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

Gemma Chaters1, P. Johnson1, Sarah Cleaveland1, Joseph Crispell2, William A. de Glanville1, T. Doherty3, Louise Matthews1, Sibylle Mohr1, Obed M. Nyasebwa4, Gianluigi Rossi3, Liliana C. M. Salvador5,6,3, Emmanuel S. Swai4, Rowland R. Kao3
1Boyd Orr Centre for Population and Ecosystem Health, Institute of Biodiversity Animal Health and Comparative Medicine, University of Glasgow, Glasgow G12 8QQ, UK
2School of Veterinary Medicine, University College Dublin, Dublin, Ireland
3Royal (Dick) School of Veterinary Studies and Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian EH25 9RG, UK
4Department of Veterinary Services, Ministry of Livestock and Fisheries, Nelson Mandela Road, Dar Es Salaam, Tanzania
5Department of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
6Institute of Bioinformatics, University of Georgia, Athens, GA, 30602, USA

Tóm tắt

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’ (R0= 3) and ‘slow’ (R0= 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’.

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Tài liệu tham khảo

10.1038/30918

10.1098/rspb.1999.0716

10.1038/35082140

10.1098/rspb.2006.3505

10.1098/rsif.2007.0214

10.1073/pnas.0308344101

10.1038/nature04292

10.1073/pnas.0906910106

10.1126/science.1125237

10.1093/infdis/jiw273

10.1056/NEJMoa1003176

10.1016/j.jtbi.2005.10.004

10.1136/vr.161.13.439

10.1016/j.prevetmed.2010.12.009

10.1371/journal.pone.0055223

10.1016/j.prevetmed.2014.09.005

10.1016/j.prevetmed.2009.08.026

10.1371/journal.pone.0019869

10.1016/j.prevetmed.2015.12.003

10.1016/j.prevetmed.2006.04.010

10.1016/j.onehlt.2016.03.001

FAO, 2009, The state of food and agriculture; livestock in the balance, 32

10.1098/rstb.2009.0097

Grace D, 2012, Mapping of poverty and likely zoonoses hotspots

ILRI. 2018 Why livestock matter 2018 [11/07/2018]. See https://www.ilri.org/whylivestockmatter.

Aklilu Y, 2008, Livestock marketing in Kenya and Ethiopia: a review of policies and practice

Musemwa L, 2012, The impact of climate change on livestock production amongst the resource-poor farmers of third world countries: a review, Asian J. Agric. Rural Dev., 2, 621

10.1371/journal.pone.0191565

10.1136/vr.98.25.501

10.1007/s11250-010-9607-1

10.1371/journal.pntd.0002324

10.1186/s12917-015-0504-8

10.1371/journal.pntd.0003536

10.3402/iee.v5.30025

10.4269/ajtmh.17-0125

Wasserman S Faust K. 1994 Social network analysis: methods and applications . Structural analysis in the social sciences vol. 8. New York NY: Cambridge University Press.

10.1016/S0378-8733(98)00010-0

10.1093/aje/kwi308

10.1016/j.prevetmed.2010.11.013

10.1371/journal.pone.0152578

10.1016/j.prevetmed.2016.12.017

10.1371/journal.pone.0074292

10.1038/s41598-017-04466-2

10.1038/nrmicro960

10.1136/vr.149.24.729

10.1017/S095026880500453X

10.1016/S0966-842X(02)02371-5

10.1098/rsif.2007.1129

10.1006/jtbi.1999.1064

10.1016/j.jtbi.2005.07.018

10.1016/j.epidem.2018.04.003

Kao RR. 2010 Networks and models with heterogeneous population structure in epidemiology. In Network science: complexity in nature and technology (eds E Estrada et al.). Berlin Germany: Springer.

10.1371/journal.pone.0068629

10.1371/journal.pone.0120567

10.1038/nature03459

10.1103/PhysRevE.73.036127

10.1038/4371251a

10.1103/PhysRevE.83.036102

10.1103/PhysRevE.84.046116

10.1103/PhysRevE.83.025102

10.1016/j.epidem.2013.03.001

10.1371/journal.pone.0003955

10.1098/rsif.2010.0142

10.1098/rspb.2007.1601

10.1371/journal.pbio.1002056

10.1371/journal.pbio.1002057

10.1371/journal.ppat.1000050

10.1016/j.tim.2014.02.011

10.1371/journal.pcbi.1005130

10.1371/journal.pcbi.1004613

10.1038/hdy.2010.78

10.1371/currents.RRN1026

10.1371/journal.pcbi.1004633

10.1371/journal.pcbi.1002768

10.1098/rspb.2014.1324

10.1214/15-AOAS898

10.1098/rspb.2011.0913

10.1534/genetics.113.154856

10.1098/rspb.2014.2878

10.1016/j.tree.2015.03.009

10.1016/j.epidem.2014.09.001

Meehan CJ et al. 2018 The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology. EBioMedicine 37 410–416. (doi:10.1016/j.ebiom.2018.10.013)

10.1093/molbev/msu179

10.1371/journal.pcbi.1003549

10.20506/rst.30.1.2022

10.1111/1365-2656.12137

10.1371/journal.pcbi.1003570

10.1017/S0031182016000044

10.1016/j.prevetmed.2013.07.013

10.1016/j.prevetmed.2013.07.014

10.1146/annurev.es.04.110173.000245

Holling CS. 1996 Engineering resilience versus ecological resilience. In Engineering within ecological constraints (ed. PE Schulze) pp. 31-44. Washington DC: National Academy.

