Outbreak analytics: A developing data science for informing the response to emerging pathogens

Philosophical Transactions of the Royal Society B: Biological Sciences

Volumn 374, Issue 1776, 2019, Pages

  Polonsky, Jonathan A ^a,c   Baidjoe, Amrish ^d   Kamvar, Zhian N ^d   Cori, Anne ^d   Durski, Kara ^b   John Edmunds, W ^a,e   Eggo, Rosalind M ^a,e   Funk, Sebastian ^a,e   Kaiser, Laurent ^c   Keating, Patrick ^a,f   Le Polain De Waroux, Olivier ^a,f,g   Marks, Michael ^f   Moraga, Paula ^h   Morgan, Oliver ^a   Nouvellet, Pierre ^d,i   Ratnayake, Ruwan ^a,e   Roberts, Chrissy H ^f   Whitworth, Jimmy ^a,f   Jombart, Thibaut ^a,d,f  

^a Department of Health Emergency Information and Risk Assessment ^*  (United Kingdom)

^b WORLD HEALTH ORGANIZATION   (Switzerland)

^c UNIVERSITY OF GENEVA   (Switzerland)

^d MEDICAL RESEARCH COUNCIL   (United Kingdom)

^e Department of Infectious Disease Epidemiology   (United Kingdom)

^f LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

^g PUBLIC HEALTH ENGLAND   (United Kingdom)

^h LANCASTER UNIVERSITY   (United Kingdom)

ⁱ UNIVERSITY OF SUSSEX   (United Kingdom)

Author keywords

Epidemics; Infectious; Methods; Pipeline; Software; Tools

Indexed keywords

ANALYTICAL METHOD; EPIDEMIC; HOST-PATHOGEN INTERACTION; INFECTIOUS DISEASE; METHODOLOGY; PATHOGENICITY; PIPELINE; POPULATION OUTBREAK; PUBLIC HEALTH; SOFTWARE;

ANIMALIA;

EID: 85065403501     PISSN: 09628436     EISSN: 14712970     Source Type: Journal    
DOI: 10.1098/rstb.2018.0276     Document Type: Review

References (152)

