The value of mathematical modelling approaches in epidemiology for public health decision making

  • Oscar Espinosa Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia. Bogotá, Colombia. https://orcid.org/0000-0003-4893-0509
  • Oscar Franco a. Department of Global Public Health, Utrecht University. Utrecht, Netherlands. b. Harvard T.H. Chan School of Public Health, Harvard University. Boston, USA.
  • Martha Ospina Florida International University, Miami, USA.
  • Mabel Carabalí Dalla Lana School of Public Health, University of Toronto. Toronto, Canadá.
  • Ricardo Baeza-Yates a. Institute for Experiential AI, Northeastern University. California, USA. b. Department of Information and Communication Technologies, Universitat Pompeu Fabra. Barcelona, Spain. c. Department of Computer Science, Universidad de Chile. Santiago de Chile, Chile.
Keywords: Mathematical modelling, Epidemiology, Public health, Decision making, Anesthesiology

Abstract

It is discussed the relevance of quantitative approaches, specifically mathematical modelling in epidemiology, in the public health decision-making process. This topic is discussed here based on the experience of various experts in mathematical epidemiology and public health. First, the definition of mathematical modelling is presented, especially in the context of epidemiology. Second, the different uses and socio-political implications, including empirical examples of recent experiences that have taken place at the international level are addressed. Finally, some general considerations regarding the challenges encountered in the use and application of mathematical modelling in epidemiology in the decision-making process at the local and national levels.

References

Brauer F. Mathematical epidemiology: past, present, and future. Infect Dis Model. 2017;2(2):113-27. doi: https://doi.org/10.1016/j.idm.2017.02.001

Mondaini R, Pardalos P, editors. Mathematical modelling of biosystems. Berlin: Springer; 2008. doi: https://doi.org/10.1007/978-3-540-76784-8

Star L, Moghadas SM. The role of mathematical modelling in public health planning and decision making. Purple Pap. 2010;(22):1-6.

Hadeler P. Topics in mathematical biology. Tübingen: Springer; 2017. doi: https://doi.org/10.1007/978-3-319-65621-2

Domotor Z. Philosophy of science, mathematical models in BT - Mathematics of complexity and dynamical systems. In: Meyers R, editor. New York: Springer; 2011. p. 1407-22. doi: https://doi.org/10.1007/978-1-4614-1806-1_89

Hosking R, Venturino E, editors. Aspects of mathematical modelling. Boston: Springer; 2008. doi: https://doi.org/10.1007/978-3-7643-8591-0

Mesterton-Gibbons M. A concrete approach to mathematical modelling. New York: John Wiley & Sons, Inc.; 2007. doi: https://doi.org/10.1002/9781118032480

Torres N, Santos G. The (mathematical) modeling process in biosciences. Front Genet. 2015;6:354. doi: https://doi.org/10.3389/fgene.2015.00354

Brauer F, Castillo-Chávez C. Mathematical models in population biology and epidemiology. New York: Springer; 2010. doi: https://doi.org/10.1007/978-1-4614-1686-9_9

International Actuarial Association. International Standard of Actuarial Practice (ISAP 1A) - Governance of models. Ottawa: International Actuarial Association; 2016. [Internet]. [Cited 16 Mar 23]. Available at: http://www.actuaries.org/CTTEES_ASC/isaps/Final_ISAPs_posted/ISAP_1A_Final_November2016_Web.pdf

Van Kerkhove M, Ferguson N. Epidemic and intervention modelling - a scientific rationale for policy decisions? Lessons from the 2009 influenza pandemic. Bull World Health Organ. 2012;90(4):306-10. doi: https://doi.org/10.2471/BLT.11.097949

World Health Organization. Consultation on the development of guidance on how to incorporate the results of modelling into WHO guidelines. Geneva; 2016. [Internet]. [Cited 16 Mar 23]. Available at: https://apps.who.int/iris/bitstream/handle/10665/258987/WHO-HIS-IER-REK-2017.2-eng.pdf?sequence=1&isAllowed=y