10.1007/s10021-001-0045-9

Anderson I, 2002, Foot and mouth disease 2001: lessons to be learned inquiry

10.1017/S0950268813002963

10.1038/35019019

10.1016/j.epidem.2015.02.007

10.20506/rst.30.2.2043

10.1098/rstb.2014.0107

10.1111/j.1865-1682.2008.01064.x

10.1017/S0950268812000635

10.1017/S0950268817001935

Miguel S, 2012, Challenges in complex systems science, Eur. Phys. J., 214, 245

10.1098/rstb.2016.0091

Godfray HCJ, 2013, A restatement of the natural science evidence base relevant to the control of bovine tuberculosis in Great Britain, Phil. Trans. R. Soc. B, 280, 20131634

10.1093/comnet/cnu016

10.1038/s41559-017-0101

Cardillo A, 2013, Modeling the multi-layer nature of the European Air Transport Network: resilience and passengers re-scheduling under random failures, Eur. Phys. J., 215, 23

10.1098/rstb.2012.0113

10.1016/j.epidem.2014.08.005

10.1371/journal.pcbi.1005301

10.1038/s41598-017-02567-6

10.3201/eid0812.010317

Sole RV, 2001, Complexity and fragility in ecological networks, Phil. Trans. R. Soc. Lond. B, 268, 2039

10.1038/nature18019

10.1103/PhysRevE.88.062816

10.1103/PhysRevE.88.052811

10.1371/journal.pone.0005016

10.1163/1568539X-00003493

10.1016/j.prevetmed.2006.04.004

10.1098/rsif.2006.0129

10.1038/srep43932

New Zealand Government. 2018 National animal identification and tracing . See https://www.mpi.govt.nz/growing-and-harvesting/livestock-and-animal-care/national-animal-identification-and-tracing/.

10.3390/s90503586

10.1016/j.prevetmed.2013.04.007

10.1016/j.rvsc.2013.02.016

10.1038/s41598-018-29999-y

10.1371/journal.pone.0053432

10.1111/j.1865-1682.2008.01053.x

10.1016/j.actatropica.2016.03.027

10.2460/ajvr.69.2.252

10.1186/1756-3305-6-281

10.1371/journal.pone.0075570

10.1016/j.actatropica.2012.12.013

10.1186/1746-6148-7-66

10.1086/422341

10.1038/nature10856

10.1103/PhysRevE.88.022812

Bouslikhane M, 2015, Cross border movements of animals and animal products and their relevance to the epidemiology of animal diseases in Africa

Muyunda C, 2009, Hidden value on the hoof: cross-border livestock trade in East Africa

10.1089/vbz.2012.1205

10.1098/rstb.2011.0362

10.1371/journal.pntd.0001557

Karimuribo ED, 2007, Prevalence of brucellosis in crossbred and indigenous cattle in Tanzania, Livest. Res. Rural Dev., 19, 148

Machangu RS, 1997, Leptospirosis in animals and humans in selected areas of Tanzania, Belg. J. Zool., 127, 97

10.1371/journal.pntd.0002787

10.1093/jn/133.11.3875S

10.1016/S1473-3099(10)70312-1

10.2460/javma.231.12.1806

Muma JB, 2014, The contribution of veterinary medicine to public health and poverty reduction in developing countries, Vet. Ital., 50, 117

Pradere JP, 2014, Improving animal health and livestock productivity to reduce poverty, Rev. Sci. Tech., 33, 735

10.2527/jas.2007-0467

Steinfeld H et al. 2006 Livestock's long shadow: environmental issues and options . Rome Italy: FAO.

Covarrubias K et al. 2012 Livestock and livelihoods in rural Tanzania: a descriptive analysis of the 2009 National Panel Survey . Washington DC: World Bank Group.

Pica-Ciamarra U et al. 2011 Linking smallholders to livestock markets: combining market and household survey data in Tanzania.

Williams TO Spycher BD Okike I. 2006 Improving livestock marketing and intra-regional trade in West Africa: determining appropriate economic incentives and policy framework. Nairobi Kenya: ILRI (International Livestock Research Institute).

10.1023/A:1026435714109

10.1098/rsos.170808

Tanzania National Bureau of Statistics. 2012 Tanzania in figures 2012 . See http://www.nbs.go.tz.

Widgren S Bauer P Eriksson R Engblom S. 2016 SimInf: an R package for data-driven stochastic disease spread simulations . https://arxiv.org/abs/1605.01421v3.

10.1038/nature01343