A data-driven approach to cattle epidemic modelling under uncertainty

Abstract

Cattle movement is an intrinsic part of animal husbandry (i.e., breeding, maintenance, slaughter of livestock). There are an estimated 1 billion cattle heads in the world used for the production of meat, milk, leather, among other products, which are consumed by billions of people. The pressures of efficiently delivering animal products to individuals, lead to a stress in the system both in the number of heads kept and traded, and in the number of possible contacts between these heads. Under these conditions, contact tracing and avoidance is an essential part of modern agriculture because highly contagious diseases such as brucellosis and foot-and-mouth disease can spread through contact, leading to heavy economic costs. Many countries track their cattle with electronic tags (e.g. Australia, Canada) which leads to a highly-precise monitoring capability. Unfortunately, several of the largest producers in the world (e.g. Brazil, Mexico, USA), do not mandate such use, and some do not even mandate the tracking of animal movement (e.g. Mexico, USA). Added to this, the lack of tracking capabilities enable people to take advantage of the system by engaging in unregulated cattle trade. The consequence is that official movement data may contain uncertainty in the number of cattle movements as well as the number of actual trades. This work focuses on understanding uncertainty in cattle movement networks and its relation to epidemic modelling.

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ORCID for Sima Farokhnejad: orcid.org/0000-0003-1892-4095

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