The Veterinary Journal 195 (2013) 59–65

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The Veterinary Journal

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Analysis of traditional poultry trader networks to improve risk-based surveillance

Emilie Vallée a,⇑,1, Agnès Waret-Szkuta a, Hassen Chaka b, Raphaël Duboz a, Melesse Balcha b,Flavie Goutard a

a CIRAD, AGIRs Unit, Campus International de Baillarguet, TA C-22/E, 34398 Montpellier Cedex 5, Franceb NAHDIC, PO Box 04, Sebeta, Ethiopia

a r t i c l e i n f o a b s t r a c t

Article history:Accepted 20 May 2012

Keywords:Social network analysisSurveillanceTypologyChickensLive bird markets

1090-0233/$ - see front matter � 2012 Elsevier Ltd. Ahttp://dx.doi.org/10.1016/j.tvjl.2012.05.017

⇑ Corresponding author. Tel.: +64 6 3569099.E-mail address: E.Vallee@massey.ac.nz (E. Vallée).

1 Present address: Institute of Veterinary, Animal andUniversity, Private Bag 11222, Palmerston North 4442,

Live bird markets and contacts between them through poultry traders are known risk factors in thespread of diseases such as Newcastle disease. A traders’ questionnaire survey was used to build networksof chicken movements among 29 shared markets during and outside festive seasons in the Oromia regionof Ethiopia. A comparison was made between typologies built using centrality indexes (in-degree, out-degree, in-closeness, out-closeness and random-walk betweenness) and descriptive characteristics ofthe markets (number of chickens, number and type of sellers and the frequency with which they use dif-ferent markets). The festive seasons did not appear to have an impact on the network structure, implyingthat it was not necessary to make structural changes to surveillance targets during these periods. Basedon centrality indices, three markets (Meki, Debre Zeit and Adulala) emerged from the typology as beingcentral to the network, which would not have been deduced from their descriptive characteristics alone.These three poultry markets ideally would be chosen in a risk-based type of surveillance system and intargeted control policies.

� 2012 Elsevier Ltd. All rights reserved.

Introduction

The majority (99%) of the 42 million poultry in Ethiopia arechickens bred traditionally and 96.6% belong to indigenous breeds(Tadelle et al., 2003; Tadesse et al., 2005; Central Statistical Agency,2010). During festive seasons in September (New Year, Meskel),January (Orthodox Christmas, Epiphany) and April (Easter), thereare increased chicken sales for cash needs and consumption (Akliluet al., 2007, 2008; Wilson, 2010).

Highly contagious diseases affecting chickens could jeopardisethe economic and sociocultural balance in Ethiopia (Aklilu et al.,2007, 2008). Newcastle disease (ND) is enzootic in Ethiopia, withprevalences ranging from 14 to 44% in different regions. Periodicoutbreaks of ND, following seasonal patterns (Spradbrow, 2001;Musa et al., 2009; Njagi et al., 2010), lead to high mortality inchickens, ranging from 50 to 100% per annum (Alemu et al.,2008), but the real impact is difficult to assess accurately (Tadesseet al., 2005; Zeleke et al., 2005). In village breeding systems, in-fected chickens incubating the disease are the main source of intro-duction of ND virus (Alexander, 2000; Njagi et al., 2010).

In Ethiopia, important avian diseases are mainly detectedthrough declaration by farmers after unusual poultry mortality.

ll rights reserved.

Biomedical Sciences, MasseyNew Zealand.

Targeted surveillance is conducted around some lakes in the RiftValley and in markets in Addis Ababa. These markets are used byfarmers to sell the chickens they have bred and by a majority ofbreeders (71%) to build or renew their flocks (Tadelle et al., 2003;Alemu et al., 2008). Unsold birds are usually brought back to thevillage (Olive, 2007; Alemu et al., 2008). Traders often transporttheir animals from market to market. Recent studies on poultrytrade have highlighted the role of traders in potential diseasetransmission (Van Kerkhove et al., 2009; Soares-Magalhães et al.,2010). Traders are involved in at least 39% of market transactionsin Ethiopia (G/Egziabher, 2007; Alemu et al., 2008), but the traders’marketing chain is still informal and little understood (Wilson,2010).

