Use of approximate Bayesian computation to assess and fit models of mycobacterium leprae to predict outcomes of the brazilian control program

PLoS ONE

Volumn 10, Issue 6, 2015, Pages

  Smith, Rebecca Lee ^a   Gröhn, Yrjö Tapio ^b  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b Animal Health Diagnostic Center   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE BAYESIAN COMPUTATION; ARTICLE; BRAZIL; CONTROLLED STUDY; INCIDENCE; INFECTION CONTROL; LEARNING ALGORITHM; LEPROSY; MATHEMATICAL MODEL; MORTALITY; MULTIBACILLARY LEPROSY; MYCOBACTERIUM LEPRAE; PAUCIBACILLARY LEPROSY; PREDICTION; PREVALENCE; ALGORITHM; BAYES THEOREM; COMMUNICABLE DISEASE CONTROL; HUMAN; MICROBIOLOGY; MONTE CARLO METHOD; PROCEDURES; PROGRAM EVALUATION; STATISTICAL MODEL;

MYCOBACTERIUM LEPRAE;

LEPROSTATIC AGENT;

ALGORITHMS; BAYES THEOREM; BRAZIL; COMMUNICABLE DISEASE CONTROL; HUMANS; INCIDENCE; LEPROSTATIC AGENTS; LEPROSY; MODELS, STATISTICAL; MONTE CARLO METHOD; MYCOBACTERIUM LEPRAE; PROGRAM EVALUATION;

EID: 84939145884     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0129535     Document Type: Article

References (28)

