A semi-stochastic model for Salmonella infection in a multi-group herd

• Published in: Mathematical biosciences Vol. 200; no. 2; pp. 214 - 233
• Main Authors: Xiao, Y., Clancy, D., French, N.P., Bowers, R.G.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2006
• Subjects: Animals; Cattle; Cattle Diseases - epidemiology; Cattle Diseases - microbiology; Epidemiology; Female; Hybrid process; Infection; Male; Models, Biological; Numerical Analysis, Computer-Assisted; Salmonella; Salmonella Infections, Animal - epidemiology; Salmonella Infections, Animal - microbiology; Salmonella typhimurium - physiology; Semi-stochastic model; Infection; Salmonella; Semi-stochastic model; Epidemiology; Hybrid process
• ISSN: 0025-5564; 1879-3134
• DOI: 10.1016/j.mbs.2006.01.006

A multi-group semi-stochastic model is formulated to identify possible causes of why different strains of Salmonella develop so much variation in their infection dynamics in UK dairy herds. The model includes demography (managed populations) and various types of transmission: direct, pseudovertical and indirect (via free-living infectious units in the environment). The effects of herd size and epidemiological parameters on mean prevalence of infection and mean time until fade out are investigated. Numerical simulation shows that higher pathogen-induced mortality, shorter infectious period, more persistent immune response and more rapid removal of faeces result in a lower mean prevalence of infection, a shorter mean time until fade out, and a greater probability of fade out of infection within 600 days. Combining these results and those for the deterministic counterpart could explain differences in observed epidemiological patterns and help to identify the factors inducing the decline in reported cases of epidemic strains such as DT104 in cattle. We further investigate the effect of group structure on the probability of a major outbreak by using the stochastic threshold theory in homogeneous populations and that in heterogeneous populations. Numerical studies suggest that group structure makes major outbreaks less likely than would be the case in a homogeneous population with the same basic reproduction number. Moreover, some control strategies are suggested by investigating the effect of epidemiological parameters on the probability of an epidemic.