Incorporating genomic methods into contact networks to reveal new insights into animal behaviour and infectious disease dynamics

in Behaviour
Restricted Access
Get Access to Full Text
Rent on DeepDyve

Have an Access Token?



Enter your access token to activate and access content online.

Please login and go to your personal user account to enter your access token.



Help

Have Institutional Access?



Access content through your institution. Any other coaching guidance?



Connect

Abstract

Utilization of contact networks has provided opportunities for assessing the dynamic interplay between pathogen transmission and host behaviour. Genomic techniques have, in their own right, provided new insight into complex questions in disease ecology, and the increasing accessibility of genomic approaches means more researchers may seek out these tools. The integration of network and genomic approaches provides opportunities to examine the interaction between behaviour and pathogen transmission in new ways and with greater resolution. While a number of studies have begun to incorporate both contact network and genomic approaches, a great deal of work has yet to be done to better integrate these techniques. In this review, we give a broad overview of how network and genomic approaches have each been used to address questions regarding the interaction of social behaviour and infectious disease, and then discuss current work and future horizons for the merging of these techniques.

Sections

References

Anderson, R.M. & May, R.M. (1991). Infectious diseases of humans: dynamics and control. — Oxford University Press, Oxford.

Archie, E.A., Luikart, G. & Ezenwa, V.O. (2009). Infecting epidemiology with genetics: a new frontier in disease ecology. — Trends Ecol. Evol. 24: 21-30.

Baele, G., Suchard, M.A., Rambaut, A. & Lemey, P. (2017). Emerging concepts of data integration in pathogen phylodynamics. — Syst. Biol. 66: e47-e65.

Becker, D.J., Streicker, D.G. & Altizer, S. (2015). Linking anthropogenic resources to wildlife-pathogen dynamics: a review and meta-analysis. — Ecol. Lett. 18: 483-495.

Biek, R., Drummond, A.J. & Poss, M. (2006). A virus reveals population structure and recent demographic history of its carnivore host. — Science 311: 538-541.

Biek, R., Henderson, J.C., Waller, L.A., Rupprecht, C.E. & Real, L.A. (2007). A high-resolution genetic signature of demographic and spatial expansion in epizootic rabies virus. — Proc. Natl. Acad. Sci. USA 104: 7993-7998.

Bird, B.H., Khristova, M.L., Rollin, P.E., Ksiazek, T.G. & Nichol, S.T. (2007). Complete genome analysis of 33 ecologically and biologically diverse Rift Valley fever virus strains reveals widespread virus movement and low genetic diversity due to recent common ancestry. — J. Virol. 81: 2805-2816.

Blanchong, J.A., Samuel, M.D., Scribner, K.T., Weckworth, B.V., Langenberg, J.A. & Filcek, K.B. (2007). Landscape genetics and the spatial distribution of chronic wasting disease. — Biol. Lett. 4: 130-133.

Blasse, A., Calvignac-Spencer, S., Merkel, K., Goffe, A.S., Boesch, C., Mundry, R. & Leendertz, F.H. (2013). Mother-offspring transmission and age-dependent accumulation of simian foamy virus in wild chimpanzees. — J. Virol. 87: 5193-5204.

Blyton, M.D.J., Banks, S.C., Peakall, R. & Gordon, D.M. (2013). High temporal variability in commensal Escherichia coli strain communities of a herbivorous marsupial. — Environ. Microbiol. 15: 2162-2172.

Blyton, M.D.J., Banks, S.C., Peakall, R., Lindenmayer, D.B. & Gordon, D.M. (2014). Not all types of host contacts are equal when it comes to E. coli transmission. — Ecol. Lett. 17: 970-978.

Buhnerkempe, M.G., Roberts, M.G., Dobson, A.P., Heesterbeek, H., Hudson, P.J. & Lloyd-Smith, J.O. (2015). Eight challenges in modelling disease ecology in multi-host, multi-agent systems. — Epidemics 10: 26-30.

Bull, C.M., Godfrey, S.S. & Gordon, D.M. (2012). Social networks and the spread of Salmonella in a sleepy lizard population. — Mol. Ecol. 21: 4386-4392.

Carnegie, N.B. (2017). Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure. — Stat. Med., in press. DOI:10.1002/sim.7259.