Since animals from diverse origins mix together and becausecommercial activity by traders involves movements of live animalsbetween markets, there is potential for transmission of infectiousagents (Shirley and Rushton, 2005; Ortiz-Pelaez et al., 2006;Soares-Magalhães et al., 2010). If these movements follow theempirical 20:80 relationship described by Woolhouse et al.(1997), 20% of markets would be responsible for 80% of spread ofa contagious disease. Due to this uneven distribution, any surveil-lance and control programme must identify and target this high-risk limited number of premises, since they have a major influenceon disease transmission patterns in the network (Woolhouse et al.,1997, 2005; Kiss et al., 2006; Duerr et al., 2007; Green et al., 2009).

Building and analysing the network of live chicken move-ments could help to identify the relevant markets. Network

Fig. 1. Market typology using the following variables: average number of chickens, average number of sellers, ratio of traders to farmers, number of market days (T, terminalmarket; P, primary markets; S, secondary markets; V, village markets). ‘Height’ indicates the arbitrary distance between two classes.

Table 1Mean (± standard deviation) of the number of chickens, number of sellers, ratio of traders to farmers and number of market days per week according to the class used to build themarket typology.

Number of chickens Number of sellers Ratio of traders to farmers Number of market days per week

Village markets 65.42 (± 56.47)c 22.8 (± 13.02)b 0.045 (± 0.06)b 1.0 (± 0.29)a

Secondary markets 462.5 (± 47.87)b 35.5 (± 13.05)b 0.01875 (± 0.02)b 1 (± 0)a

Primary markets 720 (± 168.65)a 114.7 (± 39.94)a 0.178 (± 0.21)a 1.6 (± 0.84)a

Terminal market 900 (NA) 35 (NA) 2.5 (NA) 7 (NA)

NA, not applicable.a,b,c Within a column, the same letters indicate a significant difference using a two-sided t test (P < 0.05).

60 E. Vallée et al. / The Veterinary Journal 195 (2013) 59–65

analysis identifies the ‘prominent’ actors at strategic places inthe network. Centrality indices attempt to quantify the promi-nence of an actor embedded in the network (Wasserman andFaust, 1994).

The aim of this study was to improve the understanding ofchicken trading networks in the East Shewa zone of the Oromiaregion, Ethiopia, using field data. After describing traders’ prac-tices, we compared the advantages of using market centralitymeasures with classic quantitative market descriptors to selectkey markets. The objectives were to describe, analyse and com-pare the contact networks generated by the shared use of marketsduring festive and non-festive seasons and to examine the impli-cations of such structures for the design of chicken diseasesurveillance and control activities.

Materials and methods

Study area

The study was conducted in 5/12 districts (woredas) in the East Shewa zone ofthe Oromia region: Akaki, Ada’a Chukala, Lome, Dugda Bora and Adami Tulu JidoKombolcha. The borders of these woredas were arbitrarily chosen as the limits ofthe data collection area. The selected area has a high density of chicken farms (Ak-lilu et al., 2008; Wilson, 2010) and includes one of the main chicken trading routesto Addis Ababa (Olive, 2007).

Data collection

Markets were selected using snowball sampling, with Debre Zeit market as astarting point for logistical reasons. Markets were selected within the study areafrom information collected in the previous location (Wasserman and Faust, 1994).

Table 2Mean number (%) of each class of market used for purchase and sale by traders.

Type of market Type of trade

Purchase Sale

Village markets 21 (17.7)*** 11 (11.6)***

Secondary markets 30 (25.2)*** 1 (1.1)***

Primary markets 68 (57.1)*** 74 (77.9)***

Terminal markets 0 (0)*** 9 (9.5)***

*** Fisher’s exact test, P < 0.001 realised across the four market types.

E. Vallée et al. / The Veterinary Journal 195 (2013) 59–65 61

Each market was visited once and selling traders were actively searched for andinterviewed; a trader was defined as a poultry seller who sells chickens that havebeen bought before, but were not bred by the same person. Data were also collectedfor another study from farmers (who sell their own chickens) interviewed in thesame markets. Markets identified by farmers were also included in this study.

