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Citation: Daniel Oladimeji Oluwayelu, Clement Adebajo Meseko, Adekunle Bamidele Ayinmode, Adebowale Idris Adebiyi, Mike Aneshimi Lawani, Florence Omonele Kakulu. Re-emergence of Highly Pathogenic Avian Influenza H5N1 in Nigeria, 2014–2016: Role of Social Network and Value Chain Forces in Interstate Transmission [J].VIROLOGICA SINICA, 2020, 35(4) : 494-498.

Re-emergence of Highly Pathogenic Avian Influenza H5N1 in Nigeria, 2014–2016: Role of Social Network and Value Chain Forces in Interstate Transmission

  • Corresponding author: Daniel Oladimeji Oluwayelu,, ORCID: 0000-0001-9920-879X
  • Received Date: 07 August 2019
    Accepted Date: 17 January 2020
    Published Date: 31 March 2020

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    5. Couacy-Hymann E, Kouakou VA, Aplogan GL, Awoume F, Kouakou CK, Kakpo L, Sharp BR, McClenaghan L, McKenzie P, Webster RG, Webby RJ, Ducatez MF (2012) Surveillance for influenza viruses in poultry and swine, West Africa, 2006–2008. Emerg Infect Dis 18:1446–1452
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    6. Fasina FO, Mokoele JM, Spencer BT, Van Leengoed LAML, Bevis Y, Booysen I (2015) Spatio-temporal patterns and movement analysis of pigs from smallholder farms and implications for African swine fever spread, Limpopo province. S Afr Onderstepoort J Vet Res 82:795

    7. Fournié G, Guitian J, Desvaux S, Cuong VC, Dung DH, Pfeiffer DU, Manftani P, Ghani AC (2013) Interventions for avian influenza A (H5N1) risk management in live bird market networks. Proc Natl Acad Sci USA 110:9177–9182
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    8. Fusaro A, Nelson MI, Joannis TM, Bertolotti L, Monne I, Salviato A, Olaleye O, Shittu I, Sulaiman L, Lombin LH, Capua I, Holmes EC, Cattoli G (2010) Evolutionary dynamics of multiple sublineages of H5N1 influenza viruses in Nigeria from 2006 to 2008. J Virol 84:3239–3247
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    9. Grubesic TH, Murray AT (2001) Detecting hot spots using cluster analysis and GIS. In: Proceedings fifth annual international crime mapping research conference, vol 26. Dallas, Texas, USA

    10. Joannis TM, Lombin LH, De Benedictis P, Cattoli G, Capua I (2006) Confirmation of H5N1 avian influenza in Africa. Vet Rec 158:309–310

    11. Joannis TM, Meseko CA, Oladokun AT, Gulak H, Egbuji AN, Solomon P, Nyam DC, Gado D, Luka PD, Ogedengbe ME, Yakubu B, Tyem AD, Akinyede O, Shittu I, Sulaiman L, Owolodun OA, Olawuyi K, Obishakin E, Fasina FO (2008) Serologic and virologic surveillance of avian influenza in Nigeria, 2006–7. Eurosurveillance 13:1–5

    12. Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6:888–893
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    13. Lee DH, Bahl J, Torchetti MK, Killian ML, Ip HS, DeLiberto TJ, Swayne DE (2016) Highly pathogenic avian influenza viruses and generation of novel reassortants, United States, 2014–2015. Emerg Infect Dis 22:1283–1285
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    14. Meseko CA, Oladokun AT, Shehu B (2007) An outbreak of highly pathogenic avian influenza (HPAI) in a mixed farm by the introduction of a waterfowl. Nig Vet J 28:67–69
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    15. Monne I, Meseko C, Joannis TM, Shittu I, Ahmed M, Tassoni L, Fusaro A, Cattoli G (2015) Highly pathogenic avian influenza A (H5N1) virus in poultry, Nigeria, 2015. Emerg Infect Dis 21:1275–1277
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    Re-emergence of Highly Pathogenic Avian Influenza H5N1 in Nigeria, 2014–2016: Role of Social Network and Value Chain Forces in Interstate Transmission

      Corresponding author: Daniel Oladimeji Oluwayelu,
    • 1. Department of Veterinary Microbiology, University of Ibadan, Ibadan 200284, Nigeria
    • 2. Centre for Control and Prevention of Zoonoses (CCPZ), University of Ibadan, Ibadan 200284, Nigeria
    • 3. Virology Department, National Veterinary Research Institute, Vom 930103, Nigeria
    • 4. Department of Veterinary Parasitology and Entomology, University of Ibadan, Ibadan 200284, Nigeria
    • 5. Gestational Diabetes Society of Nigeria, Mother and Child Hospital Annex, Akure 340282, Nigeria
    • 6. Department of Veterinary and Pest Control Services, Federal Ministry of Agriculture, Abuja 900247, Nigeria


    • Dear Editor,

      Since 2004, high pathogenic avian influenza (HPAI) due to H5N1 virus among others has caused a major veterinary health crisis, resulting in the loss of millions of poultry through death or culling in several countries in Asia, the Middle East, Europe, Africa and North America (Alexander 2007; Lee et al. 2016). The first African outbreak was reported from Nigeria in 2006 in domestic poultry (Joannis et al. 2006) and persisted till 2008 (Fusaro et al. 2010). These outbreaks negatively affected animal and public health as well as the economy, and were caused by viruses belonging to genetic clades 2.2, 2.2.1, 2.2.2 and (Aiki-Raji et al. 2008; Fusaro et al. 2010; Monne et al. 2015).

      In Nigeria, avian influenza virus (AIV) and or AIVspecific antibodies have been detected in chickens (Joannis et al. 2006, 2008), waterfowls (Meseko et al. 2007), spurwinged geese and whistling ducks (Snoeck et al. 2011), and turkeys (Oluwayelu et al. 2015). In 2012, CouacyHymann et al. (2012) reported that Nigeria did not have any HPAI-H5N1 outbreak since July 2008 and seemed free of the pathogen. However, this "AI-free" status was interrupted by a major HPAI-H5N1 epizootic in 2015, about nine years after the first outbreak. This epizootic was first reported on 24th December, 2014 at a live bird market (LBM) in Sabon Gari, Kano State, where high mortality of chickens, geese and turkeys occurred. Almost simultaneously, and obviously due to bird movement from Kano State to the South during the Christmas season, anecdotal reports of increased bird deaths at LBMs in Onipanu and Mushin, Lagos State were made. Fifty thousand birds were susceptible with 3, 300 deaths. Subsequently, 29 other outbreaks were recorded in 24 States and the Federal Capital Territory. The second wave of the epizootic started between 1st and 3rd January 2015, also in Kano State and parts of the Southwest, with 16 follow-up cases reported (OIE 2016a, b). Thereafter, cases of the disease continued to occur until November 2016 (Monne et al. 2015; OIE 2016a, b). Considering the recurrent outbreaks of HPAIH5N1 in Nigeria and the challenge of mounting effective surveillance especially in the absence of vaccination policy, there is a need to design targeted risk-based interventions that are realistic and sustainable for prevention and control of the disease in the country.

      We utilized social network and value chain analyses to investigate the HPAI-H5N1 outbreak which occurred in Nigeria between 24 December, 2014 and 15 November, 2016 with a view to providing better understanding of the disease dynamics and interplay of factors that contributed to its spread, severity and persistence within an epidemiological context. A total of 2, 727, 561 birds were exposed to the virus during the epizootic with an approximate mortality rate of 9.1% (248, 482 birds; range: 1–25, 000). Majority (67.4%) of the 482 field samples collected for laboratory confirmation by reverse transcriptase-polymerase chain reaction (RT-PCR) were "locally sourced" i.e., collected from birds at LBMs and "unspecified" suppliers of day-old chicks, point-of-lay pullets, spent layers and local poultry, while 32.6% were from commercial/ backyard poultry farms. Out of these 482 samples, 346 (71.8%) were positive for HPAIV H5N1, of which approximately 60% (n = 208) were obtained from LBMs and "unspecified" poultry suppliers. Although southern Nigeria had overall lower proportion of H5N1-positive cases by RT-PCR (36.4%) compared to the northern region (63.6%), there was significantly higher detection of positive cases from outbreaks in the south (126/134, 94.0%) than in the north (220/348, 63.2%) (Table 1).