Chamie, G., Wandera, B., Marquez, C., Kato-Maeda, M., Kamya, M.R., Havlir, D.V. & Charlebois, E.D. (2015). Identifying locations of recent TB transmission in rural Uganda: a multidisciplinary approach. — Trop. Med. Int. Health 20: 537-545.

Chen, S., White, B.J., Sanderson, M.W., Amrine, D.E., Ilany, A. & Lanzas, C. (2014). Highly dynamic animal contact network and implications on disease transmission. — Sci. Rep. 4: 4472.

Chiyo, P.I., Grieneisen, L.E., Wittemyer, G., Moss, C.J., Lee, P.C., Douglas-Hamilton, I. & Archie, E.A. (2014). The influence of social structure, habitat, and host traits on the transmission of Escherichia coli in wild elephants. — PLoS One 9: e93408.

Colijn, C. & Gardy, J. (2014). Phylogenetic tree shapes resolve disease transmission patterns. — Evol. Med. Publ. Health: 96-108.

Cottam, E.M., Thébaud, G., Wadsworth, J., Gloster, J., Mansley, L., Paton, D.J., King, D.P. & Haydon, D.T. (2008). Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus. — Proc. Roy. Soc. Lond. B: Biol. Sci. 275: 887-895.

Craft, M.E. (2015). Infectious disease transmission and contact networks in wildlife and livestock. — Philos. Trans. Roy. Soc. Lond. B: Biol. Sci. 370: 20140107.

Craft, M.E. & Caillaud, D. (2011). Network models: an underutilized tool in wildlife epidemiology?. — Interdiscipl. Perspect. Infect. Dis.: 676949.

Craft, M.E., Volz, E., Packer, C. & Meyers, L.A. (2009). Distinguishing epidemic waves from disease spillover in a wildlife population. — Proc. Roy. Soc. Lond. B: Biol. Sci. 276: 1777-1785.

Craft, M.E., Volz, E., Packer, C. & Meyers, L.A. (2011). Disease transmission in territorial populations: the small-world network of Serengeti lions. — J. Roy. Soc. Interface 8: 776-786.

Croft, D.P., James, R. & Krause, J. (2008). Exploring animal social networks. — Princeton University Press, Princeton, NJ.

Cullingham, C.I., Kyle, C.J., Pond, B.A., Rees, E.E. & White, B.N. (2009). Differential permeability of rivers to raccoon gene flow corresponds to rabies incidence in Ontario, Canada. — Mol. Ecol. 18: 43-53.

de Carvalho Ferreira, H.C., Weesendorp, E., Quak, S., Stegeman, J.A. & Loeffen, W.L.A. (2014). Suitability of faeces and tissue samples as a basis for non-invasive sampling for African swine fever in wild boar. — Vet. Microbiol. 172: 449-454.

De Maio, N., Wu, C.-H., O’Reilly, K.M. & Wilson, D. (2015). New routes to phylogeography: a Bayesian structured coalescent approximation. — PLoS Genet. 11: e1005421.

De Maio, N., Wu, C.-H. & Wilson, D.J. (2016). SCOTTI: efficient reconstruction of transmission within outbreaks with the structured coalescent. — PLoS Comput. Biol. 12: e1005130.

Degnan, P.H., Pusey, A.E., Lonsdorf, E.V., Goodall, J., Wroblewski, E.E., Wilson, M.L., Rudicell, R.S., Hahn, B.H. & Ochman, H. (2012). Factors associated with the diversification of the gut microbial communities within chimpanzees from Gombe National Park. — Proc. Natl. Acad. Sci. USA 109: 13034-13039.

Didelot, X., Fraser, C., Gardy, J. & Colijn, C. (2017). Genomic infectious disease epidemiology in partially sampled and ongoing outbreaks. — Mol. Biol. Evol. 34: 997-1007.

Didelot, X., Gardy, J. & Colijn, C. (2014). Bayesian inference of infectious disease transmission from whole-genome sequence data. — Mol. Biol. Evol. 31: 1869-1879.

Dizney, L. & Dearing, M.D. (2013). The role of behavioural heterogeneity on infection patterns: implications for pathogen transmission. — Anim. Behav. 86: 911-916.