A questionnaire was designed and piloted in Debre Zeit market. Data were col-lected from April to July 2009 by one person, with the assistance of three teams whoprovided translations from English to the local languages (Amharic or Oromo). Theglobal positioning system was used to identify the position of each market. The yearof the study (2009) was a typical year in Ethiopia with respect to climate, poultryconsumption and trading, and no major disease outbreak was recorded in poultry.

Each trader was interviewed to determine the origin of the chickens sold on theday of the interview (name and woreda of the market of purchase) and the trader’sregular purchasing practices (frequency of use of the different markets). Similarquestions were related to product sales. When necessary, a distinction was madebetween festive and non-festive seasons.

Data related to standard market operation came from interviewing the first twotraders who declared working at the market throughout the year. Data were col-lected on average numbers of chickens, farmers selling chickens, traders sellingchickens and market days per week.

Data analysis

A first market typology was built using principal component analysis followedby hierarchical clustering. Four variables were included in the analysis: averagenumber of chickens in the market, average number of sellers of all types, ratio oftraders to farmers and number of market days per week. These variables were com-pared among classes using a two-sided t test.

The number of traders buying and selling per market type, the average numberof markets used per trader to buy and sell chickens and the proportions of tradersworking only during festive seasons, using new markets or increasing their tradevolume were calculated. The numbers of markets used for buying and selling chick-ens were compared using a paired Wilcoxon test for non-normal distributions.

Two directed valued networks were built, with nodes representing markets andlinking chicken movements. A normalised value was allocated to each link, calcu-lated as the average monthly poultry flow between two markets. Monthly poultryflows for September, January and April were used for the festive season networksand those of the remaining months for non-festive seasons. The networks were pro-jected on a map.

We calculated actor degree centrality (number of direct neighbours; Freeman,1978), actor closeness centrality (a function of the geodesic, i.e. shortest path, dis-tances from the considered node to all other nodes of the network; Wasserman andFaust, 1994), in-degree, out-degree, in-closeness and out-closeness for directedgraphs and random-walk betweenness (a measure of the ability of a node to allowor close the contact between other nodes based on random walks) for undirectedgraphs (Newman, 2005). The five centrality indices (in-degree, out-degree, in-close-ness, out-closeness and random-walk betweenness) and density (proportion of

Table 3Classification of markets (value of index from 1 to 5) according to in-degree, out-degree a

In-degree Out-degree Random-walk between(10�3)

Festive Non-festive Festive Non-festive

Festive Non-fe

Debre Zeit(13)

Debre Zeit(11)

Meki (5) Meki (5) Debre Zeit(87)

Debre(43)

Akaki (8) Akaki (8) Adulala(4)

Adulala (5) Meki (36) Meki (

Dukem (7) Dire (5) Dire (4) Dire (4) Ziway (35) Adulal

Meki (6) Meki (5) Bulbula(4)

Bulbula (4) Dire (28) Ziway

Ziway (4) Ziway (4) Ziway (4) Ziway (4) Adulala (27) Bulbu

links among all the possible links present in the network) were calculated for thetwo binarised networks (values replaced by ‘1’ when a movement occurs, ‘0’otherwise).

Network densities were compared between the two seasons using a bootstrapsampled t test (5000 permutations), i.e. a standard t test with a permutation testto generate the significance level to avoid the need for standard assumptions ofindependence and random sampling (Hanneman and Riddle, 2005). Pearson corre-lations were calculated between the corresponding adjacency matrices of valuedflows using a quadratic assignment procedure with 10,000 permutations. The twobinarised adjacency matrices were compared by generating a matrix with ‘1’ in cellswhere both matrices have the same value and ‘0’ elsewhere. The proportion of dif-ference between the networks was calculated from the number of zeros in thisresulting matrix. Using principal component analysis and hierarchical clustering,two other typologies were built using the five centrality indices as variables for fes-tive and non-festive seasons.

All typologies were built using R 2.7.2 (R Foundation for Statistical Computing).Statistical tests and calculations related to network data were conducted with UCI-Net 6 (Analytic Technologies). A function in R was used to calculate random-walkbetweenness (Newman, 2005). Networks were visualised using NetDraw 2 (Ana-lytic Technologies) and ArcView v9 (ESRI Systems) was used for the geographicalprojection.

