Perspective | Published:

Precision epidemiology for infectious disease control

Nature Medicinevolume 25pages206211 (2019) | Download Citation


Advances in genomics and computing are transforming the capacity for the characterization of biological systems, and researchers are now poised for a precision-focused transformation in the way they prepare for, and respond to, infectious diseases. This includes the use of genome-based approaches to inform molecular diagnosis and individual-level treatment regimens. In addition, advances in the speed and granularity of pathogen genome generation have improved the capability to track and understand pathogen transmission, leading to potential improvements in the design and implementation of population-level public health interventions. In this Perspective, we outline several trends that are driving the development of precision epidemiology of infectious disease and their implications for scientists’ ability to respond to outbreaks.

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  1. 1.

    Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

  2. 2.

    Venter, J. C. et al. The sequence of the human genome. Science 291, 1304–1351 (2001).

  3. 3.

    International HapMap Consortium. The International HapMap Project. Nature 426, 789–796 (2003).

  4. 4.

    1000 Genomes Project Consortium et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  5. 5.

    Torkamani, A., Andersen, K. G., Steinhubl, S. R. & Topol, E. J. High-definition medicine. Cell 170, 828–843 (2017).

  6. 6.

    Gardy, J. L. & Loman, N. J. Towards a genomics-informed, real-time, global pathogen surveillance system. Nat. Rev. Genet. 19, 9–20 (2018).

  7. 7.

    Ksiazek, T. G. et al. A novel coronavirus associated with severe acute respiratory syndrome. N. Engl. J. Med. 348, 1953–1966 (2003).

  8. 8.

    Zaki, A. M., van Boheemen, S., Bestebroer, T. M., Osterhaus, A. D. M. E. & Fouchier, R. A. M. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N. Engl. J. Med. 367, 1814–1820 (2012).

  9. 9.

    Novel Swine-Origin Influenza A (H1N1) Virus Investigation Team et al. Emergence of a novel swine-origin influenza A (H1N1) virus in humans. N. Engl. J. Med. 360, 2605–2615 (2009).

  10. 10.

    Baize, S. et al. Emergence of Zaire Ebola virus disease in Guinea. N. Engl. J. Med. 371, 1418–1425 (2014).

  11. 11.

    Holmes, E. C., Dudas, G., Rambaut, A. & Andersen, K. G. The evolution of Ebola virus: insights from the 2013-2016 epidemic. Nature 538, 193–200 2016).

  12. 12.

    Grubaugh, N. D., Faria, N. R., Andersen, K. G. & Pybus, O. G. Genomic insights into Zika virus emergence and spread. Cell 172, 1160–1162 2018).

  13. 13.

    Dudas, G. et al. Virus genomes reveal factors that spread and sustained the Ebola epidemic. Nature 544, 309–315 (2017).

  14. 14.

    Gire, S. K. et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345, 1369–1372 (2014).

  15. 15.

    Collins, F. S. & Varmus, H. A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

  16. 16.

    Bissonnette, L. & Bergeron, M. G. Infectious disease management through point-of-care personalized medicine molecular diagnostic technologies. J. Pers. Med. 2, 50–70 (2012).

  17. 17.

    Matranga, C. al. Enhanced methods for unbiased deep sequencing of Lassa and Ebola RNA viruses from clinical and biological samples. Genome Biol. 15, 519 (2014).

  18. 18.

    Quick, J. et al. Multiplex PCR method for MinION and Illumina sequencing of Zika and other virus genomes directly from clinical samples. Nat. Protoc. 12, 1261–1276 (2017).

  19. 19.

    Wylie, T. N., Wylie, K. M., Herter, B. N. & Storch, G. A. Enhanced virome sequencing using targeted sequence capture. Genome Res. 25, 1910–1920 (2015).

  20. 20.

    Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008).

  21. 21.

    Kiso, M. et al. Resistant influenza A viruses in children treated with oseltamivir: descriptive study. Lancet 364, 759–765 (2004).

  22. 22.

    Simen, B. B. et al. Low-abundance drug-resistant viral variants in chronically HIV-infected, antiretroviral treatment-naive patients significantly impact treatment outcomes. J. Infect. Dis. 199, 693–701 (2009).

  23. 23.

    Perrin, L. HIV treatment failure: testing for HIV resistance in clinical practice. Science 280, 1871–1873 (1998).

  24. 24.