Drewe, J.A. (2010). Who infects whom? Social networks and tuberculosis transmission in wild meerkats. — Proc. Roy. Soc. Lond. B: Biol. Sci. 277: 633-642.

Drummond, A.J. & Rambaut, A. (2007). BEAST: Bayesian evolutionary analysis by sampling trees. — BMC Evol. Biol. 7: 214.

Eames, K., Bansal, S., Frost, S. & Riley, S. (2015). Six challenges in measuring contact networks for use in modelling. — Epidemics 10: 72-77.

Ferrier, S., Manion, G., Elith, J. & Richardson, K. (2007). Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. — Divers. Distrib. 13: 252-264.

Fountain-Jones, N.M., Craft, M.E., Funk, W.C., Kozakiewicz, C., Trumbo, D., Boydston, E.E., Lyren, L.M., Crooks, K., Lee, J.S., VandeWoude, S. & Carver, S. (2017a). Urban landscapes can change virus gene flow and evolution in a fragmentation-sensitive carnivore. — Mol. Ecol. 26: 6487-6498.

Fountain-Jones, N.M., Packer, C., Troyer, J.L., VanderWaal, K., Robinson, S., Jacquot, M. & Craft, M.E. (2017b). Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions. — J. Anim. Ecol. 86: 1469-1482.

Francis, S.S., Plucinski, M.M., Wallace, A.D. & Riley, L.W. (2016). Genotyping oral commensal bacteria to predict social contact and structure. — PLoS One 11: e0160201.

Gardy, J.L., Johnston, J.C., Ho Sui, S.J., Cook, V.J., Shah, L., Brodkin, E., Rempel, S., Moore, R., Zhao, Y., Holt, R., Varhol, R., Birol, I., Lem, M., Sharma, M.K., Elwood, K., Jones, S.J.M., Brinkman, F.S.L., Brunham, R.C. & Tang, P. (2011). Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. — N. Engl. J. Med. 364: 730-739.

Godfrey, S.S. (2013). Networks and the ecology of parasite transmission: a framework for wildlife parasitology. — Int. J. Parasitol. Parasit. Wildl. 2: 235-245.

Gottdenker, N.L., Streicker, D.G., Faust, C.L. & Carroll, C.R. (2014). Anthropogenic land use change and infectious diseases: a review of the evidence. — Ecohealth 11: 619-632.

Grad, Y.H., Kirkcaldy, R.D., Trees, D., Dordel, J., Harris, S.R., Goldstein, E., Weinstock, H., Parkhill, J., Hanage, W.P., Bentley, S. & Lipsitch, M. (2014). Genomic epidemiology of Neisseria gonorrhoeae with reduced susceptibility to cefixime in the USA: a retrospective observational study. — Lancet Infect. Dis. 14: 220-226.

Greene, C.E. (2012). Infectious diseases of the dog and cat, 4th edn.Elsevier/Saunders, St. Louis, MO.

Grenfell, B.T., Pybus, O.G., Gog, J.R., Wood, J.L.N., Daly, J.M., Mumford, J.A. & Holmes, E.C. (2004). Unifying the epidemiological and evolutionary dynamics of pathogens. — Science 303: 327-332.

Hall, M.D., Woolhouse, M.E.J. & Rambaut, A. (2016). Using genomics data to reconstruct transmission trees during disease outbreaks. — Rev. Sci. Technol. 35: 287-296.

Hall, M., Woolhouse, M. & Rambaut, A. (2015). Epidemic reconstruction in a phylogenetics framework: transmission trees as partitions of the node set. — PLoS Comput. Biol. 11: e1004613.

Hoffmann, C., Stockhausen, M., Merkel, K., Calvignac-Spencer, S. & Leendertz, F.H. (2016). Assessing the feasibility of fly based surveillance of wildlife infectious diseases. — Sci. Rep. 6: 37952.

Jombart, T., Eggo, R.M., Dodd, P.J. & Balloux, F. (2011). Reconstructing disease outbreaks from genetic data: a graph approach. — Heredity 106: 383-390.

Kao, R.R., Haydon, D.T., Lycett, S.J. & Murcia, P.R. (2014). Supersize me: how whole-genome sequencing and big data are transforming epidemiology. — Trends Microbiol. 22: 282-291.

Keeling, M.J. & Eames, K.T.D. (2005). Networks and epidemic models. — J. Roy. Soc. Interface 2: 295-307.

