each country at around 86 %. Trimmed mean recorded contacts were highest in NL with 9.9 (95 % confidence interval [CI] 9.0-10.8) contacts per person per day and lowest in CH at 6.0 (95 % CI 5.4-6.6). Contacts at work were lowest in the UK (1.4 contacts per person per day) and highest in NL at 2.8 contacts per person per day. Other contacts were also lower in the UK at 1.6 per person per day (95 % CI 1.4-1.9) and highest in NL at 3.4 recorded per person per day (95 % CI 43.0-4.0). The next-generation approach suggests that R 0 for a close-contact disease would be roughly half pre-pandemic levels in the UK, 80 % in NL and intermediate in the other two countries. The pandemic appears to have resulted in lasting changes in contact patterns expected to have an impact on the epidemiology of many different pathogens. Further post-pandemic surveys are necessary to confirm this finding., Competing Interests: Declaration of Competing Interest None to declare., (Crown Copyright © 2024. Published by Elsevier B.V. All rights reserved.)

During the COVID-19 pandemic, contact tracing was used to identify individuals who had been in contact with a confirmed case so that these contacted individuals could be tested and quarantined to prevent further spread of the SARS-CoV-2 virus. Many countries developed mobile apps to find these contacted individuals faster. We evaluate the epidemiological effectiveness of the Dutch app CoronaMelder, where we measure effectiveness as the reduction of the reproduction number R. To this end, we use a simulation model of SARS-CoV-2 spread and contact tracing, informed by data collected during the study period (December 2020 - March 2021) in the Netherlands. We show that the tracing app caused a clear but small reduction of the reproduction number, and the magnitude of the effect was found to be robust in sensitivity analyses. The app could have been more effective if more people had used it, and if notification of contacts could have been done directly by the user and thus reducing the time intervals between symptom onset and reporting of contacts. The model has two innovative aspects: i) it accounts for the clustered nature of social networks and ii) cases can alert their contacts informally without involvement of health authorities or the tracing app., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Children play a key role in the transmission of many infectious diseases. They have many of their close social encounters at home or at school. We hypothesized that most of the transmission of respiratory infections among children occur in these two settings and that transmission patterns can be predicted by a bipartite network of schools and households., Aim and Methods: To confirm transmission over a school-household network, SARS-CoV-2 transmission pairs in children aged 4-17 years were analyzed by study year and primary/secondary school. Cases with symptom onset between 1 March 2021 and 4 April 2021 identified by source and contact-tracing in the Netherlands were included. In this period, primary schools were open and secondary school students attended class at least once per week. Within pairs, spatial distance between the postcodes was calculated as the Euclidean distance., Results: A total of 4059 transmission pairs were identified; 51.9% between primary schoolers; 19.6% between primary and secondary schoolers; 28.5% between secondary schoolers. Most (68.5%) of the transmission for children in the same study year occurred at school. In contrast, most of the transmission of children from different study years (64.3%) and most primary-secondary transmission (81.7%) occurred at home. The average spatial distance between infections was 1.2 km (median 0.4) for primary school pairs, 1.6 km (median 0) for primary-secondary school pairs and 4.1 km (median 1.2) for secondary school pairs., Conclusion: The results provide evidence of transmission on a bipartite school-household network. Schools play an important role in transmission within study years, and households play an important role in transmission between study years and between primary and secondary schools. Spatial distance between infections in a transmission pair reflects the smaller school catchment area of primary schools versus secondary schools. Many of these observed patterns likely hold for other respiratory pathogens., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Some past epidemics of different influenza subtypes (particularly A/H3N2) in the US saw co-circulation of vaccine-type and variant strains. There is evidence that natural infection with one influenza subtype offers short-term protection against infection with another influenza subtype (henceforth, cross-immunity). This suggests that such cross-immunity for strains within a subtype is expected to be strong. Therefore, while vaccination effective against one strain may reduce transmission of that strain, this may also lead to a reduction of the vaccine-type strain's ability to suppress spread of a variant strain. It remains unclear what the joint effect of vaccination and cross-immunity is for co-circulating influenza strains within a subtype, and what is the potential benefit of a bivalent vaccine that protects against both strains. We simulated co-circulation of vaccine-type and variant strains under a variety of scenarios. In each scenario, we considered the case when the vaccine efficacy against the variant strain is lower than the efficacy against the vaccine-type strain (monovalent vaccine), as well the case when vaccine is equally efficacious against both strains (bivalent vaccine). Administration of a bivalent vaccine results in a significant reduction in the overall incidence of infection compared to administration of a monovalent vaccine, even with lower coverage by the bivalent vaccine. Additionally, we found that with greater cross-immunity, increasing coverage levels for the monovalent vaccine becomes less beneficial, while introducing the bivalent vaccine becomes more beneficial. Our work exhibits the limitations of influenza vaccines that have low efficacy against non-vaccine strains, and demonstrates the benefits of vaccines that offer good protection against multiple influenza strains. The results elucidate the need for guarding against the potential co-circulation of non-vaccine strains for an influenza subtype, at least during select seasons, possibly through inclusion of multiple strains within a subtype (particularly A/H3N2) in a vaccine., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)

