Disease prediction models and operational readiness

PLoS ONE

Volumn 9, Issue 3, 2014, Pages

  Corley, Courtney D ^a   Pullum, Laura L ^b   Hartley, David M ^c   Benedum, Corey ^a   Noonan, Christine ^a   Rabinowitz, Peter M ^d   Lancaster, Mary J ^a  

^a PACIFIC NORTHWEST NATIONAL LABORATORY   (United States)

^b OAK RIDGE NATIONAL LABORATORY   (United States)

^c GEORGETOWN UNIVERSITY MEDICAL CENTER   (United States)

^d YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOSURVEILLANCE; COMPUTER PREDICTION; DATA BASE; DISEASE PREDICTION MODEL; DYNAMICAL MODEL; ECOLOGICAL NICHE; EVENT DETECTION MODEL; EVENT PREDICTION MODEL; HUMAN; REMOTE SENSING; RISK ASSESSMENT; SPATIAL MODEL; STATISTICAL MODEL; SYSTEMATIC REVIEW; VALIDATION STUDY; BIOLOGICAL MODEL; DECISION SUPPORT SYSTEM; DISASTER PLANNING; DISEASES; FORECASTING; META ANALYSIS; REPRODUCIBILITY; STATISTICS;

BIOSURVEILLANCE; DECISION SUPPORT TECHNIQUES; DISASTER PLANNING; DISEASE; FORECASTING; HUMANS; MODELS, BIOLOGICAL; REPRODUCIBILITY OF RESULTS; STATISTICS AS TOPIC;

EID: 84898622920     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0091989     Document Type: Article

References (115)

