Mathematical modelling for antibiotic resistance control policy: do we know enough?
| Autor: | Katherine E. Atkins, Sonja Lehtinen, Marc Lipsitch, Elizabeth J. Klemm, Julie V. Robotham, Martin J. Llewelyn, Dov J. Stekel, Francesc Coll, Rebecca E Glover, Mark Jit, Laith Yakob, Jodi A. Lindsay, Mike Sharland, Danna R. Gifford, Nicholas G Davies, Ana Mateus, Tjibbe Donker, Caroline Colijn, Gwenan M. Knight |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Modern medicine Computer science Debate 030106 microbiology Control (management) Decision Making Resistance (psychoanalysis) Safeguarding Antimicrobial resistance (AMR) lcsh:Infectious and parasitic diseases Anti-Bacterial Agents/pharmacology 03 medical and health sciences Antibiotic resistance Humans lcsh:RC109-216 Antibiotic resistance (ABR) Health Policy Drug Resistance Microbial Models Theoretical 3. Good health Anti-Bacterial Agents Dilemma 030104 developmental biology Infectious Diseases Harm Risk analysis (engineering) Spite Drug Resistance Microbial/drug effects Decision-making Dynamic modelling |
| Zdroj: | Knight, G M, Davies, N G, Colijn, C, Coll, F, Donker, T, Gifford, D R, Glover, R E, Jit, M, Klemm, E, Lehtinen, S, Lindsay, J A, Lipsitch, M, Llewelyn, M J, Mateus, A L P, Robotham, J V, Sharland, M, Stekel, D, Yakob, L & Atkins, K E 2019, ' Mathematical modelling for antibiotic resistance control policy : Do we know enough? ', BMC Infectious Diseases, vol. 19, no. 1, 1011 . https://doi.org/10.1186/s12879-019-4630-y BMC Infectious Diseases Knight, G M, Davies, N G, Colijn, C, Coll, F, Donker, T, Gifford, D R, Glover, R E, Jit, M, Klemm, E, Lehtinen, S & Atkins, K 2019, ' Mathematical modelling for antibiotic resistance control policy: do we know enough? ', BMC Infectious Diseases, vol. 19, no. 1 . https://doi.org/10.1186/s12879-019-4630-y BMC Infectious Diseases, Vol 19, Iss 1, Pp 1-9 (2019) |
| ISSN: | 1471-2334 |
| DOI: | 10.1186/s12879-019-4630-y |
| Popis: | Background Antibiotics remain the cornerstone of modern medicine. Yet there exists an inherent dilemma in their use: we are able to prevent harm by administering antibiotic treatment as necessary to both humans and animals, but we must be mindful of limiting the spread of resistance and safeguarding the efficacy of antibiotics for current and future generations. Policies that strike the right balance must be informed by a transparent rationale that relies on a robust evidence base. Main text One way to generate the evidence base needed to inform policies for managing antibiotic resistance is by using mathematical models. These models can distil the key drivers of the dynamics of resistance transmission from complex infection and evolutionary processes, as well as predict likely responses to policy change in silico. Here, we ask whether we know enough about antibiotic resistance for mathematical modelling to robustly and effectively inform policy. We consider in turn the challenges associated with capturing antibiotic resistance evolution using mathematical models, and with translating mathematical modelling evidence into policy. Conclusions We suggest that in spite of promising advances, we lack a complete understanding of key principles. From this we advocate for priority areas of future empirical and theoretical research. |
| Databáze: | OpenAIRE |
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