      Number of cases %
      Sources of birds during outbreaks
        Commercial/backyard farms 157 32.6
        LBMs/"unspecified" poultry suppliers 325 67.4
        Positive 346 71.8
        Negative 136 28.2
      Source of positive cases
        Commercial/backyard farms 139 40.2
        LBMs/"unspecified" poultry suppliers 207 59.8
      Geographic origin of positive cases
        Northern region 220 63.6
        Southern region 126 36.4
      Within-region distribution of positive cases
        Northern region 220 (out of 348 cases) 63.2
        Southern region 126 (out of 134 cases) 94
      Statistical analysis was performed using SPSS version 21 (SPSS Inc., Chicago, IL). Range and proportions were calculated for relevant variables. The relationships between HPAI (H5N1)-positive cases by reverse transcriptase-polymerase chain reaction (RT-PCR) and corresponding variables were determined by Fisher’s exact test at P < 0.05.

      Table 1.  Summary of HPAIH5N1 outbreak markers (December 2014 to November 2016).

      Social network analysis showed that there were 43 vertices, 27 unique edges and 380 edges. There was only one connected component while the maximum shortest path between any two distances was 8. However, 27% of the graph was full of ties (Graph density = 0.0271) with an average geodesic distance of 3.186 and a clustering coefficient of 0.000 (Fig. 1).

      Figure 1.  Interstate poultry market network following HPAI-H5N1 outbreaks in Nigeria (December 2014–November 2016). Outbreak data for the period were obtained from the Office International des Epizooties (OIE 2016a, b). Data were formatted for analysis on NodeXL Basic template 2014 (Social Media Research Foundation, California, USA). Network nodes and corresponding vertices were used to identify individual and central players within the local poultry market chain. Each node represented a hatchery, farm known for poultry (broilers, point-of-lay pullets, or spent layers) distribution or proximate state where HPAI was reported. The corresponding arrows on the other hand indicated poultry transport network with subsequent incoming and out-going infections. Harel–Koren Fast multiscale layout algorithm was used to analyze the relationships giving a spring-embedded visualization. Networks were grouped into clusters using Clauset–Newman–Moore cluster algorithm while graph density and average geodesic distance between nodes were used as informative tools to describe the presenting graphical network connections.

      Analysis of outbreak data showed that Kano, Plateau, Rivers and Lagos States were the most affected point locations of the epizootic during the study period (Fig. 2). Plateau (99% CI, P < 0.0005) and Kano States (95% CI, P < 0.0005) were hotspots for outbreaks while the southern states of Rivers and Bayelsa were cold spots (Fig. 2). Although several outbreaks were recorded in Lagos, Ogun, Oyo, Edo, Enugu, Ebonyi and Rivers States, they were not significant following hotspot analysis.

      Figure 2.  Point locations and hotspots of HPAI-H5N1 outbreaks with interstate network of commercial poultry movement in Nigeria. Coordinates of point locations of HPAI outbreaks within states in Nigeria were generated using Google Earth Pro. Data were collated on Microsoft Excel sheet and exported into ESRI's ArcGIS 10.1 software. Cartographical outputs were visualized on ArcView and results displayed as points symbolizing locations of resultant outbreaks. Hotspot analysis was used to identify statistically significant hotspots and cold spots, respectively. A hotspot was defined as a geographical area or location with high risk of HPAI occurrence (Grubesic and Murray 2001). Conversely, a cold spot referred to a geo-location with less likelihood of HPAI occurrence.

      The findings of this study (Table 1, Fig. 1) show that the re-emergence of HPAI-H5N1 in Nigeria between December 2014 and November 2016 was first detected in LBMs, corroborating previous report (Monne et al. 2015). Live bird market networks are known to epidemiologically connect regions that otherwise may have remained isolated; as such they support large-scale and transboundary disease spread (Fournié et al. 2013). In Nigeria, LBMs are common and usually located in peri-urban areas where various bird species produced by multiple suppliers are mixed together, thus providing opportunity for maximum interaction and efficient contracting of infectious agents among the birds, and between humans and birds (Aiki-Raji et al. 2015). This is buttressed by the prospect of silent circulation and likely persistence of influenza viruses, including HPAI-H5N1, in LBMs as well as by reports of virological and serological evidence of AIVs in some LBMs in Nigeria (Coker et al. 2014; Aiki-Raji et al. 2015). Based on our findings in this study, there appears to be an upsurge of AIV outbreaks in Nigeria during the cold harmattan period that is coincident with increased bird movement (especially interstate transportation) and sales in the December festive season (Monne et al. 2015). Movement of live birds is a familiar risk factor for AIV dissemination to poultry flocks (Soares Magalhaes et al. 2010).

