Abstract
1. Understanding how an emergent pathogen successfully establishes itself and persists in a previously unaffected population is a crucial problem in disease ecology, with important implications for disease management. In multi-host pathogen systems this problem is particularly difficult, as the importance of each host species to transmission is often poorly characterised, and the disease epidemiology is complex. Opportunities to observe and analyse such emergent scenarios are few.
2. Here, we exploit a unique dataset combining densely-collected data on the epidemiological and evolutionary characteristics of an outbreak of Mycobacterium bovis (the causative agent of bovine tuberculosis, bTB) in a population of cattle and badgers in an area considered low-risk for bTB, with no previous record of either persistent infection in cattle, or of any infection in wildlife. We analyse the outbreak dynamics using a combination of mathematical modelling, Bayesian evolutionary analyses, and machine learning.
3. Comparison to M. bovis whole-genome sequences from Northern Ireland confirmed this to be a single introduction of the pathogen from the latter region, with evolutionary analysis supporting an introduction directly into the local cattle population six years prior to its first discovery in badgers.
4. Once introduced, the evidence supports M. bovis epidemiological dynamics passing through two phases, the first dominated by cattle-to-cattle transmission before becoming established in the local badger population.
5. Synthesis and applications. The raw data object of this analysis were used to support decisions regarding the control of a M. bovis emergent outbreak, of considerable concern because of the geographical distance from previously known high-risk areas. Our further analyses, estimating the time of introduction (and therefore the likely magnitude of any hidden outbreak) and the rates of cross-species transmission, provided valuable confirmation that the extent and focus of the imposed controls were appropriate. Not only these findings strengthen the call for genomic surveillance, but they also pave the path for future outbreaks control, providing insights for more rapid and decisive evidence-based decision-making. As the methods we used and developed are agnostic to the disease itself, they are also valuable for other slowly transmitting pathogens.
2. Here, we exploit a unique dataset combining densely-collected data on the epidemiological and evolutionary characteristics of an outbreak of Mycobacterium bovis (the causative agent of bovine tuberculosis, bTB) in a population of cattle and badgers in an area considered low-risk for bTB, with no previous record of either persistent infection in cattle, or of any infection in wildlife. We analyse the outbreak dynamics using a combination of mathematical modelling, Bayesian evolutionary analyses, and machine learning.
3. Comparison to M. bovis whole-genome sequences from Northern Ireland confirmed this to be a single introduction of the pathogen from the latter region, with evolutionary analysis supporting an introduction directly into the local cattle population six years prior to its first discovery in badgers.
4. Once introduced, the evidence supports M. bovis epidemiological dynamics passing through two phases, the first dominated by cattle-to-cattle transmission before becoming established in the local badger population.
5. Synthesis and applications. The raw data object of this analysis were used to support decisions regarding the control of a M. bovis emergent outbreak, of considerable concern because of the geographical distance from previously known high-risk areas. Our further analyses, estimating the time of introduction (and therefore the likely magnitude of any hidden outbreak) and the rates of cross-species transmission, provided valuable confirmation that the extent and focus of the imposed controls were appropriate. Not only these findings strengthen the call for genomic surveillance, but they also pave the path for future outbreaks control, providing insights for more rapid and decisive evidence-based decision-making. As the methods we used and developed are agnostic to the disease itself, they are also valuable for other slowly transmitting pathogens.
| Original language | English |
|---|---|
| Number of pages | 13 |
| Journal | Journal of Applied Ecology |
| Early online date | 1 Nov 2021 |
| DOIs | |
| Publication status | E-pub ahead of print - 1 Nov 2021 |