A Mathematical Model of Campylobacter Dynamics Within a Broiler Flock

Abstract

Globally, the bacterial genus Campylobacter is one of the leading causes of human gastroenteritis, with its primary route of infection being through poultry meat. The application of biosecurity measures is currently limited by a lack of understanding of the transmission dynamics within a flock. Our work is the first to undertake a mathematical modeling approach to Campylobacter population dynamics within a flock of broilers (chickens bred specifically for meat). A system of stochastic differential equations is used to model the routes of infection between co-housed birds. The presented model displays the strong correlation between housing density and Campylobacter prevalence, and shows how stochastic variation is the driving factor determining which strains of Campylobacter will emerge first within a flock. The model also shows how the system will rapidly select for phenotypic advantages, to quickly eliminate demographically-weaker strains. A global sensitivity analysis is performed, highlighting that the growth and death rate of other native bacterial species likely contributes the greatest to preventing flock outbreaks, presenting a promising approach to hypothesizing new methods of combatting disease transmission.

A mathematical modelling approach to uncover factors influencing the spread of Campylobacter in a flock of chickens

“…One can also observe slight oscillating behaviour in the total number of infections across a few weeks, most notably in the May to September period. Experimental and theoretical studies have both confirmed the presence of these oscillations 2117 , caused by the immune response within each chicken creating predator-prey style oscillations between the immune system and invading bacteria. One can also note from figures 1 to 3 that C. jejuni appears to be the most frequently appearing species of Campylobacter in the summer months, before C. coli takes over as the most frequently appearing species in the winter.…”

“…Note however from figure 2 that, despite the higher number of C. jejuni STs, each week is primarily dominated by only two or three STs. This phenomena was found through theoretical study to be a result of stochastic drivers within the system, as opposed to any demographic advantages unique to certain STs 17 .…”

“…After confirming from this model the existence of variation in bird transition probabilities, we asked what the impact of this variation could be on the proliferation of Campylobacter STs. Using a previously published stochastic differential equation model of Campylobacter population dynamics within a broiler flock 17 we simulated two variant scenarios, one simulating a homogenous flock of chickens, and another simulating variation in immune response such as that observed in figure 9. The simulations are presented in Appendix 2, where it was seen that demographically equal strains of Campylobacter can be sustained at broadly different levels across the flock due only to variation in bird immune response.…”

“…After confirming from model 5 the existence of variation in bird transition probabilities, we asked what the impact of this variation could be on the proliferation of Campylobacter STs. Using a previously published stochastic differential equation model of Campylobacter population dynamics within a broiler flock 17 we simulated two variant scenarios. Figure A1 displays a case study of the spread of five demographically identical strains of Campylobacter within a flock of 400 demographically identical broilers.…”

“…However, another study suggests that strains are instead lost or transmitted randomly, regardless of their genotypic differences 16 . Indeed recent mathematical modelling approaches have demonstrated that stochastic simulations can effectively capture the broad dynamical differences between strains of equal demographic ability 17 .…”