The dynamics of scarlet fever in The Netherlands, 1906-1920: a historical analysis.

Background. Scarlet fever, an infectious disease caused by Streptococcus pyogenes, largely disappeared in developed countries during the twentieth century. In recent years, scarlet fever is on the rise again, and there is a need for a better understanding of possible factors driving transmission. Methods. Using historical case notification data from the three largest cities in The Netherlands (Amsterdam, Rotterdam and The Hague) from 1906 to 1920, we inferred the transmission rate for scarlet fever using time-series susceptible-infected-recovered (TSIR) methods. Through additive regression modelling, we investigated the contributions of meteorological variables and school term times to transmission rates. Results. Estimated transmission rates varied by city, and were highest overall for Rotterdam, the most densely populated city at that time. High temperature, seasonal precipitation levels and school term timing were associated with transmission rates, but the roles of these factors were limited and not consistent over all three cities. Conclusions. While weather factors alone can only explain a small portion of the variability in transmission rates, these results help understand the historical dynamics of scarlet fever infection in an era with less advanced sanitation and no antibiotic treatment and may offer insights into the driving factors associated with its recent resurgence.

A scenario modelling analysis to anticipate the impact of COVID-19 vaccination in adolescents and children on disease outcomes in the Netherlands, summer 2021.

BackgroundSince the roll-out of COVID-19 vaccines in late 2020 and throughout 2021, European governments have relied on mathematical modelling to inform policy decisions about COVID-19 vaccination.AimWe present a scenario-based modelling analysis in the Netherlands during summer 2021, to inform whether to extend vaccination to adolescents (12-17-year-olds) and children (5-11-year-olds).MethodsWe developed a deterministic, age-structured susceptible-exposed-infectious-recovered (SEIR) model and compared modelled incidences of infections, hospital and intensive care admissions, and deaths per 100,000 people across vaccination scenarios, before the emergence of the Omicron variant.ResultsOur model projections showed that, on average, upon the release of all non-pharmaceutical control measures on 1 November 2021, a large COVID-19 wave may occur in winter 2021/22, followed by a smaller, second wave in spring 2022, regardless of the vaccination scenario. The model projected reductions in infections/severe disease outcomes when vaccination was extended to adolescents and further reductions when vaccination was extended to all people over 5 years-old. When examining projected disease outcomes by age group, individuals benefitting most from extending vaccination were adolescents and children themselves. We also observed reductions in disease outcomes in older age groups, particularly of parent age (30-49 years), when children and adolescents were vaccinated, suggesting some prevention of onward transmission from younger to older age groups.ConclusionsWhile our scenarios could not anticipate the emergence/consequences of SARS-CoV-2 Omicron variant, we illustrate how our approach can assist decision making. This could be useful when considering to provide booster doses or intervening against future infection waves.

A model of tuberculosis clustering in low incidence countries reveals more transmission in the United Kingdom than the Netherlands between 2010 and 2015.

Tuberculosis (TB) remains a public health threat in low TB incidence countries, through a combination of reactivated disease and onward transmission. Using surveillance data from the United Kingdom (UK) and the Netherlands (NL), we demonstrate a simple and predictable relationship between the probability of observing a cluster and its size (the number of cases with a single genotype). We demonstrate that the full range of observed cluster sizes can be described using a modified branching process model with the individual reproduction number following a Poisson lognormal distribution. We estimate that, on average, between 2010 and 2015, a TB case generated 0.41 (95% CrI 0.30,0.60) secondary cases in the UK, and 0.24 (0.14,0.48) secondary cases in the NL. A majority of cases did not generate any secondary cases. Recent transmission accounted for 39% (26%,60%) of UK cases and 23%(13%,37%) of NL cases. We predict that reducing UK transmission rates to those observed in the NL would result in 538(266,818) fewer cases annually in the UK. In conclusion, while TB in low incidence countries is strongly associated with reactivated infections, we demonstrate that recent transmission remains sufficient to warrant policies aimed at limiting local TB spread.

[Quantifying the impact of mass vaccination programmes on notified cases in the Netherlands]. / Vaccinatieprogramma's in Nederland.

OBJECTIVE: To quantify the impact of long-standing vaccination programmes on notified cases in the Netherlands. DESIGN: Estimates based on model projections of historical morbidity data. METHOD: We collected and digitised previously unavailable monthly case notifications of diphtheria, poliomyelitis, mumps and rubella in the Netherlands over the period 1919-2015. Poisson regression models accounting for seasonality, multi-year cycles, secular trends and auto-correlation were fit to pre-vaccination periods. Cases averted were calculated as the difference between observed and expected cases based on model projections. RESULTS: In the first 13 years of mass vaccinations, case notifications declined rapidly with 18,900 (95%-CI: 12,000-28,600) notified cases of diphtheria averted, 5100 (95%-CI: 2200-13,500) cases of poliomyelitis, and 1800 (95%-CI: 1000-3200) cases of mumps. Vaccination of 11-year-old girls against rubella averted 13700 (95%-CI: 1400-38,300) cases, while universal rubella vaccination averted 700 (95%-CI: 80-2300) cases. CONCLUSION: These findings show that vaccination programmes have contributed substantially to the reduction of infectious diseases in the Netherlands.