Results

Questionnaire results

Data were collected from 63 traders interviewed in 27 markets.Two other markets, identified as buying places by some traders,were included in the analysis, even though they were not visited.Of the 27 markets visited, 12 were village markets (V), four weresecondary markets (S), 10 were primary markets (P) and one wasa terminal market (T) (Fig. 1). Primary markets (Table 1) had thehighest average number of sellers (n = 115) (P < 0.5). The averagenumber of chickens increased from village to terminal markets,also characterised by the highest average number of market days(7 days) and ratio of traders to farmers (2.5).

The use of markets by traders varied according to market type(Table 2). Traders used significantly more markets to buy chickens(mean ± standard deviation 2.2 ± 1.3, range 1–6 markets) than tosell chickens (1.6 ± 0.8, range 1–4; paired Wilcoxon test, P <0.05).Of interviewed traders, 11/63 (17.5%) worked only during festiveseasons, 10/63 (15.9%) added one or more new markets to theirroutine during festive seasons and 42/63 (66.6%) only increasedthe number of chickens they traded during festive seasons.

Network analysis

The two networks of chicken trading between markets duringfestive and non-festive seasons had the same number of nodes(n = 29). Four nodes for the festive seasons and five nodes for theother seasons were not connected because none of the interviewedtraders visited them; these markets were not included in the com-putation of centrality indices. The number of links was slightlyhigher for the festive season network (n = 57) than for the non-fes-

nd betweenness for festive and non-festive seasons.

ness In-closeness Out-closeness

stive Festive Non-festive Festive Non-festive

Zeit Bole (13.8) Debre Zeit(15.3)

Koka (6.6) Koka (6.2)

39) Debre Zeit(13.4)

Dukem (14.7) Mojo (6.6) Adulala (6.0)

a (39) Dukem (13.1) Akaki (14.4) Alemtena(6.4)

Alemtena(6.0)

(36) Dire (12.8) Dire (14.3) Adulala (6.4) Mojo (6.0)

la (31) Akaki (12.8) Bali (13.7) AbuSera (6.3) AbuSera (5.9)

62 E. Vallée et al. / The Veterinary Journal 195 (2013) 59–65

tive season network (n = 52). Density was not significantly differ-ent (P >0.05) between festive (7.0%) and non-festive (6.4%) seasons.The two matrices differed by 11/841 (1.3%) links.

In both seasons, Debre Zeit and Meki markets had the highestrandom-walk betweenness (Table 3), with the highest in-degreefor Debre Zeit and Akaki markets and the highest out-degree forMeki, Adulala and Dire markets. Debre Zeit, Akaki, Dukem and Direwere amongst the five markets with highest in-closeness. With theexception of Adulala, which also had a relatively high out-degree

Fig. 2. Geographical projection of networks of poultry flows between markets during fmarkets. Poultry flows are grouped in six classes, represented by the thickness of the ar

and random-walk betweenness, the markets with highest out-closeness were markets with no other high index.

Chicken trading for festive and non-festive seasons was signifi-cantly correlated (Pearson’s correlation coefficient = 0.882,P <0.001), indicating that a high chicken flow between two marketsduring festive seasons is more likely to be high during other seasonsas well. Chicken flows ranged from 1 to 2660 chickens per monthduring festive seasons and from 1 to 2490 chickens per monthduring other periods (Fig. 2).

estive (A) and non-festive (B) seasons using global positioning system positions ofrow line.

E. Vallée et al. / The Veterinary Journal 195 (2013) 59–65 63

In the typologies constructed using five centrality indices, DebreZeit, Meki, Dire and Ziway were in the group with the highestin-degree, out-degree and random-walk betweenness for bothperiods and highest in-closeness for festive seasons (Fig. 3; Table 4).Koka, Mojo, Bakajo and Abu Sera were in the group with highestout-closeness and relatively low in-degree and in-closeness. Groupcomposition changed from festive to non-festive seasons, Dukemand Akaki being in the most central group only in the festive sea-son, whereas Adulala was in the group of highest centrality fornon-festive seasons, but not during the festive season.

Discussion

This study presents networks of monthly chicken flows linkedwith trading activity in the East Shewa zone of the Oromia region,Ethiopia. Most sales by traders were made in primary markets,while purchases were shared between villages, secondary andprimary markets. The traders used more markets to buy than tosell chickens.

