Use of proxy indicators for automated surveillance of severe acute respiratory infection, the Netherlands, 2017 to 2023: a proof-of-concept study.

BackgroundEffective pandemic preparedness requires robust severe acute respiratory infection (SARI) surveillance. However, identifying SARI patients based on symptoms is time-consuming. Using the number of reverse transcription (RT)-PCR tests or contact and droplet precaution labels as a proxy for SARI could accurately reflect the epidemiology of patients presenting with SARI.AimWe aimed to compare the number of RT-PCR tests, contact and droplet precaution labels and SARI-related International Classification of Disease (ICD)-10 codes and evaluate their use as surveillance indicators.MethodsPatients from all age groups hospitalised at Leiden University Medical Center between 1 January 2017 up to and including 30 April 2023 were eligible for inclusion. We used a clinical data collection tool to extract data from electronic medical records. For each surveillance indicator, we plotted the absolute count for each week, the incidence proportion per week and the correlation between the three surveillance indicators.ResultsWe included 117,404 hospital admissions. The three surveillance indicators generally followed a similar pattern before and during the COVID-19 pandemic. The correlation was highest between contact and droplet precaution labels and ICD-10 diagnostic codes (Pearson correlation coefficient: 0.84). There was a strong increase in the number of RT-PCR tests after the start of the COVID-19 pandemic.DiscussionAll three surveillance indicators have advantages and disadvantages. ICD-10 diagnostic codes are suitable but are subject to reporting delays. Contact and droplet precaution labels are a feasible option for automated SARI surveillance, since these reflect trends in SARI incidence and may be available real-time.

Estimation of the infection attack rate of mumps in an outbreak among college students using paired serology.

Mumps virus is a highly transmissible pathogen that is effectively controlled in countries with high vaccination coverage. Nevertheless, outbreaks have occurred worldwide over the past decades in vaccinated populations. Here we analyse an outbreak of mumps virus genotype G among college students in the Netherlands over the period 2009-2012 using paired serological data. To identify infections in the presence of preexisting antibodies we compared mumps specific serum IgG concentrations in two consecutive samples (n=746), whereby the first sample was taken when students started their study prior to the outbreaks, and the second sample was taken 2-5 years later. We fit a binary mixture model to the data. The two mixing distributions represent uninfected and infected classes. Throughout we assume that the infection probability increases with the ratio of antibody concentrations of the second to first sample. The estimated infection attack rate in this study is higher than reported earlier (0.095 versus 0.042). The analyses yield probabilistic classifications of participants, which are mostly quite precise owing to the high intraclass correlation of samples in uninfected participants (0.85, 95%CrI: 0.82-0.87). The estimated probability of infection increases with decreasing antibody concentration in the pre-outbreak sample, such that the probability of infection is 0.12 (95%CrI: 0.10-0.13) for the lowest quartile of the pre-outbreak samples and 0.056 (95%CrI: 0.044-0.068) for the highest quartile. We discuss the implications of these insights for the design of booster vaccination strategies.

Effectiveness of a COVID-19 contact tracing app in a simulation model with indirect and informal contact tracing.

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.