塞内加尔牛和小反刍动物贸易网络的描述及其对动物疾病监测的影响
Description of the cattle and small ruminants trade network in Senegal and implication for the surveillance of animal diseases
摘要：
牲畜的流动，特别是小型和大型反刍动物的流动，是西非生产和贸易的主要支柱之一：牲畜四处流动，寻找更好的放牧地，或在市场上出售，供国内消费和与节日有关的活动。这些运输路线长达数千公里，能够连接整个西非地区，从而促进了许多动物疾病和人畜共患病的传播。即使在正常年份，也有几个因素决定了流动模式，监测系统需要考虑到这些变化。在本文中，我们提出了一个基于时间网络理论的程序，使用两个指标来确定可能的哨兵位置：脆弱性（即被疾病到达的概率）和感染时间（即疾病首次到达的时间）。在我们对变化网络的结构分析中使用这些指标，使我们能够确定一组可用于预警系统的节点。作为案例研究，我们模拟了F.A.S.T.的引入。(Foot该研究使用了塞内加尔兽医服务局颁发的2020年卫生证书（LPS，卫生通行证）中的数据，以重建国家流动网络。我们的分析表明，静态的方法可以显着高估的速度和疾病传播的程度，而时间分析显示，可达性和脆弱性的不同的行政部门（用作节点的流动性网络）在一年中的变化。出于这个原因，在一年中的不同时期确定了几组前哨淋巴结，强调了时间性在形成疾病扩散模式中的作用。
英文摘要：
Livestock mobility, particularly that of small and large ruminants, is one of the main pillars of production and trade in West Africa: livestock is moved around in search of better grazing or sold in markets for domestic consumption and for festival-related activities. These movements cover several thousand kilometers and have the capability of connecting the whole West African region thus facilitating the diffusion of many animal and zoonotic diseases. Several factors shape mobility patterns even in normal years and surveillance systems need to account for such changes. In this paper, we present a procedure based on temporal network theory to identify possible sentinel locations using two indicators: vulnerability (i.e. the probability of being reached by the disease) and time of infection (i.e. the time of first arrival of the disease). Using these indicators in our structural analysis of the changing network enabled us to identify a set of nodes that could be used in an early warning system. As a case study we simulated the introduction of F.A.S.T. (Foot and Mouth Similar Transboundary) diseases in Senegal and used data taken from 2020 Sanitary certificates (LPS, laissez-passer sanitaire) issued by the Senegalese Veterinary Services to reconstruct the national mobility network. Our analysis showed that a static approach can significantly overestimate the speed and the extent of disease propagation, whereas temporal analysis revealed that the reachability and vulnerability of the different administrative departments (used as nodes of the mobility network) change over the course of the year. For this reason, several sets of sentinel nodes were identified in different periods of the year, underlining the role of temporality in shaping patterns of disease diffusion.
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