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Quantifying Transmission

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  • Author: Mark Woolhouse1
  • Editors: Fernando Baquero2, Emilio Bouza3, J.A. Gutiérrez-Fuentes4, Teresa M. Coque5
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Centre for Immunity, Infection and Evolution, University of Edinburgh, Edinburgh, United Kingdom.; 2: Hospital Ramón y Cajal (IRYCIS), Madrid, Spain; 3: Hospital Ramón y Cajal (IRYCIS), Madrid, Spain; 4: Complutensis University, Madrid, Spain; 5: Hospital Ramón y Cajal (IRYCIS), Madrid, Spain
  • Source: microbiolspec July 2017 vol. 5 no. 4 doi:10.1128/microbiolspec.MTBP-0005-2016
  • Received 28 July 2016 Accepted 17 June 2017 Published 21 July 2017
  • Mark Woolhouse, mark.woolhouse@ed.ac.uk
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  • Abstract:

    Transmissibility is the defining characteristic of infectious diseases. Quantifying transmission matters for understanding infectious disease epidemiology and designing evidence-based disease control programs. Tracing individual transmission events can be achieved by epidemiological investigation coupled with pathogen typing or genome sequencing. Individual infectiousness can be estimated by measuring pathogen loads, but few studies have directly estimated the ability of infected hosts to transmit to uninfected hosts. Individuals’ opportunities to transmit infection are dependent on behavioral and other risk factors relevant given the transmission route of the pathogen concerned. Transmission at the population level can be quantified through knowledge of risk factors in the population or phylogeographic analysis of pathogen sequence data. Mathematical model-based approaches require estimation of the per capita transmission rate and basic reproduction number, obtained by fitting models to case data and/or analysis of pathogen sequence data. Heterogeneities in infectiousness, contact behavior, and susceptibility can have substantial effects on the epidemiology of an infectious disease, so estimates of only mean values may be insufficient. For some pathogens, super-shedders (infected individuals who are highly infectious) and super-spreaders (individuals with more opportunities to transmit infection) may be important. Future work on quantifying transmission should involve integrated analyses of multiple data sources.

  • Keywords: susceptibility; genome sequencing; contact tracing; phylogenetics; phylogeography; super-shedding; super-spreading; infectiousness

  • Citation: Woolhouse M. 2017. Quantifying Transmission. Microbiol Spectrum 5(4):MTBP-0005-2016. doi:10.1128/microbiolspec.MTBP-0005-2016.

Key Concept Ranking

Severe Acute Respiratory Syndrome
0.47293615
Infectious Diseases
0.4430888
0.47293615