1. Anderson RM, May RM (1991) Infectious diseases of humans: dynamics and control: Oxford University Press.
2. Boily MC, Masse B (1997) Mathematical models of disease transmission: a precious tool for the study of sexually transmitted diseases. Can J Public Health 88: 255-265.
3. Angulo JJ (1987) Interdisciplinary approaches in epidemic studies-II: Four geographic models of the flow of contagious disease. Soc Sci Med 24: 57-69.
4. Riley S (2007) Large-scale Spatial-transmission Models of Infectious Disease. Science 316: 1298-1301.
5. Perez L, Dragicevic S (2009) An agent-based approach for modeling dynamics of contagious disease spread. Int J Health Geogr 8: 50.
6. Van den Broeck W, Gioannini C, Goncalves B, Quaggiotto M, Colizza V, et al. (2011) The GLEaMviz computational tool, publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infect Dis 11: 50.
7. Miller JC, Slim AC, Volz EM (2012) Edge-based compartmental modelling for infectious disease spread. J Royal Soc Interface 9: 890-906
8. Siettos CI, Russo L (2013) Mathematical modeling of infectious disease dynamics. Virulence 4: 297-306.
9. Brownstein JS, Holford TR, Fish D (2003) A climate-based model predicts the spatial distribution of the Lyme disease vector Ixodes scapularis in the United States. Environmental Health Perspectives 111: 1152-1157. (Pubitemid 36939394)
10. Vasquez-Prokopec GM, Bisanzio D, Stoddard ST, Paz-Soldan V, Morrison AC, et al. (2013) Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS One 8: e58802.
11. Chan EH, Sahai V, Conrad C, Bronstein JS (2011) Using Web Search Query Data to Monitor Dengue Epidemics: A New Model for Neglected Tropical Disease Surveillance. PLoS Negl Trop Dis 5: e1206.
12. Bush RM, Bender CA, Subbarao K, Cox NJ, Fitch WM (1999) Predicting the Evolution of Human Influenza A. Science 286: 1921-1925. (Pubitemid 129515893)
13. Liao Y-C, Lee M-S, Ko C-Y, Hsiung CA (2008) Bioinformatics models for predicting antigenic variants of influenza A/H3N2 virus. Bioinformatics 24: 505-512. (Pubitemid 351256294)
14. Johnson DA, Alldredge JR, Vakoch DL (1996) Potato late blight forecasting models for the semiarid environment of south-central Washington. Phytopathology 86: 480-484. (Pubitemid 126629970)
15. Yuen JE, Hughes G (2002) Bayesian analysis of plant disease prediction. Plant Pathology 51: 407-412. (Pubitemid 34923146)
16. Hashimoto S, Murakami Y, Taniguchi K, Nagaid M (2000) Detection of epidemics in their early stage through infectious disease surveillance. International Journal of Epidemiology 29: 905-910.
17. Jackson C, Vynnycky E, Hawker J, Olowokure B, Mangtani P (2013) School closures and influenza: systematic review of epidemiological studies. BMJ Open 3: e002149.
18. Kawaguchi R, Miyazono M, Noda T, Takayama Y, Sasai Y, et al. (2009) Influenza (H1N1) 2009 outbreak and school closure, Osaka Prefecture, Japan. Emerg Infect Dis 15: 1685.
19. Brown ST, Tai JH, Bailey RR, Cooley PC, Wheaton WD, et al. (2011) Would school closure for the 2009 H1N1 influenza epidemic have been worth the cost? A computational simulation of Pennsylvania. BMC Public Health 11: 353.
20. Ford TE, Colwell RR, Rose JB, Morse SS, Rogers DJ, et al. (2009) Using satellite images of environmental changes to predict infectious disease outbreaks. Emerging Infectious Diseases 15: 1341-1346.
21. de Magny GC, Murtugudde R, Sapiano MRP, Nizam A, Brown CW, et al. (2008) Environmental signatures associated with cholera epidemics. Proceedings of the National Academy of Sciences of the United States of America 105: 17676-17681.
22. Linthicum KJ, Anyamba A, Tucker CJ, Kelley PW, Myers MF, et al. (1999) Climate and satellite indicators to forecast Rift Valley fever epidemics in Kenya. Science 285: 397-400. (Pubitemid 29343959)
23. Corley CD, Lancaster MJ, Brigantic RT, Chung JS, Walters RA, et al. (2012) Assessing the continuum of event-based biosurveillance through an operational lens. Biosecur Bioterror 10: 131-141.
24. Halliday J, Daborn C, Auty H, Mtema Z, Lembo T, et al. (2012) Bringing together emerging and endemic zoonoses surveillance: shared challenges and a common solution. Philosophical Transactions of the Royal Society B: Biological Sciences 367: 2872-2880.