Markets such as Meki or Ziway, with high out-degree associatedwith high in-degree, are of epidemiological interest, being morelikely to receive diseased chickens and to transmit disease to sev-eral markets. Markets such as Debre Zeit, Meki or Ziway had a highrandom-walk betweenness, suggesting a role in the maintenanceor closure of disease transmission between the other nodes inthe network and thus a potential focus for control of diseasetransmission.

Fig. 3. Market typology using the following variables: in-degree, out-degree, random-wseasons. ‘Height’ indicates the arbitrary distance between two classes.

Closeness centrality is a good index for the probabilities ofinfection (Bell et al., 1999; Ghani and Garnett, 2000), giving infor-mation on which market is more likely to receive and dissemi-nate infection in a worst case scenario, where infections wouldfollow the shortest paths and thus the fastest possible spread.An epidemic starting in a market with a high out-closeness, suchas Koka, will spread further and faster throughout the networkthan if it started from a node with low out-closeness (Nataleet al., 2009).

An important difference between seasons is the quantity ofpoultry traded, festive seasons being at higher risk for diseasetransmission (Spradbrow, 2000). However, there are other modifi-cations, such as the centrality values for some markets and thecomposition of the groups in the typology, showing that centralityindices and typologies are very sensitive to the network structure.

In a similar study conducted in northern Vietnam by Soares Ma-galhães et al. (2010), the role of traders is potentially double: bring-ing the disease from villages to market (with potential transmissionfrom village to village during collection) and spread throughout themarket network. In comparison, live chicken traders in Ethiopia canpotentially disseminate and maintain circulation of highly patho-genic avian diseases through the regular cycle of market days, butnot between villages, since they do not collect animals directly fromvillages. In Cambodia, Van Kerkhove et al. (2009) derived similar re-sults when comparing festive and non-festive seasons; there was nochange in the general structure of the network, but an increase in

alk betweenness, in-closeness and out-closeness for festive (A) and non-festive (B)

Table 4Mean values of in-degree, out-degree, random-walk betweenness, in-closeness andout-closeness for the different classes of market typologies using centrality indices forfestive (A) and non-festive (B) seasons.

Class 1 Class 2 Class 3 Class 4

AIn-degree 6.8 0.3 1.5 0Out-degree 3.7 2.5 1.8 1Random-walk betweenness (10�3) 36 18 12 3In-closeness 12.6 3.5 11.4 3.4Out-closeness 6.0 6.4 5.6 6.1

BIn-degree 3.9 0.4 3.3 0Out-degree 4 1.8 1.4 1Random-walk betweenness (10�3) 34 14 9 2In-closeness 11.6 3.5 13.2 3.4Out-closeness 5.7 6.0 5.6 5.8

The names of classes were attributed according to decreasing random-walkbetweenness and correspond to the names in Fig. 3.

64 E. Vallée et al. / The Veterinary Journal 195 (2013) 59–65

the quantity of poultry traded, consistent with an increased risk ofdisease transmission during festive seasons.

Asking traders to recall their regular practices over 1 year mighthave introduced bias to our data, even if with a carefully designedquestionnaire. This bias was probably increased by translation dif-ficulties, although we tried to reduce this through training oftranslators.

Snowball sampling is commonly used in network analysis.However, one of its limitations is that it does not identify isolatedmarkets. Our sampling also involved farmers, who were inter-viewed for another study. With this method, we identified fourmarkets for festive seasons and five for other seasons that wereconnected only by farmers and thus were not included in our cen-trality index computation. We tried to cross-check our marketidentification with data from the regional agriculture offices, butno reliable list of markets was available. Nevertheless, we can rea-sonably assume that the completeness of our information balancesthe lack of representativity, due to the selection of Debre Zeit asour first market for practical reasons.

The definition of the study area, which was limited due to con-straints of time and resources, may also have introduced some biasin the centrality measures of the markets at the periphery. Sometraders indicated that they bought and sold in markets outsidethe study area, including Addis Ababa.

