Forecasting Epidemiological and Evolutionary Dynamics of Infectious Diseases

Trends in Ecology and Evolution

Volumn 31, Issue 10, 2016, Pages 776-788

  Gandon, Sylvain ^a   Day, Troy ^b   Metcalf, C Jessica E ^c   Grenfell, Bryan T ^c  

^a UNIV MONTPELLIER   (France)

^b QUEEN S UNIVERSITY   (Canada)

^c PRINCETON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EPIDEMIOLOGY; EVOLUTIONARY BIOLOGY; INFECTIOUS DISEASE; NUMERICAL MODEL; PATHOGEN; PEST OUTBREAK; PUBLIC HEALTH;

BIOLOGICAL MODEL; COMMUNICABLE DISEASE; EPIDEMIC; FORECASTING; HUMAN;

COMMUNICABLE DISEASES; DISEASE OUTBREAKS; FORECASTING; HUMANS; MODELS, BIOLOGICAL;

EID: 84990172828     PISSN: 01695347     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tree.2016.07.010     Document Type: Review

References (100)

1. 1 Richardson, L.F., Weather Prediction by Numerical Process. 1922, Cambridge University Press.
2. 2 Bauer, P., et al. The quiet revolution of numerical weather prediction. Nature 525 (2015), 47–55.
3. 3 Anderson, R.M., May, R.M., Infectious Diseases of Humans. 1991, Oxford University Press.
4. 4 Diekmann, O., Heesterbeek, J.A.P., Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation. 2000, Wiley.
5. 5 Keeling, M.J., Rohani, P., Modelling Infectious Diseases. 2008, Princeton University Press.
6. 6 Bernoulli, D., Essai d'une nouvelle analyse de la mortalité causée par la petite vérole, et des avantages de l'inoculation pour la prévenir. In Histoire de l'Académie Royale des Sciences: Mémoires de Mathématiques et de Physique, Imprimerie Royale (in French), 1766, 1–45.
7. 7 Ross, R., Some a priori pathometric equations. BMJ, 1, 1915 546-447.
8. 8 Kermack, W.O., McKendrick, A.G., A contribution to the mathematical theory of epidemics. Proc. R. Soc. A 115 (1927), 700–721.
9. 9 MacDonald, G., Epidemiological basis of malaria control. Bull. World Health Organ. 15 (1956), 613–626.
10. 10 Wolfson, L.J., et al. Estimates of measles case fatality ratios: a comprehensive review of community-based studies. Int. J. Epidemiol. 38 (2009), 195–205.
11. 11 Finkenstädt, B.F., Grenfell, B.T., Time series modelling of childhood diseases: a dynamical systems approach. J. R. Stat. Soc. Ser. C Appl. Stat. 49 (2000), 187–205.
12. 12 Bjornstad, O.N., et al. Dynamics of measles epidemics: estimating scaling of transmission rates using a time series SIR model. Ecol. Monogr. 72 (2002), 169–184.
13. 13 Grenfell, B.T., et al. Dynamics of measles epidemics: scaling noise, determinism, and predictability with the TSIR model. Ecol. Monogr. 72 (2002), 185–202.
14. 14 Takahashi, S., et al. Reduced vaccination and the risk of measles and other childhood infections post-Ebola. Science 347 (2015), 1240–1242.
15. 15 Olsen, L., Schaffer, W., Chaos versus noisy periodicity: alternative hypotheses for childhood epidemics. Science 249 (1990), 499–504.
16. 16 Ferrari, M.J., et al. The dynamics of measles in sub-Saharan Africa. Nature 451 (2008), 679–684.
17. 17 Dalziel, B.D., et al. Persistent chaos of measles epidemics in the prevaccination United States caused by a small change in seasonal transmission patterns. PLoS Comput. Biol., 12, 2016, e1004655.
18. 18 Jansen, V.A.A., et al. Measles outbreaks in a population with declining vaccine uptake. Science, 301, 2003, 804.
19. 19 Roy, M., et al. Epidemic cholera spreads like wildfire. Sci. Rep., 4, 2014, 3710.
20. 20 Caudron, Q., et al. Predictability in a highly stochastic system: final size of measles epidemics in small populations. J. R. Soc. Interface, 12, 2015, 20141125.