1. Fraser C et al. 2009 Pandemic potential of a strain of influenza A (H1N1): early findings. Science 324, 1557-1561. (doi:10.1126/science.1176062)
2. Assiri A et al. 2013 Hospital outbreak of Middle East respiratory syndrome coronavirus. N. Engl. J. Med. 369, 407-416. (doi:10.1056/ NEJMoa1306742)
3. Cauchemez S, Fraser C, Van Kerkhove MD, Donnelly CA, Riley S, Rambaut A, Enouf V, van der Werf S, Ferguson NM. 2014 Middle East respiratory syndrome coronavirus: quantification of the extent of the epidemic, surveillance biases, and transmissibility. Lancet Infect. Dis. 14, 50-56. (doi:10.1016/S1473-3099(13)70304-9)
4. Cauchemez S et al. 2016 Unraveling the drivers of MERS-CoV transmission. Proc. Natl Acad. Sci. USA 113, 9081-9086. (doi:10.1073/pnas. 1519235113)
5. Campos GS, Bandeira AC, Sardi SI. 2015 Zika virus outbreak, Bahia, Brazil. Emerg. Infect. Dis. 21, 1885-1886. (doi:10.3201/eid2110.150847)
6. European Centre for Disease Prevention and Control. 2015 Zika virus epidemic in the Americas: potential association with microcephaly and Guillain-Barré syndrome (first update), 21 January 2016. (See https://ecdc.europa.eu/sites/portal/files/media/en/publications/Publications/zika-virus-americas-association-with-microcephaly-rapid-risk-assessment.pdf).
7. WHO Ebola Response Team. 2014 Ebola virus disease in West Africa - the first 9 months of the epidemic and forward projections. N. Engl. J. Med. 371, 1481-1495. (doi:10.1056/ NEJMoa1411100)
8. WHO Ebola Response Team et al. 2015 West African Ebola epidemic after one year - slowing but not yet under control. N. Engl. J. Med. 372, 584-587. (doi:10.1056/NEJMc1414992)
9. Moon S et al. 2015 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. (doi:10.1016/S0140-6736(15)00946-0)
10. Van Kerkhove MD, Bento AI, Mills HL, Ferguson NM, Donnelly CA. 2015 A review of epidemiological parameters from Ebola outbreaks to inform early public health decision-making. Sci Data 2, 150019. (doi:10.1038/sdata.2015.19)
11. Senga M et al. 2017 Contact tracing performance during the Ebola virus disease outbreak in Kenema district, Sierra Leone. Phil. Trans. R. Soc. B 372, 20160300. (doi:10.1098/rstb.2016.0300)
12. International Ebola Response Team. 2016 Exposure patterns driving Ebola transmission in West Africa: a retrospective observational study. PLoS Med. 13, e1002170. (doi:10.1371/journal.pmed.1002170)
13. Cori A et al. 2017 Key data for outbreak evaluation: building on the Ebola experience. Phil. Trans. R. Soc. B 372, 20160371. (doi:10.1098/rstb.2016.0371)
14. Lewnard JA. 2018 Ebola virus disease: 11 323 deaths later, how far have we come? Lancet 392, 189-190. (doi:10.1016/S0140-6736(18)31443-0)
15. Ebola Outbreak Epidemiology Team. 2018 Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April-May, 2018: an epidemiological study. Lancet 392, 213-221. (doi:10.1016/S0140-6736(18)31387-4)
16. Polonsky J et al. 2019 Lessons learnt from Ebola virus disease surveillance in Équateur Province, May-July 2018. Weekly Epidemiological Record 94, 23-27.
17. 2017 WHO j Ebola outbreak Democratic Republic of the Congo 2017. See https://www.who.int/emergencies/ebola-DRC-2017/en/.
18. Hartung C, Lerer A, Anokwa Y, Tseng C, Brunette W, Borriello G. 2010 Open Data Kit: Tools to build information services for developing regions. In Proceedings of the 4th ACM/IEEE International Conference on Information and Communication Technologies and Development, pp. 18:1-18:12. New York, NY: ACM.
19. Brunette W, Sundt M, Dell N, Chaudhri R, Breit N, Borriello G. 2013 Open Data Kit 2.0: Expanding and refining information services for developing regions. In Proceedings of the 14th Workshop on Mobile Computing Systems and Applications, pp. 10:1-10:6. New York, NY: ACM.
20. Nagraj VP, Randhawa N, Campbell F, Crellen T, Sudre B, Jombart T. 2018 epicontacts: Handling, visualisation and analysis of epidemiological contacts. F1000Res. 7, 566. (doi:10.12688/ f1000research.14492.1)
