{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T23:04:23Z","timestamp":1774134263148,"version":"3.50.1"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"vor","delay-in-days":247,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Antibiotics are the major tool for treating bacterial infections. Rising antibiotic resistance, however, calls for a better use of antibiotics. While classical recommendations favor long and aggressive treatments, more recent clinical trials advocate for moderate regimens. In this debate, two axes of \u2018aggression\u2019 have typically been conflated: treatment intensity (dose) and treatment duration. The third dimension of treatment timing along each individual\u2019s infection course has rarely been addressed. By using a generic mathematical model of bacterial infection controlled by immune response, we examine how the relative effectiveness of antibiotic treatment varies with its timing, duration and antibiotic kill rate. We show that short or long treatments may both be beneficial depending on treatment onset, the target criterion for success and on antibiotic efficacy. This results from the dynamic trade-off between immune response build-up and resistance risk in acute, self-limiting infections, and uncertainty relating symptoms to infection variables. We show that in our model early optimal treatments tend to be \u2018short and strong\u2019, while late optimal treatments tend to be \u2018mild and long\u2019. This suggests a shift in the aggression axis depending on the timing of treatment. We find that any specific optimal treatment schedule may perform more poorly if evaluated by other criteria, or under different host-specific conditions. Our results suggest that major advances in antibiotic stewardship must come from a deeper empirical understanding of bacterial infection processes in individual hosts. To guide rational therapy, mathematical models need to be constrained by data, including a better quantification of personal disease trajectory in humans.<\/jats:p>\n                  <jats:p>Lay summary: Bacterial infections are becoming more difficult to treat worldwide because bacteria are becoming resistant to the antibiotics used. Addressing this problem requires a better understanding of how treatment along with other host factors impact antibiotic resistance. Until recently, most theoretical research has focused on the importance of antibiotic dosing on antibiotic resistance, however, duration and timing of treatment remain less explored. Here, we use a mathematical model of a generic bacterial infection to study three aspects of treatment: treatment dose\/efficacy (defined by the antibiotic kill rate), duration, and timing, and their impact on several infection endpoints. We show that short and long treatment success strongly depends on when treatment begins (defined by the symptom threshold), the target criterion to optimize, and on antibiotic efficacy. We find that if administered early in an infection, \u201cstrong and short\u201d therapy performs better, while if treatment begins at higher bacterial densities, a \u201cmild and long\u201d course of antibiotics is favored. In the model host immune defenses are key in preventing relapses, controlling antibiotic resistant bacteria and increasing the effectiveness of moderate intervention. In order to improve rational treatments of human infections, we call for a better quantification of individual disease trajectories in bacteria-immunity space.<\/jats:p>","DOI":"10.1093\/emph\/eoaa033","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T15:12:44Z","timestamp":1598454764000},"page":"249-263","source":"Crossref","is-referenced-by-count":16,"title":["Treatment timing shifts the benefits of short and long antibiotic treatment over infection"],"prefix":"10.1093","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0493-3102","authenticated-orcid":false,"given":"Erida","family":"Gjini","sequence":"first","affiliation":[{"name":"Mathematical Modeling of Biological Processes Laboratory, Instituto Gulbenkian de Ci\u00eancia, Rua da Quinta Grande, 6, Oeiras, 2780-156, Portugal"}]},{"given":"Francisco F S","family":"Paup\u00e9rio","sequence":"additional","affiliation":[{"name":"Mathematical Modeling of Biological Processes Laboratory, Instituto Gulbenkian de Ci\u00eancia, Rua da Quinta Grande, 6, Oeiras, 2780-156, Portugal"},{"name":"Departamento de Inform\u00e1tica, Faculdade de Ci\u00eancias, Universidade de Lisboa, Campo Grande, Lisbon, 1749-016, Portugal"}]},{"given":"Vitaly V","family":"Ganusov","sequence":"additional","affiliation":[{"name":"Department of Microbiology, University of Tennessee, Knoxville, TN 37996, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,11,23]]},"reference":[{"key":"2020113013264692600_eoaa033-B1","doi-asserted-by":"crossref","first-page":"S122","DOI":"10.1038\/nm1145","article-title":"Antibacterial resistance worldwide: causes, challenges and responses","volume":"10","author":"Levy","year":"2004","journal-title":"Nat Med"},{"key":"2020113013264692600_eoaa033-B2","doi-asserted-by":"crossref","first-page":"e1004857","DOI":"10.1371\/journal.pcbi.1004857","article-title":"Integrating antimicrobial therapy with host immunity to fight drug-resistant infections: classical vs. adaptive treatment","volume":"12","author":"Gjini","year":"2016","journal-title":"PLoS Comput Biol"},{"key":"2020113013264692600_eoaa033-B3","doi-asserted-by":"publisher","author":"Acosta","year":"2020","DOI":"10.1093\/emph\/eoaa016"},{"key":"2020113013264692600_eoaa033-B4","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/nrmicro3380","article-title":"Molecular mechanisms of antibiotic resistance","volume":"13","author":"Blair","year":"2015","journal-title":"Nat Rev Microbiol"},{"key":"2020113013264692600_eoaa033-B5","doi-asserted-by":"crossref","first-page":"e1004689","DOI":"10.1371\/journal.pcbi.1004689","article-title":"Does high-dose antimicrobial chemotherapy prevent the evolution of resistance?","volume":"12","author":"Day","year":"2016","journal-title":"PLoS Comput Biol"},{"key":"2020113013264692600_eoaa033-B6","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1038\/nrmicro.2017.75","article-title":"Prediction of antibiotic resistance: time for a new preclinical paradigm","volume":"15","author":"Sommer","year":"2017","journal-title":"Nat Rev Microbiol"},{"key":"2020113013264692600_eoaa033-B7","doi-asserted-by":"crossref","DOI":"10.1128\/mBio.00186-18","article-title":"Rapid growth of uropathogenic Escherichia coli during human urinary tract infection","volume":"9","author":"Forsyth","year":"2018","journal-title":"mBio"},{"key":"2020113013264692600_eoaa033-B8","doi-asserted-by":"crossref","first-page":"i939","DOI":"10.1136\/bmj.i939","article-title":"Global prevalence of antibiotic resistance in paediatric urinary tract infections caused by Escherichia coli and association with routine use of antibiotics in primary care: systematic review and meta-analysis","volume":"352","author":"Bryce","year":"2016","journal-title":"BMJ"},{"key":"2020113013264692600_eoaa033-B9","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1086\/605630","article-title":"Hospital and societal costs of antimicrobial-resistant infections in a chicago teaching hospital: implications for antibiotic stewardship","volume":"49","author":"Roberts","year":"2009","journal-title":"Clin Infect Dis"},{"key":"2020113013264692600_eoaa033-B10","doi-asserted-by":"crossref","DOI":"10.1128\/IAI.00636-17","article-title":"What is a host? 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