25. Halliday JEB, Meredith AL, Knobel DL, Shaw DJ, Bronsvoort BMDC, et al. (2007) A framework for evaluating animals as sentinels for infectious disease surveillance. Journal of the Royal Society Interface 4: 973-984. (Pubitemid 47523501)
26. Scotch M, Brownstein J, Vegso S, Galusha D, Rabinowitz P (2011) Human vs. Animal Outbreaks of the 2009 Swine-Origin H1N1 Influenza A epidemic. EcoHealth 8: 376-380.
27. Scotch M, Odofin L, Rabinowitz P (2009) Linkages between animal and human health sentinel data. Bmc Veterinary Research 5: 1-9.
28. Woolhouse MEJ, Matthews L, Coen P, Stringer SM, Foster JD, et al. (1999) Population dynamics of scrapie in a sheep flock. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 354: 751-756. (Pubitemid 29365623)
29. Woolhouse M, Gaunt E (2007) Ecological origins of novel human pathogens. Critical Reviews in Microbiology 33: 231-242. (Pubitemid 350146703)
30. Daszak P, Cunningham AA, Hyatt AD (2000) Emerging Infectious Diseases of Wildlife - Threats to Biodiversity and Human Health. Science 287: 443-449.
31. Rabinowitz P, Gordon Z, Chudnov D, Wilcox M, Odofin L, et al. (2006) Animals as sentinels of bioterrorism agents. Emerging Infectious Diseases 12: 647-652.
32. Models PCoI-S, Workshop GfCAtaIOHBS-A, Medicine Io (2012) Information Sharing and Collaboration: Applications to Integrated Biosurveillance: Workshop Summary: The National Academies Press.
33. Daszak P (2009) A Call for "Smart Surveillance": A Lesson Learned from H1N1. EcoHealth 6: 1-2.
34. Lloyd-Smith JO, George D, Pepin KM, Pitzer VE, Pulliam JRC, et al. (2009) Epidemic Dynamics at the Human-Animal Interface. Science 326: 1362-1367.
35. Feighner BH, Eubank S, Glass RJ, Davey VJ, Chrétien JP, et al. (2009) Infectious disease modeling and military readiness. Emerging Infectious Diseases 15: e1.
36. Bravata DM, Sundaram V, McDonald KM, Smith WM, Szeto H, et al. (2004) Evaluating detection and diagnostic decision support systems for bioterrorism response. Emerg Infect Dis 10: 100-108. (Pubitemid 38040700)
37. Rolka H, Burkom H, Cooper GF, Kulldorff M, Madigan D, et al. (2007) Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: Research needs ‡. Statistics in Medicine 26: 1834-1856. (Pubitemid 46455640)
38. Prieto DM, Das TK, Savachkin AA, Uribe A, Izurieta R, et al. (2012) A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels. BMC Public Health 12: 251.
39. Costa J, Peterson AT, Beard CB (2002) Ecologic niche modeling and differentiation of poplations of Triatoma brasiliensis Neiva, 1911, the most important Chagas' disease vector in northeastern Brazil (Hemiptera, Reduviidae, Triatominae). Am J Trop Med Hyg 67: 516-520. (Pubitemid 35403168)
40. Knorr-Held L, Besag J (1998) Modelling risk from a disease in time and space. Stat Med 17: 2045-2060. (Pubitemid 28442014)
41. Jentes ES, Poumerol G, Gershman MD, Hill DR, Lemarchand J, et al. (2011) The revised global yellow fever risk map and recommendations for vaccination, 2010: consensus of the Informal WHO Working Group on Geographic Risk for Yellow Fever. The Lancet Infectious Diseases 11: 622-632.
42. Eisen L, Eisen RJ (2011) Using geographic information systems and decision support systems for the prediction, prevention and control of vector-borne diseases. Annu Rev Entomol 56: 41-61.
43. Tatem AJ, Hay SI, Rogers DJ (2006) Global traffic and disease vector dispersal. Proceedings of the National Academy of Sciences of the United States of America 103: 6242-6247.
44. Matsuda F, Ishimura S, Wagatsuma Y, Higashi T, Hayashi T, et al. (2008) Prediction of epidemic cholera due to Vibrio cholerae O1 in children younger than 10 years using climate data in Bangladesh. Epidemiology and Infection 136: 73-79.
45. Brooker S, Hotez PJ, Bundy DAP (2010) The Global Atlas of Helminth Infection: Mapping the Way Forward in Neglected Tropical Disease Control. PLoS Negl Trop Dis 4: e779.
46. Murray KA, Retallick RWR, Puschendorf R, Skerratt LF, Rosauer D, et al. (2011) Assessing spatial patterns of disease risk to biodiversity: implications for the management of the amphibian pathogen, Batrachochytrium dendrobatidis. Journal of Applied Ecology 49: 163-173.
47. Tatem AJ, Baylis M, Mellor PS, Purse BV, Capela R, et al. (2003) Prediction of bluetongue vector distribution in Europe and north Africa using satellite imagery. Veterinary Microbiology 97: 13-29. (Pubitemid 37456661)
