On the Epidemiological Evolution of Colistin-Resistant Acinetobacter Baumannii in the City of Valencia: An Agent-Based Modelling Approach

Juan A. Aledo¹ ; Carlos Andreu-Vilarroig² ; Juan-Carlos Cortés² ; Juan C. Orengo^3, 4 ; Rafael-Jacinto Villanueva²

¹ Departamento de Matemáticas, Universidad de Castilla-La Mancha, Albacete, Spain
² Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, Valencia, Spain
³ Public Health Program, Ponce Health Sciences University, Ponce, Puerto Rico
⁴ MSD (IS), LLC, Guaynabo, Puerto Rico

Mathematical modelling of natural phenomena, Tome 18 (2023), article no. 33.

Résumé

Antibiotic resistance is one of the greatest public health threats today, mainly due to the non-rational use of antibiotics. Acinetobacter baumannii is an example of a microorganism with high antibiotic resistance that has developed rapidly in recent years. Consequently, only a few lastresort antibiotics, such as colistin, are currently effective against it. In this work, we propose a random agent-based computational model to describe the evolution of colistin-resistant A. baumannii in the population of Valencia (Spain) and to predict its impact both on the whole population and by age groups. The agent- based model uses a synthetic population of individuals with a vector of characteristics or state variables. These variables change over time based on a series of random events with certain conditional probabilities. The synthetic population statistical features and the probabilities have been found in demographic and hospital databases. One of these probabilities, the probability of infection by a resistant strain, has been modeled using random differential equations. The model takes into account antibiotic consumption as the primary driving force of variation and assumes non-rewersibility of resistance as the worst-case scenario. The agent-based model calibration and the selection of a real-world representative set of solutions have been carried out using the Partide Swarm Optimization evolutionary algorithm. This approach takes into account the inherent stochasticity of the model and the uncertainty of the data. Finally, projections of the incidence and absolute cases of colistin-resistant A. baumannii have been performed. Our results suggest that, if the same consumption pattern continues, the ervolution of the colistin-resistant strain proportion will be exponential, exceeding 50% in 2025. Additionally, the results reveal that, despite the low incidence in Valencian hospitals, the impact on people over 60 years old will be more significant in terms of the number of cases. Based on these findings, it can be deduced that colistin will cense to be an effective antibiotic in the coming years, negatively impacting the human population, especially the most advanced age groups.

Détail

DOI : 10.1051/mmnp/2023037

Bibliographie

[1] D.I Andersson, D. Hughes Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 2010 260 271

[2] 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.

[3] 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 Antibiotic resistance: a rundown of a global crisis Infect. Drug Resist. 2018 1645

[4] S. Banisch, Markov Chain Aggregation for Agent-based Models. Springer (2015).

[5] B.G. Bell, F. Schellevis, E. Stobberingh, H. Goossens, M. Pringle A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance BMC Infect. Dis. 2014 1 25

[6] F. Blanquart, S. Lehtinen, M. Lipsitch, C. Fraser The evolution of antibiotic resistance in a structured host population J. Roy. Soc. Interface 2018 20180040

[7] E. Bonabeau Agent-based modeling: methods and techniques for simulating human systems Proc. Natl. Acad. Sci. U.S.A. 2002 7280 7287

[8] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press (2004).

[9] F. Brauer Compartmental models in epidemiology Math. Epidemiol. 2008 19 79

[10] F. Brauer Mathematical epidemiology: Past, present, and future Infect. Dis. Model. 2017 113 127

[11] L.D. Brown, T.T. Cai, A. Dasgupta Interval estimation for a binomial proportion Stat. Sci. 2001 101 117

[12] C. Burgos, J.-C. Cortés, D. Martínez-Rodríguez, 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. 2019 1950009

[13] G. Casella and R.L. Berger, Statistical Inference. Cengage Learning (2021).

[14] 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.

[15] W. Chen Host innate immune responses to Acinetobacter baumannii infection Front. Cell. Infect. Microbiol. 2020

[16] 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).

[17] M.L. Cohen Epidemiology of drug resistance: implications for a post-antimicrobial era Science 1992 1050 1055

[18] G.J. Da Silva, S. Domingues Interplay between colistin resistance, virulence and fitness in Acinetobacter baumannii Antibiotics 2017 28

[19] N.G. Davies, S. Flasche, M. Jit, K.E. Atkins Within-host dynamics shape antibiotic resistance in commensal bacteria Nat. Ecol. Evol. 2019 440 449

[20] T.N. Doan, D.C.M. Kong, C. Marshall, C.M.J. Kirkpatrick, E.S. Mcbryde Modeling the impact of interventions against Acinetobacter baumannii transmission in intensive care units Virulence 2016 141 152

[21] G.M. Eliopoulos, L.L. Maragakis, T.M. Perl Acinetobacter baumannii: epidemiology, antimicrobial resistance, and treatment options Clin. Infect. Dis. 2008 1254 1263

[22] P. Espinal, S. Martí, J. Vila Effect of biofilm formation on the survival of Acinetobacter Baumannii on dry surfaces J. Hosp. Infect. 2012 56 60

[23] 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].

[24] V. Grimm, U. Berger, D.L. Deangelis, J.G. Polhill, J. Giske, S.F. Railsback The odd protocol: a review and first update Ecol. Model. 2010 2760 2768

[25] The Julia Programming Language. https://julialang.org/ [Accessed: 25/11/2022].

[26] J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN’95-International Conference on Neural Networks, Vol. 4. IEEE (1995) 1942–1948.