Keeling, M.J. & Rohani, P. (2011). Modeling infectious diseases in humans and animals. — Princeton University Press, Princeton, NJ.

Klinkenberg, D., Backer, J.A., Didelot, X., Colijn, C. & Wallinga, J. (2017). Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks. — PLoS Comput. Biol. 13: e1005495.

Lee, J.S., Ruell, E.W., Boydston, E.E., Lyren, L.M., Alonso, R.S., Troyer, J.L., Crooks, K.R. & Vandewoude, S. (2012). Gene flow and pathogen transmission among bobcats (Lynx rufus) in a fragmented urban landscape. — Mol. Ecol. 21: 1617-1631.

Leigh Brown, A.J., Lycett, S.J., Weinert, L., Hughes, G.J., Fearnhill, E., Dunn, D.T. & UK HIV Drug Resistance Collaboration (2011). Transmission network parameters estimated from HIV sequences for a nationwide epidemic. — J. Infect. Dis. 204: 1463-1469.

Lembo, T., Hampson, K., Haydon, D.T., Craft, M., Dobson, A., Dushoff, J., Ernest, E., Hoare, R., Kaare, M., Mlengeya, T., Mentzel, C. & Cleaveland, S. (2008). Exploring reservoir dynamics: a case study of rabies in the Serengeti ecosystem. — J. Appl. Ecol. 45: 1246-1257.

Lemey, P., Rambaut, A., Bedford, T., Faria, N., Bielejec, F., Baele, G., Russell, C.A., Smith, D.J., Pybus, O.G., Brockmann, D. & Suchard, M.A. (2014). Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2. — PLoS Pathog. 10: e1003932.

Lemey, P., Rambaut, A., Drummond, A.J. & Suchard, M.A. (2009). Bayesian phylogeography finds its roots. — PLoS Comput. Biol. 5: e1000520.

Lemey, P., Rambaut, A., Welch, J.J. & Suchard, M.A. (2010). Phylogeography takes a relaxed random walk in continuous space and time. — Mol. Biol. Evol. 27: 1877-1885.

Leventhal, G.E., Kouyos, R., Stadler, T., von Wyl, V., Yerly, S., Böni, J., Cellerai, C., Klimkait, T., Günthard, H.F. & Bonhoeffer, S. (2012). Inferring epidemic contact structure from phylogenetic trees. — PLoS Comput. Biol. 8: e1002413.

Levins, R. (1966). The strategy of model building in population biology. — Am. Sci. 54: 421-431.

Lewis, F., Hughes, G.J., Rambaut, A., Pozniak, A. & Leigh Brown, A.J. (2008). Episodic sexual transmission of HIV revealed by molecular phylodynamics. — PLoS Med. 5: e50.

Lloyd-Smith, J.O., George, D., Pepin, K.M., Pitzer, V.E., Pulliam, J.R.C., Dobson, A.P., Hudson, P.J. & Grenfell, B.T. (2009). Epidemic dynamics at the human-animal interface. — Science 326: 1362-1367.

Lloyd-Smith, J.O., Schreiber, S.J., Kopp, P.E. & Getz, W.M. (2005). Superspreading and the effect of individual variation on disease emergence. — Nature 438: 355-359.

MacIntosh, A.J.J., Jacobs, A., Garcia, C., Shimizu, K., Mouri, K., Huffman, M.A. & Hernandez, A.D. (2012). Monkeys in the middle: parasite transmission through the social network of a wild primate. — PLoS One 7: e51144.

Marquetoux, N., Heuer, C., Wilson, P., Ridler, A. & Stevenson, M. (2016). Merging DNA typing and network analysis to assess the transmission of paratuberculosis between farms. — Prev. Vet. Med. 134: 113-121.

Martínez-López, B., Perez, A.M. & Sánchez-Vizcaíno, J.M. (2009). Social network analysis. Review of general concepts and use in preventive veterinary medicine. — Transbound. Emerg. Dis. 56: 109-120.

McCloskey, R.M., Liang, R.H. & Poon, A.F.Y. (2016). Reconstructing contact network parameters from viral phylogenies. — Virus Evol. 2: vew029.

