Modeling pneumococcal resistance to penicillin in southern Sweden using artificial neural networks

Microbial Drug Resistance

Volumn 12, Issue 3, 2006, Pages 149-157

  Geli, Patricia ^a,b,c   Rolfhamre, Per ^b,c   Almeida, Jonas ^d   Ekdahl, Karl ^c,e,f  

^a STOCKHOLM UNIVERSITY   (Sweden)

^b SWEDISH INSTITUTE FOR INFECTIOUS DISEASE CONTROL   (Sweden)

^c Stockholm Group for Epidemic Modelling   (Sweden)

^d UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^e KAROLINSKA INSTITUTE   (Sweden)

^f EUROPEAN CENTRE FOR DISEASE PREVENTION AND CONTROL ECDC   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIBIOTIC AGENT; PENICILLIN DERIVATIVE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONFOUNDING VARIABLE; CORRELATION ANALYSIS; DATA ANALYSIS; DRUG USE; EPIDEMIC; OBSERVATION; PENICILLIN RESISTANCE; PREDICTION; PRIORITY JOURNAL; RETROSPECTIVE STUDY; SENSITIVITY ANALYSIS; SPACE; STATISTICAL MODEL; STREPTOCOCCUS PNEUMONIAE; SWEDEN; TIME;

HUMANS; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); PENICILLIN RESISTANCE; PNEUMOCOCCAL INFECTIONS; PNEUMONIA, PNEUMOCOCCAL; PREDICTIVE VALUE OF TESTS; SENSITIVITY AND SPECIFICITY; STREPTOCOCCUS PNEUMONIAE; SWEDEN;

STREPTOCOCCUS PNEUMONIAE;

EID: 33751247008     PISSN: 10766294     EISSN: None     Source Type: Journal    
DOI: 10.1089/mdr.2006.12.149     Document Type: Article

References (27)