References

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6. Charleston B, Bankowski BM, Gubbins S, Chase-Topping ME, Schley D, Howey R, Barnett PV, Gibson D, Juleff ND, Woolhouse ME. 2011. Relationship between clinical signs and transmission of an infectious disease and the implications for control. Science 332:726–729. http://dx.doi.org/10.1126/science.1199884.
7. Greiner AL, Angelo KM, McCollum AM, Mirkovic K, Arthur R, Angulo FJ. 2015. Addressing contact tracing challenges-critical to halting Ebola virus disease transmission. Int J Infect Dis 41:53–55. http://dx.doi.org/10.1016/j.ijid.2015.10.025. [PubMed]
8. Bessell PR, Shaw DJ, Savill NJ, Woolhouse ME. 2010. Estimating risk factors for farm-level transmission of disease: foot and mouth disease during the 2001 epidemic in Great Britain. Epidemics 2:109–115. http://dx.doi.org/10.1016/j.epidem.2010.06.002.
9. Chase-Topping ME, McKendrick IJ, Pearce MC, MacDonald P, Matthews L, Halliday J, Allison L, Fenlon D, Low JC, Gunn G, Woolhouse ME. 2007. Risk factors for the presence of high-level shedders of Escherichia coli O157 on Scottish farms. J Clin Microbiol 45:1594–1603. http://dx.doi.org/10.1128/JCM.01690-06. [PubMed]
10. Haydon DT, Chase-Topping M, Shaw DJ, Matthews L, Friar JK, Wilesmith J, Woolhouse ME. 2003. The construction and analysis of epidemic trees with reference to the 2001 UK foot-and-mouth outbreak. Proc Biol Sci 270:121–127. http://dx.doi.org/10.1098/rspb.2002.2191. [PubMed]
11. Woolhouse M, Chase-Topping M, Haydon D, Friar J, Matthews L, Hughes G, Shaw D, Wilesmith J, Donaldson A, Cornell S, Keeling M, Grenfell B. 2001. Epidemiology. Foot-and-mouth disease under control in the UK. Nature 411:258–259. http://dx.doi.org/10.1038/35077149.
12. Cottam EM, Haydon DT, Paton DJ, Gloster J, Wilesmith JW, Ferris NP, Hutchings GH, King DP. 2006. Molecular epidemiology of the foot-and-mouth disease virus outbreak in the United Kingdom in 2001. J Virol 80:11274–11282. http://dx.doi.org/10.1128/JVI.01236-06.
13. Stadler T, Kühnert D, Rasmussen DA, du Plessis L. 2014. Insights into the early epidemic spread of Ebola in Sierra Leone provided by viral sequence data. PLoS Curr 6:ecurrents.outbreaks.02bc6d927ecee7bbd33532ec8ba6a25f. http://dx.doi.org/10.1371/currents.outbreaks.02bc6d927ecee7bbd33532ec8ba6a25f.
14. Hall MD, Knowles NJ, Wadsworth J, Rambaut A, Woolhouse ME. 2013. Reconstructing geographical movements and host species transitions of foot-and-mouth disease virus serotype SAT 2. mBio 4:e00591-e13. http://dx.doi.org/10.1128/mBio.00591-13.
15. Ward MJ, Gibbons CL, McAdam PR, van Bunnik BA, Girvan EK, Edwards GF, Fitzgerald JR, Woolhouse ME. 2014. Time-scaled evolutionary analysis of the transmission and antibiotic resistance dynamics of Staphylococcus aureus CC398. Appl Environ Microbiol 80:7275–7282. http://dx.doi.org/10.1128/AEM.01777-14.
16. Lycett S, McLeish NJ, Robertson C, Carman W, Baillie G, McMenamin J, Rambaut A, Simmonds P, Woolhouse M, Leigh Brown AJ. 2012. Origin and fate of A/H1N1 influenza in Scotland during 2009. J Gen Virol 93:1253–1260. http://dx.doi.org/10.1099/vir.0.039370-0. [PubMed]
17. Woolhouse ME, Rambaut A, Kellam P. 2015. Lessons from Ebola: improving infectious disease surveillance to inform outbreak management. Sci Transl Med 7:307rv5. http://dx.doi.org/10.1126/scitranslmed.aab0191.
18. Anderson RM, May RM. 1991. Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, New York, NY.
19. Keeling MJ, Rohani P. 2008. Modeling Infectious Diseases in Humans and Animals. Princeton University Press, Princeton, NJ.
20. Cottam EM, Thébaud G, Wadsworth J, Gloster J, Mansley L, Paton DJ, King DP, Haydon DT. 2008. Integrating genetic and epidemiological data to determine transmission pathways of foot-and-mouth disease virus. Proc Biol Sci 275:887–895. http://dx.doi.org/10.1098/rspb.2007.1442.
21. Woolhouse ME, Dye C, Etard JF, Smith T, Charlwood JD, Garnett GP, Hagan P, Hii JL, Ndhlovu PD, Quinnell RJ, Watts CH, Chandiwana SK, Anderson RM. 1997. Heterogeneities in the transmission of infectious agents: implications for the design of control programs. Proc Natl Acad Sci U S A 94:338–342. http://dx.doi.org/10.1073/pnas.94.1.338.
22. Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. 2005. Superspreading and the effect of individual variation on disease emergence. Nature 438:355–359. http://dx.doi.org/10.1038/nature04153. [PubMed]
23. Chase-Topping M, Gally D, Low C, Matthews L, Woolhouse M. 2008. Super-shedding and the link between human infection and livestock carriage of Escherichia coli O157. Nat Rev Microbiol 6:904–912. http://dx.doi.org/10.1038/nrmicro2029.
24. Canini L, Woolhouse MEJ, Maines TR, Carrat F. 2016. Heterogeneous shedding of influenza by human subjects and its implications for epidemiology and control. Sci Rep 6:38749. http://dx.doi.org/10.1038/srep38749. [PubMed]
25. Matthews L, Low JC, Gally DL, Pearce MC, Mellor DJ, Heesterbeek JAP, Chase-Topping M, Naylor SW, Shaw DJ, Reid SWJ, Gunn GJ, Woolhouse ME. 2006. Heterogeneous shedding of Escherichia coli O157 in cattle and its implications for control. Proc Natl Acad Sci U S A 103:547–552. http://dx.doi.org/10.1073/pnas.0503776103. [PubMed]
26. Woolhouse ME, Watts CH, Chandiwana SK. 1991. Heterogeneities in transmission rates and the epidemiology of schistosome infection. Proc Biol Sci 245:109–114. http://dx.doi.org/10.1098/rspb.1991.0095.
27. Gates MC, Woolhouse ME. 2015. Controlling infectious disease through the targeted manipulation of contact network structure. Epidemics 12:11–19. http://dx.doi.org/10.1016/j.epidem.2015.02.008.
28. Woolhouse ME, Shaw DJ, Matthews L, Liu WC, Mellor DJ, Thomas MR. 2005. Epidemiological implications of the contact network structure for cattle farms and the 20-80 rule. Biol Lett 1:350–352. http://dx.doi.org/10.1098/rsbl.2005.0331. [PubMed]
29. Read JM, Edmunds WJ, Riley S, Lessler J, Cummings DA. 2012. Close encounters of the infectious kind: methods to measure social mixing behaviour. Epidemiol Infect 140:2117–2130. http://dx.doi.org/10.1017/S0950268812000842.
30. Hall M, Woolhouse M, Rambaut A. 2015. Epidemic reconstruction in a phylogenetics framework: transmission trees as partitions of nodes. PLoS Comput Biol 11:e1004613. http://dx.doi.org/10.1371/journal.pcbi.1004613.
31. Volz EM, Kosakovsky Pond SL, Ward MJ, Leigh Brown AJ, Frost SD. 2009. Phylodynamics of infectious disease epidemics. Genetics 183:1421–1430. http://dx.doi.org/10.1534/genetics.109.106021.
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2017-07-21
2017-09-21