21. 21 Keeling, M.J., et al. Seasonally forced disease dynamics explored as switching between attractors. Physica D 148 (2001), 317–335.
22. 22 Rozhnova, G., et al. Characterizing the dynamics of rubella relative to measles: the role of stochasticity. J. R. Soc. Interface, 10, 2013, 20130643.
23. 23 Epstein, E.S., Stochastic dynamic prediction. Tellus 21 (1969), 739–759.
24. 24 Leith, C.E., Theoretical skill of Monte Carlo forecasts. Mon. Weather Rev. 102 (1974), 409–418.
25. 25 Axelsen, J.B., et al. Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers. Proc. Natl. Acad. Sci. U.S.A. 111 (2014), 9538–9542.
26. 26 Lynch, M., Walsh, B., Genetics and Analysis of Quantitative Traits. 1998, Sinauer Associates.
27. 27 Berngruber, T.W., et al. Evolution of virulence in emerging epidemics. PLoS Pathog., 9, 2013, e1003209.
28. 28 Gandon, S., et al. Imperfect vaccines and the evolution of pathogen virulence. Nature 414 (2001), 751–756.
29. 29 Gandon, S., Day, T., The evolutionary epidemiology of vaccination. J. R. Soc. Interface 4 (2007), 803–817.
30. 30 Mackinnon, M.J., Read, A.F., Effects of immunity on relationships between growth rate, virulence and transmission in semi-immune hosts. Parasitology 126 (2003), 103–112.
31. 31 Barclay, V.C., et al. The evolutionary consequences of blood-stage vaccination on the rodent malaria Plasmodium chabaudi. PLoS Biol., 10, 2012, e1001368.
32. 32 Read, A.F., et al. Imperfect vaccination can enhance the transmission of highly virulent pathogens. PLoS Biol., 13, 2015, e1002198.
33. 33 Goossens, H., et al. Outpatient antibiotic use in Europe and association with resistance: a cross-national database study. Lancet 365 (2005), 579–587.
34. 34 Seppälä, H., et al. The effect of changes in the consumption of macrolide antibiotics on erythromycin resistance in group A streptococci in Finland. Finnish Study Group for Antimicrobial Resistance. N. Engl. J. Med. 337 (1997), 441–446.
35. 35 Austin, D.J., et al. The relationship between the volume of antimicrobial consumption in human communities and the frequency of resistance. Proc. Natl. Acad. Sci. U.S.A. 96 (1999), 1152–1156.
36. 36 Lipsitch, M., The rise and fall of antimicrobial resistance. Trends Microbiol. 9 (2001), 438–444.
37. 37 Lipsitch, M., et al. The epidemiology of antibiotic resistance in hospitals: paradoxes and prescriptions. Proc. Natl Acad. Sci. U.S.A. 97 (2000), 1938–1943.
38. 38 Wargo, A.R., et al. Competitive release and facilitation of drug resistant parasites following therapeutic chemotherapy in a rodent malaria model. Proc. Natl. Acad. Sci. U.S.A. 104 (2007), 19914–19919.
39. 39 Day, T., Read, A.F., When does high-dose antimicrobial chemotherapy prevent the emergence of resistance?. PLoS Comput. Biol., 12, 2016, e1004689.
40. 40 Luciani, F., et al. The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. U.S.A. 106 (2009), 14711–14715.
41. 41 Kucharski, A.J., et al. Capturing the dynamics of pathogens with many strains. J. Math. Biol. 72 (2015), 1–24.
42. 42 Gog, J.R., Grenfell, B.T., Dynamics and selection of many-strain pathogens. Proc. Natl. Acad. Sci. U.S.A. 99 (2002), 17209–17214.
43. 43 Shrestha, S., et al. Statistical inference for multi-pathogen systems. PLoS Comput. Biol., 7, 2011, e1002135.
44. 44 Koelle, K., et al. Epochal evolution shapes the phylodynamics of interpandemic influenza A (H3N2) in humans. Science 314 (2006), 1898–1903.
45. 45 Reich, N.G., et al. Interactions between serotypes of dengue highlight epidemiological impact of cross-immunity. J. R. Soc. Interface, 10, 2013, 20130414.
46. 46 Mongkolsapaya, J., et al. Original antigenic sin and apoptosis in the pathogenesis of dengue hemorrhagic fever. Nat. Med. 9 (2003), 921–927.