1. Abidi, S., and A. Goh. 1998. Applying knowledge discovery to predict infectious disease epidemics. Lecture Notes in Artificial Intelligence 1531- PRICAI'98. Springer Verlag, Berlin.
2. Albrich, W.C., D.L. Monnet, and S. Harbarth. 2004. Antibiotic selection pressure and resistance in Streptococcus pneumoniae and Streptococcus pyogenes. Emerg. Infect. Dis. 10:514-517.
3. Almeida, J.S. 2002. Predictive non-linear modeling of complex data by artificial neural networks, Curr. Opin. Biotechnol. 13:72-76.
4. Almeida, J.S., R. Stanislaus, E. Krug, and J. Arthur. 2005. Normalization and analysis of residual variation in 2D gel electrophoresis for quantitative differential proteomics. Proteomics 5:1242-1249.
5. Appelbaum, P.C. 1992. Antimicrobial resistance in Streptococcus pneumoniae: an overview. Clin. Infect. Dis. 15:77-83.
6. nd ed. Springer, New York.
7. Bronzwaer, S.L., O. Cars, U. Buchholz, S. Molstad, W. Goettsch, I.K. Veldhuijzen, J.L. Kool, M.J. Sprenger, and J.E. Degener. 2002. European antimicrobial resistance surveillance system. A European study on the relationship between antimicrobial use and antimicrobial resistance. Emerg. Infect. Dis. 8:278-282.
8. Diekema, D., A. Brueggemann, and G. Doern. 2000. Antibacterial drug use and changes in resistance in Streptococcus pneumoniae. Emerg. Infect. Dis. 6:552-556.
9. Ekdahl, K., and C. Kamme. 1994. Increasing resistance to penicillin in Streptococcus pneumoniae in southern Sweden. Scand. J. Infect. Dis. 26:301-305.
10. Forsgren, A., and M. Walder. 1994. Antimicrobial susceptibility of bacterial isolates in south Sweden including a 13-year follow-up study of some respiratory tract pathogens. APMIS 102:227-235.
11. Garcia, S., and J.S. Almeida. 2005. Nearest neighbor embedding with different time delays. Phys. Rev. E 71:037204.
12. Garcia-Rey, C., L. Aguilar, F. Baquero, J. Casal, and R. Dal-Re. 2002. Importance of local variations in antibiotic consumption and geographical differences of erythromycin and penicillin resistance in Streptococcus pneumoniae. J. Clin. Microbiol. 40:159-164.
13. Henriqus Normark, B., B. Christensson, A. Sandgren, B. Noreen, S. Sylvan, L.G. Burman, and B. Olsson-Liljequist. 2003. Clonal analysis of Streptococcus pneumoniae nonsusceptible to penicillin at day-care centers with index cases, in a region with low incidence of resistance: emergence of an invasive type 35B clone among carriers. Microb. Drug. Resist. 9:337-344.
14. Högberg, L., T. Oke, P. Geli, C.S. Lundborg, O. Cars, and K. Ekdahl. 2005. Reduction in outpatient antibiotic sales for pre-school children: interrupted time series analysis of weekly antibiotic sales data in Sweden 1992-2002. J. Antimicrob. Chemother. 506:208-215.
15. Jansson, A., M. Arneborn, K. Skärlund, and K. Ekdahl. 2004. Timeliness of case reporting in the Swedish statutory surveillance of communicable diseases 1998-2002. Scand. J. Infect. Dis. 36:865-872.
16. Kamme, C., K. Ekdahl, and S. Molstad. 1999. Penicillin-resistant pneumococci in southern Sweden, 1993-1997. Microb. Drug. Resist. 5:31-36.
17. Malmgren, H., M. Borga, and L. Niklasson (eds.). 2000. Artificial neural networks in medicine and biology. Proc. ANNIMAB-1. XII, 352.
18. Marton, A., M. Gulyas, R. Munoz, and A. Tomasz. 1991. Extremely high incidence of antibiotic resistance in clinical isolates of Streptococcus pneumoniae in Hungary. J. Infect. Dis. 163:542-548.
19. Melander, E., K. Ekdahl, H.B. Hansson, C. Kamme, M. Laurell, P. Nilsson, K. Persson, M. Soderstrom, and S. Molstad. 1998. Introduction and clonal spread of penicillin- and trimethoprim/sulfamethoxazole-resistant Streptococcus pneumoniae, serotype 9V, in southern Sweden. Microb. Drug. Resist. 4:71-78.
20. Melander, E., K. Ekdahl, G. Jonsson, and S. Molstad. 2000. Frequency of penicillin-resistant pneumococci in children is correlated to community utilization of antibiotics. Pediatr. Infect. Dis. J. 19:1172-1177.
21. Mueller, M., C.L. Wagner, D.J. Annibale, T.C. Hulsey, R.G. Knapp, and J.S. Almeida. 2004. Predicting extubation outcome in preterm newborns: a comparison of neural networks with clinical expertise and statistical modeling. Pediatr. Res. 56:11-18.
22. Nasrin, D., P.J. Collignon, L. Roberts, E.J. Wilson, L.S. Pilotto, and R.M. Douglas. 2002. Effect of beta lactam antibiotic use in children on pneumococcal resistance to penicillin: prospective cohort study. Br. Med. J. 324:28-30.
23. Olsson-Liljequist, B., L.G. Burman, and I. Kallings. 1992. Antibiotic susceptibility of upper respiratory tract pathogens in Sweden: a seven year follow-up study including loracarbef. Swedish Respiratory Tract Study Group. Scand. J. Infect. Dis. 24:485-493.
24. Statistics Sweden. 2002. National travel survey (NTS). Statistics Sweden. Available from http://www.sika-institute.se/databas/data/ss2002_2.pdf.
25. Voit, E.O., and J.S. Almeida. 2004. Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics 20:1670-1681.
26. Wolf, G., J.S. Almeida, C. Pinheiro, V. Correia, C. Rodrigues, M.A.M. Reis, and J.G. Crespo. 2001. Two-dimensional fluorometry coupled with artificial neural networks: a novel method for on-line monitoring of complex biological processes. Biotechnol. Bioeng. 72:297-306.
27. Wolf, G., J.S. Almeida, M.A.M. Reis, and J.G. Crespo. 2005. Modelling of the extractive membrane bioreactor process based on natural fluorescence fingerprints and process operation history. Water Sci. Tech. 51:51-58.