Abstract:

Transmissibility is the defining characteristic of infectious diseases. Quantifying transmission matters for understanding infectious disease epidemiology and designing evidence-based disease control programs. Tracing individual transmission events can be achieved by epidemiological investigation coupled with pathogen typing or genome sequencing. Individual infectiousness can be estimated by measuring pathogen loads, but few studies have directly estimated the ability of infected hosts to transmit to uninfected hosts. Individuals’ opportunities to transmit infection are dependent on behavioral and other risk factors relevant given the transmission route of the pathogen concerned. Transmission at the population level can be quantified through knowledge of risk factors in the population or phylogeographic analysis of pathogen sequence data. Mathematical model-based approaches require estimation of the per capita transmission rate and basic reproduction number, obtained by fitting models to case data and/or analysis of pathogen sequence data. Heterogeneities in infectiousness, contact behavior, and susceptibility can have substantial effects on the epidemiology of an infectious disease, so estimates of only mean values may be insufficient. For some pathogens, super-shedders (infected individuals who are highly infectious) and super-spreaders (individuals with more opportunities to transmit infection) may be important. Future work on quantifying transmission should involve integrated analyses of multiple data sources.

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Figures

Image of FIGURE 1
FIGURE 1

Time line of exposure, infectiousness, and clinical signs. Following exposure to infection, there is a period when the host is infected but not yet infectious—the latent period (yellow)—and a period when the host is infected but not yet showing clinical signs—the incubation period (yellow and orange). In this example, there is a (brief) period when the host is infectious but asymptomatic (orange), as occurs for infections such as influenza or FMD.

Source: microbiolspec July 2017 vol. 5 no. 4 doi:10.1128/microbiolspec.MTBP-0005-2016
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Image of FIGURE 2
FIGURE 2

Infectiousness of FMDV in experimental infections of cattle. Distribution of the incubation period minus the latent period, predicted using a Bayesian analysis, where a negative value indicates when clinical signs appear before an animal is infectious. The plot shows that when infectiousness is measured directly by the exposure of in-contact susceptible cattle, the analysis indicates that only a small fraction of infectiousness is found to occur before the appearance of clinical signs (black line). In contrast, if infectiousness is measured indirectly using virus isolation from blood (red) or nasal fluid (green), the bulk of infectiousness is estimated to occur well before clinical signs appear. Data from reference 6 ; figure kindly supplied by Simon Gubbins.

Source: microbiolspec July 2017 vol. 5 no. 4 doi:10.1128/microbiolspec.MTBP-0005-2016
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Image of FIGURE 3
FIGURE 3

Transmission heterogeneities. Heterogeneous infectiousness. Frequency distribution of bacterial counts for O157 in cattle fecal samples (horizontal axis, log scale). Raw data (histogram) are compared with a fitted mixture distribution log normal model that identified two distributions (red lines) and their sum (green). Arrowheads indicate the mean for each distribution. Reproduced with permission from reference 23 . Heterogeneous contact rates. The graph shows the cumulative, fractional contribution to the value of of individuals arranged in order (highest to lowest along the horizontal axis) of their observed contact rates. For a range of infectious diseases (multiple examples covering HIV/AIDS, malaria, schistosomiasis, and leishmaniasis), 20% of the population contribute 80% of the basic reproduction number, , as estimated given observed heterogeneities in contact behavior (sexual contacts, vector biting rates, water contact, as appropriate). Data from reference 21 .

Source: microbiolspec July 2017 vol. 5 no. 4 doi:10.1128/microbiolspec.MTBP-0005-2016
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