21. Moraga P, Dorigatti I, Kamvar ZN, Piatkowski P, Toikkanen SE, Nagraj VP, Donnelly CA, Jombart T. 2018 epiflows: an R package for risk assessment of travel-related spread of disease. F1000Res. 7, 1374. (doi:10.12688/f1000research.16032.1)
22. Pigott DM et al. 2017 Local, national, and regional viral haemorrhagic fever pandemic potential in Africa: a multistage analysis. Lancet 390, 2662-2672. (doi:10.1016/S0140-6736(17)32092-5)
23. Messina JP et al. 2016 Mapping global environmental suitability for Zika virus. Elife 5, e15272. (doi:10.7554/eLife.15272)
24. Pigott DM et al. 2014 Mapping the zoonotic niche of Ebola virus disease in Africa. Elife 3, e04395. (doi:10.7554/eLife.04395)
25. Jombart T, Eggo RM, Dodd PJ, Balloux F. 2011 Reconstructing disease outbreaks from genetic data: a graph approach. Heredity 106, 383-390. (doi:10. 1038/hdy.2010.78)
26. Famulare M, Hu H. 2015 Extracting transmission networks from phylogeographic data for epidemic and endemic diseases: Ebola virus in Sierra Leone, 2009 H1N1 pandemic influenza and polio in Nigeria. Int. Health 7, 130-138. (doi:10.1093/ inthealth/ihv012)
27. Cori A, Ferguson NM, Fraser C, Cauchemez S. 2013 A new framework and software to estimate time-varying reproduction numbers during epidemics. Am. J. Epidemiol. 178, 1505-1512. (doi:10.1093/ aje/kwt133)
28. Nouvellet P et al. 2017 A simple approach to measure transmissibility and forecast incidence. Epidemics 22, 29-35. (doi:10.1016/j.epidem.2017. 02.012)
29. Nouvellet P et al. 2015 The role of rapid diagnostics in managing Ebola epidemics. Nature 528, S109-S116. (doi:10.1038/nature16041)
30. Finger F, Funk S, White K, Siddiqui R, John Edmunds W, Kucharski AJ. 2018 Real-time analysis of the diphtheria outbreak in forcibly displaced Myanmar nationals in Bangladesh. bioRxiv. 388645. (doi:10.1101/388645)
31. Bausch DG, Edmunds J. 2018 Real-time modeling should be routinely integrated into outbreak response. Am. J. Trop. Med. Hyg. 98, 1214-1215. (doi:10.4269/ajtmh.18-0150)
32. Feil EJ, Li BC, Aanensen DM, Hanage WP, Spratt BG. 2004 eBURST: inferring patterns of evolutionary descent among clusters of related bacterial genotypes from multilocus sequence typing data. J. Bacteriol. 186, 1518-1530. (doi:10.1128/JB.186. 5.1518-1530.2004)
33. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu CH, Xie D, Suchard MA, Rambaut A, Drummond AJ. 2014 BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537. (doi:10.1371/journal.pcbi.1003537)
34. Holmes EC, Rambaut A, Andersen KG. 2018 Pandemics: spend on surveillance, not prediction. Nature 558, 180-182. (doi:10.1038/d41586-018-05373-w)
35. Campbell F, Strang C, Ferguson N, Cori A, Jombart T. 2018 When are pathogen genome sequences informative of transmission events? PLoS Pathog. 14, e1006885. (doi:10.1371/journal. ppat.1006885)
36. Carroll D, Daszak P, Wolfe ND, Gao GF, Morel CM, Morzaria S, Pablos-Méndez A, Tomori O, Mazet JAK. 2018 The Global Virome Project. Science 359, 872-874. (doi:10.1126/science.aap7463)
37. Birrell PJ, De Angelis D, Presanis AM. 2018 Evidence synthesis for stochastic epidemic models. Stat. Sci. 33, 34-43. (doi:10.1214/17-STS631)
38. Wikipedia contributors. 2018 Data science. Wikipedia, The Free Encyclopedia. See https://en.wikipedia.org/w/index.php?title=Data_ science&oldid=868658447 (accessed on 16 November 2018).
39. Bausch DG, Clougherty MM. 2015 Ebola virus: sensationalism, science, and human rights. J. Infect. Dis. 212(Suppl. 2), S79-S83. (doi:10. 1093/infdis/jiv359)
40. Kwong JC et al. 2012 The impact of infection on population health: results of the Ontario burden of infectious diseases study. PLoS ONE 7, e44103. (doi:10.1371/journal.pone.0044103)
41. Vos T et al. 2012 Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2163-2196. (doi:10.1016/S0140-6736(12)61729-2)
42. Global Burden of Disease Study 2013 Collaborators. 2015 Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 386, 743-800. (doi:10.1016/S0140-6736(15)60692-4)
43. Prüss-Üstün A et al. 2003 Introduction and methods: assessing the environmental burden of disease at national and local levels. WHO Environmental Burden of Disease Series, No. 1. Geneva, Switzerland: World Health Organization.