Despite these limitations, our results could be used to improvethe current surveillance of avian diseases, since many chickensgoing through many peripheral markets end up at Addis Ababa (Ol-ive, 2007). Most of the indices calculated here are linked to theprobability of acquiring infection at the market. Risk-based surveil-lance (Stärk et al., 2006) focussing only on premises with a highprobability of infection may be of value in Ethiopia, where re-sources are scarce. For early disease detection, surveillance couldtarget markets with high in-closeness or high in-degree, such asDebre Zeit or Meki. These continue to act as central markets duringfestive and non-festive periods, with higher numbers of chickensbeing traded during festive seasons.

Control and prevention measures would preferentially targetthe most central markets. Although market closure can be shownto be efficient for control and prevention in simulations of node re-moval (Natale et al., 2009), in practice this would be difficult toimplement and would not stop movements of chickens. Market‘rest days’, which have proved to be efficient in South-East Asia(Lau et al., 2007; Fournié et al., 2011) may not be relevant in Ethi-opia, where markets are held one to three times per week andchickens usually do not stay in markets overnight. Thus, there isless risk of viral amplification in Ethiopian markets, but environ-mental contamination remains a risk.

To confirm these hypotheses, spread simulations of differentavian diseases could be performed to estimate the final sizes ofoutbreaks and the impacts of network modification, such as noderemoval, on disease transmission (Natale et al., 2009). To be moreaccurate, these simulations should take into account the valuedflows of animals and the dynamics of movements. Ideally, theywould be based on real outbreak data to determine the efficacyof the indices in predicting the degree of disease spread and theprobability of infection. Social studies could also be undertakento investigate the behaviour of traders and farmers in the contextof disease outbreaks. It is also important to be aware that otherpatterns of transmission exist, for example through wild birds orhumans (Fiebig et al., 2009). These other patterns can be taken inaccount using multi-relational networks, which can be used for dif-ferent scenario modelling.

Conclusions

This study examined the buying and selling practices of chickentraders and the use of live bird markets in the East Shewa zone ofthe Oromia region, Ethiopia. The networks of chicken flows be-tween markets for festive and non-festive seasons, the ranking ofmarkets according to in-degree, out-degree, random-walkbetweenness, in-closeness and out-closeness and market typolo-gies could be exploited in future surveillance and control plans.Our findings indicate that three poultry markets (Meki, Debre Zeitand Adulala) would ideally be chosen in a risk-based type of sur-veillance system, in addition to the passive surveillance systemcurrently in place, and in targeted control policies. The design ofsurveillance plans targeting these markets would not need to bemodified during festive seasons, but merely strengthened due tothe increased number of animals.

Conflict of interest statement

None of the authors of this paper has a financial or personalrelationship with other people or organisations that could inappro-priately influence or bias the content of the paper.

Acknowledgements

This work was funded by the French Ministry of Foreign Affairsvia the FSP (Fonds de Solidarité Prioritaire) project (GRIPAVI 2006-26). The authors are grateful to Dr Berhe Gebre-Egziabher, Head ofthe Animal and Plant Health Regulatory Department at the Minis-try of Agriculture and Rural Development, Addis Ababa, Ethiopia,and Dr Mesfin Sahle, Director of the National Animal Health Dis-ease Investigation Centre, Sebeta, Ethiopia, for supporting thiswork and to the laboratory technicians, who served as interpretersand drivers, for their help during the field investigation.

References

Aklilu, H.A., Almekinders, C.J.M., Udo, H.M.J., Van Der Zijpp, A.J., 2007. Villagepoultry consumption and marketing in relation to gender, religious festival andmarket access. Tropical Animal Health Production 39, 165–177.

Aklilu, H.A., Udo, H.M.J., Almekinders, C.J.M., Van Der Zijpp, A.J., 2008. How resourcepoor households value and access poultry: Village poultry keeping in Tigray,Ethiopia. Agricultural Systems 96, 175–183.

Alemu, D., Degefe, T., Ferede, S., Nzietcheung, S., Roy, D., 2008. Overview andbackground paper on Ethiopia’s poultry sector: Relevance for HPAI research inEthiopia, Africa/Indonesia Team Working Paper No. 1, pp. 22–33. http://www.dfid.gov.uk/r4d/PDF/Outputs/HPAI/PRE080703_Background-paper.pdf(accessed 20.05.12).

Alexander, D.J., 2000. Newcastle disease and other avian paramyxoviruses. RevueScientifique et Technique de l’Office Internationale des Epizooties 19, 443–462.

