Progress and Challenges in Infectious Disease Cartography

Trends in Parasitology

Volumn 32, Issue 1, 2016, Pages 19-29

  Kraemer, Moritz U G ^a   Hay, Simon I ^b,c,d   Pigott, David M ^a,b   Smith, David L ^a,d,e   Wint, G R William ^a   Golding, Nick ^b  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c UNIVERSITY OF WASHINGTON   (United States)

^d NATIONAL INSTITUTES OF HEALTH   (United States)

^e SANARIA INC   (United States)

Author keywords

Disease modelling; Human mobility; Mapping; Pathogen dispersal; Spatial epidemiology

Indexed keywords

CHIKUNGUNYA; DENGUE; DISEASE CARRIER; ENVIRONMENTAL FACTOR; EPIDEMIC; GEOSTATISTICAL ANALYSIS; HUMAN; INFECTION; INFECTION RISK; MALARIA; MIXED INFECTION; PHYLOGEOGRAPHY; REVIEW; SOCIOECONOMICS; STATISTICAL MODEL; COMMUNICABLE DISEASES; GEOGRAPHIC MAPPING; INFORMATION DISSEMINATION; MOBILE PHONE; STANDARDS; TRENDS;

CELL PHONES; COMMUNICABLE DISEASES; EPIDEMIOLOGIC METHODS; EPIDEMIOLOGY; GEOGRAPHIC MAPPING; HUMANS; INFORMATION DISSEMINATION;

EID: 84952864803     PISSN: 14714922     EISSN: 14715007     Source Type: Journal    
DOI: 10.1016/j.pt.2015.09.006     Document Type: Review

References (94)

1. Nordhaus W.D. Geography and macroeconomics: new data and new findings. Proc. Natl. Acad. Sci. U.S.A. 2006, 103:3510-3517.
2. Tatem A.J. Mapping population and pathogen movements. Int. Health 2014, 6:5-11.
3. Elith J., et al. Novel methods improve prediction of species' distributions from occurrence data. Ecogeography 2006, 29:129-151.
4. Hastie T., et al. The Elements of Statistical Learning 2009, Springer. 2nd edn.
5. Lindström T., et al. A bayesian ensemble approach for epidemiological projections. PLoS Comput. Biol. 2015, 11:e1004187.
6. Hay S.I., et al. Big data opportunities for global infectious disease surveillance. PLoS Med. 2013, 10:e1001413.
7. Lounibos L.P. Invasion by insect vectors of human disease. Annu. Rev. Entomol. 2002, 47:233-266.
8. Tsetsarkin K.a., Weaver S.C. Sequential adaptive mutations enhance efficient vector switching by Chikungunya virus and its epidemic emergence. PLoS Pathog. 2011, 7:e1002412.
9. Bhatt S., et al. The global distribution and burden of dengue. Nature 2013, 496:504-507.
10. Hay S.I., et al. A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med. 2009, 6:e1000048.
11. Hii Y.L., et al. Forecast of dengue incidence using temperature and rainfall. PLoS Negl. Trop. Dis. 2012, 6:e1908.
12. Weiss D.J., et al. Air temperature suitability for Plasmodium falciparum malaria transmission in Africa 2000-2012: a high-resolution spatiotemporal prediction. Malar. J. 2014, 13:171.
13. Hahn M.B., et al. Influence of deforestation, logging, and fire on malaria in the Brazilian Amazon. PLoS ONE 2014, 9:e85725.
14. Cunningham A., a, et al. Disease invasion: impacts on biodiversity and human health. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 2012, 367:2804-2806.
15. Gong P., et al. Urbanisation and health in China. Lancet 2012, 379:843-852.
16. Tatem A.J. Mapping the denominator: spatial demography in the measurement of progress. Int. Health 2014, 6:153-155.
17. Tatem A.J., et al. Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births. Int. J. Health Geogr. 2014, 13:1-11.
18. Murray C.J.L., et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012, 380:2197-2223.
19. Alirol E., et al. Urbanisation and infectious diseases in a globalised world. Lancet Infect. Dis. 2011, 11:131-141.
20. Bharti N., et al. Explaining seasonal fluctuations of measles in Niger using nighttime lights imagery. Science 2011, 334:1424-1427.
21. Sutton P., et al. Census from Heaven: an estimate of the global human population using night-time satellite imagery. Int. J. Remote Sens. 2001, 22:3061-3076.
22. Deville P., et al. Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. U.S.A. 2014, 111:15888-15893.
23. Bakillah M., et al. Fine-resolution population mapping using OpenStreetMap points-of-interest. Int. J. Geogr. Inf. Sci. 2014, 28:1940-1963.
24. Linard C., et al. Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS ONE 2012, 7:e31743.
25. Bengtsson L., et al. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS Med. 2011, 8:e1001083.
26. Huang Z., et al. An open-access modeled passenger flow matrix for the global air network in 2010. PLoS ONE 2013, 8:e64317.
27. Lambin E.F., Meyfroidt P. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl. Acad. Sci. U.S.A. 2011, 108:3465-3472.
28. Brockmann D., Helbing D. The hidden geometry of complex, network-driven contagion phenomena. Science 2013, 342:1337-1342.
29. Nunes M.R.T., et al. Air travel is associated with intracontinental spread of dengue virus serotypes 1-3 in Brazil. PLoS Negl. Trop. Dis. 2014, 8:e2769.
30. Gubler D.J. Dengue, urbanization and globalization: the unholy trinity of the 21st century. Trop. Med. Health 2011, 39:3-11.
31. Guagliardo S.A., et al. Patterns of geographic expansion of Aedes aegypti in the Peruvian Amazon. PLoS Negl. Trop. Dis. 2014, 8:e3033.
32. Horby P.W., et al. Prospects for emerging infections in east and southeast Asia 10 years after severe acute respiratory syndrome. Emerg. Infect. Dis. 2013, 19:853-860.
33. Kraemer M., et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 2015, 4:e08347.
34. Schaffner F., et al. Public health significance of invasive mosquitoes in Europe. Clin. Microbiol. Infect. 2013, 19:685-692.
35. Tatem A.J., et al. Global traffic and disease vector dispersal. Proc. Natl. Acad. Sci. U.S.A. 2006, 103:6242-6247.
36. Kilpatrick A.M., Randolph S.E. Drivers, dynamics, and control of emerging vector-borne zoonotic diseases. Lancet 2012, 380:1946-1955.
37. Moyes C.L., et al. Defining the geographical range of the Plasmodium knowlesi reservoir. PLoS Negl. Trop. Dis. 2014, 8:e2780.
38. Bogoch I.I., et al. Assessment of the potential for international dissemination of Ebola virus via commercial air travel during the 2014 west African outbreak. Lancet 2014, 385:29-35.
39. Khan K., et al. Potential for the International Spread of Middle East Respiratory Syndrome in Association with Mass Gatherings in Saudi Arabia. PLoS Curr. 2013, Published online July 17, 2013. 10.1371/currents.outbreaks.a7b70897ac2fa4f79b59f90d24c860b8.
40. Khan K., et al. Assessing the origin of and potential for international spread of Chikungunya virus from the Caribbean. PLoS Curr. 2014, Published online June 6, 2014. 10.1371/currents.outbreaks.2134a0a7bf37fd8d388181539fea2da5.
41. Smith K.F., et al. Globalization of human infectious diseases. Ecology 2007, 88:1903-1910.
42. Stoddard S.T., et al. House-to-house human movement drives dengue virus transmission. Proc. Natl. Acad. Sci. U.S.A. 2013, 110:994-999.
43. Abel G.J., Sander N. Quantifying global international migration flows. Science 2014, 343:1520-1522.
44. Vayena E., et al. Ethical challenges of big data in public health. PLOS Comput. Biol. 2015, 11:e1003904.
45. Montjoye Y., De, et al. Unique in the shopping mall: on the reidentifiability of credit card metadata. Science 2015, 347:356-359.
46. Weaver S.C., Lecuit M. Chikungunya virus and the global spread of a mosquito-borne disease. N. Engl. J. Med. 2015, 372:1231-1239.
47. Hawley W.A., et al. Aedes albopictus in North America: probable introduction in used tires from northern Asia. Science 1987, 236:1114-1116.
48. Gilbert M., et al. Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia. Nat. Commun. 2014, 5:4116.
49. Kilpatrick A.M., et al. Predicting the global spread of H5N1 avian influenza. Proc. Natl. Acad. Sci. U.S.A. 2006, 103:19368-19373.