44. Camacho A, Eggo RM, Funk S, Watson CH, Kucharski AJ, Edmunds WJ. 2015 Estimating the probability of demonstrating vaccine efficacy in the declining Ebola epidemic: a Bayesian modelling approach. BMJ Open 5, e009346. (doi:10.1136/bmjopen-2015-009346)
45. Camacho A et al. 2017 Real-time dynamic modelling for the design of a cluster-randomized phase 3 Ebola vaccine trial in Sierra Leone. Vaccine 35, 544-551. (doi:10.1016/j.vaccine.2016.12.019)
46. Garske T, Van Kerkhove MD, Yactayo S, Ronveaux O, Lewis RF, Staples JE, Perea W, Ferguson NM, Yellow Fever Expert Committee. 2014 Yellow fever in Africa: estimating the burden of disease and impact of mass vaccination from outbreak and serological data. PLoS Med. 11, e1001638. (doi:10.1371/ journal.pmed.1001638)
47. Kraemer MUG et al. 2017 Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015-16: a modelling study. Lancet Infect. Dis. 17, 330-338. (doi:10.1016/ S1473-3099(16)30513-8)
48. Dorigatti I, Hamlet A, Aguas R, Cattarino L, Cori A, Donnelly CA, Garske T, Imai N, Ferguson NM. 2017 International risk of yellow fever spread from the ongoing outbreak in Brazil, December 2016 to May 2017. Euro Surveill. 22, 30572. (doi:10.2807/1560-7917.ES.2017.22.28.30572)
49. Brookmeyer R, You X. 2006 A hypothesis test for the end of a common source outbreak. Biometrics 62, 61-65. (doi:10.1111/j.1541-0420.2005.00421.x)
50. Nishiura H, Miyamatsu Y, Chowell G, Saitoh M. 2015 Assessing the risk of observing multiple generations of Middle East respiratory syndrome (MERS) cases given an imported case. Euro Surveill. 20, 21181. (doi:10.2807/1560-7917.ES2015.20.27.21181)
51. Nishiura H, Miyamatsu Y, Mizumoto K. 2016 Objective determination of end of MERS outbreak, South Korea, 2015. Emerg. Infect. Dis. 22, 146-148. (doi:10.3201/eid2201.151383)
52. Fähnrich C et al. 2015 Surveillance and outbreak response management system (SORMAS) to support the control of the Ebola virus disease outbreak in West Africa. Euro Surveill. 20, 21071. (doi:10.2807/ 1560-7917.ES2015.20.12.21071)
53. Polonsky JA, Martínez-Pino I, Nackers F, Chonzi P, Manangazira P, Van Herp M, Maes P, Porten K, Luquero FJ. 2014 Descriptive epidemiology of typhoid fever during an epidemic in Harare, Zimbabwe, 2012. PLoS One 9, e114702. (doi:10. 1371/journal.pone.0114702)
54. Page A-L et al. 2015 Geographic distribution and mortality risk factors during the cholera outbreak in a rural region of Haiti, 2010-2011. PLoS Neglect. Trop. Dis. 9, e0003605. (doi:10.1371/journal.pntd. 0003605)
55. Aanensen DM, Huntley DM, Feil EJ, al-Own F, Spratt BG. 2009 EpiCollect: linking smartphones to web applications for epidemiology, ecology and community data collection. PLoS One 4, e6968. (doi:10.1371/journal.pone.0006968)
56. Tom-Aba D et al. 2015 Innovative technological approach to Ebola virus disease outbreak response in Nigeria using the open data kit and form hub technology. PLoS One 10, e0131000. (doi:10.1371/ journal.pone.0131000)
57. Xie Y, Allaire JJ, Grolemund G. 2018 R markdown: The definitive guide. Boca Raton, FL: Chapman and Hall/CRC. https://bookdown.org/yihui/rmarkdown/.
58. Xie Y. 2016 Bookdown: authoring books and technical documents with R markdown. Boca Raton, FL: CRC Press.
59. Karo B, Haskew C, Khan AS, Polonsky JA, Mazhar MKA, Buddha N. 2018 World Health Organization early warning, alert, and response system in the Rohingya Crisis, Bangladesh, 2017-2018. Emerg. Infect. Dis. 24, 2074-2076. (doi:10.3201/ eid2411.181237)
60. Cauchemez S, Valleron A-J, Boëlle P-Y, Flahault A, Ferguson NM. 2008 Estimating the impact of school closure on influenza transmission from Sentinel data. Nature 452, 750-754. (doi:10.1038/ nature06732)
61. Cauchemez S, Bhattarai A, Marchbanks TL, Fagan RP, Ostroff S, Ferguson NM, Swerdlow D, Pennsylvania H1N1 working group. 2011 Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza. Proc. Natl Acad. Sci. USA 108, 2825-2830. (doi:10.1073/pnas.1008895108)