      Analysis of point locations of HPAI-H5N1 outbreaks (Fig. 2) revealed that Kano, Plateau, Lagos and Rivers were the most severely affected states. However, only Kano and Plateau States were identified as hotspots of HPAI-H5N1 during the outbreak period even though several cases were recorded in the southern states of Lagos, Ogun, Oyo, Edo, Enugu, Ebonyi and Rivers (Fig. 2). It is possible that the regulatory authorities in these southern states were more able to respond rapidly and effectively following occurrence of the index cases, as well as in implementing biosecurity and other control measures including enforcement of movement restrictions, slaughter of infected birds and decontamination of infected farms and LBMs.

      An interesting epidemiological scenario was observed during this period with the first reports of outbreaks in two poultry farms in Enugu and Abia States, southeast Nigeria. Remarkably, in the process of tracing back the infection source(s), we discovered that both farms purchased pointof-lay pullets from a commercial farm in Ibadan, Oyo State, southwest Nigeria (Fig. 2). Subsequent testing of biological specimens from birds in the source farm confirmed they were HPAI-H5N1-infected. This finding highlights the critical role of poultry market chain actors such as suppliers of point-of-lay pullets and day-old chicks in the interstate transmission of HPAI-H5N1. It also emphasizes the need for regulation of both interstate and intra-state movements of poultry and poultry products/inputs in Nigeria. Consequently, it is instructive to equip existing animal control posts across Nigeria with rapid influenza antigen detection kits so that birds that test positive can be quarantined and their specimens sent to zonal/national reference laboratories for confirmatory diagnosis. Thus, we have shown that trade-movement networks are an important risk factor for the dissemination of AIV to poultry flocks in Nigeria where poultry movement data is generally unavailable. We recommend enforcement of movement restrictions on poultry and other birds, issuance of veterinary permit before movement of birds and restructuring of poultry marketing networks across the country, as has been used successfully elsewhere (Fasina et al. 2015).

      Further, social network and value chain analyses can be applied within an epidemiological context to assist in identifying persistence of HPAI infections or points of concentration along the poultry value chain in order to aid description of the disease transmission patterns and guide control policies (Kitsak et al. 2010). As shown in this study, the role of interstate transport of day-old chicks and point-of-lay pullets (in the southwest-north and southwestsoutheast/south-south directions) as well as indigenous chickens (in the north–south direction) in spread and persistence of the December 2014 to November 2016 HPAIH5N1 outbreaks in Nigeria cannot be underestimated.

      In order to forestall future outbreaks of HPAI in Nigeria, surveillance efforts should not only be targeted at preventing emergence of new cases, they should also be riskbased and focused on stopping dissemination in order to avoid farm-to-farm transmission, and medium- or longdistance jumps due to agro-commercial practices. Transportation of indigenous poultry with domestic ruminants from northern Nigeria to live animal markets in the south should also be discouraged. We advocate the use of social network and value chain analyses combined with epidemiological surveillance for identifying high risk areas along Nigeria's poultry value chain where knowledge of poultry trading patterns and the LBM network structure, and their capacity for maintaining HPAI-H5N1 infection may be employed to enhance the success of existing control measures. This novel approach will also facilitate contact trace back in future HPAI emergencies.

    • We are grateful to Mr. Joseph Olumoyegun (Department of Geography, University of Ibadan, Ibadan, Nigeria) for his excellent contributions in preparing the figures in this manuscript.

    • The authors declare that they have no conflict of interest.

    • This article does not contain any studies with human or animal subjects performed by any of the authors.

    Figure (2)  Table (1) Reference (20) Relative (20)



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