{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T19:22:25Z","timestamp":1777922545780,"version":"3.51.4"},"reference-count":0,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2006,9,1]],"date-time":"2006-09-01T00:00:00Z","timestamp":1157068800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Microbial Drug Resistance"],"published-print":{"date-parts":[[2006,9]]},"abstract":"<jats:p>In recent decades, penicillin-resistant pneumococci (PRP) have emerged and spread rapidly between and\nwithin countries over the world. In this study we developed an iterative artificial neural network (ANN) model\nto describe and predict the spread of PRP in space and time as a function of antibiotic consumption and a\nnumber of different confounders. Retrospective data from 1997 to 2000 on an international epidemic PRP\nclone (serotype 9V) and antibiotic consumption data from Southern Sweden were used to train the ANN models\nand data from 2001 to 2003 for evaluation of the model predictions. Five different ANN models were\ntrained, each with independent topology optimization for alternative sets of input variables to find the most\ndescriptive model. The model containing all variables was the only one performing better than the reference\nlinear models, as assessed by the correlation between predictions and observations. The inability to identify\na smaller subset of most predictive parameters may reflect either diffuse causal mechanisms or just the absence\nof critical experimental indicators from the dataset. The iterative ANN model identified is useful to predict\nfuture data. The sensitivity analysis of the model suggests that past incidence has a small effect on the\nnumber of PRP cases.<\/jats:p>","DOI":"10.1089\/mdr.2006.12.149","type":"journal-article","created":{"date-parts":[[2006,9,26]],"date-time":"2006-09-26T10:30:33Z","timestamp":1159266633000},"page":"149-157","source":"Crossref","is-referenced-by-count":8,"title":["Modeling Pneumococcal Resistance to Penicillin in Southern Sweden Using Artificial Neural Networks"],"prefix":"10.1177","volume":"12","author":[{"given":"Patricia","family":"Geli","sequence":"first","affiliation":[{"name":"Division of Mathematical Statistics, Stockholm University, Stockholm, Sweden."},{"name":"Department of Epidemiology, Swedish Institute for Infectious Disease Control (SMI), Stockholm, Sweden."},{"name":"Stockholm Group for Epidemic Modeling, Stockholm, Sweden."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Per","family":"Rolfhamre","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Swedish Institute for Infectious Disease Control (SMI), Stockholm, Sweden."},{"name":"Stockholm Group for Epidemic Modeling, Stockholm, Sweden."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonas","family":"Almeida","sequence":"additional","affiliation":[{"name":"Department for Biostatistics and Applied Mathematics, University of Texas M.D. Anderson Cancer Center, Texas."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karl","family":"Ekdahl","sequence":"additional","affiliation":[{"name":"Stockholm Group for Epidemic Modeling, Stockholm, Sweden."},{"name":"Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden."},{"name":"European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2006,9]]},"container-title":["Microbial Drug Resistance"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/mdr.2006.12.149","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1089\/mdr.2006.12.149","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T20:23:28Z","timestamp":1777667008000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.1089\/mdr.2006.12.149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,9]]},"references-count":0,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2006,9]]}},"alternative-id":["10.1089\/mdr.2006.12.149"],"URL":"https:\/\/doi.org\/10.1089\/mdr.2006.12.149","relation":{},"ISSN":["1076-6294","1931-8448"],"issn-type":[{"value":"1076-6294","type":"print"},{"value":"1931-8448","type":"electronic"}],"subject":[],"published":{"date-parts":[[2006,9]]}}}