62. Gignoux E, Polonsky J, Ciglenecki I, Bichet M, Coldiron M, Thuambe Lwiyo E, Akonda I, Serafini M, Porten K. 2018 Risk factors for measles mortality and the importance of decentralized case management during an unusually large measles epidemic in eastern Democratic Republic of Congo in 2013. PLoS One 13, e0194276. (doi:10.1371/ journal.pone.0194276)
63. Saurabh S, Prateek S. 2017 Role of contact tracing in containing the 2014 Ebola outbreak: a review. Afr. Health Sci. 17, 225-236. (doi:10.4314/ahs.v17i1.28)
64. Funk S, Camacho A, Kucharski AJ, Eggo RM, Edmunds WJ. 2018 Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model. Epidemics 22, 56-61. (doi:10. 1016/j.epidem.2016.11.003)
65. Viboud C et al. 2018 The RAPIDD Ebola forecasting challenge: synthesis and lessons learnt. Epidemics 22, 13-21. (doi:10.1016/j.epidem.2017.08.002)
66. Wickham H. 2014 Tidy data. J. Stat. Softw. 59, 1-23. (doi:10.18637/jss.v059.i10)
67. Dallman T et al. 2016 Phylogenetic structure of European Salmonella enteritidis outbreak correlates with national and international egg distribution network. Microb Genom 2, e000070. (doi:10.1099/ mgen.0.000070)
68. Jenkins C et al. 2015 Public health investigation of two outbreaks of shiga toxin-producing Escherichia coli O157 associated with consumption of watercress. Appl. Environ. Microbiol. 81, 3946-3952. (doi:10.1128/AEM.04188-14)
69. Inns T et al. 2015 A multi-country Salmonella enteritidis phage type 14b outbreak associated with eggs from a German producer: 'near real-time' application of whole genome sequencing and food chain investigations, United Kingdom, May to September 2014. Eurosurveillance 20, 21098. (doi:10.2807/1560-7917.ES2015.20.16.21098)
70. Bousema T et al. 2016 The impact of hotspot-targeted interventions on malaria transmission in Rachuonyo South District in the Western Kenyan highlands: a cluster-randomized controlled trial. PLoS Med. 13, e1001993. (doi:10.1371/journal. pmed.1001993)
71. Baidjoe AY et al. 2016 Factors associated with high heterogeneity of malaria at fine spatial scale in the Western Kenyan highlands. Malar. J. 15, 307. (doi:10.1186/s12936-016-1362-y)
72. Ahmed R, Robinson R, Elsony A, Thomson R, Squire SB, Malmborg R, Burney P, Mortimer K. 2018 A comparison of smartphone and paper datacollection tools in the Burden of Obstructive Lung Disease (BOLD) study in Gezira state, Sudan. PLoS One 13, e0193917. (doi:10.1371/journal.pone. 0193917)
73. Solomon AW et al. 2018 Quality assurance and quality control in the global trachoma mapping project. Am. J. Trop. Med. Hyg. 99, 858-863. (doi:10.4269/ajtmh.18-0082)
74. King JD et al. 2013 A novel electronic data collection system for large-scale surveys of neglected tropical diseases. PLoS One 8, e74570. (doi:10.1371/journal. pone.0074570)
75. Njuguna HN et al. 2014 A comparison of smartphones to paper-based questionnaires for routine influenza sentinel surveillance, Kenya, 2011-2012. BMC Med. Inform. Decis. Mak. 14, 107. (doi:10.1186/s12911-014-0107-5)
76. Poushter J. 2016 Smartphone ownership and internet usage continues to climb in emerging economies. Pew Res. Center 22, 1-44.
77. Bogoch II, Koydemir HC, Tseng D, Ephraim RKD, Duah E, Tee J, Andrews JR, Ozcan A. 2017 Evaluation of a mobile phone-based microscope for screening of Schistosoma haematobium infection in rural Ghana. Am. J. Trop. Med. Hyg. 96, 1468-1471. (doi:10.4269/ajtmh.16-0912)
78. Kühnemund M et al. 2017 Targeted DNA sequencing and in situ mutation analysis using mobile phone microscopy. Nat. Commun. 8, 13913. (doi:10.1038/ncomms13913)
79. Quesada-González D, Merkoçi A. 2017 Mobile phone-based biosensing: an emerging 'diagnostic and communication' technology. Biosens. Bioelectron. 92, 549-562. (doi:10.1016/j.bios. 2016.10.062)
80. Macharia P, Dunbar MD, Sambai B, Abuna F, Betz B, Njoroge A, Bukusi D, Cherutich P, Farquhar C. 2015 Enhancing data security in open data kit as an mHealth application. In 2015 International Conference on Computing, Communication and Security (ICCCS), Pamplemousses, Mauritius, 4-5 December 2015. (doi:10.1109/cccs.2015.7374205)
81. Crawley MJ. 2012 The R book. Hoboken, NJ: John Wiley & Sons.