Bell, C.B., Atkinson, J.S., Carlson, J.W., 1999. Centrality measures for diseasetransmission network. Social Networks 21, 1–21.

E. Vallée et al. / The Veterinary Journal 195 (2013) 59–65 65

Central Statistical Agency, 2010. Agricultural Sample Survey 2009/10 [2002 E.C.],Volume II, Report on Livestock and Livestock Characteristics (Private PeasantHoldings). Statistical Bulletin 468, Federal Democratic Republic of Ethiopia,Central Statistical Agency, Addis Ababa, Ethiopia, pp. 18–20. http://www.csa.gov.et/surveys/Livestock/LiveStock_2009_2010/survey0/data/docs%5CFinal_Livestok-2009_Report_Final.pdf (accessed 20.05.12).

Duerr, H.-P., Schwehm, M., Leary, C.C., De Vlas, S.J., 2007. The impact of contactstructure on infectious disease control: Influenza and antiviral agents.Epidemiology and Infection 135, 1124–1132.

Fiebig, L., Smieszek, T., Saurina, J., Hattendorf, J., Zinsstag, J., 2009. Contacts betweenpoultry farms, their spatial dimension and their relevance for avian influenzapreparedness. Geospatial Health 4, 79–95.

Fournié, G., Guitian, F.J., Mangtani, P., Ghani, A.C., 2011. Impact of theimplementation of rest days in live bird markets on the dynamics of H5N1highly pathogenic avian influenza. Journal of the Royal Society Interface 8,1079–1089.

Freeman, L.C., 1978. Centrality in social networks conceptual clarification. SocialNetworks 1, 215–239.

G/Egziabher, M.M., 2007. Characterization of Smallholder Poultry Production andMarketing System of Dale, Wonsho and Loka Abaya Weredas of SouthernEthiopia, MSc Thesis in Animal Production, Hawassa University, Awassa,Ethiopia, pp. 69–70. http://www.ipms-ethiopia.org/content/files/Documents/publications/MscTheses/FinalThesis_MekonnenGebreEgziabher.pdf (accessed20.05.12).

Ghani, A.C., Garnett, G.P., 2000. Risk of acquiring and transmitting sexuallytransmitted diseases in sexual partner networks. Sexually TransmittedDiseases 27, 579–587.

Green, D.M., Gregory, A., Munro, L.A., 2009. Small- and large-scale networkstructure of live fish movements in Scotland. Preventive Veterinary Medicine91, 261–269.

Hanneman, R.A., Riddle, M., 2005. Introduction to Social Network Methods.University of California, Riverside, California, USA, 114 pp..

Kiss, I.Z., Green, D.M., Kao, R.R., 2006. The effect of contact heterogeneity andmultiple routes of transmission on final epidemic size. MathematicalBiosciences 203, 124–136.

Lau, E.H.Y., Leung, Y.H.C., Zhang, L.J., Cowling, B.J., Mak, S.P., Guan, Y., Leung, G.M.,Peiris, J.S.M., 2007. Effect of interventions on influenza A (H9N2) isolation inHong Kong’s live poultry markets, 1999–2005. Emerging Infectious Diseases 13,1340–1347.

Musa, U., Abdu, P.A., Dafwang, I.I., Umoh, J.U., Sa’idu, L., Mera, U.M., Edache, J.A.,2009. Seroprevalence, seasonal occurrence and clinical manifestation ofNewcastle disease in rural household chickens in Plateau state, Nigeria.International Journal of Poultry Science 8, 200–204.

Natale, F., Giovannini, A., Savini, L., Palma, D., Possenti, L., Fiore, G., Calistri, P., 2009.Network analysis of Italian cattle trade patterns and evaluation of risks forpotential disease spread. Preventive Veterinary Medicine 92, 341–350.

Newman, M.E.J., 2005. A measure of betweenness centrality based on randomwalks. Social Networks 27, 39–54.

Njagi, L.W., Nyaga, P.N., Mbuthia, P.G., Bebora, L.C., Michieka, J.N., Minga, U.M.,2010. A retrospective study of factors associated with Newcastle diseaseoutbreaks in village indigenous chickens. Bulletin of Animal Health andProduction in Africa 58, 22–33.

