Abstract
Infectious diseases in livestock can be transmitted through fomites: objects able to convey infectious agents. Between-farm spread of infections through fomites is mostly due to indirect contacts generated by on-farm visits of personnel that can carry pathogens on their clothes, equipment, or vehicles. However, data on farm visitors are often difficult to obtain because of the heterogeneity of their nature and privacy issues. Thus, models simulating disease spread between farms usually rely on strong assumptions about the contribution of indirect contacts on infection spread. By using data on veterinarian on-farm visits in a dairy farm system, we built a simple simulation model to assess the role of indirect contacts on epidemic dynamics compared to cattle movements (i.e. direct contacts). We showed that including in the simulation model only specific subsets of the information available on indirect contacts could lead to outputs widely different from those obtained with the full-information model. Then, we provided a simple preferential attachment algorithm based on the probability to observe consecutive on-farm visits from the same operator that allows overcoming the information gaps. Our results suggest the importance of detailed data and a deeper understanding of visit dynamics for the prevention and control of livestock diseases.
Highlights
Contagious infectious diseases, such as foot-and-mouth disease (FMD), classical swine fever, and highly pathogenic avian influenza, represent major threats for the livestock industries worldwide[1, 2]
To understand the role of indirect contacts on epidemic dynamics, we ran simulations of the stochastic epidemic model assuming that direct contacts always generate an infection in the receiving farm and testing a range of probability values for between-farm transmission due to indirect contacts
We showed that all partial-information models failed in accurately describing the epidemic dynamics, suggesting that the lack of information affecting these models limited their ability to reproduce the behaviour of the real contact network
Summary
Contagious infectious diseases, such as foot-and-mouth disease (FMD), classical swine fever, and highly pathogenic avian influenza, represent major threats for the livestock industries worldwide[1, 2]. In order to reduce potential losses, veterinary public health agencies have focused on building surveillance systems able to quickly identify ongoing outbreaks, and set up control plans able to effectively and rapidly extinguish livestock epidemics. To this aim, scenario analyses based on knowledge of the infection spread patterns are essential[5]. Routes of between-farm transmission other than animal movements, such as infection through fomites (i.e. contaminated clothes, equipment, and vehicles), have been found to be an important component of the spread of many diseases, including FMD12 in ruminants and swine, classical swine fever in swine[13], and influenza A in both swine[14] and poultry[15]. Epidemiological simulation-based models often accounted for the effect of indirect contacts in between-farm transmission by using limited information on visitors and vehicle movements
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