82. Wickham H. 2016 Ggplot2: elegant graphics for data analysis. Berlin, Germany: Springer.
83. Höhle M. 2007 surveillance: An R package for the monitoring of infectious diseases. Comput. Stat. 22, 571-582. (doi:10.1007/s00180-007-0074-8)
84. Jombart T et al. 2014 OutbreakTools: a new platform for disease outbreak analysis using the R software. Epidemics 7, 28-34. (doi:10.1016/j. epidem.2014.04.003)
85. Jombart T, Kamvar ZN, FitzJohn R. 2018 Incidence: compute, handle, plot and model incidence of dated events. R package version 1.5.4. https://CRAN.R-project.org/package¼incidence.
86. King AA, Domenech de Cellès M, Magpantay FMG, Rohani P. 2015 Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola. Proc. R. Soc. B 282, 20150347. (doi:10.1098/rspb.2015.0347)
87. Snow J. 1855 On the mode of communication of cholera. London, UK: John Churchill.
88. Wertheim HFL, Horby P, Woodall JP. 2012 Atlas of human infectious diseases. Hoboken, NJ: John Wiley & Sons.
89. Nunes MRT et al. 2015 Emergence and potential for spread of Chikungunya virus in Brazil. BMC Med. 13, 102. (doi:10.1186/s12916-015-0348-x)
90. In press. Radiant Earth Foundation - Earth imagery for impact. See https://www.radiant.earth (accessed on 18 November 2018).
91. In press. Spatial epidemiology of Viral Hemorrhagic Fevers. See http://www.healthdata.org/datavisualization/spatial-epidemiology-viral-hemorrhagic-fevers (accessed on 19 September 2018).
92. Hadfield J, Megill C, Bell SM, Huddleston J, Potter B, Callender C, Sagulenko P, Bedford T, Neher RA. 2018 Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121-4123. (doi:10. 1093/bioinformatics/bty407)
93. Argimón S et al. 2016 Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb Genom 2, e000093. (doi:10. 1099/mgen.0.000093)
94. Freifeld CC, Mandl KD, Reis BY, Brownstein JS. 2008 HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J. Am. Med. Inform. Assoc. 15, 150-157. (doi:10.1197/jamia. M2544)
95. Backer JA, Wallinga J. 2016 Spatiotemporal analysis of the 2014 Ebola epidemic in West Africa. PLoS Comput. Biol. 12, e1005210. (doi:10.1371/journal. pcbi.1005210)
96. Wallinga J, Teunis P. 2004 Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. Am. J. Epidemiol. 160, 509-516. (doi:10.1093/aje/kwh255)
97. Wallinga J, Lipsitch M. 2007 How generation intervals shape the relationship between growth rates and reproductive numbers. Proc. R. Soc. B 274, 599-604. (doi:10.1098/rspb.2006.3754)
98. Cauchemez S, Van Kerkhove MD, Riley S, Donnelly CA, Fraser C, Ferguson NM. 2013 Transmission scenarios for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) and how to tell them apart. Euro Surveill. 18, 20503.
99. Shrivastava SR, Shrivastava PS, Ramasamy J. 2014 Utility of contact tracing in reducing the magnitude of Ebola disease. Germs 4, 97-99. (doi:10.11599/ germs.2014.1063)
100. WHO Ebola Response Team et al. 2015 Ebola virus disease among children in West Africa. N. Engl. J. Med. 372, 1274-1277. (doi:10.1056/ NEJMc1415318)
101. WHO Ebola Response Team. 2016 Ebola virus disease among male and female persons in West Africa. N. Engl. J. Med. 374, 96-98. (doi:10.1056/ NEJMc1510305)
102. Donnelly CA et al. 2003 Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong. Lancet 361, 1761-1766. (doi:10.1016/S0140-6736(03)13410-1)
103. Anderson RM, Fraser C, Ghani AC, Donnelly CA, Riley S, Ferguson NM, Leung GM, Lam TH, Hedley AJ. 2004 Epidemiology, transmission dynamics and control of SARS: the 2002-2003 epidemic. Phil. Trans. R. Soc. Lond. B 359, 1091-1105. (doi:10. 1098/rstb.2004.1490)
104. Anderson RM, May RM. 1991 Infectious diseases of humans, vol. 1. Oxford, UK: Oxford University Press.
105. Farrington CP, Andrews NJ, Beale AD, Catchpole MA. 1996 A statistical algorithm for the early detection of outbreaks of infectious disease. J. R. Stat. Soc. Ser. A Stat. Soc. 159, 547-563. (doi:10.2307/ 2983331)
106. Park SW, Champredon D, Weitz J, Dushoff J. 2018 A practical generation interval-based approach to inferring the strength of epidemics from their speed. bioRxiv. 312397. (doi:10.1101/312397)
107. Keeling M, Rohani P. 2008 Modeling infectious diseases in humans and animals. Clin. Infect. Dis. 47, 864-866. (doi:10.1086/591197)
108. McKinley T, Cook AR, Deardon R. 2009 Inference in epidemic models without likelihoods. Int. J. Biostat. 5(1): Article 24. (doi:10.2202/1557-4679.1171)
109. Obadia T, Haneef R, Boëlle P-Y. 2012 The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks. BMC Med. Inform. Decis. Mak. 12, 147. (doi:10.1186/1472-6947-12-147)
110. Chowell G, Viboud C, Simonsen L, Merler S, Vespignani A. 2017 Perspectives on model forecasts of the 2014-2015 Ebola epidemic in West Africa: lessons and the way forward. BMC Med. 15, 42. (doi:10.1186/s12916-017-0811-y)
111. Held L, Meyer S, Bracher J. 2017 Probabilistic forecasting in infectious disease epidemiology: the 13th Armitage lecture. Stat. Med. 36, 3443-3460. (doi:10.1002/sim.7363)
112. Funk S, Camacho A, Kucharski AJ, Lowe R, Eggo RM, Edmunds WJ. 2017 Assessing the performance of real-time epidemic forecasts. bioRxiv. 177451. (doi:10.1101/177451)
113. Kucharski AJ, Camacho A, Flasche S, Glover RE, Edmunds WJ, Funk S. 2015 Measuring the impact of Ebola control measures in Sierra Leone. Proc. Natl Acad. Sci. USA 112, 14 366-14 371. (doi:10.1073/ pnas.1508814112)
114. Buring JE. 1987 Epidemiology in medicine. Philadelphia, PA: Lippincott Williams & Wilkins.
115. Grandesso F et al. 2014 Risk factors for cholera transmission in Haiti during inter-peak periods: insights to improve current control strategies from two case-control studies. Epidemiol. Infect. 142, 1625-1635. (doi:10.1017/S0950268813002562)
116. Gross M. 1976 Oswego County revisited. Public Health Rep. 91, 168-170.
117. Buchholz U et al. 2011 German outbreak of Escherichia coli O104:H4 associated with sprouts. N. Engl. J. Med. 365, 1763-1770. (doi:10.1056/ NEJMoa1106482)
118. Ebola ça Suffit Ring Vaccination Trial Consortium. 2015 The ring vaccination trial: a novel cluster randomised controlled trial design to evaluate vaccine efficacy and effectiveness during outbreaks, with special reference to Ebola. BMJ 351, h3740. (doi:10.1136/bmj.h3740)
119. Grais RF, Conlan AJK, Ferrari MJ, Djibo A, Le Menach A, Bjørnstad ON, Grenfell BT. 2008 Time is of the essence: exploring a measles outbreak response vaccination in Niamey, Niger. J. R. Soc. Interface 5, 67-74. (doi:10.1098/rsif.2007.1038)
120. Harris SR et al. 2013 Whole-genome sequencing for analysis of an outbreak of meticillin-resistant Staphylococcus aureus: a descriptive study. Lancet Infect. Dis. 13, 130-136. (doi:10.1016/S1473-3099(12)70268-2)
121. Gire SK et al. 2014 Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345, 1369-1372. (doi:10.1126/ science.1259657)
122. Cotten M et al. 2013 Transmission and evolution of the Middle East respiratory syndrome coronavirus in Saudi Arabia: a descriptive genomic study. Lancet 382, 1993-2002. (doi:10.1016/S0140-6736(13) 61887-5)
123. Robinson ER, Walker TM, Pallen MJ. 2013 Genomics and outbreak investigation: from sequence to consequence. Genome Med. 5, 36. (doi:10.1186/ gm440)
124. Hatherell H-A, Didelot X, Pollock SL, Tang P, Crisan A, Johnston JC, Colijn C, Gardy JL. 2016 Declaring a tuberculosis outbreak over with genomic epidemiology. Microb. Genomics 2, e000060. (doi:10.1099/mgen.0.000060)
125. Dudas G et al. 2017 Virus genomes reveal factors that spread and sustained the Ebola epidemic. Nature 544, 309-315. (doi:10.1038/ nature22040)
126. Faria NR et al. 2017 Establishment and cryptic transmission of Zika virus in Brazil and the Americas. Nature 546, 406-410. (doi:10.1038/ nature22401)
127. Quick J et al. 2016 Real-time, portable genome sequencing for Ebola surveillance. Nature 530, 228-232. (doi:10.1038/nature16996)
128. Pallen MJ, Loman NJ, Penn CW. 2010 High-throughput sequencing and clinical microbiology: progress, opportunities and challenges. Curr. Opin. Microbiol. 13, 625-631. (doi:10.1016/j.mib.2010. 08.003)
129. Spratt BG, Hanage WP, Li B, Aanensen DM, Feil EJ. 2004 Displaying the relatedness among isolates of bacterial species-the eBURST approach. FEMS Microbiol. Lett. 241, 129-134. (doi:10.1016/j. femsle.2004.11.015)
