Eliminating infectious diseases of livestock: A metapopulation model of infection control

National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australia. Electronic address: .
Theoretical Population Biology (Impact Factor: 1.7). 03/2013; 85(1). DOI: 10.1016/j.tpb.2013.02.002
Source: PubMed


When novel disease outbreaks occur in livestock, policy makers must respond promptly to eliminate disease, and are typically called on to make control decisions before detailed analysis of disease parameters can be undertaken. We present a flexible metapopulation model of disease spread that incorporates variation in livestock density and includes occasional high-mixing locations or events, such as markets or race meetings. Using probability generating functions derived from this branching process model, we compare the likely success of reactive control strategies in eliminating disease spread. We find that the optimal vaccine strategy varies according to the disease transmission rate, with homogeneous vaccination most effective for low transmission rates, and heterogeneous vaccination preferable for high levels of transmission. Quarantine combines well with vaccination, with the chance of disease elimination enhanced even for vaccines with low efficacy. Control decisions surrounding horse race meetings were of particular concern during the 2007 outbreak of equine influenza in Australia. We show that this type of high-mixing event is a powerful spread mechanism, even when the proportion of time spent at such events is low. If such locations remain open, elimination will require a highly effective vaccine with high coverage. However, a policy of banning animals from quarantined regions from attending such events can provide an effective alternative if full closure of events is economically or politically untenable.

8 Reads
  • Source
    • "Ideally, the exposition—and especially abstracts, results sections, and figures—enables both theorists and non-theorists to extract the main biological conclusions. The empirical relevance of the work is demonstrated, for example, by an illustration with data (Cowell, 2013; Matthews and Garenne, 2013), by simulation or computation using parameter values relevant to empirical scenarios (Barraquand and Yoccoz, 2013; Glass and Barnes, 2013; Sverdlov and Thompson, 2013), by centering the work around a specific empirical problem (Boni et al., 2013; Dexter and Kowalewski, 2013), or through discussion sections that comment both on the value of the work as theory and on its contributions to the biological question at hand (Bansaye and Lambert, 2013; Good and Desai, 2013; Schreiber and Killingback, 2013; Wittmann et al., 2013). "

    Theoretical Population Biology 12/2013; 92. DOI:10.1016/j.tpb.2013.11.003 · 1.70 Impact Factor

Similar Publications