47. 47 Weinberger, D.M., et al. Serotype replacement in disease following pneumococcal vaccination: a discussion of the evidence. Lancet 378 (2011), 1962–1973.
48. 48 Croucher, N.J., et al. Population genomics of post-vaccine changes in pneumococcal epidemiology. Nat. Genet. 45 (2013), 656–663.
49. 49 Ferguson, N.M., et al. Ecological and immunological determinants of influenza evolution. Nature 422 (2003), 428–433.
50. 50 Bedford, T., et al. Canalization of the evolutionary trajectory of the human influenza virus. BMC Biol., 10, 2012, 38.
51. 51 Bush, R.M., et al. Predicting the evolution of human influenza A. Science 286 (1999), 1921–1925.
52. 52 Plotkin, J.B., et al. Hemagglutinin sequence clusters and the antigenic evolution of influenza A virus. Proc. Natl. Acad. Sci. U.S.A. 99 (2002), 6263–6268.
53. 53 Luksza, M., Lässig, M., A predictive fitness model for influenza. Nature 507 (2014), 57–61.
54. 54 Bedford, T., et al. Integrating influenza antigenic dynamics with molecular evolution. Elife, 3, 2014, e01914.
55. 55 Neher, R.A., et al. Predicting evolution from the shape of genealogical trees. Elife, 3, 2014, e03568.
56. 56 Neher, R.A., et al. Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses. Proc. Natl. Acad. Sci. U.S.A. 99 (2016), 6263–6268.
57. 57 Gerrish, P., Lenski, R., The fate of competing beneficial mutations in an asexual population. Genetica 102-103 (1998), 127–144.
58. 58 Desai, M.M., Fisher, D.S., Beneficial mutation selection balance and the effect of genetic linkage on positive selection. Genetics 176 (2007), 1759–1798.
59. 59 Good, B.H., et al. Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proc. Natl. Acad. Sci. U.S.A. 109 (2012), 4950–4955.
60. 60 Gerrish, P.J., Sniegowski, P.D., Real time forecasting of near-future evolution. J. R. Soc. Interface. 9 (2012), 2268–2278.
61. 61 Drake, J.W., et al. Rates of spontaneous mutation. Genetics 148 (1998), 1667–1686.
62. 62 Holmes, E.C., The Evolution and Emergence of RNA Viruses. 2009, Oxford University Press.
63. 63 Day, T., Computability, Gödel's incompleteness theorem, and an inherent limit on the predictability of evolution. J. R. Soc. Interface 9 (2011), 624–639.
64. 64 Orr, H.A., The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6 (2005), 119–127.
65. 65 Martin, G., Lenormand, T., A general multivariate extension of Fisher's geometric model and the distribution of mutation fitness effects across species. Evolution 60 (2006), 893–907.
66. 66 Kassen, R., Bataillon, T., Distribution of fitness effects among beneficial mutations before selection in experimental populations of bacteria. Nat. Genet. 38 (2006), 484–488.
67. 67 Tenaillon, O., The utility of Fisher's geometric model in evolutionary genetics. Annu. Rev. Ecol. Evol. Syst. 45 (2015), 179–201.
68. 68 Martin, G., et al. Distributions of epistasis in microbes fit predictions from a fitness landscape model. Nat. Genet. 39 (2007), 555–560.
69. 69 Couce, A., Tenaillon, O., The rule of declining adaptability in microbial evolution experiments. Front. Genet., 6, 2015, 99.
70. 70 Chevereau, G., et al. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biol., 13, 2015, e1002299.
71. 71 Boni, M.F., et al. Homologous recombination is very rare or absent in human influenza A virus. J. Virol. 82 (2008), 4807–4811.
72. 72 Koelle, K., Rasmussen, D.A., The effects of a deleterious mutation load on patterns of influenza A/H3N2s antigenic evolution in humans. Elife, 4, 2015, e07361.
73. 73 Gandon, S., Day, T., Evolutionary epidemiology and the dynamics of adaptation. Evolution 63 (2009), 826–838.