Metzker, M.L., Mindell, D.P., Liu, X.-M., Ptak, R.G., Gibbs, R.A. & Hillis, D.M. (2002). Molecular evidence of HIV-1 transmission in a criminal case. — Proc. Natl. Acad. Sci. USA 99: 14292-14297.

Meyers, L.A. (2007). Contact network epidemiology: bond percolation applied to infectious disease prediction and control. — Bull. Am. Math. Soc. 44: 63-87.

Minot, S., Bryson, A., Chehoud, C., Wu, G.D., Lewis, J.D. & Bushman, F.D. (2013). Rapid evolution of the human gut virome. — Proc. Natl. Acad. Sci. USA 110: 12450-12455.

Mollentze, N., Nel, L.H., Townsend, S., le Roux, K., Hampson, K., Haydon, D.T. & Soubeyrand, S. (2014). A Bayesian approach for inferring the dynamics of partially observed endemic infectious diseases from space-time-genetic data. — Proc. Roy. Soc. Lond. B: Biol. Sci. 281: 20133251.

Natoli, E., Say, L., Cafazzo, S., Bonanni, R., Schmid, M. & Pontier, D. (2005). Bold attitude makes male urban feral domestic cats more vulnerable to feline immunodeficiency virus. — Neurosci. Biobehav. Rev. 29: 151-157.

Numminen, E., Chewapreecha, C., Sirén, J., Turner, C., Turner, P., Bentley, S.D. & Corander, J. (2014). Two-phase importance sampling for inference about transmission trees. — Proc. Roy. Soc. Lond. B: Biol. Sci. 281: 20141324.

Nunn, C.L., Jordán, F., McCabe, C.M., Verdolin, J.L. & Fewell, J.H. (2015). Infectious disease and group size: more than just a numbers game. — Philos. Trans. Roy. Soc. Lond. B: Biol. Sci.: 370.

Pellis, L., Ball, F., Bansal, S., Eames, K., House, T., Isham, V. & Trapman, P. (2015). Eight challenges for network epidemic models. — Epidemics 10: 58-62.

Perkins, S.E., Cagnacci, F., Stradiotto, A., Arnoldi, D. & Hudson, P.J. (2009). Comparison of social networks derived from ecological data: implications for inferring infectious disease dynamics. — J. Anim. Ecol. 78: 1015-1022.

Picard, C., Dallot, S., Brunker, K., Berthier, K., Roumagnac, P., Soubeyrand, S., Jacquot, E. & Thébaud, G. (2017). Exploiting genetic information to trace plant virus dispersal in landscapes. — Annu. Rev. Phytopathol. 55: 139-160.

Pluciński, M.M., Starfield, R. & Almeida, R.P.P. (2011). Inferring social network structure from bacterial sequence data. — PLoS One 6: e22685.

Pope, L.C., Butlin, R.K., Wilson, G.J., Woodroffe, R., Erven, K., Conyers, C.M., Franklin, T., Delahay, R.J., Cheeseman, C.L. & Burke, T. (2007). Genetic evidence that culling increases badger movement: implications for the spread of bovine tuberculosis. — Mol. Ecol. 16: 4919-4929.

Porphyre, T., Stevenson, M., Jackson, R. & McKenzie, J. (2008). Influence of contact heterogeneity on TB reproduction ratio R0 in a free-living brushtail possum Trichosurus vulpecula population. — Vet. Res. 39: 31.

Rasmussen, A.L. (2015). Probing the viromic frontiers. — MBio 6: e01767-15.

Ray, B., Ghedin, E. & Chunara, R. (2016). Network inference from multimodal data: a review of approaches from infectious disease transmission. — J. Biomed. Inform. 64: 44-54.

Reynolds, J.J.H., Hirsch, B.T., Gehrt, S.D. & Craft, M.E. (2015). Raccoon contact networks predict seasonal susceptibility to rabies outbreaks and limitations of vaccination. — J. Anim. Ecol. 84: 1720-1731.

Robert, K., Garant, D. & Pelletier, F. (2012). Keep in touch: does spatial overlap correlate with contact rate frequency?. — J. Wildl. Manage. 76: 1670-1675.

Robinson, K., Fyson, N., Cohen, T., Fraser, C. & Colijn, C. (2013). How the dynamics and structure of sexual contact networks shape pathogen phylogenies. — PLoS Comput. Biol. 9: e1003105.