Correction: Latent tuberculosis infection in foreign-born communities: Import vs. transmission in The Netherlands derived through mathematical modelling.

[This corrects the article DOI: 10.1371/journal.pone.0192282.].

Latent tuberculosis infection in foreign-born communities: Import vs. transmission in The Netherlands derived through mathematical modelling.

While tuberculosis (TB) represents a significant disease burden worldwide, low-incidence countries strive to reach the WHO target of pre-elimination by 2035. Screening for TB in immigrants is an important component of the strategy to reduce the TB burden in low-incidence settings. An important option is the screening and preventive treatment of latent TB infection (LTBI). Whether this policy is worthwhile depends on the extent of transmission within the country, and introduction of new cases through import. Mathematical transmission models of TB have been used to identify key parameters in the epidemiology of TB and estimate transmission rates. An important application has also been to investigate the consequences of policy scenarios. Here, we formulate a mathematical model for TB transmission within the Netherlands to estimate the size of the pool of latent infections, and to determine the share of importation-either through immigration or travel- versus transmission within the Netherlands. We take into account importation of infections due to immigration, and travel to the country of origin, focusing on the three ethnicities most represented among foreign-born TB cases (after exclusion of those overrepresented among asylum seekers): Moroccans, Turkish and Indonesians. We fit a system of ordinary differential equations to the data from the Netherlands Tuberculosis Registry on (extra-)pulmonary TB cases from 1995-2013. We estimate that about 27% of Moroccans, 25% of Indonesians, and 16% of Turkish, are latently infected. Furthermore, we find that for all three foreign-born communities, immigration is the most important source of LTBI, but the extent of within-country transmission is much lower (about half) for the Turkish and Indonesian communities than for the Moroccan. This would imply that contact investigation would have a greater yield in the latter community than in the former. Travel remains a minor factor contributing LTBI, suggesting that targeting returning travelers might be less effective at preventing LTBI than immigrants upon entry in the country.

Temporal cross-correlation between influenza-like illnesses and invasive pneumococcal disease in The Netherlands.

BACKGROUND: While the burden of community-acquired pneumonia and invasive pneumococcal disease (IPD) is still considerable, there is little insight in the factors contributing to disease. Previous research on the lagged relationship between respiratory viruses and pneumococcal disease incidence is inconclusive, and studies correcting for temporal autocorrelation are lacking. OBJECTIVES: To investigate the temporal relation between influenza-like illnesses (ILI) and IPD, correcting for temporal autocorrelation. METHODS: Weekly counts of ILI were obtained from the Sentinel Practices of NIVEL Primary Care Database. IPD data were collected from the Dutch laboratory-based surveillance system for bacterial meningitis from 2004 to 2014. We analysed the correlation between time series, pre-whitening the dependent time series with the best-fit seasonal autoregressive integrated moving average (SARIMA) model to the independent time series. We performed cross-correlations between ILI and IPD incidences, and the (pre-whitened) residuals, in the overall population and in the elderly. RESULTS: We found significant cross-correlations between ILI and IPD incidences peaking at lags -3 overall and at 1 week in the 65+ population. However, after pre-whitening, no cross-correlations were apparent in either population group. CONCLUSION: Our study suggests that ILI occurrence does not seem to be the major driver of IPD incidence in The Netherlands.

Contribution of seasonality in transmission of Mycobacterium tuberculosis to seasonality in tuberculosis disease: a simulation study.

A seasonal rise in tuberculosis (TB) notifications has been confirmed in several studies. Here, we examined one hypothesis for its cause: increased transmission of TB during wintertime due to crowding. Seasonality analysis was performed on actual and simulated notifications of clustered TB cases, which are considered to be representative of recent transmission, diagnosed from 1993 to 2004 in the Netherlands (n = 4,746). To test the hypothesis of winter crowding, notifications were simulated by adding patient delay and incubation period to an infection date randomly taken to be in winter in 80% of cases. The incubation periods were derived from frequency distributions for different TB disease localizations drawn from the literature. Seasonality analysis was performed using autocorrelation function plots and spectral analysis. Actual notifications showed strong seasonality in clustered TB and clustered extrapulmonary TB cases but not in clustered pulmonary TB cases. Analysis of simulated notifications revealed barely significant seasonality only in extrapulmonary TB cases. Our results suggest that increased transmission of TB during wintertime is unlikely to be the only cause of the seasonal peak in TB notifications. A factor closer to the notification date probably contributes to the seasonality observed in TB notifications.

Role of avidity and breadth of the CD4 T cell response in progression to AIDS.

The great variability in the time between infection with HIV and the onset of AIDS has been the object of intense study. In the current work, we examine a mathematical model that focuses on the role of immune response variability between patients. We study the effect of variation in both the avidity and the breadth of the immune response on within-patient disease dynamics, viral setpoint and time to AIDS. We conclude that immune response variability can explain the observed variability in disease progression to a large extent. It turns out that the avidity, more than the breadth of the immune response, determines disease progression, and that the average avidity of the five best clones is a much better correlate for disease progression than the total number of clones responding. For the design of vaccines, this would suggest that, if given the choice between stimulating a broader, but average avidity response or a narrower high-avidity response, the latter option would yield better control of virus load and consequently slow down disease progression.