Dose-response relationships for foot and mouth disease in cattle and sheep

Abstract

The relationships between the inhaled dose of foot and mouth disease virus and the outcomes of infection and disease were examined by fitting dose-response models to experimental data. The parameters for both the exponential and beta-poisson models were estimated using maximum likelihood and Bayesian methods. The median probability of infection given a single inhaled TCID50 was estimated to be 0.031 with 95% Bayesian credibility intervals (CI) of 0.018-0.052 for cattle, and 0.045 (CI = 0.024-0.080) for sheep. These estimates were used to construct dose-response curves and uncertainty distributions for use in quantitative risk assessments.

A meta-analysis of the effect of dose and age at exposure on shedding ofMycobacterium aviumsubspeciesparatuberculosis(MAP) in experimentally infected calves and cows

“…However, the relationship between challenge dose and progression to infectiousness has not been fully quantified. Dose-dependent or age-dependent response to infection is common for many pathogens, including MAP [8][9][10]. Previous work with other pathogens illustrates the potential impact of changes to pathogenesis due to age at exposure subsequently resulting in a change in infection transmission dynamics in a population [11].…”

Dynamical behaviour of epidemiological models with sub-optimal immunity and nonlinear incidence

“…For some infections data suggests that severity of disease increases with intimacy of contact or, more generally, with the intensity of exposure to the infectious agent (e.g. measles [26], polio [20], foot-and-mouth disease [8,2]). But dose of exposure may also be important for subsequent infectiousness as suggested by experiments on bovine tuberculosis [18,19].…”

“…There is good documentation on dose-response relationships from experimental studies in terms of infection, disease, and immunity. The majority of such empirical studies conclude that there is a minimal dose required to induce a response, and above this threshold the probability of response increases nonlinearly with the dose of exposure [8,18]. To incorporate this observation into a model of transmission we need a scheme that processes the shedding and spread of infectious agent to determine the average dose challenging a susceptible.…”

Markov Chain Monte Carlo-Based Bayesian Analysis of Binary Response Regression, with Illustration in Dose-Response Assessment

“…Metz et al 1982 amongst many others). However, the steady increase in popularity of the Bayesian methodology (Clark 2005) has prompted substantial interest in implementing it in the D-R and exposure risk assessment settings (e.g., French et al 2002;Teunis et al 2002;Englehardt 2004;Englehardt & Swartout. 2004, 2006Mutshinda et al 2008).…”