Open Access
Issue |
Math. Model. Nat. Phenom.
Volume 18, 2023
|
|
---|---|---|
Article Number | 33 | |
Number of page(s) | 31 | |
Section | Population dynamics and epidemiology | |
DOI | https://doi.org/10.1051/mmnp/2023037 | |
Published online | 30 November 2023 |
- D.I Andersson and D. Hughes, Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8 (2010) 260–271. [CrossRef] [PubMed] [Google Scholar]
- C. Andreu-Vilarroig, J. Ceberio, J.-C. Cortés, F.F. de Vega, J.-I. Hidalgo and R.-J Villanueva, Evolutionary approach to model calibration with uncertainty: an application to breast cancer growth model. Proc. Genet. Evol. Comput. Conf. Companion (2022) 1895–1901. [Google Scholar]
- B. Aslam, W. Wang, M.I. Arshad, M. Khurshid, S. Muzammil, M.H. Rasool, M.A. Nisar, R.F. Alvi, M.A. Aslam, M.U. Qamar, et al., Antibiotic resistance: a rundown of a global crisis. Infect. Drug Resist. 11 (2018) 1645. [CrossRef] [Google Scholar]
- S. Banisch, Markov Chain Aggregation for Agent-based Models. Springer (2015). [Google Scholar]
- B.G. Bell, F. Schellevis, E. Stobberingh, H. Goossens and M. Pringle, A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC Infect. Dis. 14 (2014) 1–25. [CrossRef] [Google Scholar]
- F. Blanquart, S. Lehtinen, M. Lipsitch and C. Fraser, The evolution of antibiotic resistance in a structured host population. J. Roy. Soc. Interface 15 (2018) 20180040. [CrossRef] [PubMed] [Google Scholar]
- E. Bonabeau, Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. U.S.A. 99 (2002) 7280–7287. [CrossRef] [PubMed] [Google Scholar]
- S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press (2004). [Google Scholar]
- F. Brauer, Compartmental models in epidemiology. Math. Epidemiol. 1945 (2008) 19–79. [CrossRef] [Google Scholar]
- F. Brauer, Mathematical epidemiology: Past, present, and future. Infect. Dis. Model. 2 (2017) 113–127. [Google Scholar]
- L.D. Brown, T.T. Cai and A. DasGupta, Interval estimation for a binomial proportion. Stat. Sci. (2001) 16 101–117. [CrossRef] [Google Scholar]
- C. Burgos, J.-C. Cortés, D. Martínez-Rodríguez and R.-J. Villanueva, Computational modeling with uncertainty of frequent users of e-commerce in Spain using an age-group dynamic nonlinear model with varying size population. Adv. Complex Syst. 22 (2019) 1950009. [CrossRef] [Google Scholar]
- G. Casella and R.L. Berger, Statistical Inference. Cengage Learning (2021). [Google Scholar]
- J. Chastre, Infections due to Acinetobacter baumannii in the ICU, in Seminars in Respiratory and Critical Care Medicine, Vol. 24. Thieme Medical Publishers, Inc., New York (2003) 069–078. [CrossRef] [Google Scholar]
- W. Chen, Host innate immune responses to Acinetobacter baumannii infection. Front. Cell. Infect. Microbiol. 10 (2020). [Google Scholar]
- C.A. Coello Coello and M.S. Lechuga, MOPSO: a proposal for multiple objective particle swarm Optimization, in Proceedings of the 2002 Congress on Evolutionary Computation CEC’02. IEEE (2002). [Google Scholar]
- M.L. Cohen, Epidemiology of drug resistance: implications for a post-antimicrobial era. Science 257 (1992) 1050–1055. [CrossRef] [PubMed] [Google Scholar]
- G.J. Da Silva and S. Domingues, Interplay between colistin resistance, virulence and fitness in Acinetobacter baumannii. Antibiotics 6 (2017) 28. [CrossRef] [PubMed] [Google Scholar]
- N.G. Davies, S. Flasche, M. Jit and K.E. Atkins, Within-host dynamics shape antibiotic resistance in commensal bacteria. Nat. Ecol. Evol. 3 (2019) 440–449. [CrossRef] [Google Scholar]
