Abstract
BackgroundA discrete time, stochastic, compartmental model simulating the spread of Mycoplasma hyopneumoniae within a batch of industrially raised pigs was developed to understand infection dynamics and to assess the impact of a range of husbandry practices. A ‘disease severity’ index was calculated based on the ratio between the cumulative numbers of acutely and chronically diseased and infectious pigs per day in each age category, divided by the length of time that pigs spent in this age category. This is equal to the number of pigs per day, either acutely or chronically infectious and diseased, divided by the number of all pigs per all days in the model. The impact of risk and protective factors at batch level was examined by adjusting ‘acclimatisation of gilts’, ‘length of suckling period’, ‘vaccination of suckling pigs against M. hyopneumoniae’, ‘contact between fattening pigs of different age during restocking of compartments’ and ‘co-infections in fattening pigs’.ResultsThe highest ‘disease severity’ was predicted, when gilts do not have contact with live animals during their acclimatisation, suckling period is 28 days, no vaccine is applied, fatteners have contact with pigs of other ages and are suffering from co-infections. Pigs in this scenario become diseased/infectious for 26.1 % of their lifetime. Logistic regression showed that vaccination of suckling pigs was influential for ‘disease severity’ in growers and finishers, but not in suckling and nursery pigs. Lack of contact between gilts and other live pigs during the acclimatisation significantly influenced the ‘disease severity’ in suckling pigs but had less impact in growing and finishing pigs. The length of the suckling period equally affected the severity of the disease in all age groups with the strongest association in nursery pigs. The contact between fatteners of different groups influenced the course of infection among finishers, but not among other pigs. Finally, presence of co-infections was relevant in growers and finishers, but not in younger pigs.ConclusionThe developed model allows comparison of different prevention programmes and strategies for controlling transmission of M. hyopneumoniae.Electronic supplementary materialThe online version of this article (doi:10.1186/s40813-016-0026-1) contains supplementary material, which is available to authorized users.
Highlights
A discrete time, stochastic, compartmental model simulating the spread of Mycoplasma hyopneumoniae within a batch of industrially raised pigs was developed to understand infection dynamics and to assess the impact of a range of husbandry practices
When all protective factors were present ([P] = Positive; Vac[P], Acclimatisation of gilts (Acc)[P], suckling period (Suc)[P]) and risk factors were absent ([N] = Negative; Con[N], in growing and finishing pigs (Inf)[N]), the percentage of finishing pigs susceptible to M. hyopneumoniae at the end of the fattening period was >85 % on average and the percentage of pigs, which had been infected during their growth period was
The observation of less than 10 % potentially seropositive pigs and the absence of biologically significant within-herd transmission in most simulations are consistent with findings of a recent study [26]
Summary
A discrete time, stochastic, compartmental model simulating the spread of Mycoplasma hyopneumoniae within a batch of industrially raised pigs was developed to understand infection dynamics and to assess the impact of a range of husbandry practices. A ‘disease severity’ index was calculated based on the ratio between the cumulative numbers of acutely and chronically diseased and infectious pigs per day in each age category, divided by the length of time that pigs spent in this age category. For the infection of pigs with M. hyopneumoniae and the corresponding disease several risk factors, e.g. poor management practices, co-infections with other bacteria, viruses and/or parasites, seasonal effects, have been described [4, 6,7,8,9]. Vaccination can reduce the impact of disease in endemically infected herds [14], but does not eliminate the pathogen from an infected herd [15]
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