74. 74 Luo, S., Koelle, K., Navigating the devious course of evolution: the importance of mechanistic models for identifying eco-evolutionary dynamics in nature. Am. Nat. 181 (2013), S58–S75.
75. 75 Clark, J.S., et al. Ecological forecasts: an emerging imperative. Science 293 (2001), 657–660.
76. 76 Petchey, O.L., et al. The ecological forecast horizon, and examples of its uses and determinants. Ecol. Lett. 18 (2015), 597–611.
77. 77 Mouquet, N., et al. Predictive ecology in a changing world. J. Appl. Ecol. 52 (2015), 1293–1310.
78. 78 Woolhouse, M., How to make predictions about future infectious disease risks. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366 (2011), 2045–2054.
79. 79 Shaman, J., Karspeck, A., Forecasting seasonal outbreaks of influenza. Proc. Natl. Acad. Sci. U.S.A. 109 (2012), 20425–20430.
80. 80 Shaman, J.A., et al. Real-time influenza forecasts during the 2012-2013 season. Nat. Commun., 4, 2013, 2837.
81. 81 Rodó, X., et al. Climate change and infectious diseases: can we meet the needs for better prediction?. Clim. Change 118 (2013), 625–640.
82. 82 Corley, C.D., et al. Disease prediction models and operational readiness. PLoS ONE, 9, 2014, e91989.
83. 83 Heesterbeek, H., et al. Modeling infectious disease dynamics in the complex landscape of global health. Science, 347, 2015, aaa4339.
84. 84 Roy, M., et al. Predictability of epidemic malaria under non-stationary conditions with process-based models combining epidemiological updates and climate variability. Malar. J., 14, 2015, 419.
85. 85 Yang, W., et al. Forecasting influenza epidemics in Hong Kong. PLoS Comput. Biol., 11, 2015, e1004383.
86. 86 Hilborn, R., Mangel, M., The Ecological Detective: Confronting Models with Data. 1997, Princeton University Press.
87. 87 Neher, R.A., Bedford, T., nextflu: real-time tracking of seasonal influenza virus evolution in humans. Bioinformatics 31 (2015), 3546–3548.
88. 88 Grundmann, H., Towards a global antibiotic resistance surveillance system: a primer for a roadmap. Ups. J. Med. Sci. 119 (2014), 87–95.
89. 89 Grenfell, B.T., et al. Unifying the epidemiological and evolutionary dynamics of pathogens. Science 303 (2004), 327–332.
90. 90 Stadler, T., Inferring epidemiological parameters on the basis of allele frequencies. Genetics 188 (2011), 663–672.
91. 91 Luksza, M., et al. Epidemiological and evolutionary analysis of the 2014 Ebola virus outbreak. 2014 arxiv:1411.1722.
92. 92 Stadler, T., Inferring speciation and extinction processes from extant species data. Proc. Natl. Acad. Sci. U.S.A. 108 (2011), 16145–16146.
93. 93 Stack, J.C., et al. Protocols for sampling viral sequences to study epidemic dynamics. J. R. Soc. Interface 7 (2010), 1119–1127.
94. 94 Day, T., Gandon, S., Evolutionary epidemiology of multilocus drug resistance. Evolution 66 (2012), 1582–1587.
95. 95 Pybus, O.G., et al. Evolutionary epidemiology: preparing for an age of genomic plenty. Philos. Trans. R. Soc. Lond. B Biol. Sci., 368, 2013, 20120193.
96. 96 Shukla, J., Predictability in the midst of chaos: a scientific basis for climate forecasting. Science 282 (1998), 728–731.
97. 97 Haldane, J.B.S., Adventures of a Biologist. 1937, Harper & Bros.
98. 98 Day, T., Gandon, S., Applying population-genetic models in theoretical evolutionary epidemiology. Ecol. Lett. 10 (2007), 876–888.
99. 99 Lande, R., Natural-selection and random genetic drift in phenotypic evolution. Evolution 30 (1976), 314–334.
100. 100 Day, T., Proulx, S.R., A general theory for the evolutionary dynamics of virulence. Am. Nat. 163 (2004), E40–E63.