Kogan N, Clemente L, Liautaud P, Kaashoek J, Link N, Nguyen A, et al. An early warning approach to monitor covid-19 activity with multiple digital traces in near real time. Sci Adv. 2021;7(10):eabd6989. doi: https://doi.org/10.1126/sciadv.abd6989

van de Goor I, Hämäläinen R, Syed A, Juel Lau C, Sandu P, Spitters H, et al. Determinants of evidence use in public health policy making: results from a study across six EU countries. Health Policy. 2017;121(3):273-81. doi: https://doi.org/10.1016/j.healthpol.2017.01.003

The COVID-19 Multi-Model Comparison Collaboration (CMCC) Policy Group. Guidance on use of modelling for policy responses to covid-19. 2020. [Internet]. [Cited 16 Mar 23]. Available at: https://decidehealth.world/en/news/guidance-use-modelling-policy-responses-covid-19

Harari YN. Lessons from a year of Covid. Financial Times. 2021. [Internet]. [Cited 16 Mar 23]. Available at: https://www.ft.com/content/f1b30f2c-84aa-4595-84f2-7816796d6841

Instituto Nacional de Salud. Modelos COVID-19. 2023. [Internet]. [Cited 16 Mar 23]. Available at: https://www.ins.gov.co/Direcciones/ONS/modelos-covid-19

Espinosa O, Rodríguez J, Robayo A, Arregocés L, Agudelo N, Suárez C, et al. Advance Market Commitments (AMC) model application for Colombian purchase strategy of COVID-19 vaccines. Vaccine X. 2022;12:100197. doi: https://doi.org/10.1016/j.jvacx.2022.100197

Morales-Zamora G, Espinosa O, Puertas E, Fernández J, Hernández J, Zakzuk V, et al. Cost-effectiveness analysis of strategies of COVID-19 vaccination in Colombia: comparison of high-risk prioritization and no prioritization strategies with the absence of a vaccination plan. Value Heal Reg Issues. 2022;31:101-10. doi: https://doi.org/10.1016/j.vhri.2022.04.004

Espinosa O, Rodríguez J, Robayo A, Arias L, Moreno S, Ospina M, et al. Vulnerability interactive geographic viewer against COVID‐19 at the block level in Colombia: Analytical tool based on machine learning techniques. Reg Sci Policy Pract. 2021;13(S1):187-97. doi: https://doi.org/10.1111/rsp3.12469

Metcalf C, Edmunds W, Lessler J. Six challenges in modelling for public health policy. Epidemics. 2015;10:93-6. doi: https://doi.org/10.1016/j.epidem.2014.08.008

Kretzschmar M. Disease modeling for public health: added value, challenges, and institutional constraints. J Public Health Policy. 2020;41(1):39-51. doi: https://doi.org/10.1057/s41271-019-00206-0

Alahmadi A, Belet S, Black A, Cromer D, Flegg J, House T, et al. Influencing public health policy with data-informed mathematical models of infectious diseases: Recent developments and new challenges. Epidemics. 2020;32:100393. doi: https://doi.org/10.1016/j.epidem.2020.100393

How to Cite
1.
Espinosa O, Franco O, Ospina M, Carabalí M, Baeza-Yates R. The value of mathematical modelling approaches in epidemiology for public health decision making. Colomb. J. Anesthesiol. [Internet]. 2023 May 26 [cited 2024 May 3];52(1). Available from: https://www.revcolanest.com.co/index.php/rca/article/view/1079

Downloads

Download data is not yet available.
Published
2023-05-26
How to Cite
1.
Espinosa O, Franco O, Ospina M, Carabalí M, Baeza-Yates R. The value of mathematical modelling approaches in epidemiology for public health decision making. Colomb. J. Anesthesiol. [Internet]. 2023 May 26 [cited 2024 May 3];52(1). Available from: https://www.revcolanest.com.co/index.php/rca/article/view/1079
Section
Essay

Altmetric

Article metrics
Abstract views
Galley vies
PDF Views
HTML views
Other views
QR Code

Some similar items: