Spatial and temporal epidemiological analysis in the Big Data era

Preventive Veterinary Medicine

Volumn 122, Issue 1-2, 2015, Pages 213-220

  Pfeiffer, Dirk U ^a   Stevens, Kim B ^a  

^a ROYAL VETERINARY COLLEGE   (United Kingdom)

Author keywords

Data science; Exploratory analysis; Internet of things; Modelling; Multi criteria decision analysis; Spatial analysis; Visualisation

Indexed keywords

ANIMAL; ANIMAL DISEASES; CLOUD COMPUTING; FACTUAL DATABASE; GEOGRAPHIC INFORMATION SYSTEM; STATISTICAL ANALYSIS; VETERINARY;

ANIMAL DISEASES; ANIMALS; CLOUD COMPUTING; DATA INTERPRETATION, STATISTICAL; DATABASES, FACTUAL; EPIDEMIOLOGIC RESEARCH DESIGN; GEOGRAPHIC INFORMATION SYSTEMS;

EID: 84952636621     PISSN: 01675877     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.prevetmed.2015.05.012     Document Type: Article

References (132)

1. Alvarado-Serrano D.F., Knowles L.L. Ecological niche models in phylogeographic studies: applications, advances and precautions. Mol. Ecol. Resources 2013, 14:233-248.
2. Anderson C. The end of theory: the data deluge makes the scientific method obsolete. Wired Mag. 2008, 16:07.
3. Andrienko N., Andrienko G. Visual analytics of movement: an overview of methods, tools and procedures. Inf. Visual. 2012, 12:3-24.
4. Anon Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease 2011, The National Academies Press, Washington DC, USA.
5. Anon Frontiers in Massive Data Analysis 2013, National Research Council of the National Academies, Washington DC, USA, 176pp.
6. Anon Big Data and Privacy A Technological Perspective 2014, Executive Office of the President, President's Council of Advisors on Science and Technology, Washington DC, USA, 57pp.
7. Anon The Internet of Things: Making the Most of the Second Digital Revolution 2014, The Government Office for Science, London, UK, 38pp.
8. Baker S., Holt K.E., Clements A.C.A., Karkey A., Arjyal A., Boni M.F., Dongol S., Hammond N., Koirala S., Duy P.T., Nga T.V.T., Campbell J.I., Dolecek C., Basnyat B., Dougan G., Farrar J.J. Combined high-resolution genotyping and geospatial analysis reveals modes of endemic urban typhoid fever transmission. Open Biol. 2011, 1:110008. 10.1098/rsob.110008.
9. Bell G., Hey T., Szalay A. Computer science. Beyond the data deluge. Science 2009, 323:1297-1298.
10. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, USA-$9. Wadsworth & Brooks Monterey, California.
11. Brownstein J.S., Freifeld C.C., Madoff L.C. Digital disease detection - harnessing the web for public health surveillance. N. Engl. J. Med. 2009, 360:2153-2157.
12. Brownstein J.S., Freifeld C.C., Reis B.Y., Mandl K.D. Surveillance sans frontieres: internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Med. 2008, 5:e151.
13. Brownstein J.S., Freifeld C.C., Chan E.H., Keller M., Sonricker A.L., Mekaru S.R., Buckeridge D.L. Information technology and global surveillance of cases of 2009H1N1 influenza. N. Engl. J. Med. 2010, 362:1731-1735.
14. Brunker K., Hampson K., Horton D.L., Biek R. Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model. Parasitology 2012, 139:1899-1913.
15. Butler D. Mashups mix data into global service. Nature 2006, 439:6-7.
16. Butler D. When Google got flu wrong. Nature 2013, 494:155-156.
17. Carneiro H.A., Mylonakis E. Google trends: a web-based tool for real-time surveillance of disease outbreaks. Clin. Infect. Dis. 2009, 49:1557-1564.
18. Carrel M., Emch M. Genetics: a new landscape for medical geography. Ann. Assoc. Am. Geogr. 2013, 103:1452-1467.
19. Carroll L.N., Au A.P., Detwiler L.T., Fu T.-C., Painter I.S., Abernethy N.F. Visualization and analytics tools for infectious disease epidemiology: a systematic review. J. Biomed. Inf. 2014, 51:287-298.
20. Chan L.M., Brown J.L., Yoder A.D. Integrating statistical genetic and geospatial methods brings new power to phylogeography. Mol. Phylogenet. Evol. 2011, 59:523-537.
21. Chew C., Eysenbach G. Pandemics in the age of Twitter: content analysis of tweets during the 2009H1N1 outbreak. PLoS ONE 2010, 5:e14118.
22. Chunara R., Smolinski M., Brownstein J. Why we need crowdsourced data in infectious disease surveillance. Curr. Infect. Dis. Rep. 2013, 15:316-319.
23. Clements A.C.A., Pfeiffer D.U., Martin V. Application of knowledge-driven spatial modelling approaches and uncertainty management to a study of Rift Valley fever in Africa. Int. J. Health Geographics 2006, 5:57.
24. Collier N., Doan S., Kawazoe A., Goodwin R.M., Conway M., Tateno Y., Ngo Q.-H., Dien D., Kawtrakul A., Takeuchi K., Shigematsu M., Taniguchi K. BioCaster: detecting public health rumors with a web-based text mining system. Bioinformatics 2008, 24:2940-2941.
25. Collins F.S., Varmus H. A new initiative on precision medicine. N. Engl. J. Med. 2015, 372:793-795.
26. Conway, D., 2010. The data science Venn Diagram. (, last accessed 04.15.). http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram.
27. Correa T.R., Assuncao R.M., Costa M.A. A critical look at prospective surveillance using a scan statistic. Stat. Med. 2014, 34:1081-1093.
28. Costa M., Kulldorff M. Maximum linkage space-time permutation scan statistics for disease outbreak detection. Int. J. Health Geographics 2014, 13:20. 10.1186/1476-072X-13-20.
29. Cowen P., Garland T., Hugh-Jones M.E., Shimshony A., Handysides S., Kaye D., Madoff L.C., Pollack M.P., Woodall J. Evaluation of ProMED-mail as an electronic early warning system for emerging animal diseases: 1996 to 2004. J. Am. Vet. Med. Assoc. 2006, 229:1090-1099.
30. de Glanville W.A., Vial L., Costard S., Wieland B., Pfeiffer D.U. Spatial multi-criteria decision analysis to predict suitability for African swine fever endemicity in Africa. BMC Vet. Res. 2014, 10:9. 10.1186/1746-6148-10-9.
31. Dhar V. Data science and prediction. Commun. ACM 2013, 56:64-73.
32. Dodge M., Kitchin R. Crowdsourced cartography: mapping experience and knowledge. Environ. Plann. A 2013, 45:19-36.
33. Eastman J.R. Decision support: uncertainty management. IDRISI Guide to GIS and Image Processing 2009, 156-172.
34. Elith J., Graham C.H. Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models. Ecography 2009, 32:66-77.
35. Elith J., Leathwick J.R. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 2009, 40:677-697.
36. Eysenbach G. What is e-health. J. Med. Internet Res. 2001, 3:e20.
37. Faghmous J.H., Kumar V. A big data guide to understanding climate change: the case for theory-guided data science. Big Data 2014, 2:155-163.
38. Faria N.R., Suchard M.A., Rambaut A., Lemey P. Toward a quantitative understanding of viral phylogeography. Curr. Opin. Virol. 2011, 1:423-429.
39. Feizizadeh B., Jankowski P., Blaschke T. A GIS-based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis. Comput. Geosci. 2014, 64:81-95.
40. Fernández A., del Río S., López V., Bawakid A., del Jesus M.J., Benítez J.M., Herrera F. Big data with cloud computing: an insight on the computing environment, MapReduce, and programming frameworks. Wiley Interdiscip. Rev.: Data Min. Knowl. Discovery 2014, 4:380-409.
41. Firestone S.M., Christley R.M., Ward M.P., Dhand N.K. Adding the spatial dimension to the social network analysis of an epidemic: investigation of the 2007 outbreak of equine influenza in Australia. Prev. Vet. Med. 2012, 106:123-135.
42. Firestone S.M., Ward M.P., Christley R.M., Dhand N.K. The importance of location in contact networks: describing early epidemic spread using spatial social network analysis. Prev. Vet. Med. 2011, 102:185-195.
43. França S., Cabral H.N. Predicting fish species richness in estuaries: which modelling technique to use. Environ. Model. Software 2015, 66:17-26.
44. Freifeld C.C., Mandl K.D., Reis B.Y., Brownstein J.S. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J. Am. Med. Inf. Assoc. 2008, 15:150-157.
