Analysing livestock network data for infectious disease control: an argument for routine data collection in emerging economies

• Published in: Philosophical transactions of the Royal Society of London. Series B. Biological sciences Vol. 374; no. 1776; p. 20180264
• Main Authors: Chaters, G L, Johnson, P C D, Cleaveland, S, Crispell, J, de Glanville, W A, Doherty, T, Matthews, L, Mohr, S, Nyasebwa, O M, Rossi, G, Salvador, L C M, Swai, E, Kao, R R
• Format: Journal Article
• Language: English
• Published: England The Royal Society 08-07-2019
• Subjects: Animal Diseases - economics; Animal Diseases - epidemiology; Animals; Communicable Diseases - economics; Communicable Diseases - epidemiology; Communicable Diseases - veterinary; Data Collection; Developing Countries - economics; Livestock; Models, Biological; Population Surveillance - methods; zoonoses; targeted interventions; centrality measures; network analysis; livestock networks; Tanzania
• ISSN: 0962-8436; 1471-2970; 1471-2970
• DOI: 10.1098/rstb.2018.0264

Livestock movements are an important mechanism of infectious disease transmission. Where these are well recorded, network analysis tools have been used to successfully identify system properties, highlight vulnerabilities to transmission, and inform targeted surveillance and control. Here we highlight the main uses of network properties in understanding livestock disease epidemiology and discuss statistical approaches to infer network characteristics from biased or fragmented datasets. We use a 'hurdle model' approach that predicts (i) the probability of movement and (ii) the number of livestock moved to generate synthetic 'complete' networks of movements between administrative wards, exploiting routinely collected government movement permit data from northern Tanzania. We demonstrate that this model captures a significant amount of the observed variation. Combining the cattle movement network with a spatial between-ward contact layer, we create a multiplex, over which we simulated the spread of 'fast' ( R
= 3) and 'slow' ( R
= 1.5) pathogens, and assess the effects of random versus targeted disease control interventions (vaccination and movement ban). The targeted interventions substantially outperform those randomly implemented for both fast and slow pathogens. Our findings provide motivation to encourage routine collection and centralization of movement data to construct representative networks. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.