    Schön, al. Mycobacterium tuberculosis drug-resistance testing: challenges, recent developments and perspectives. Clin. Microbiol. Infect. 23, 154–160 2017).

  25. 25.

    Both, L. et al. Monoclonal antibodies for prophylactic and therapeutic use against viral infections. Vaccine 31, 1553–1559 (2013).

  26. 26.

    Kugelman, J. R. et al. Emergence of ebola virus escape variants in infected nonhuman primates treated with the MB-003 antibody cocktail. Cell Rep. 12, 2111–2120 (2015).

  27. 27.

    Doud, M. B., Hensley, S. E. & Bloom, J. D. Complete mapping of viral escape from neutralizing antibodies. PLoS. Pathog. 13, e1006271 (2017).

  28. 28.

    Cann, A. J. et al. Reversion to neurovirulence of the live-attenuated Sabin type 3 oral poliovirus vaccine. Nucleic Acids Res. 12, 7787–7792 (1984).

  29. 29.

    Stern, A. et al. The Evolutionary Pathway to Virulence of an RNA Virus. Cell 169, 35–46.e19 (2017).

  30. 30.

    Kugelman, J. R. et al. Evaluation of the potential impact of Ebola virus genomic drift on the efficacy of sequence-based candidate therapeutics. MBio 6, e02227-14 (2015).

  31. 31.

    Kozel, T. R. & Burnham-Marusich, A. R. Point-of-care testing for infectious diseases: past, present, and future. J. Clin. Microbiol. 55, 2313–2320 (2017).

  32. 32.

    Sozhamannan, S. et al. Evaluation of signature erosion in ebola virus due to genomic drift and its impact on the performance of diagnostic assays. Viruses 7, 3130–3154 (2015).

  33. 33.

    Greninger, A. L. et al. Rapid metagenomic identification of viral pathogens in clinical samples by real-time nanopore sequencing analysis. Genome Med. 7, 99 (2015).

  34. 34.

    Miller, S. et al. Laboratory validation of a clinical metagenomic sequencing assay for pathogen detection in cerebrospinal Ffuid. Preprint at (2018).

  35. 35.

    Biek, R., Pybus, O. G., Lloyd-Smith, J. O. & Didelot, X. Measurably evolving pathogens in the genomic era. Trends. Ecol. Evol. 30, 306–313 (2015).

  36. 36.

    Mate, S. E. et al. Molecular evidence of sexual transmission of ebola virus. N. Engl. J. Med. 373, 2448–2454 (2015).

  37. 37.

    Butler, D. What first case of sexually transmitted Ebola means for public health. Nature News (2015).

  38. 38.

    Andersen, K. G. et al. Clinical sequencing uncovers origins and evolution of lassa virus. Cell 162, 738–750 (2015).

  39. 39.

    Siddle, K. J. et al. Genomic analysis of Lassa virus during an increase in cases in Nigeria in 2018. N. Engl. J. Med. 379,1745–1753 (2018).

  40. 40.

    Timme, R. E. et al. GenomeTrakr proficiency testing for foodborne pathogen surveillance: an exercise from 2015. Microb Genom 4, (2018).

  41. 41.

    Center for Food Safety & Nutrition, A. Whole Genome Sequencing Program—GenomeTrakr Network (2018).

  42. 42.

    Allard, M. W. et al. Genomics of foodborne pathogens for microbial food safety. Curr. Opin. Biotechnol. 49, 224–229 (2018).

  43. 43.

    Stevens, E. L. et al. The public health impact of a publically available, environmental database of microbial genomes. Front. Microbiol. 8, 808 (2017).

  44. 44.

    Jackson, B. R. et al. Implementation of nationwide real-time whole-genome sequencing to enhance listeriosis outbreak detection and investigation. Clin. Infect. Dis. 63, 380–386 (2016).

  45. 45.

    Grabowski, M. K. et al. The role of viral introductions in sustaining community-based HIV epidemics in rural Uganda: evidence from spatial clustering, phylogenetics, and egocentric transmission models. PLoS Med. 11, e1001610 (2014).

  46. 46.

    Little, S. J. et al. Using HIV networks to inform real time prevention interventions. PLoS ONE. 9, e98443 (2014).

  47. 47.

    Grubaugh, N. D. et al. Genomic epidemiology reveals multiple introductions of Zika virus into the United States. Nature 546, 401–405 (2017).

  48. 48.