The identification of key "driver" groups in influenza epidemics is of much interest for the implementation of effective public health response strategies, including vaccination programs. However, the relative importance of different age groups in propagating epidemics is uncertain. During a communicable disease outbreak, some groups may be disproportionately represented during the outbreak's ascent due to increased susceptibility and/or contact rates. Such groups or subpopulations can be identified by considering the proportion of cases within the subpopulation occurring before (Bp) and after the epidemic peak (Ap) to calculate the subpopulation's relative risk, RR=Bp/Ap. We estimated RR for several subpopulations (age groups) using data on laboratory-confirmed US influenza hospitalizations during epidemics between 2009-2014. Additionally, we simulated various influenza outbreaks in an age-stratified population, relating the RR to the impact of vaccination in each subpopulation on the epidemic's initial effective reproductive number R_e(0). We found that children aged 5-17 had the highest estimates of RR during the five largest influenza A outbreaks, though the relative magnitude of RR in this age group compared to other age groups varied, being highest for the 2009 A/H1N1 pandemic. For the 2010-2011 and 2012-2013 influenza B epidemics, adults aged 18-49, and 0-4 year-olds had the highest estimates of RR respectively. For 83% of simulated epidemics, the group with the highest RR was also the group for which initial distribution of a given quantity of vaccine would result in the largest reduction of R_e(0). In the largest 40% of simulated outbreaks, the group with the highest RR and the largest vaccination impact was children 5-17. While the relative importance of different age groups in propagating influenza outbreaks varies, children aged 5-17 play the leading role during the largest influenza A epidemics. Extra vaccination efforts for this group may contribute to reducing the epidemic's impact in the whole community.

The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R. OutbreakTools is developed by a community of epidemiologists, statisticians, modellers and bioinformaticians, and implements classes and methods for storing, handling and visualizing outbreak data. It includes real and simulated outbreak datasets. Together with a number of tools for infectious disease epidemiology recently made available in R, OutbreakTools contributes to the emergence of a new, free and open-source platform for the analysis of disease outbreaks., (Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.)

Increasing incidence has led to the re-appearance of pertussis as a public health problem in developed countries. Pertussis infection is usually mild in vaccinated children and adults, but it can be fatal in infants who are too young for effective vaccination (≤3 months). Tailoring of control strategies to prevent infection of the infant hinges on the availability of estimates of key epidemiological quantities. Here we estimate the serial interval of pertussis, i.e., the time between symptoms onset in a case and its infector, using data from a household-based study carried out in the Netherlands in 2007-2009. We use statistical methodology to tie infected persons to probable infector persons, and obtain statistically supported stratifications of the data by person-type (infant, mother, father, sibling). The analyses show that the mean serial interval is 20 days (95% CI: 16-23 days) when the mother is the infector of the infant, and 28 days (95% CI: 23-33 days) when the infector is the father or a sibling. These time frames offer opportunities for early mitigation of the consequences of infection of an infant once a case has been detected in a household. If preventive measures such as social distancing or antimicrobial treatment are taken promptly they could decrease the probability of infection of the infant., (Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.)

Following the emergence of a novel strain of influenza A(H1N1) in Mexico and the United States in April 2009, its epidemiology in Europe during the summer was limited to sporadic and localised outbreaks. Only the United Kingdom experienced widespread transmission declining with school holidays in late July. Using statistical modelling where applicable we explored the following causes that could explain this surprising difference in transmission dynamics: extinction by chance, differences in the susceptibility profile, age distribution of the imported cases, differences in contact patterns, mitigation strategies, school holidays and weather patterns. No single factor was able to explain the differences sufficiently. Hence an additive mixed model was used to model the country-specific weekly estimates of the effective reproductive number using the extinction probability, school holidays and weather patterns as explanatory variables. The average extinction probability, its trend and the trend in absolute humidity were found to be significantly negatively correlated with the effective reproduction number - although they could only explain about 3% of the variability in the model. By comparing the initial epidemiology of influenza A (H1N1) across different European countries, our analysis was able to uncover a possible role for the timing of importations (extinction probability), mixing patterns and the absolute humidity as underlying factors. However, much uncertainty remains. With better information on the role of these epidemiological factors, the control of influenza could be improved., (Copyright © 2011 Elsevier B.V. All rights reserved.)