Abstract
Simple SummaryMaximizing the efficiency of pork production in line with sustainability and environmental restrictions presents a challenge for the pig industry in the coming years. It is necessary to develop practices based on cost/benefit analyses of the effects of disease on animal performance. Diseases can be controlled in various ways, such as vaccination programs and management protocols, among others, to control pathogens. We have developed a model to disentangle the effects of management and vaccination strategies to control one of the most important pig viral diseases, Aujeszky disease. Our results suggest that after confirming the diagnosis, early vaccination of most of the population is critical to decrease the spread of the virus and minimize its impact on pig productivity. However, the effect of management is negligible for the control of this virus. Thus, this model can be used to evaluate preventive medicine programs in the control of known diseases and for new ones that could appear in the future.Aujeszky’s disease is one of the main pig viral diseases and results in considerable economic losses in the pork production industry. The disease can be controlled using preventive measures such as improved stock management and vaccination throughout the pig-rearing period. We developed a stochastic model based on Population Dynamics P systems (PDP) models for a standard pig production system to differentiate between the effects of pig farm management regimes and vaccination strategies on the control of Aujeszky’s disease under several different epidemiological scenarios. Our results suggest that after confirming the diagnosis, early vaccination of most of the population (>75%) is critical to decrease the spread of the virus and minimize its impact on pig productivity. The direct economic cost of an outbreak of Aujeszky’s disease can be extremely high on a previously uninfected farm (from 352–792 Euros/sow/year) and highlights the positive benefits of investing in vaccination measures to control infections. We demonstrate the usefulness of computational models as tools in the evaluation of preventive medicine programs aimed at limiting the impact of disease on animal production.
Highlights
According to the United Nations Food and Agriculture Organization (FAO), food production must increase by 70% to feed the world’s population by the year 2050 [1]
The main goal of this paper is to develop a stochastic model, based on our Population Dynamics P systems (PDP) model for porcine reproductive and respiratory syndrome virus (PRRSV), to mimic the intra-herd dynamics of a standard pig production system in order to disentangle the effects of pig farm management measures and vaccination strategies in the control of Aujeszky’s disease, under several different epidemiological scenarios
We have used a stochastic and more advanced model (PDP) [10,21], which works with individuals in which each pig moves around and acts according to their own specific rules and is grouped into smaller groups in order to better mimic the intra-herd dynamics of a standard pig production system [10]
Summary
According to the United Nations Food and Agriculture Organization (FAO), food production must increase by 70% to feed the world’s population by the year 2050 [1]. A sustained increase in pig production will be necessary to cope with this challenge and provide enough pork worldwide, by increasing the number of animals in production and/or improving the efficiency of the sector. Increasing the number of animals worldwide is problematic due to environmental restrictions as well as the decrease in pig production in certain countries [2]. Production efficiency globally suffers from the multiple effects of infectious and non-infectious diseases, such as mortality losses, reduced feed conversion ratio, increased veterinary costs, and the lost or lowered value of infected carcasses [3]. It is necessary to evaluate the economic consequences of disease on animal performance in order to select the best husbandry practices based on cost/benefit analyses [4]
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