    Faria, N. R. et al. Establishment and cryptic transmission of Zika virus in Brazil and the Americas. Nature 506, 406–410 (2017).

  49. 49.

    Popovich, K. J. & Snitkin, E. S. Whole genome sequencing-implications for infection prevention and outbreak investigations. Curr. Infect. Dis. Rep. 19, 15 (2017).

  50. 50.

    Harris, S. R. et al. Whole-genome sequencing for analysis of an outbreak of meticillin-resistant Staphylococcus aureus: a descriptive study. Lancet. Infect. Dis. 13, 130–136 (2013).

  51. 51.

    du Plessis, L. & Stadler, T. Getting to the root of epidemic spread with phylodynamic analysis of genomic data. Trends Microbiol. 23, 383–386 (2015).

  52. 52.

    Fraser, C. et al. Pandemic potential of a strain of influenza A (H1N1): early findings. Science 324, 1557–1561 (2009).

  53. 53.

    Kühnert, D., Stadler, T., Vaughan, T. G. & Drummond, A. J. Phylodynamics with migration: a computational framework to quantify population structure from genomic data. Mol. Biol. Evol. 33, 2102–2116 (2016).

  54. 54.

    Stadler, T., Kühnert, D., Rasmussen, D. A. & du Plessis, L. Insights into the early epidemic spread of Ebola in Sierra Leone provided by viral sequence data. PLoS Curr. 6, (2014).

  55. 55.

    Neher, R. A. & Bedford, T. nextflu: real-time tracking of seasonal influenza virus evolution in humans. Bioinformatics 31, 3546–3548 (2015).

  56. 56.

    Ampofo, W. K. et al. Strengthening the influenza vaccine virus selection and development process: Report of the 3rd WHO Informal Consultation for Improving Influenza Vaccine Virus Selection held at WHO headquarters, Geneva, Switzerland, 1-3 April 2014. Vaccine 33, 4368–4382 (2015).

  57. 57.

    Yozwiak, N. L. et al. Roots, not parachutes: research collaborations combat outbreaks. Cell 166, 5–8 (2016).

  58. 58.

    Yozwiak, N. L., Schaffner, S. F. & Sabeti, P. C. Data sharing: make outbreak research open access. Nature 518, 477–479 (2015).

  59. 59.

    Moon, S. et al. Will Ebola change the game? Ten essential reforms before the next pandemic. The report of the Harvard-LSHTM independent panel on the global response to ebola. Lancet 386, 2204–2221 (2015).

  60. 60.

    H3Africa Consortium. et al. Research capacity. Enabling the genomic revolution in Africa. Science 344, 1346–1348 (2014).

  61. 61.

    World Health Organization. Guidance for Managing Ethical Issues in Infectious Disease Outbreaks (World Health Organization, 2016).

  62. 62.

    Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).

  63. 63.

    Brownstein, J. S. & Freifeld, C. C. HealthMap: the development of automated real-time internet surveillance for epidemic intelligence. Euro. Surveill. 12, E071129.5 (2007).

  64. 64.

    Johansson, M. A., Reich, N. G., Meyers, L. A. & Lipsitch, M. Preprints: an underutilized mechanism to accelerate outbreak science. PLoS. Med. 15, e1002549 (2018).

  65. 65.

    WHO. Policy statement on data sharing by WHO in the context of public health emergencies (as of 13 April 2016). Wkly. Epidemiol. Rec. 91, 237–240 (2016).

  66. 66.

    WHO. R&D Blueprint Meeting on Pathogen Genetic Sequence Data (GSD) Sharing in the Context of Public Health Emergencies, 28-29 September 2017 (WHO, 2017).

  67. 67.

    Christie, A. et al. Possible sexual transmission of Ebola virus - Liberia, 2015. MMWR. Morb. Mortal. Wkly. Rep. 64, 479–481 (2015).

  68. 68.

    Poon, A. F. Y. et al. Near real-time monitoring of HIV transmission hotspots from routine HIV genotyping: an implementation case study. Lancet HIV 3, e231–e238 (2016).

  69. 69.

    Eyre, D. W. et al. A Candida Auris outbreak and its control in an intensive care setting. N. Engl. J. Med. 379, 1322–1331 (2018).

  70. 70.

    Faria, N. R. et al. Genomic and epidemiological monitoring of yellow fever virus transmission potential. Science 361, 894–899 (2018).

  71. 71.