[27] N. Khemka, C. Jacob Exploratory toolkit for evolutionary and swarm-based optimization Math. J. 2008 376

[28] M.-F. Lin, C.-Y. Lan Antimicrobial resistance in Acinetobacter baumannii: from bench to bedside World J. Clin. Cases 2014 787

[29] F.D. Lowy Antimicrobial resistance: the example of Staphylococcus aureus J. Clin. Invest. 2003 1265 1273

[30] L.L. Maragakis, E.N. Perencevich, S.E. Cosgrove Clinical and economic burden of antimicrobial resistance Expert Rev. Anti-infective Ther. 2008 751 763

[31] F. Marini, B. Walczak Particle swarm optimization (PSO). A tutorial Chemometrics Intell. Lab. Syst. 2015 153 165

[32] D. Molina, A. Latorre, 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. 2018 517 544

[33] J.A. Nelder, R. Mead A simplex method for function minimization Comput. J. 1965 308 313

[34] J. O’Neill, Antimicrobial resistance: tackling a crisis for the health and wealth of nations. Rev. Antimicrob. Resist. 1 (2014) [Accessed: 25/11/2022].

[35] L. Opatowski, D. Guillemot, P.-Y. Boëlle, L. Temime Contribution of mathematical modeling to the fight against bacterial antibiotic resistance Curr. Opin. Infect. Dis. 2011 279 287

[36] 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].

[37] Python. https://www.python.org/ [Accessed: 25/11/2022].

[38] P. Renard, A. Alcolea and D. Ginsbourger, Stochastic versus deterministic approaches, in Environmental Modelling. John Wiley Sons, Ltd (2013) 133–149.

[39] I. Roca, M. Akova, F. Baquero, J. Carlet, M. Cavaleri, S. Coenen, J. Cohen, D. Findlay, I. Gyssens, O.E. Heure The global threat of antimicrobial resistance: science for intervention New Microbes New Infect. 2015 22 29

[40] J. Rodríguez-Baño, J.M. Cisneros, F. Fernández-Cuenca, A. Ribera, J. Vila, A. Pascual, L. Martínez-Martínez, G. Bou, J. Pachón Clinical features and epidemiology of acinetobacter baumannii colonization and infection in Spanish hospitals Infect. Control Hosp. Epidemiol. 2004 819 824

[41] M. Sassone-Corsi, M. Raffatellu No vacancy: how beneficial microbes cooperate with immunity to provide colonization resistance to pathogens J. Immunol. 2015 4081 4087

[42] T.T. Soong, Random Differential Equations in Science and Engineering (1973).

[43] Spanish Government. Instituto Nacional de Estadística (INE), https://www.ine.es/en/index.htm [Accessed: 25/11/2022].

[44] Spanish Ministry of Health, Statistical Portal. Management Intelligence Area. https://pestadistico.inteligenciadegestion.mscbs.es/publicoSNS/N/siae [Accessed: 25/11/2022].

[45] M. Sundqvist Reversibility of antibiotic resistance Upsala J. Med. Sci. 2014 142 148

[46] M. Sundqvist, P. Geli, D.I. Andersson, M. Sjölund-Karlsson, A. Runehagen, H. Cars, K. Abelson-Storby, O. Cars, G. Kahlmeter Little evidence for reversibility of trimethoprim resistance after a drastic reduction in trimethoprim use J. Antimicrob. Chemother. 2010 350 360

[47] 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 Multidrug-resistant Acinetobacter infection mortality rate and length of hospitalization Emerg. Infect. Dis. 2007 97

[48] F.C. Tenover Mechanisms of antimicrobial resistance in bacteria Am. J. Med. 2006 S3 S10

[49] K.A. Thom, C. Rock, S.S. Jackson, J.K. Johnson, A. Srinivasan, L. Magder, M.-C. Roghmann, R.A. Bonomo, A.D. Harris Factors leading to transmission risk of Acinetobacter baumannii Crit. Care Med. 2017 e633

[50] Valencian Government, Microbiological Surveillance Network of the Valencian Community (REDMIVA). https://www.sp.san.gva.es/sscc/opciones2.jsp?CodPor=1210pcion=SANMS513000=1601=SANMS50000=1 [Accessed: 25/11/2022].

[51] Valencian Government. Statistical Portal. Population estimates. https://pegv.gva.es/es/estimaciones-de-poblacion-de-la-comunitat-valenciana [Accessed: 25/11/2022].

[52] R.-J. Villanueva, J.I. Hidalgo, C. Cervigón, J. Villanueva-Oller, J.-C. Cortés Calibration of an agent-based simulation model to the data of women infected by human papillomavirus with uncertainty Appl. Soft Comput. 2019 546 556

[53] X. Wang, Y. Chen, W. Zhao, Y. Wang, Q. Song, H. Liu, J. Zhao, X. Han, X. Hu, H. Grundmann A data-driven mathematical model of multi-drug resistant Acinetobacter baumannii transmission in an intensive care unit Sci. Rep. 2015 1 8

[54] C. Wendt, B. Dietze, E. Dietz, H. Ruoden Survival of Acinetobacter baumannii on dry surfaces J. Clin. Microbiol. 1997 1394 1397

[55] R. Wise Antimicrobial resistance: priorities for action J. Antimicrob. Chemother. 2002 585 586

[56] World Health Organization (WHO), Antimicrobial resistance. https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance (2020) [Accessed: 25/11/2022].