48. Estrada-Peña A, Zatansever Z, Gargili A, Aktas M, Uzun R, et al. (2007) Modeling the spatial distribution of crimean-congo hemorrhagic fever outbreaks in Turkey. Vector Borne & Zoonotic Diseases 7: 667-678.
49. Liccardo A, Fierro A (2013) A lattice model for influenza spreading. PLoS One 8: e63935.
50. Keller M, Blench M, Tolentino H, Freifeld CC, Mandl KD, et al. (2009) Use of unstructured event-based reports for global infectious disease surveillance. Emerg Infect Dis 15: 689-695.
51. Takla A, Velasco E, Benzler J (2012) The FIFA Women's World Cup in Germany 2011: A practical example for tailoring an event-specific enhanced infectious disease surveillance system. BMC Public Health 12: 576.
52. ISO/IEC (2008) ISO/IEC 15288:2008 Systems and software engineering - System life cycle processes. International Organization for Standardization/ International Electrotechnical Commission.
53. IEEE (2012) IEEE Std 1012-2012 IEEE Standard for System and Software Verification and Validation. IEEE Standards Association.
54. ISO/IEC-IEEE (2008) ISO/IEC 12207:2008, IEEE Std 12207-2008 Systems and Software Engineering - Software Life Cycle Processes. International Organization for Standardization/International Electrotechnical Commission and Institute of Electrical and Electronics Engineers.
55. NASA (2008) NASA-STD-7009 Standard for Models and Simulations. National Aeronautics and Space Administration.
56. U.S DoD (2011) Verification, Validation, and Accreditation (VV&A) Recommended Practices Guide (RPG). Modeling & Simulation Coordination Office, U.S. Department of Defense.
57. U.S Army (1998) TRADOC Reg 5-11 U.S. Army Training and Doctrine Command (TRADOC) Models and Simulations (M&S) and Data Management. United States Army Training and Doctrine Command.
58. U.S Navy (2005) Best Practices Guide for Verification, Validation, and Accreditation of Legacy Modeling and Simulation. Department of the Navy, Navy Modeling & Simulation Office.
59. U.S DoD (2008) MIL-STD-3022 Documentation of Verification, Validation & Accreditation (VV&A) for Models and Simulations. U.S. Department of Defense, Modeling and Simulation Coordination Office.
60. Boehm BW (1981) Software Engineering Economics; Yeh RT, editor: Prentice-Hall.
61. Favier C, Chalvet-Monfray K, Sabatier P, Lancelot R, Fontenille D, et al. (2006) Rift Valley fever in West Africa: the role of space in endemicity. Trop Med Int Health 11: 1878-1888. (Pubitemid 44813883)
62. Eisen RJ, Eisen L (2008) Spatial Modeling of human risk of exposure to vector-borne pathogens based on epidemiological versus arthropod vector data. Journal of Medical Entomology 45: 181-192. (Pubitemid 351421725)
63. Margevicius KJ, Generous N, Taylor-McCabe KJ, Brown M, Daniel WB, et al. (2014) Advancing a framework to enable characterization and evaluation of data streams useful for biosurveillance. PLoS ONE 9: e83730.
64. Hartley DM, Barker CM, Le Menach A, Niu T, Gaff HD, et al. (2012) Effects of Temperature on Emergence and Seasonality of West Nile Virus in California. The American Journal of Tropical Medicine and Hygiene 86: 884-894.
65. Pascual M (2000) Cholera Dynamics and El Nino-Southern Oscillation. Science 289: 1766-1769.
66. Pullum LL, Cui X. Techniques and Issues in Agent-Based Model Validation; 2012; Boston, MA.
67. Pullum LL, Cui X. A Hybrid Sensitivity Analysis Approach for Agent-based Disease Spread Models; 2012; Boston, MA.
68. Koopman J (2004) Modeling Infection Transmission. Annual Review of Public Health 25: 303-326.
69. Bernard KW (2013) Health and national security: A contemporary collision of cultures. Biosecurity and Bioterrorism: Biodefense Strategy, Practice, and Science 11: 157-162.
70. Mankins JC (1995) Technology Readiness Levels.
71. Hartley DM (2014) Using social media and other Internet data for public health surveillance: The importance of talking. Milbank Quarterly In press.
72. Métras R, Collins LM, White RG, Alonso S, Chevalier V, et al. (2011) Rift Valley fever epidemiology, surveillance, and control: what have models contributed? Vector-Borne and Zoonotic Diseases 11: 761-771.
73. Pitman R, Fisman D, Zaric GS, Postma M, Kretzschmar M, et al. (2012) Dynamic Transmission Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-5. Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research 15: 828-834.