- T.N. Doan, D.C.M. Kong, C. Marshall, C.M.J. Kirkpatrick and E.S. McBryde, Modeling the impact of interventions against Acinetobacter baumannii transmission in intensive care units. Virulence 7 (2016) 141–152. [CrossRef] [PubMed] [Google Scholar]
- G.M. Eliopoulos, L.L. Maragakis and T.M. Perl, Acinetobacter baumannii: epidemiology, antimicrobial resistance, and treatment options. Clin. Infect. Dis. 46 (2008) 1254–1263. [CrossRef] [PubMed] [Google Scholar]
- P. Espinal, S. Martí and J. Vila, Effect of biofilm formation on the survival of Acinetobacter Baumannii on dry surfaces. J. Hosp. Infect. 80 (2012) 56–60. [CrossRef] [Google Scholar]
- European Center for Disease Prevention and Control (ECDC). Antimicrobial consumption database (ESAC-Net). https://www.ecdc.europa.eu/en/antimicrobial-consumption/surveillance-and-disease-data/database [Accessed: 25/11/2022]. [Google Scholar]
- V. Grimm, U. Berger, D.L. DeAngelis, J.G. Polhill, J. Giske and S.F. Railsback, The odd protocol: a review and first update. Ecol. Model. 221 (2010) 2760–2768. [CrossRef] [Google Scholar]
- The Julia Programming Language. https://julialang.org/ [Accessed: 25/11/2022]. [Google Scholar]
- J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95-International Conference on Neural Networks, Vol. 4. IEEE (1995) 1942–1948. [CrossRef] [Google Scholar]
- N. Khemka and C. Jacob, Exploratory toolkit for evolutionary and swarm-based optimization. Math. J. 11 (2008) 376. [Google Scholar]
- M.-F. Lin and C.-Y. Lan, Antimicrobial resistance in Acinetobacter baumannii: from bench to bedside. World J. Clin. Cases 2 (2014) 787. [CrossRef] [Google Scholar]
- F.D. Lowy, Antimicrobial resistance: the example of Staphylococcus aureus. J. Clin. Invest. 111 (2003) 1265–1273. [CrossRef] [PubMed] [Google Scholar]
- L.L. Maragakis, E.N. Perencevich and S.E. Cosgrove, Clinical and economic burden of antimicrobial resistance. Expert Rev. Anti-infective Ther. 6 (2008) 751–763. [CrossRef] [PubMed] [Google Scholar]
- F. Marini and B. Walczak, Particle swarm optimization (PSO). A tutorial. Chemometrics Intell. Lab. Syst. 149 (2015) 153–165. [CrossRef] [Google Scholar]
- D. Molina, A. LaTorre and F. Herrera, An insight into bio-inspired and evolutionary algorithms for global optimization: review, analysis, and lessons learnt over a decade of competitions. Cogn. Comput. 10 (2018) 517–544. [CrossRef] [Google Scholar]
- J.A. Nelder and R. Mead, A simplex method for function minimization. Comput. J. 7 (1965) 308–313. [Google Scholar]
- J. O’Neill, Antimicrobial resistance: tackling a crisis for the health and wealth of nations. Rev. Antimicrob. Resist. 1 (2014) [Accessed: 25/11/2022]. [Google Scholar]
- L. Opatowski, D. Guillemot, P.-Y. Boëlle and L. Temime, Contribution of mathematical modeling to the fight against bacterial antibiotic resistance. Curr. Opin. Infect. Dis. 24 (2011) 279–287. [CrossRef] [PubMed] [Google Scholar]
- Puerta de Hierro Health Research Institute – Segovia de Arana. Prevalence Study of Nosocomial Diseases in Spain (EPINE). https://epine.es/resultados/espania [Accessed: 25/11/2022]. [Google Scholar]
- Python. https://www.python.org/ [Accessed: 25/11/2022]. [Google Scholar]
- P. Renard, A. Alcolea and D. Ginsbourger, Stochastic versus deterministic approaches, in Environmental Modelling. John Wiley & Sons, Ltd (2013) 133–149. [CrossRef] [Google Scholar]
- I. Roca, M. Akova, F. Baquero, J. Carlet, M. Cavaleri, S. Coenen, J. Cohen, D. Findlay, I. Gyssens, O.E. Heure, et al., The global threat of antimicrobial resistance: science for intervention. New Microbes New Infect. 6 (2015) 22–29. [CrossRef] [Google Scholar]
