The Testing of Epidemiological Concepts during the COVID-19 Pandemic
Main Article Content
Mauricio Canals L.
http://orcid.org/0000-0001-5256-4439
http://orcid.org/0000-0001-5256-4439
Abstract
The COVID-19 pandemic had a profound global impact, marked by high morbidity and mortality rates. It also placed substantial strain on epidemiological understanding, complicating efforts to measure the pandemic's scale and speed of transmission, as well as the implementation of mitigation and control strategies. Throughout the pandemic, key epidemiological concepts—such as the basic reproductive number, serial interval, incidence rate, fatality rate, and effective reproductive number—came under intense scrutiny. New concepts, like incidence moments, were even developed to better assess the scale of the outbreak and support temporal forecasting.
In this analysis, we trace the chronological relationship between the progression of the pandemic and the application of these key epidemiological concepts. We briefly examine their historical development, definitions, significance, interconnections, and how they informed both non-pharmaceutical interventions and, later, pharmaceutical measures with the introduction of vaccines. We show that the reproductive number proved to be a particularly valuable tool, guiding the design of epidemiological interventions and helping to define conditions for herd immunity and the vaccination threshold. In contrast, crude case fatality rates were often misleading, requiring adjustments to account for the time lag between infection and death. While incidence rates and reproductive numbers were essential for understanding disease burden and transmission dynamics, they were unsufficent in forecast future case counts. The concept of herd immunity was also critical, but its implications were frequently misunderstood. It was necessary to clarify that reaching the herd immunity threshold does not abruptly end an epidemic, and to highlight its dependence on vaccination thresholds and vaccine effectiveness.
Article Details
How to Cite
L., Mauricio Canals.
The Testing of Epidemiological Concepts during the COVID-19 Pandemic.
Medical Research Archives, [S.l.], v. 13, n. 4, apr. 2025.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/6476>. Date accessed: 15 may 2025.
doi: https://doi.org/10.18103/mra.v13i4.6476.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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20. Morens DM, Breman JG, Calisher GH, Doherty PC, Hahn BH, Keush GT, et al. The origin of COVID-19 and why it matters. Am J Trop Med Hyg. 2020; 103(3): 955-959. doi: 10.4269/ajtmh.20-0849.
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29. Kermack WO, McKendrick AG. A Contribution to the Mathematical Theory of Epidemics. Proc Roy Soc Lond. A. 1927; 115: 700-721.
30. Macdonald G. The analysis of the sporozoite rate. Trop Dis Bull. 1952; 49: 569-586.
31. Bailey N. The mathematical theory of infectious diseases and its applications. Griffin 1975; London.
32. Dietz K. Transmission and control of arbovirus diseases. In Ludwig D, Cooke KL (eds) Epidemiology (.) SIAM. 1975; 104-121. Philadelphia, 1975. p. 104-21.
33. Dietz K. The incidence of infectious diseases under influence of seasonal fluctuations. Lect Notes Biomath. 1976; 11: 1-15.
34. Anderson R, May R. Population biology of infectious diseases: Part I. Nature 1979; 280: 361-367.
35. May R, Anderson RM. 1979. Population biology of infectious diseases: Part II. Nature. 1979; 280, 455-461.
36. Anderson R. Epidemiology. In: Cox FEG (ed) Modern Parasitology 1993; 75-116. Blackwell Scientific Publications, Oxford.
37. 37.- Canals M. Learning from the COVID-19 pandemic: Concepts for good decision-making. Rev Med Chile 2020;148: 415-20. https://www.scielo.cl/pdf/rmc/v148n3/0717-6163 rmc-148-03-0418.pdf.
38. Canals M. Concepts for good decision-making during the COVID-19 pandemic in Chile. Point of View. Rev Chil Infectol. 2020; 37(2): 170-2. doi: 10.4067/s071610182920000200170.
39. Davies NG, Abbott S, Barnard RC, Jarvis CL, Kucharski AJ, Munday JD. Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England. Science 2021; 372. doi: 10.1126/science. abg3055.
40. Banho CA, Sacchetto L, Campos GR, Bittar C, Possebon FS, Ullman LS, et al. Impact of SARS-CoV-2 Gamma lineage introduction and COVID-19 vaccination on the epidemiological landscape of a Brazilian city. Comm. Med. 2022; 2: 41. doi: 10.1038/s43856-022-00108-5.
41. Liu Y, Rocklöv J. The effective reproductive number of the Omicron variant of SARS-CoV-2 is several times relative to Delta. J Trav Med. 2022;1-4. doi.org/10.1093/jtm/taac037
42. Nishiura H, Klinkenberg D, Roberts M, Heesterbeek JAP. Early epidemiological assesment of the virulence of infectious diseases: a case study o fan influenza pandemic. PLoSONE. 2009; 4(8):e6852. doi:10.1371/journal.pone.0006852.
43. Smith CEG. Prospects for the control of infectious disease. Proc Roy Soc Med. 1970; 63: 1181-90. PMID: 5530322
44. Gostic KM, McGough L, Baskerville E, Abbott S, Joshi K, Tedijanto C, et al. Practical considerations for measuring the effective reproductive number, Rt. PLoS Comput Biol .2020; 16(12): e1008409. doi: 10.1371/journal.pcbi.1008409.
45. Gostic KM, McGough L, Baskerville EB, Abbott S, Joshi K, Tedijanto C, et al. Correction: Practical considerations for measuring the effective reproductive number, Rt. PLoS Comput Biol. 2021; 17(12): e1009679. doi:10.1371/journal.pcbi.1009679.
46. Wallinga J, Teunis P. Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures. Amer J Epidemiol. 2004;160(6):509–516. doi:10.1093/aje/kwh255.
47. Wallinga J, Lipsitch M. How generation intervals shape the relationship between growth rates and reproductive numbers. Proc Biol Sci. 2007;274(1609):599–604. doi:10.1098/rspb.2006.3754
48. Bettencourt LMA, Ribeiro RM. Real Time Bayesian Estimation of the Epidemic Potential of Emerging Infectious Diseases. PLoS ONE. 2008;3(5):e2185. doi:10.1371/journal.pone.0002185.
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