50. Metcalf C.J.E., et al. Seven challenges in modeling vaccine preventable diseases. Epidemics 2014, 10:11-15.
51. Lessler J., Grenfell B.T. Measurement of vaccine-derived immunity: how do we use all the data?. Expert Rev. Vaccines 2012, 11:747-749.
52. Ferguson N.M., et al. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 2005, 437:209-214.
53. Ferguson N.M., et al. Strategies for mitigating an influenza pandemic. Nature 2006, 442:448-452.
54. Cauchemez S., et al. Local and regional spread of chikungunya fever in the Americas. Eurosurveillance 2014, 19:20854.
55. Ebola virus disease in West Africa: the first 9 months of the epidemic and forward projections. N. Engl. J. Med. 2014, 371:1481-1495. WHO Ebola Response Team.
56. McCormack J.E., et al. Applications of next-generation sequencing to phylogeography and phylogenetics. Mol. Phylogenet. Evol. 2013, 66:526-538.
57. Pybus O.G., et al. Unifying the spatial epidemiology and molecular evolution of emerging epidemics. Proc. Natl. Acad. Sci. U.S.A. 2012, 109:15066-15071.
58. Frost S.D.W., et al. Eight challenges in phylodynamic inference. Epidemics 2015, 10:88-92.
59. Lemey P., et al. Unifying viral genetics and human transportation data to predict the global transmission dynamics of human influenza H3N2. PLoS Pathog. 2014, 10:e1003932.
60. Faria N.R., et al. The early spread and epidemic ignition of HIV-1 in human populations. Science 2014, 346:56-61.
61. Carroll M.W., et al. Temporal and spatial analysis of the 2014-2015 Ebola virus outbreak in West Africa. Nature 2015, 524:97-101.
62. Tatem A.J., Smith D.L. International population movements and regional Plasmodium falciparum malaria elimination strategies. Proc. Natl. Acad. Sci. U.S.A. 2010, 107:12222-12227.
63. Wesolowski A., et al. Quantifying the impact of human mobility on malaria. Science 2012, 338:267-270.
64. Hay S.I., et al. Global mapping of infectious disease. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 2013, 368:20120250.
65. Brownstein J.S., et al. Digital disease detection - harnessing the web for public health surveillance. N. Engl. J. Med. 2009, 360:2153-2157.
66. Madoff L.C. ProMED-mail: an early warning system for emerging diseases. Clin. Infect. Dis. 2004, 39:227-232.
67. Collier N., et al. BioCaster: detecting public health rumors with a Web-based text mining system. Bioinformatics 2008, 24:2940-2941.
68. Salathé M., et al. Digital epidemiology. PLoS Comput. Biol. 2012, 8:e1002616.
69. Chan E.H., et al. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Negl. Trop. Dis. 2011, 5:e1206.
70. Broniatowski D.A., et al. Twitter: Big data opportunities. Science 2014, 345:148.
71. Pigott D.M., et al. Mapping the zoonotic niche of Ebola virus disease in Africa. Elife 2014, 3:e04395.
72. Bhattacharya D., et al. Estimating the impacts of means-tested subsidies under treatment externalities with application to anti-malarial bednets. NBER Working Pap. Ser. 2013, 18833.
73. Kim D., a, et al. Social network targeting to maximise population behaviour change: a cluster randomised controlled trial. Lancet 2015, 386:145-153.
74. Lazer D., et al. Big data. The parable of Google Flu: traps in big data analysis. Science 2014, 343:1203-1205.
75. Jeon C.Y., Murray M.B. Diabetes mellitus increases the risk of active tuberculosis: a systematic review of 13 observational studies. PLoS Med. 2008, 5:1091-1101.
76. Brooker S., et al. Epidemiology of Plasmodium-helminth co-infection in Africa: populations at risk, potential impact on anemia, and prospects for combining control. Am. J. Trop. Med. Hyg. 2007, 77:88-98.
77. Mulu A., et al. Epidemiological and clinical correlates of malaria-helminth co-infections in Southern Ethiopia. Malar. J. 2013, 12:227.
78. Bygbjerg I.C. Double burden of noncommunicable and infectious diseases in developing countries. Science 2012, 337:1499-1501.
79. Global, regional and national levels of age-specific mortality and 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015, 385:117-171. GBD 2013 Mortality and Causes of Death Collaborators.
80. Snow R.W., et al. The malaria transition on the Arabian Peninsula: progress toward a malaria-free region between 1960-2010. Adv. Parasitol. 2013, 82:205-251.
81. Murray C.J.L., et al. Global malaria mortality between 1980 and 2010: a systematic analysis. Lancet 2012, 379:413-431.
82. Chiyaka C., et al. The stability of malaria elimination. Science 2013, 339:909-910.
83. Andrus J.K., et al. Strengthening surveillance: confronting infectious diseases in developing countries. Vaccine 2011, 29(Suppl. 4):D126-D130.
84. Pigott D.M., Kraemer M.U. Enhancing infectious disease mapping with open access resources. Eurosurveillance 2014, 19:20989.
85. Brady O.J., et al. Global temperature constraints on Aedes aegypti and Ae. albopictus persistence and competence for dengue virus transmission. Parasit. Vectors 2014, 7:338.
86. Gething P.W., et al. A new world malaria map: Plasmodium falciparum endemicity in 2010. Malar. J. 2011, 10:378.
87. Brady O.J., et al. Modelling adult Aedes aegypti and Aedes albopictus survival at different temperatures in laboratory and field settings. Parasit. Vectors 2013, 6:351.
88. Gething P.W., et al. Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax. Parasit. Vectors 2011, 4:92.
89. Lai Y-S., et al. Spatial distribution of schistosomiasis and treatment needs in sub-Saharan Africa: a systematic review and geostatistical analysis. Lancet Infect. Dis. 2015, 15:927-940.
90. Stone M. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B 1974, 36:111-147.
91. Purse B.V., Golding N. Tracking the distribution and impacts of diseases with biological records and distribution modelling. Biol. J. Linn. Soc. 2015, 115:664-677.
92. Wesolowski A., et al. Commentary: containing the ebola outbreak - the potential and challenge of mobile network data. PLoS Curr. 2014, Published online September 29, 2014. http://dx.doi.org/0.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e.
93. Tizzoni M., et al. On the use of human mobility proxies for modeling epidemics. PLoS Comput. Biol. 2014, 10:e1003716.
94. Wesolowski A., et al. The impact of biases in mobile phone ownership on estimates of human mobility. J. R. Soc. Interface 2013, 10:20120986.