45. Gandomi A., Haider M. Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 2015, 35:137-144.
46. Gartner Gartner's 2014Hype Cycle for Emerging Technologies Maps the Journey to Digital Business 2014, 2014. Gartner, Inc.
47. Gibney E. DeepMind algorithm beats people at classic video games. Nature 2015, 518:465-466.
48. Giebultowicz S., Ali M., Yunus M., Emch M. The simultaneous effects of spatial and social networks on cholera transmission. Interdiscip. Perspect. Infect. Dis. 2011, 2011. Article ID 604372. 10.1155/2011/604372.
49. Gilbert M., Golding N., Zhou H., Wint G.R., Robinson T.P., Tatem A.J., Lai S., Zhou S., Jiang H., Guo D., Huang Z., Messina J.P., Xiao X., Linard C., Van Boeckel T.P., Martin V., Bhatt S., Gething P.W., Farrar J.J., Hay S.I., Yu H. Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia. Nat. Commun. 2014, 5. Article number 4116. 10.1038/ncomms5116.
50. Ginsberg J., Mohebbi M.H., Patel R.S., Brammer L., Smolinski M.S., Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2008, 457:1012-1014.
51. Goodchild M. Citizens as sensors: the world of volunteered geography. GeoJournal 2007, 69:211-221.
52. Goodchild M.F., Li L. Assuring the quality of volunteered geographic information. Spat. Stat. 2012, 1:110-120.
53. Grein T.W., Kamara K.B., Rodier G., Plant A.J., Bovier P., Ryan M.J., Ohyama T., Heymann D.L. Rumors of disease in the global village: outbreak verification. Emerg. Infect. Dis. 2000, 6:97-102.
54. Haklay M. How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environ. Plann. B: Plann. Des. 2010, 37:682-703.
55. Hartley D.M., Nelson N.P., Arthur R.R., Barboza P., Collier N., Lightfoot N., Linge J.P., van der Goot E., Mawudeku A., Madoff L.C., Vaillant L., Walters R., Yangarber R., Mantero J., Corley C.D., Brownstein J.S. An overview of Internet biosurveillance. Clin. Microbiol. Infect. 2013, 19:1006-1013.
56. Hay S.I., Graham A., Rogers D.J. Global mapping of infectious diseases: methods, examples and emerging applications. Emerg. Infect. Dis. 2006, 13:674. 10.3201/eid1304.070037.
57. Hay S.I., George D.B., Moyes C.L., Brownstein J.S. Big data opportunities for global infectious disease surveillance. PLoS Med. 2013, 10:e1001413. 10.1371/journal.pmed.1001413.
58. Heipke C. Crowdsourcing geospatial data. ISPRS J. Photogramm. Remote Sens. 2010, 65:550-557.
59. Heymann D.L., Rodier G.R. Hot spots in a wired world: WHO surveillance of emerging and re-emerging infectious diseases. Lancet Infect. Dis. 2001, 1:345-353.
60. Hirzel A.H., Hausser J., Chessel D., Perrin N. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data. Ecology 2002, 83:2027-2036.
61. Hirzel A.H., Le Lay G., Helfer V., Randin C., Guisan A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 2006, 199:142-152.
62. Hodson, H., 2014. Online army helps map Guinea's Ebola outbreak. New Sci. 2964, (; last accessed 04.15). http://www.newscientist.com/article/mg22229644.400-online-army-helps-map-guineas-ebola-outbreak.html#.VToUHfJ0yM8.
63. Hongoh V., Hoen A.G., Aenishaenslin C., Waaub J.P., Belanger D., Michel P., Lyme M.C. Spatially explicit multi-criteria decision analysis for managing vector-borne diseases. Int. J. Health Geographics 2011, 10:70. 10.1186/1476-072X-10-70.
64. Istepanian R., Jovanov E., Zhang Y. Introduction to the special section on m-Health: beyond seamless mobility and global wireless health-care connectivity. IEEE Trans. Inf. Technol. Biomed. 2004, 8:405-414.
65. Jankowski P., Fraley G., Pebesma E. An exploratory approach to spatial decision support. Comput. Environ. Urban Syst. 2014, 45:101-113.
66. Jones N. Computer science: the learning machines. Nature 2014, 505:146-148.
67. Kambatla K., Kollias G., Kumar V., Grama A. Trends in big data analytics. J. Parallel Distrib. Comput. 2014, 74:2561-2573.
68. Kamel Boulos M., Al-Shorbaji N. On the Internet of Things, smart cities and the WHO Healthy Cities. Int. J. Health Geographics 2014, 13:10.
69. Kamel Boulos M., Resch B., Crowley D., Breslin J., Sohn G., Burtner R., Pike W., Jezierski E., Chuang K.-Y. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int. J. Health Geographics 2011, 10:67. 10.1186/1476-072X-10-67.
70. Keller M., Blench M., Tolentino H., Freifeld C., Mandl K., Mawudeku A., Eysenbach G., Brownstein J. Use of unstructured event-based reports for global infectious disease surveillance. Emerg. Infect. Dis. 2009, 15:689-695.
71. Kuhn M., Johnson K. Who's afraid of the big black box? Statisticians' vital role in big data and predictive modelling. Significance 2014, 11:35-37.
72. Lawson A.B. Hierarchical modeling in spatial epidemiology. Wiley Interdiscip. Rev.: Comput. Stat. 2014, 6:405-417.
73. Lazer D., Kennedy R., King G., Vespignani A., Big data The parable of Google Flu: traps in big data analysis. Science 2014, 343:1203-1205.
74. Lee J., Kao H.-A., Yang S. Service innovation and smart analytics for Industry 4. 0 and Big Data environment. Procedia CIRP 2014, 16:3-8.
75. Leetaru K. Why big data missed the early warning signs of Ebola. Foreign Policy 2014, http://foreignpolicy.com/2014/09/26/why-big-data-missed-the-early-warning-signs-of-ebola/.
76. Li J., Heap A.D. Spatial interpolation methods applied in the environmental sciences: a review. Environ. Model. Software 2014, 53:173-189.
77. Liang L., Xu, Chen B., Liu Y., Cao Y., Fang W., Feng L., Goodchild L., Gong M.F., P Combining spatial-temporal and phylogenetic analysis approaches for improved understanding on global H5N1 transmission. PloS ONE 2010, 5:e13575. 10.1371/journal.pone.0013575.
78. Ligmann-Zielinska A., Jankowski P. Spatially-explicit integrated uncertainty and sensitivity analysis of criteria weights in multicriteria land suitability evaluation. Environ. Model. Software 2014, 57:235-247.
79. Malak, M., 2014. The fourth bubble in the Data Science Venn Diagram: social sciences (; last accessed 04.15.). http://datascienceassn.org/content/fourth-bubble-data-science-venn-diagram-social-sciences.
80. Malczewski J. GIS-based multicriteria decision analysis: a survey of the literature. Int, J, Geog. Inf. Sci. 2006, 20:703-726.
81. Malczewski J. Multiple criteria decision analysis and geographic information systems. Trends in Multiple Criteria Decision Analysis 2010, 369-395. Springer, New York. M. Ehrgott, S. Greco, J.R. Figueira (Eds.).
82. Martin V., Pfeiffer D.U., Zhou X., Xiao X., Prosser D.J., Guo F., Gilbert M. Spatial distribution and risk factors of highly pathogenic avian influenza (HPAI) H5N1 in China. PLoS Pathog. 2011, 7:e1001308.
83. Mayer-Schönberger V., Cukier K. Big Data: A Revolution That Will Transform How We Live, Work, and Think 2014, Mariner Books, Houghton Mifflin Harcourt Boston, 272pp.
84. Milinovich G.J., Magalhães R.J.S., Hu W. Role of big data in the early detection of Ebola and other emerging infectious diseases. Lancet Global Health 2015, 3:e20-e21.
85. Milinovich G.J., Williams G.M., Clements A.C.A., Hu W. Internet-based surveillance systems for monitoring emerging infectious diseases. Lancet Infect. Dis. 2014, 14:160-168.
86. Mortari A., Lorenzelli L. Recent sensing technologies for pathogen detection in milk: a review. Biosens. Bioelectron. 2014, 60:8-21.
87. Mullins J.C., Garofolo G., Van Ert M., Fasanella A., Lukhnova L., Hugh-Jones M.E., Blackburn J.K. Ecological niche modeling of Bacillus anthracis on three continents: evidence for genetic-ecological divergence. PLoS ONE 2013, 8:e72451.
88. Murray A.T., Grubesic T.H., Wei R. Spatially significant cluster detection. Spat. Stat. 2014, 10:103-116.
89. Mykhalovskiy E., Weir L. The Global Public Health Intelligence Network and early warning outbreak detection: a Canadian contribution to global public health. Can. J. Public Health 2011, 97:42-44.
90. Norvig, P., 2009. All we want are the facts, ma'am (; last accessed 04.15.). http://norvig.com/fact-check.html.
91. O'Driscoll A., Daugelaite J., Sleator R.D. 'Big data', Hadoop and cloud computing in genomics. J. Biomed. Inf. 2013, 46:774-781.
92. Okabe A., Sugihara K. Spatial Analysis Along Networks - Statistical and Computational Methods 2012, John Wiley & Sons Chichester, UK, 306pp.
93. Oliver M.A., Webster R. A tutorial guide to geostatistics: computing and modelling variograms and kriging. CATENA 2014, 113:56-69.
94. Olsen J. Using secondary data. Modern Epidemiology 2008, 481-491. Lippincott Williams & Wilkins, Philadelphia. K.J. Rothman, S. Greenland, T.L. Lash (Eds.).
95. Peters D.P.C., Havstad K.M., Cushing J., Tweedie C., Fuentes O., Villanueva-Rosales N. Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology. Ecosphere 2014, 5:art67.
96. Pfeiffer D.U., Robinson T.P., Stevenson M., Stevens K.B., Rogers D.J., Clements A.C.A. Introduction. Spatial Analysis in Epidemiology 2008, 1-8. Oxford University Press, Oxford, UK.
97. Pfeiffer D.U., Robinson T.P., Stevenson M., Stevens K.B., Rogers D.J., Clements A.C.A. Spatial risk assessment and management of disease. Spatial Analysis in Epidemiology 2008, 119-120. Oxford University Press, Oxford, UK.
98. Pigliucci M. The end of theory in science. EMBO Rep. 2009, 10:534. 10.1038/embor.2009.111.
99. Pigott D.M., Golding N., Mylne A., Huang Z., Henry A.J., Weiss D.J., Brady O.J., Kraemer M.U., Smith D.L., Moyes C.L., Bhatt S., Gething P.W., Horby P.W., Bogoch I.I., Brownstein J.S., Mekaru S.R., Tatem A.J., Khan K., Hay S.I. Mapping the zoonotic niche of Ebola virus disease in Africa. eLife 2014, 3:e04395.
100. Porter J.H., Hanson P.C., Lin C.C. Staying afloat in the sensor data deluge. Trends Ecol. Evol. 2012, 27:121-129.
101. Prates M.O., Kulldorff M., Assuncao R.M. Relative risk estimates from spatial and space-time scan statistics: are they biased. Stat. Med. 2014, 33:2634-2644.
102. Pybus O.G., Suchard M.A., Lemey P., Bernardin F.J., Rambaut A., Crawford F.W., Gray R.R., Arinaminpathy N., Stramer S.L., Busch M.P., Delwart E.L. Unifying the spatial epidemiology and molecular evolution of emerging epidemics. Proc. Natl. Acad. Sci. U. S. A. 2012, 109:15066-15071.
103. Rutten C.J., Velthuis A.G., Steeneveld W., Hogeveen H. Invited review: sensors to support health management on dairy farms. J. Dairy Sci. 2013, 96:1928-1952.
104. Sanchez-Matamoros A., Sanchez-Vizcaino J.M., Rodriguez-Prieto V., Iglesias E., Martinez-Lopez B. Identification of suitable areas for African horse sickness virus infections in Spanish equine populations. Transbound. Emerg. Dis. 2014, 10.1111/tbed.12302.
105. Sarojinie Fernando W.T.P., Hazelton M.L. Generalizing the spatial relative risk function. Spat. Spatio-Temporal Epidemiol. 2014, 8:1-10.
106. Schadt E.E. The changing privacy landscape in the era of big data. Mol. Syst. Biol. 2012, 8:612.
107. Scholkopf B. Artificial intelligence: learning to see and act. Nature 2015, 518:486-487.
108. Schutt R., O'Neil C. Doing Data Science 2013, O'Reilly Media Sebastopol, California, USA, 408pp.
109. See L., Comber A., Salk C., Fritz S., van der Velde M., Perger C., Schill C., McCallum I., Kraxner F., Obersteiner M. Comparing the quality of crowdsourced data contributed by expert and non-experts. PLoS ONE 2013, 8:e69958.
110. Signorini A., Segre A.M., Polgreen P.M. The use of Twitter to track levels of disease activity and public concern in the U. S. during the Influenza A H1N1 pandemic. PLoS ONE 2011, 6:e19467.
111. Solanas A., Patsakis C., Conti M., Vlachos L.S., Ramos V., Falcone F., Postolache O., Pérez-Martínez P.A., Di Pietro R., Perrea D.N., Martínez-Ballesté A. Smart health: a context-aware health paradigm within smart cities. IEEE Commun. Mag. 2014, 52:74-81.
112. Sorensen H.T., Sabroe S., Olsen J. A framework for evaluation of secondary data sources for epidemiological research. Int J Epidemiol 1996, 25:435-442.