74. Jewell CP, Kypraios T, Christley RM, Roberts GO (2009) A novel approach to real-time risk prediction for emerging infectious diseases: A case study in Avian Influenza H5N1. Preventive Veterinary Medicine 91: 19-28.
75. Erraguntla M, Ramachandran S, Chang-Nien W, Mayer RJ (2010) Avian Influenza Datamining Using Environment, Epidemiology, and Etiology Surveillance and Analysis Toolkit (E3SAT). 2010 43rd Hawaii International Conference on System Sciences (HICSS-43); Honolulu, HI. IEEE. pp. 7 pp.
76. Hadorn DC, Racloz V, Schwermer H, Stark KDC (2009) Establishing a cost-effective national surveillance system for Bluetongue using scenario tree modelling. Veterinary Research40: Article 57.
77. Hutber AM, Kitching RP, Pilipcinec E (2006) Predictions for the timing and use of culling or vaccination during a foot-and-mouth disease epidemic. Research in Veterinary Science 81: 31-36. (Pubitemid 43323773)
78. Mayer D, Reiczigel J, Rubel F (2008) A Lagrangian particle model to predict the airborne spread of foot-and-mouth disease virus. Atmospheric Environment 42: 466-479.
79. Martínez-López B, Ivorra B, Ramos AM, Sánchez- Vizcaíno JM (2011) A novel spatial and stochastic model to evaluate the within- and between-farm transmission of classical swine fever virus. I. General concepts and description of the model. Veterinary Microbiology 147: 300-309.
80. Rubel F, Fuchs K (2005) A decision-support system for real-time risk assessment of airborne spread of the foot-and-mouth disease virus. Methods Inf Med 44: 590-595. (Pubitemid 41345385)
81. Bos MEH, Van Boven M, Nielen M, Bouma A, Elders ARW, et al. (2007) Estimating the day of highly pathogenic avian influenza (H7N7) virus introduction into a poultry flock based on mortality data. Veterinary Research 38: 493-504. (Pubitemid 350012168)
82. Verdugo C, Cardona CJ, Carpenter TE (2009) Simulation of an early warning system using sentinel birds to detect a change of a low pathogenic avian influenza virus (LPAIV) to high pathogenic avian influenza virus (HPAIV). Preventive Veterinary Medicine 88: 109-119. (Pubitemid 50317945)
83. Mongkolsawat C, Kamchai T (2009) GIS Modeling for Avian Influenza Risk Areas. International Journal of Geoinformatics 5: 7-12.
84. Ortiz-Pelaez A, Pfeiffer DU, Tempia S, Otieno FT, Aden HH, et al. (2010) Risk mapping of Rinderpest sero-prevalence in Central and Southern Somalia based on spatial and network risk factors. Bmc Veterinary Research 6: 22.
85. Racloz V, Venter G, Griot C, Stark KDC (2008) Estimating the temporal and spatial risk of bluetongue related to the incursion of infected vectors into Switzerland. BMC Vet Res 4: 42.
86. Radosavljevic V, Belojevic G (2009) A new model of bioterrorism risk assessment. Biosecur Bioterror 7: 443-451.
87. Purse BV, Baylis M, Tatem AJ, Rogers DJ, Mellor PS, et al. (2004) Predicting the risk of bluetongue through time: climate models of temporal patterns of outbreaks in Israel. Revue Scientifique Et Technique-Office International Des Epizooties 23: 761-775. (Pubitemid 40623355)
88. Cappelle J, Girard O, Fofana B, Gaidet N, Gilbert M Ecological Modeling of the Spatial Distribution of Wild Waterbirds to Identify the Main Areas Where Avian Influenza Viruses are Circulating in the Inner Niger Delta, Mali. EcoHealth ePub: 1-11. In Press.
89. Mubangizi M, Mwebaze E, Quinn JA (2009) Computational Prediction of Cholera Outbreaks; Kampala. ICCIR.
90. Schaafsma AW, Hooker DC (2007) Climatic models to predict occurrence of Fusarium toxins in wheat and maize. Int J Food Microbiol 119: 116-125. (Pubitemid 350051479)
91. Marechal F, Ribeiro N, Lafaye M, Guell A (2008) Satellite imaging and vector-borne diseases: the approach of the French National Space Agency (CNES). Geospatial Health 3: 1-5.
92. Wagner MM (2002) Models of computer-based outbreak detection. The Reference Librarian 39: 343-362.
93. Yamamoto T, Tsutsui T, Nishiguchi A, Kobayashi S (2008) Evaluation of surveillance strategies for bovine brucellosis in Japan using a simulation model. Preventive Veterinary Medicine 86: 57-74.
94. Fichet-Calvet E, Rogers DJ (2009) Risk maps of lassa fever in West Africa. Plos Neglected Tropical Diseases 3: e388.