Olive, M.M., 2007. Modeling the Risk of Dissemination of Highly Pathogenic AvianDiseases through Trade Routes in Ethiopia, A Quantitative Approach. Master of

Science Thesis, 2nd year Biology, Earth Science, Agroressources andEnvironment, Montpellier, France, CIRAD, pp. 30–38.

Ortiz-Pelaez, A., Pfeiffer, D.U., Soares-Magalhães, R.J., Guitian, F.J., 2006. Use of socialnetwork analysis to characterize the pattern of animal movements in the initialphases of the 2001 foot and mouth disease (FMD) epidemic in UK. PreventiveVeterinary Medicine 76, 40–55.

Shirley, M.D.F., Rushton, S.P., 2005. Where diseases and networks collide: Lessons tobe learnt from a study of the 2001 foot-and-mouth disease epidemic.Epidemiology and Infection 133, 1023–1032.

Soares-Magalhães, R.J., Ortiz-Pelaez, A., Thi, K.L., Otte, J., Pfeiffer, D.U., 2010.Associations between attributes of live poultry trade and HPAI H5N1 outbreaks:A descriptive and network analysis study in northern Vietnam. BMC VeterinaryResearch 22, 6–10.

Spradbrow, P.B., 2000. Epidemiology of Newcastle disease and the economics of itscontrol. In: Dolberg, F., Petersen, P.H. (Eds.), Proceedings of a Workshop onPoultry as a Tool in Poverty Eradication and Promotion of Gender Equality.Tune, Denmark, pp. 165–173.

Spradbrow, P.B., 2001. The epidemiology of Newcastle disease in village chickens.In: Alders, R.G., Spradbrow, P.B. (Eds.), Australian Centre for InternationalAgricultural Research (ACIAR). Proceedings 103: SADC Planning Workshop onNewcastle Disease Control in Village Chickens, Maputo. Mozambique, ACIAR,Canberra, Australia, pp. 53–55.

Stärk, K.D.C., Regula, G., Hernandez, J., Knopf, L., Fuchs, K., Morris, R.S., Davies, P.,2006. Concepts for risk-based surveillance in the field of veterinary medicineand veterinary public health: Review of current approaches. BMC HealthServices Research 6, 20.

Tadelle, D., Million, T., Alemu, Y., Peters, K.J., 2003. Village chicken productionsystems in Ethiopia: 1. Flock characteristics and performance. LivestockResearch for Rural Development 15, 1–10.

Tadesse, S., Ashenafi, H., Aschalew, Z., 2005. Seroprevalence study of Newcastledisease in local chickens in central Ethiopia. International Journal of AppliedResearch in Veterinary Medicine 3, 25–29.

Van Kerkhove, M.D., Vong, S., Guitian, J., Holl, D., Mangtani, P., San, S., Ghani, A.C.,2009. Poultry movement networks in Cambodia: Implications for surveillanceand control of highly pathogenic avian influenza (HPAI/H5N1). Vaccine 27,6345–6352.

Wasserman, S., Faust, K., 1994. Social Network Analysis. Cambridge UniversityPress, New York/Cambridge, Methods and Applications, 825 pp..

Wilson, R.T., 2010. Poultry production and performance in the Federal DemocraticRepublic of Ethiopia. World’s Poultry Science Journal 66, 441–454.

Woolhouse, M.E.J., Dye, C., Etard, J.-F., Smith, T., Charlwood, J.D., Garnett, G.P.,Hagan, P., Hii, J.L.K., Ndhlovu, P.D., Quinnell, R.J., Watts, C.H., Chandiwana, S.K.,Anderson, R.M., 1997. Heterogeneities in the transmission of infectious agents:Implications for the design of control programs. Proceedings of the NationalAcademy of Sciences of USA 94, 338–342.

Woolhouse, M.E.J., Shaw, D.J., Matthews, L., Liu, W.-C., Mellor, D.J., Thomas, M.R.,2005. Epidemiological implications of the contact network structure for cattlefarms and the 20–80 rule. Biology Letters 1, 350–352.

Zeleke, A., Sori, T., Gelaye, E., Ayelet, G., 2005. Newcastle disease in village chickensin the southern and Rift Valley districts in Ethiopia. International Journal ofPoultry Science 4, 507–510.