113. St Louis C., Zorlu G. Can Twitter predict disease outbreaks. Br. Med. J. 2012, 344:e2353. 10.1136/bmj.e2353.
114. Stevens K.B., Pfeiffer D.U. Spatial modelling of disease using data- and knowledge-driven approaches. Spat. Spatio-Temporal Epidemiol. 2011, 2:125-133.
115. Stevens K.B., Gilbert M., Pfeiffer D.U. Modeling habitat suitability for occurrence of highly pathogenic avian influenza virus H5N1 in domestic poultry in Asia: a spatial multicriteria decision analysis approach. Spat. Spatio-Temporal Epidemiol. 2013, 4:1-14.
116. Tatem A.J. Mapping population and pathogen movements. Int. Health 2014, 6:5-11.
117. Tatem A.J., Huang Z., Narib C., Kumar U., Kandula D., Pindolia D.K., Smith D.L., Cohen J.M., Graupe B., Uusiku P., Lourenco C. Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malar. J. 2014, 13:52. 10.1186/1475-2875-13-52.
118. Tolentino H., Kamadjeu R., Fontelo P., Liu F., Matters M., Pollack M., Madoff L. Scanning the emerging infectious diseases horizon - visualizing ProMED emails using EpiSPIDER. Adv. Dis. Surveillance 2007, 2:169.
119. Tran A., Ippoliti C., Balenghien T., Conte A., Gely M., Calistri P., Goffredo M., Baldet T., Chevalier V. A geographical information system-based multicriteria evaluation to map areas at risk for Rift Valley fever vector-borne transmission in Italy. Transbound. Emerg. Dis. 2013, 60(Suppl. 2):14-23.
120. van Zyl T. Algorithmic considerations for geospatial and/or temporal big data. Big Data- Techniques and Technologies in Geoinformatics 2014, 117-132. CRC Press, Boca Raton, Florida, USA. H.A. Karimi (Ed.).
121. van Zyl T. Machine learning on geospatial big data. Big Data - Techniques and Technologies in Geoinformatics 2014, 133-148. CRC Press, Boca Raton, Florida, USA. H.A. Karimi (Ed.).
122. Vatsavai R.R., Ganguly A., Chandola V., Stefanidis A., Klasky S., Shekhar S. Spatiotemporal data mining in the era of big spatial data: algorithms and applications. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data 2012, 1-10.
123. Wesolowski A., Buckee C.O., Bengtsson L., Wetter E., Lu X., Tatem A.J. Commentary: containing the Ebola outbreak - the potential and challenge of mobile network data. PLoS Curr. Outbreaks 2014, 6. 10.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e.
124. Wesolowski A., Stresman G., Eagle N., Stevenson J., Owaga C., Marube E., Bousema T., Drakeley C., Cox J., Buckee C.O. Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones. Scientific Reports 2014, 4:5678. 10.1038/srep05678.
125. Wilson K., Brownstein J.S. Early detection of disease outbreaks using the Internet. Can. Med. Assoc. J. 2009, 180:829-831.
126. Wilson K., von Tigerstrom B., McDougall C. Protecting global health security through the International Health Regulations: requirements and challenges. Can. Med. Assoc. J. 2008, 179:44-48.
127. Wilson N., Mason K., Tobias M., Peacey M., Huang Q.S., Baker M. Interpreting Google Flu Trends data for pandemic H1N1 influenza: the New Zealand experience. Eurosurveillance 2009, 14. pii=19386.
128. Wing J.M. Computational thinking and thinking about computing. Philos. Trans. Ser. A. Math. Phys. Eng. Sci. 2008, 366:3717-3725.
129. Yemshanov D., Koch F.H., Ben-Haim Y., Downing M., Sapio F., Siltanen M. A new multicriteria risk mapping approach based on a multiattribute frontier concept. Risk Anal. 2013, 33:1694-1709.
130. You J. Artifical intelligence: DARPA sets out to automate research. Science 2015, 347:465.
131. Zeldenrust M., Rahamat-Langendoen J., Postma M., van Vliet J. The value of ProMED-mail for the Early Warning Committee in the Netherlands: more specific approach recommended. Eurosurveillance 2008, 13. 8033.
132. Ziegler A., König I.R. Mining data with random forests: current options for real-world applications. Wiley Interdiscip. Rev.: Data Min. Knowl. Discovery 2013, 4:55-63.