130. Enright MC, Robinson DA, Randle G, Feil EJ, Grundmann H, Spratt BG. 2002 The evolutionary history of methicillin-resistant Staphylococcus aureus (MRSA). Proc. Natl Acad. Sci. USA 99, 7687-7692. (doi:10.1073/pnas.122108599)
131. King SJ, Leigh JA, Heath PJ, Luque I, Tarradas C, Dowson CG, Whatmore AM. 2002 Development of a multilocus sequence typing scheme for the pig pathogen Streptococcus suis: identification of virulent clones and potential capsular serotype exchange. J. Clin. Microbiol. 40, 3671-3680. (doi:10.1128/JCM.40.10.3671-3680.2002)
132. Urwin R, Maiden MCJ. 2003 Multi-locus sequence typing: a tool for global epidemiology. Trends Microbiol. 11, 479 - 487. (doi:10.1016/j.tim.2003.08.006)
133. Felsenstein J. 2004 Inferring phylogenies. Sunderland, MA: Sinauer Associates Sunderland.
134. Popescu A-A, Huber KT, Paradis E. 2012 ape 3.0: New tools for distance-based phylogenetics and evolutionary analysis in R. Bioinformatics 28, 1536-1537. (doi:10.1093/bioinformatics/bts184)
135. Felsenstein J. 1981 Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17, 368-376. (doi:10.1007/BF01734359)
136. Schliep KP. 2011 phangorn: phylogenetic analysis in R. Bioinformatics 27, 592-593. (doi:10.1093/ bioinformatics/btq706)
137. Ronquist F, Huelsenbeck JP. 2003 MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572-1574. (doi:10. 1093/bioinformatics/btg180)
138. Grubaugh ND, Faria NR, Andersen KG, Pybus OG. 2018 Genomic insights into zika virus emergence and spread. Cell 172, 1160-1162. (doi:10.1016/j. cell.2018.02.027)
139. Smith GJD et al. 2009 Origins and evolutionary genomics of the 2009 swine-origin H1N1 influenza A epidemic. Nature 459, 1122-1125. (doi:10.1038/ nature08182)
140. Siddle KJ et al. 2018 Genomic analysis of lassa virus during an increase in cases in Nigeria in 2018. N. Engl. J. Med. 379, 1745-1753. (doi:10.1056/ NEJMoa1804498)
141. Grenfell BT, Pybus OG, Gog JR, Wood JLN, Daly JM, Mumford JA, Holmes EC. 2004 Unifying the epidemiological and evolutionary dynamics of pathogens. Science 303, 327-332. (doi:10.1126/ science.1090727)
142. 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. R. Soc. B 275, 887-895. (doi:10.1098/rspb.2007.1442)
143. Ypma RJF, Bataille AMA, Stegeman A, Koch G, Wallinga J, van Ballegooijen WM. 2012 Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data. Proc. R. Soc. B 279, 444-450. (doi:10.1098/rspb.2011. 0913)
144. Ypma RJF, van Ballegooijen WM, Wallinga J. 2013 Relating phylogenetic trees to transmission trees of infectious disease outbreaks. Genetics 195, 1055-1062. (doi:10.1534/genetics. 113.154856)
145. Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C, Ferguson N. 2014 Bayesian reconstruction of disease outbreaks by combining epidemiologic and genomic data. PLoS Comput. Biol. 10, e1003457. (doi:10. 1371/journal.pcbi.1003457)
146. Klinkenberg D, Backer JA, Didelot X, Colijn C, Wallinga J. 2017 Simultaneous inference of phylogenetic and transmission trees in infectious disease outbreaks. PLoS Comput. Biol. 13, e1005495. (doi:10.1371/journal.pcbi.1005495)
147. Didelot X, Gardy J, Colijn C. 2014 Bayesian inference of infectious disease transmission from whole-genome sequence data. Mol. Biol. Evol. 31, 1869-1879. (doi:10.1093/molbev/msu121)
148. De Maio N, Wu C-H, Wilson DJ. 2016 SCOTTI: efficient reconstruction of transmission within outbreaks with the structured coalescent. PLoS Comput. Biol. 12, e1005130. (doi:10.1371/journal. pcbi.1005130)
149. Springborn M, Chowell G, MacLachlan M, Fenichel EP. 2015 Accounting for behavioral responses during a flu epidemic using home television viewing. BMC Infect. Dis. 15, 21. (doi:10.1186/s12879-014-0691-0)
150. R Core Team. 2018 R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
151. RECON-R Epidemics Consortium. 2018 R epidemics consortium. See https://www.repidemicsconsortium.org/ (accessed on 26 September 2018).
152. 2018 RECON learn. See https://www.reconlearn.org (accessed on 26 September 2018).