Biology subjects

Wallinga, J.

Publications and source records attributed to Wallinga, J..

3 recordsLinked to original sources

bioRxiv2019

Realized generation times: contraction and impact of infectious period, reproduction number and population size

One of the key characteristics of the transmission dynamics of infectious diseases is the generation time which refers to the time interval between the infection of a secondary case and the infection of its infector. The generation time distribution together with the reproduction number determines the rate at which an infection spreads in a population. When defining the generation time distribution at a calendar time t two definitions are plausible according whether we regard t as the infection time of the infector or the infection time of the infectee. The resulting measurements are respectively called forward generation time and backward generation time. It has been observed that the mean forward generation time contracts around the peak of an epidemic. This contraction effect has previously been attributed to either competition among potential infectors or depletion of susceptibles in the population. The first explanation requires many infectives for contraction to occur whereas the latter explanation suggests that contraction occurs even when there are few infectives. With a simulation study we show that both competition and depletion cause the mean forward generation time to contract. Our results also reveal that the distribution of the infectious period and the reproduction number have a strong effect on the size and timing of the contraction, as well as on the mean value of the generation time in both forward and backward scheme.\n\nAuthor summaryInfectious diseases remain one of the greatest threats to human health and commerce, and the analysis of epidemic data is one of the most important applications of statistics in public health. Thus, having reliable estimates of fundamental infectious diseases parameters is critical for public health decision-makers in order to take appropriate actions for the global prevention and management of outbreaks and other health emergencies. A key example is given by the prediction models of the reproduction numbers: these rely on the generation time distribution that is usually estimated from contact tracing data collected at a precise calendar time. The forward scheme is used in such a prediction model and the knowledge of its evolution over time is crucial to correctly estimate the parameters of interest. It is therefore important to characterize the causes that lead to the contraction of the mean forward generation time during the course of an outbreak.\n\nIn this paper, we firstly identify the impact of the epidemiological quantities as reproduction number, infectious period and population size on the mean forward and backward generation time. Moreover, we analyze the phenomena of competition among infectives and depletion of susceptible individuals highlighting their effects on the contraction of the mean forward generation time. The upshot of this investigation is that the variance of the infectious period distribution and the reproduction number have a strong impact on the generation times affecting both the mean value and the evolution over time. Furthermore, competition and depletion can both cause contraction even for small values of the reproduction number suggesting that, in epidemic models where the generation time is considered time-inhomogeneous, estimators accounting for both depletion and competing risks are to be preferred in the inference of the generation interval distributions.

epidemiology↗

bioRxiv2018

Optimizing the use of the Men-ACWY conjugated vaccine to control the developing meningococcal W disease outbreak in the Netherlands, a rapid analysis.

BackgroundThere is a developing outbreak of Neisseria meningitidis serotype W (MenW) in the Netherlands. In response, those aged 14 months and 14 years are vaccinated with the conjugated MenACWY vaccine. In the spring of 2018 we aimed to explore the impact of adding a one-off catch-up campaign targeting those aged 15-18 years on the transmission of MenW and the cost-effectiveness of such a campaign.\n\nMethodsWe estimated the growth rate of the MenW outbreak and quantified the impact of various targeted vaccination strategies on the reproductive number, and subsequently projected the future incidence with and without vaccination. Future cases were expressed in costs and QALYS and the incremental cost-effectiveness ratio was obtained.\n\nResultsWe estimate a reproductive number of around 1.4 (95%CI 1.2-1.7) over the period February 2016-February 2018. Adding the catch-up campaign reduces the reproductive number five years earlier than without a catch-up campaign, to a level around 1.2. The vaccination campaign, including the catch-up, will prevent around 100 cases per year in our base case scenario. Given the projected impact and realistic assumptions on costs and QALYs, adding the catch-up can be considered cost-effective using a threshold of {euro}20,000 per QALY.\n\nConclusionAdding the catch-up campaign targeting those aged 15-18 brings the impact of vaccination on reducing transmission five years forward and directly prevents a high-incidence age group from carriage and disease. Such a campaign can be considered cost-effective. Our study did underpin the decision to introduce a catch-up campaign in spring 2019. Furthermore, our applied method can be of interest for anyone solving vaccine allocation questions in a developing outbreak.

epidemiology↗

bioRxiv2018

A systematic review of social contact surveys to inform transmission models of close contact infections

Social contact data are increasingly being used to inform models for infectious disease spread with the aim of guiding effective policies on disease prevention and control. In this paper, we undertake a systematic review of the study design, statistical analyses and outcomes of the many social contact surveys that have been published. Our primary focus is to identify the designs that have worked best and the most important determinants and to highlight the most robust [fi]ndings.\n\nTwo publicly accessible online databases were systematically searched for articles regarding social contact surveys. PRISMA guidelines were followed as closely as possible. In total, 64 social contact surveys were identi[fi]ed. These surveys were conducted in 24 countries, and more than 80% of the surveys were conducted in high-income countries. Study settings included general population (58%), schools/universities (37%) and health care/conference/research institutes (5%). The majority of studies did not focus on a speci[fi]c age group (38%), whereas others focused on adults (32%) or children (19%). Retrospective and prospective designs were used mostly (45% and 41% of the surveys, respectively) with 6% using both for comparison purposes. The de[fi]nition of a contact varied among surveys, e.g. a non-physical contact may require conversation, close proximity or both. Age, time schedule (e.g., weekday/weekend) and household size were identi[fi]ed as relevant determinants for contact pattern across a large number of studies. The surveys present a wide range of study designs. Throughout, we found that the overall contact patterns were remarkably robust for the study details. By considering the most common approach in each aspect of design (e.g., sampling schemes, data collection, de[fi]nition of contact), we could identify a common practice approach that can be used to facilitate comparison between studies and for benchmarking future studies.

epidemiology↗