Romano, C.M., de Carvalho-Mello, I.M.V.G., Jamal, L.F., de Melo, F.L., Iamarino, A., Motoki, M., Pinho, J.R.R., Holmes, E.C., de Andrade Zanotto, P.M. & VGDN Consortium (2010). Social networks shape the transmission dynamics of hepatitis C virus. — PLoS One 5: e11170.

Rushmore, J., Caillaud, D., Hall, R.J., Stumpf, R.M., Meyers, L.A. & Altizer, S. (2014). Network-based vaccination improves prospects for disease control in wild chimpanzees. — J. Roy. Soc. Interface 11: 20140349.

Rushmore, J., Caillaud, D., Matamba, L., Stumpf, R.M., Borgatti, S.P. & Altizer, S. (2013). Social network analysis of wild chimpanzees provides insights for predicting infectious disease risk. — J. Anim. Ecol. 82: 976-986.

Sah, P., Leu, S.T., Cross, P.C., Hudson, P.J. & Bansal, S. (2017). Unraveling the disease consequences and mechanisms of modular structure in animal social networks. — Proc. Natl. Acad. Sci. USA 114: 4165-4170.

Sharp, P.M. & Hahn, B.H. (2010). The evolution of HIV-1 and the origin of AIDS. — Philos. Trans. Roy. Soc. Lond. B: Biol. Sci. 365: 2487-2494.

Silk, M.J., Croft, D.P., Delahay, R.J., Hodgson, D.J., Weber, N., Boots, M. & McDonald, R.A. (2017). The application of statistical network models in disease research. — Methods Ecol. Evol. 8: 1026-1041.

Sintchenko, V. & Holmes, E.C. (2015). The role of pathogen genomics in assessing disease transmission. — Br. Med. J. 350: h1314.

Smiley Evans, T., Gilardi, K.V.K., Barry, P.A., Ssebide, B.J., Kinani, J.F., Nizeyimana, F., Noheri, J.B., Byarugaba, D.K., Mudakikwa, A., Cranfield, M.R., Mazet, J.A.K. & Johnson, C.K. (2016). Detection of viruses using discarded plants from wild mountain gorillas and golden monkeys. — Am. J. Primatol. 78: 1222-1234.

Springer, A., Mellmann, A., Fichtel, C. & Kappeler, P.M. (2016). Social structure and Escherichia coli sharing in a group-living wild primate, Verreaux’s sifaka. — BMC Ecol. 16: 6.

Streicker, D.G., Winternitz, J.C., Satterfield, D.A., Condori-Condori, R.E., Broos, A., Tello, C., Recuenco, S., Velasco-Villa, A., Altizer, S. & Valderrama, W. (2016). Host-pathogen evolutionary signatures reveal dynamics and future invasions of vampire bat rabies. — Proc. Natl. Acad. Sci. USA 113: 10926-10931.

VanderWaal, K.L., Atwill, E.R., Isbell, L.A. & McCowan, B. (2014). Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis). — J. Anim. Ecol. 83: 406-414.

VanderWaal, K.L. & Ezenwa, V.O. (2016). Heterogeneity in pathogen transmission: mechanisms and methodology. — Funct. Ecol. 30: 1606-1622.

Vasylyeva, T.I., Friedman, S.R., Paraskevis, D. & Magiorkinis, G. (2016). Integrating molecular epidemiology and social network analysis to study infectious diseases: towards a socio-molecular era for public health. — Infect. Genet. Evol. 46: 248-255.

Villaseñor-Sierra, A., Quiñonez-Alvarado, M.G. & Caballero-Hoyos, J.R. (2007). Interpersonal relationships and group A Streptococcus spread in a Mexican day-care center. — Salud Publica Mex. 49: 323-329.

Welch, D. (2011). Is network clustering detectable in transmission trees?. — Viruses 3: 659-676.

Welch, D., Bansal, S. & Hunter, D.R. (2011). Statistical inference to advance network models in epidemiology. — Epidemics 3: 38-45.

Wheeler, D.C., Waller, L.A. & Biek, R. (2010). Spatial analysis of feline immunodeficiency virus infection in cougars. — Spat. Spatiotemporal Epidemiol. 1: 151-161.