95. Green AL, Dargatz DA, Herrero MV, Seitzinger AH, Wagner BA, et al. (2005) Risk factors associated with herd-level exposure of cattle in Nebraska, North Dakota, and South Dakota to bluetongue virus. American Journal of Veterinary Research 66: 853-860. (Pubitemid 40812679)
96. Kim DR, Ali M, Thiem VD, Park JK, von Seidlein L, et al. (2008) Geographic analysis of shigellosis in Vietnam. Health and Place 14: 755-767.
97. Kolivras KN, Comrie AC (2003) Modeling valley fever (coccidioidomycosis) incidence on the basis of climate conditions. Int J Biometeorol 47: 87-101. (Pubitemid 37231643)
98. Lipp EK, Huq A, Colwell R (2002) Effects Of Global Climate On Infectious Disease: The Cholera Model. Clinical Microbiology Reviews 15: 757-770. (Pubitemid 41184086)
99. Lockhart CY (2008) Surveillance for diseases of poultry with specific reference to avian influenza: Massey University.
100. Baptista-Rosas RC, Hinojosa A, Riquelme M (2007) Ecological Niche Modeling of Coccidioides spp. in Western North American Deserts. Annals of the New York Academy of Sciences 1111: 35-46. (Pubitemid 47573007)
101. Chhetri BK, Perez AM, Thurmond MC (2010) Factors associated with spatial clustering of foot-and-mouth disease in Nepal. Tropical Animal Health and Production 42: 1441-1449.
102. Cooke III WH, Grala K, Wallis RC (2006) Avian GIS models signal human risk for West Nile virus in Mississippi. International Journal of Health Geographics 5: Article 36.
103. Daniel M, Kolar J, Zeman P, Pavelka K, Sadlo J (1998) Predictive map of Ixodes vicinus high-incidence habitats and a tick-borne encephalitis risk assessment using satellite data. Experimental & Applied Acarology 22: 417-433. (Pubitemid 28329459)
104. Eisen RJ, Griffith KS, Borchert JN, MacMillan K, Apangu T, et al. (2010) Assessing human risk of exposure to plague bacteria in northwestern Uganda based on remotely sensed predictors. American Journal of Tropical Medicine and Hygiene 82: 904-911.
105. Eisen RJ, Reynolds PJ, Ettestad P, Brown T, Enscore RE, et al. (2007) Residence-linked human plague in New Mexico: A habitat-suitability model. American Journal of Tropical Medicine and Hygiene 77: 121-125. (Pubitemid 351844363)
106. Adjemian JCZ, Girvetz EH, Beckett L, oley JE (2006) Analysis of Genetic Algorithm for Rule-Set Production (GARP)Modeling Approach for Predicting Distributions of Fleas Implicatedas Vectors of Plague, Yersinia pestis, in California. Journal of Medical Entomology 43: 93-103. (Pubitemid 43165069)
107. Anyamba A, Chretien J-P, Small J, Tucker CJ, Formenty PB, et al. (2009) Prediction of a Rift Valley fever outbreak. Proceedings of the National Academy of Sciences of the United States of America 106: 955-959.
108. Kleinman K, Lazarus R, Platt R (2004) A generalized linear mixed models approach for detecting incident clusters of disease in small areas, with an application to biological terrorism. Am J Epidemiol 159: 217-224. (Pubitemid 38141649)
109. Munar-Vivas O, Morales-Osorio JG, Castañeda-Sánchez DA (2010) Use of field-integrated information in GIS-based maps to evaluate Moko disease (Ralstonia solanacearum) in banana growing farms in Colombia. Crop Protection 29: 936-941.
110. Hartley DM, Nelson NP, Walters RA, Arthur R, Yangarber R, et al. (2010) The landscape of international event-based biosurveillance. Emerging Health Threats Journal 3: Article e3.
111. Kong X, Wallstrom GL, Hogan WR (2008) A temporal extension of the Bayesian aerosol release detector. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Raleigh, NC. Springer Verlag. pp. 97-107.
112. Lu HM, Zeng D, Chen H (2008) Bioterrorism event detection based on the Markov switching model: A simulated anthrax outbreak study; Taipei, Taiwan.IEEE. pp. 76-81.
113. Nordin JD, Goodman MJ, Kulldorff M, Ritzwoller DP, Abrams AM, et al. (2005) Simulated anthrax attacks and syndromic surveillance. Emerg Infect Dis 11: 1394-1398. (Pubitemid 41209133)
114. Martin PAJ, Cameron A.R., Greiner M. (2006) Demonstrating freedom from disease using multiple complex data sources 1: A new methodology based on scenario trees. Preventive Veterinary Medicine.
115. Kolivras KN (2010) Changes in dengue risk potential in Hawaii, USA, due to climate variability and change. Climate Research 42: 1-11.