1. Bennett B, Parker DL, Robson M. Leprosy: Steps along the journey of eradication. Public Heal Rep. University of Medicine and Dentistry of New Jersey School of Public Health, Piscataway, NJ, USA. 2008; 123: 198-205.
2. Goulart LR, Goulart IMB. Leprosy pathogenetic background: A review and lessons from other mycobacterial diseases. Arch Dermatol Res. 2009; 301: 123-137.
3. Saunderson PR. Leprosy elimination: Not as straightforward as it seemed. Public Heal Rep. American Leprosy Missions, Greenville, SC 29601, USA. 2008; 123: 213-216.
4. Leprosy fact sheet (revised in February 2010). Wkly Epidemiol Rec. 2010; 85: 46-48.
5. Penna MLF, de Oliveira MLW, Penna GO. The epidemiological behaviour of leprosy in Brazil. Lepr Rev. Consultant to the Brazilian National Hansen's Disease Control Program, Secretariat of Health Surveillance, Federal Ministry of Health, Rio de Janeiro, Brazil. 2009; 80: 332-344. Available: http://www.ncbi.nlm.nih.gov/pubmed/19961107.
6. IDB Brasil. Indicadores e Dados Básicos-Brasil [Internet]. 2012 [cited 7 Feb 2014]. Available: http://tabnet.datasus.gov.br/cgi/idb2012/matriz.htm.
7. Misra R. Leprosy: A Reference Guide For Medical Practitioners, Programme Managers And Leprosy Workers. Concept Publishing Company; 1993.
8. Lechat M, Misson J, Vellut C, Misson C, Bouckaert A. Un modèle épidémiométrique de la lèpre. Bull World Health Organ. 1974; 51: 361-373. Available: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2366301/PMID: 4549490.
9. Meima A, Gupte MD, van Oortmarssen GJ, Habbema JDF. SIMLEP: A simulation model for leprosy transmission and control. Int J Lepr other Mycobact Dis. International Journal of Leprosy; 1999; 67: 215-236. Available: http://www-fgg.eur.nl/medbib/EUR-diss/040428-Meima-A/04.pdf.
10. Mushayabasa S, Bhunu CP, Dhlamini M. Understanding non-compliance with WHO multidrug therapy among leprosy patients: Insights from a mathematical model. In: Mushayabasa S, Bhunu C, editors. Understanding the Dynamics of Emerging and Re-Emerging Infectious Diseases using mathematical models. Kerala, India: Transworld Research Network; 2012. pp. 1-22. Available: http://www.trnres.com/ebook/uploads/steadycontent/T-1354259023Steady1.pdf.
11. Mushayabasa S, Bhunu CP. Modelling the effects of chemotherapy and relapse on the transmission dynamics of leprosy. Math Sci. 2012; 6: 12. doi: 10.1186/2251-7456-6-12.
12. Chiyaka ET. Theoretical Assessment of the Transmission Dynamics of Leprosy. Appl Math. 2013; 04: 387-401. doi: 10.4236/am.2013.42059.
13. Meima A, Irgens LM, van Oortmarssen GJ, Richardus JH, Habbema JDF. Disappearance of leprosy from Norway: An exploration of critical factors using an epidemiological modelling approach. Int J Epidemiol. 2002; 31: 991-1009. PMID: 12435774.
14. Blok DJ, de Vlas SJ, Fischer EAJ, Richardus JH. Mathematical Modelling of Leprosy and Its Control. Adv Parasitol. 2015; 87: 33-51. doi:10.1016/bs.apar.2014.12.002 PMID: 25765193.
15. Gillis TP. Is there a role for a vaccine in leprosy control ? Lepr Rev. 2010; 78: 338-342. Available: http://www.ncbi.nlm.nih.gov/pubmed/18309707.
16. Nery JS, Pereira SM, Rasella D, Penna MLF, Aquino R, Rodrigues LC, et al. Effect of the brazilian conditional cash transfer and primary health care programs on the new case detection rate of leprosy. PLoS Negl Trop Dis. 2014; 8: E3357. doi:10.1371/journal.pntd.0003357 PMID: 25412418.
17. Lu Z, Mitchell RM, Smith RL, Karns JS, Van Kessel JS, Wolfgang DR, et al. Invasion and transmission of Salmonella Kentucky in an adult dairy herd using approximate Bayesian computation. BMC Vet Res. 2013; 9: 245. doi:10.1186/1746-6148-9-245 PMID: 24304969.
18. Conlan AJK, McKinley TJ, Karolemeas K, Pollock EB, Goodchild A V, Mitchell AP, et al. Estimating the hidden burden of bovine tuberculosis in Great Britain. PLoS Comput Biol. 2012; 8: E1002730. doi:10.1371/journal.pcbi.1002730PMID: 23093923.
19. Cook AR, McKinley TJ, Deardon R. Inference in Epidemic Models without Likelihoods. Int J Biostat. 2009; 5: 24. doi: 10.2202/1557-4679.1171.
20. Toni T, Stumpf MPH. Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics. 2010; 26: 104-110. doi:10.1093/bioinformatics/btp619 PMID: 19880371.
21. Liepe J, Kirk P, Filippi S, Toni T, Barnes CP, Stumpf MPH. A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation. Nat Protoc. Nature Publishing Group; 2014; 9: 439-56. doi: 10.1038/nprot.2014.025.
22. Toni T, Welch PD, Strelkowa N, Ipsen A, Stumpf MPH. Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J R Soc Interface. 2009; 6: 187-202. PMID: 19205079.
23. Beaumont MA, Zhang W, Balding DJ. Approximate Bayesian computation in population genetics. Genetics. 2002; 162: 2025-2035. Available: http://www.genetics.org/content/162/4/2025.short PMID: 12524368.
24. WHO. Eliminate Leprosy as a Public Health Problem [Internet]. 1st ed. Leprosy Elimination Group, editor. Geneva: World Health Organization; 2000. Available: http://www.who.int/lep/resources/Guide-Int-E.pdf?ua=1.
25. Brooks-Pollock E, Roberts GO, Keeling MJ. A dynamic model of bovine tuberculosis spread and control in Great Britain. Nature. Nature Publishing Group; 2014; 511: 228-231. doi:10.1038/nature13529.
26. Robert CP, Cornuet J-M, Marin J-M, Pillai NS. Lack of confidence in approximate Bayesian computation model choice. Proc Natl Acad Sci. 2011; 108: 15112-15117. doi:10.1073/pnas.1102900108 PMID: 21876135.
27. Barnes CP, Filippi S, Stumpf MPH, Thorne T. Considerate approaches to constructing summary statistics for ABC model selection. Stat Comput. 2012; 22: 1181-1197. doi: 10.1007/s11222-012-9335-7.
28. Queiroz JW, Dias GH, Nobre ML, De Sousa Dias MC, Araújo SF, Barbosa JD, et al. Geographic information systems and applied spatial statistics are efficient tools to study Hansen's disease (leprosy) and to determine areas of greater risk of disease. Am J Trop Med Hyg. Health Post-Graduate Program, Department of Internal Medicine, Health Science Center; Department of Biochemistry, Bioscience Center, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil. jwq@supercabo.com.bn; 2010; 82: 306-14. doi:10.4269/ajtmh.2010.08-0675.