Estimating Infectious Disease Parameters from Data on Social Contacts and Serological Status

Abstract

In dynamic models of infectious disease transmission, typically various mixing patterns are imposed on the so-called Who-Acquires-Infection-From-Whom matrix (WAIFW). These imposed mixing patterns are based on prior knowledge of agerelated social mixing behavior rather than observations. Alternatively, one can assume that transmission rates for infections transmitted predominantly through non-sexual social contacts, are proportional to rates of conversational contact which can be estimated from a contact survey. In general, however, contacts reported in social contact surveys are proxies of those events by which transmission may occur and there may exist age-specific characteristics related to susceptibility and infectiousness which are not captured by the contact rates. Therefore, in this paper, transmission is modeled as the product of two age-specific variables: the age-specific contact rate and an age-specific proportionality factor, which entails an improvement of fit for the seroprevalence of the varicella-zoster virus (VZV) in Belgium. Furthermore, we address the impact on the estimation of the basic reproduction number, using non-parametric bootstrapping to account for different sources of variability and using multi-model inference to deal with model selection uncertainty. The proposed method makes it possible to obtain important information on transmission dynamics that cannot be inferred from approaches traditionally applied hitherto.

Collecting close-contact social mixing data with contact diaries: reporting errors and biases

“…13]. In addition, various studies have shown that empirical contact data can successfully be applied in epidemiological models to replicate serological data [14][15][16].…”

Childhood varicella-zoster virus vaccination in Belgium: Cost-effective only in the long run or without exogenous boosting?

“…For each of the vaccination options, the number of chickenpox and zoster episodes with and without vaccination is compared based on the direct costs (health care payer perspective) and consequences (life-years lost and Quality-Adjusted Life-Years (QALYs) lost) related to the episodes. The age-specific number of primary and breakthrough Age-specific force of infection Figure S2 • Social contact data from Belgium shown to fit best seroprevalence data [49][50][51] • Belgian VZV seroprevalence data 2003 (n = 3256, ages 0-80 y) 52 (Fig. S2) • Chickenpox and herpes zoster incidence data from Belgium 53…”

Six challenges in measuring contact networks for use in modelling

“…duration or frequency of interaction; presence or absence of physical contact) well-correlated with risk? Studies indicate that some measures of link properties generate better model fits to incidence data than others (Goeyvaerts et al, 2010) (see Challenge 2, above), but can we conclude that these measures of weight are more generally suitable? If we can robustly quantify link strength using available data for a pathogen of interest, we will be able to make network studies substantially more effective.…”

“…The key challenge for the use of self-reported contact data to inform network models is to validate the relationship between reported contacts and infection. Modelling work has used different measured contact patterns to fit age-structured incidence or serology data (Goeyvaerts et al, 2010;Melegaro et al, 2011), but further work is needed to understand how to interpret the results. For example, if patterns of interactions involving physical contact provide the best fit to serological sampling, does this mean that infection actually spreads via physical contact, or just that such contacts provide a good proxy in a particular population?…”