    Briese, T. et al. Genetic detection and characterization of Lujo virus, a new hemorrhagic fever-associated arenavirus from southern Africa. PLoS. Pathog. 5, e1000455 (2009).

  72. 72.

    Neher, R. A., Bedford, T., Daniels, R. S., Russell, C. A. & Shraiman, B. I. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses. Proc. Natl Acad. Sci. USA 113, E1701–E1709 (2016).

  73. 73.

    Grad, Y. H. et al. Genomic epidemiology of the Escherichia coli O104:H4 outbreaks in Europe, 2011. Proc. Natl Acad. Sci. USA 109, 3065–3070 (2012).

  74. 74.

    Robinson, P. N. Deep phenotyping for precision medicine. Hum. Mutat. 33, 777–780 (2012).

  75. 75.

    Pan, H. et al. Using PhenX measures to identify opportunities for cross-study analysis. Hum. Mutat. 33, 849–857 (2012).

  76. 76.

    Robinson, P. N. et al. The human phenotype ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet. 83, 610–615 (2008).

  77. 77.

    Bogue, M. A. et al. Mouse phenome database: an integrative database and analysis suite for curated empirical phenotype data from laboratory mice. Nucleic Acids Res. 46, D843–D850 (2018).

  78. 78.

    Cheng, K. C., Xin, X., Clark, D. P. & La Riviere, P. Whole-animal imaging, gene function, and the Zebrafish Phenome Project. Curr. Opin. Genet. Dev. 21, 620–629 (2011).

  79. 79.

    Alexandrov, V. et al. Large-scale phenome analysis defines a behavioral signature for Huntington’s disease genotype in mice. Nat. Biotechnol. 34, 838–844 (2016).

  80. 80.

    Zola, S. M., Manzanares, C. M., Clopton, P., Lah, J. J. & Levey, A. I. A behavioral task predicts conversion to mild cognitive impairment and Alzheimer’s disease. Am. J. Alzheimers Dis. Other Demen. 28, 179–184 (2013).

  81. 81.

    Harrington, J., Schramm, P. J., Davies, C. R. & Lee-Chiong, T. L. Jr. An electrocardiogram-based analysis evaluating sleep quality in patients with obstructive sleep apnea. Sleep. Breath. 17, 1071–1078 (2013).

  82. 82.

    Boulding, H. & Webber, C. Large-scale objective association of mouse phenotypes with human symptoms through structural variation identified in patients with developmental disorders. Hum. Mutat. 33, 874–883 (2012).

  83. 83.

    Brownstein, J. S., Freifeld, C. C. & Madoff, L. C. Digital disease detection — harnessing the web for public health surveillance. N. Engl. J. Med. 360, 2153–2157 (2009).

  84. 84.

    Bourgeois, F. T. et al. The value of patient self-report for disease surveillance. J. Am. Med. Inform. Assoc. 14, 765–771 (2007).

  85. 85.

    Milinovich, G. J. et al. Using internet search queries for infectious disease surveillance: screening diseases for suitability. BMC. Infect. Dis. 14, 690 (2014).

  86. 86.

    Charles-Smith, L. E. et al. Using social media for actionable disease surveillance and outbreak management: a systematic literature review. PLoS. ONE. 10, e0139701 (2015).

  87. 87.

    Nsoesie, E. O. et al. Social media as a sentinel for disease surveillance: what does sociodemographic status have to do with It? PLoS Curr. 8, (2016).

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O.G.P. is supported by the Oxford Martin School. K.G.A. is a Pew Biomedical Scholar and is supported by NIH NCATS CTSA UL1TR001114, NIAID HHSN272201400048C, NIAID R21AI137690, NIAID U19AI135995, and The Ray Thomas Foundation. J.T.L. is supported by the State of Arizona Technology and Research Initiative Fund (TRIF), administered by the Arizona Board of Regents, through Northern Arizona University.

Author information


  1. Pathogen and Microbiome Institute, Northern Arizona University, Flagstaff, AZ, USA

    • Jason T. Ladner
  2. Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, USA

    • Nathan D. Grubaugh
  3. Department of Zoology, University of Oxford, Oxford, UK

    • Oliver G. Pybus
  4. Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA

    • Kristian G. Andersen
  5. Scripps Research Translational Institute, La Jolla, CA, USA

    • Kristian G. Andersen


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The authors declare no competing interests.

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Correspondence to Kristian G. Andersen.

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