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

Abstract

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 0 = 3) and ‘slow’ ( R 0 = 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’.

Peste des petits ruminants Virus Transmission Scaling and Husbandry Practices That Contribute to Increased Transmission Risk: An Investigation among Sheep, Goats, and Cattle in Northern Tanzania

“…The relevant methods are summarized below. The SEEDZ study sampling design and questionnaire was originally designed to investigate zoonotic diseases in northern Tanzania [27,28]; however, these data are used here to study PPRV.…”

Multilayer and Multiplex Networks: An Introduction to Their Use in Veterinary Epidemiology

“…Appropriate collection of network data is a fundamental challenge in the design of network studies, and it is important to ensure enough data is collected to provide a realistic insight into the study system (76)(77)(78)(79). This problem is enhanced when using network modeling approaches in epidemiology, where missing edges can result in substantial underestimates of outbreak sizes (80).…”

Detection, forecasting and control of infectious disease epidemics: modelling outbreaks in humans, animals and plants

“…Using stochastic simulations to assess the impact of existing interventions on epidemiological dynamics often involves exploring epidemiological dynamics with and without a proposed control strategy-a problem considered by Lessler et al [93]. Potential interventions include prophylactic controls such as vaccination, considered in the context of wildlife rabies by Baker et al [78] and Ebola in humans by Getz et al [79] and movement bans, an intervention assessed in the context of livestock diseases by Chaters et al [50]. Probert et al [94] and Bussell et al [87] also focus on using models to guide interventions-using reinforcement learning and optimal control theory, respectively-and these approaches could also potentially apply to pathogens irrespective of the type of host.…”

“…Different types of data can be used to parameterize epidemiological models. (a) Data on the locations of hosts in the landscape (here the density of cattle in the UK, adapted from[50]-see that paper for further details). (b) Inference of the values of core parameters governing epidemic dynamics, such as the basic reproduction number, requires temporal data-here, time series of the numbers of new cases in each time period (adapted from[51], see that paper for further details).…”