White, L.A., Forester, J.D. & Craft, M.E. (2017). Using contact networks to explore mechanisms of parasite transmission in wildlife. — Biol. Rev. Camb. Philos. Soc. 92: 389-409.

Worby, C.J., Lipsitch, M. & Hanage, W.P. (2014). Within-host bacterial diversity hinders accurate reconstruction of transmission networks from genomic distance data. — PLoS Comput. Biol. 10: e1003549.

Wylie, J.L., Cabral, T. & Jolly, A.M. (2005). Identification of networks of sexually transmitted infection: a molecular, geographic, and social network analysis. — J. Infect. Dis. 191: 899-906.

Ypma, R.J.F., Bataille, A.M.A., Stegeman, A., Koch, G., Wallinga, J. & van Ballegooijen, W.M. (2012). Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data. — Proc. Roy. Soc. Lond. B: Biol. Sci. 279: 444-450.

Ypma, R.J.F., Jonges, M., Bataille, A., Stegeman, A., Koch, G., van Boven, M., Koopmans, M., van Ballegooijen, W.M. & Wallinga, J. (2013a). Genetic data provide evidence for wind-mediated transmission of highly pathogenic avian influenza. — J. Infect. Dis. 207: 730-735.

Ypma, R.J.F., van Ballegooijen, W.M. & Wallinga, J. (2013b). Relating phylogenetic trees to transmission trees of infectious disease outbreaks. — Genetics 195: 1055-1062.

Figures

  • Conceptual flow from a contact network to a transmission network. Areas with a green background represent components informed by empirical data; areas with a white background represent components informed by simulations. Network (a) shows a contact network that would be defined by, for example, observational data of direct contacts. A disease process, such as an SI (susceptible-infectious) model, (b) could be applied to the contact network many times (pictured three times here, but realistically, a simulation would be run on the order of 1000 times). In this example, the index case was randomly seeded, and darkened nodes represent those nodes that were infected in the course of a simulation. The who-infected-whom, transmission networks (c) could be a final output of network model simulations.

    View in gallery
  • Conceptual flow from (a) a transmission tree to (b) a transmission network; (c) depicts a transmission network in the context of the rest of the population, where gray nodes represent uninfected individuals. The green background highlights that this approach is based on empirical data, rather than simulations. Coloured circles represent sequenced samples from infected individuals. In (a), branching events in the transmission tree indicate transmission events, with colour changes occurring at these events; lettered labels at internal nodes represent infecting individuals. The coloured lines and labels at branching events in the transmission tree highlight a primary difference between phylogenetic trees and transmission trees: pathogens sampled at the ‘tips’ of the transmission tree are allowed to be ancestors of other samples. This then allows for the inference of who infected whom, as demonstrated by the directed networks in (b) and (c), but with some uncertainty that cannot be fully resolved (represented here by uncertain transmission from individual ‘h’ to individual ‘d’).

    View in gallery
  • Two examples of proposed integrations of contact networks and genomic tools, in this case focusing on utilizing transmission networks derived from inference of transmission trees. Areas with a green background represent components informed by empirical data; areas with a white background represent components informed by simulations. Panel (a) highlights some characteristics of pathogens and types of host data that may be well-suited to transmission tree approaches, but these are by no means all-inclusive. Panel (b) demonstrates a SNA application, in which observed networks such as grooming or spatial overlap networks, are compared to high-resolution transmission networks to determine what social or behavioural factors have the greatest impact on pathogen transmission. Panel (c) depicts a network modelling application, in which a transmission network derived from an apathogenic or commensal organism is used to determine environmental and ecological factors that best predict transmission in a population. These ‘rules’ are then used to create a ‘virtual contact network’ on which epidemics with a related (pathogenic) organism could be modelled. This approach could help determine the best preventive or intervention measures to be applied prior to an outbreak of a pathogen of concern.

    View in gallery
  • Advantages and disadvantages of currently used and proposed methods for integrating network and genomic approaches. Methods are listed in the order in which they appear in the text.

    View in gallery
  • (Continued.)

    View in gallery
  • (Continued.)

    View in gallery

Information

Content Metrics

Content Metrics

All Time Past Year Past 30 Days
Abstract Views 15 15 12
Full Text Views 4 4 4
PDF Downloads 0 0 0
EPUB Downloads 0 0 0