- J. Rodríguez-Baño, J.M. Cisneros, F. Fernández-Cuenca, A. Ribera, J. Vila, A. Pascual, L. Martínez-Martínez, G. Bou, J. Pachón, Grupo de Estudio de Infección Hospitalaria, et al., Clinical features and epidemiology of acinetobacter baumannii colonization and infection in Spanish hospitals. Infect. Control Hosp. Epidemiol. 25 (2004) 819–824. [CrossRef] [PubMed] [Google Scholar]
- M. Sassone-Corsi and M. Raffatellu, No vacancy: how beneficial microbes cooperate with immunity to provide colonization resistance to pathogens. J. Immunol. 194 (2015) 4081–4087. [CrossRef] [PubMed] [Google Scholar]
- T.T. Soong, Random Differential Equations in Science and Engineering (1973). [Google Scholar]
- Spanish Government. Instituto Nacional de Estadística (INE), https://www.ine.es/en/index.htm [Accessed: 25/11/2022]. [Google Scholar]
- Spanish Ministry of Health, Statistical Portal. Management Intelligence Area. https://pestadistico.inteligenciadegestion.mscbs.es/publicoSNS/N/siae [Accessed: 25/11/2022]. [Google Scholar]
- M. Sundqvist, Reversibility of antibiotic resistance. Upsala J. Med. Sci. 119 (2014) 142–148. [CrossRef] [PubMed] [Google Scholar]
- M. Sundqvist, P. Geli, D.I. Andersson, M. Sjölund-Karlsson, A. Runehagen, H. Cars, K. Abelson-Storby, O. Cars and G. Kahlmeter, Little evidence for reversibility of trimethoprim resistance after a drastic reduction in trimethoprim use. J. Antimicrob. Chemother. 65 (2010) 350–360. [CrossRef] [PubMed] [Google Scholar]
- R.H. Sunenshine, M.-O. Wright, L.L. Maragakis, A.D. Harris, X. Song, J. Hebden, S.E. Cosgrove, A. Anderson, J. Carnell, D.B. Jernigan, et al., Multidrug-resistant Acinetobacter infection mortality rate and length of hospitalization. Emerg. Infect. Dis. 13 (2007) 97. [CrossRef] [PubMed] [Google Scholar]
- F.C. Tenover, Mechanisms of antimicrobial resistance in bacteria. Am. J. Med. 119 (2006) S3–S10. [CrossRef] [Google Scholar]
- K.A. Thom, C. Rock, S.S. Jackson, J.K. Johnson, A. Srinivasan, L. Magder, M.-C. Roghmann, R.A. Bonomo and A.D. Harris, Factors leading to transmission risk of Acinetobacter baumannii. Crit. Care Med. 45 (2017) e633. [CrossRef] [PubMed] [Google Scholar]
- Valencian Government, Microbiological Surveillance Network of the Valencian Community (REDMIVA). https://www.sp.san.gva.es/sscc/opciones2.jsp?CodPor=121&0pcion=SANMS513000&CodPunto=1601&MenuSup=SANMS50000&Nivel=1 [Accessed: 25/11/2022]. [Google Scholar]
- Valencian Government. Statistical Portal. Population estimates. https://pegv.gva.es/es/estimaciones-de-poblacion-de-la-comunitat-valenciana [Accessed: 25/11/2022]. [Google Scholar]
- R.-J. Villanueva, J.I. Hidalgo, C. Cervigón, J. Villanueva-Oller and J.-C. Cortés, Calibration of an agent-based simulation model to the data of women infected by human papillomavirus with uncertainty. Appl. Soft Comput. 80 (2019) 546–556. [CrossRef] [Google Scholar]
- X. Wang, Y. Chen, W. Zhao, Y. Wang, Q. Song, H. Liu, J. Zhao, X. Han, X. Hu, H. Grundmann, et al., A data-driven mathematical model of multi-drug resistant Acinetobacter baumannii transmission in an intensive care unit. Sci. Rep. 5 (2015) 1–8. [CrossRef] [Google Scholar]
- C. Wendt, B. Dietze, E. Dietz and H. Ruoden, Survival of Acinetobacter baumannii on dry surfaces. J. Clin. Microbiol. 35 (1997) 1394–1397. [CrossRef] [PubMed] [Google Scholar]
- R. Wise, Antimicrobial resistance: priorities for action. J. Antimicrob. Chemother. 49 (2002) 585–586. [CrossRef] [PubMed] [Google Scholar]
- World Health Organization (WHO), Antimicrobial resistance. https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance (2020) [Accessed: 25/11/2022]. [Google Scholar]
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