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The concept of Ro in epidemic theory

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  • J. A. P. Heesterbeek
  • K. Dietz

Abstract

In epidemiology R0 denotes the average number of secondary cases of an infectious disease that one case would generate in a completely susceptible population. This concept is among the foremost and most valuable ideas that mathematical thinking has brought to epidemic theory. In this contribution, we first review the historical development of Ro, from demography to epidemiology, proceed to give an exposition of the recently formalised theory to define and calculate R0 for structured populations, return to the interaction of demography and epidemiology for an example of the use of the concept to study vaccination campaigns and finally we deal with statistical aspects of estimating R0. In the appendix we discuss some issues of current attention.

Suggested Citation

  • J. A. P. Heesterbeek & K. Dietz, 1996. "The concept of Ro in epidemic theory," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 50(1), pages 89-110, March.
    • Handle: RePEc:bla:stanee:v:50:y:1996:i:1:p:89-110
    DOI: 10.1111/j.1467-9574.1996.tb01482.x
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    Cited by:

    1. Imelda Trejo & Nicolas W Hengartner, 2022. "A modified Susceptible-Infected-Recovered model for observed under-reported incidence data," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-23, February.
    2. Besjana MEMA & Sabina TOSUNI, 2024. "Implementation of mathematics model in public health: Albanian case study," Smart Cities and Regional Development (SCRD) Journal, Smart-EDU Hub, vol. 8(3), pages 41-54, April.
    3. Rezapour, Shabnam & Baghaian, Atefe & Naderi, Nazanin & Sarmiento, Juan P., 2023. "Infection transmission and prevention in metropolises with heterogeneous and dynamic populations," European Journal of Operational Research, Elsevier, vol. 304(1), pages 113-138.
    4. Antoine Djogbenou & Christian Gouriéroux & Joann Jasiak & Paul Rilstone, 2022. "An Econometric Panel Data Model of the COVID-19 Pandemic," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 11(1), pages 1-3.
    5. Lin William Cong & Ke Tang & Bing Wang & Jingyuan Wang, 2021. "An AI-assisted Economic Model of Endogenous Mobility and Infectious Diseases: The Case of COVID-19 in the United States," Papers 2109.10009, arXiv.org.
    6. Chang, Joseph T. & Kaplan, Edward H., 2023. "Modeling local coronavirus outbreaks," European Journal of Operational Research, Elsevier, vol. 304(1), pages 57-68.
    7. Eugenio Valdano & Davide Colombi & Chiara Poletto & Vittoria Colizza, 2023. "Epidemic graph diagrams as analytics for epidemic control in the data-rich era," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    8. Carnehl, Christoph & Fukuda, Satoshi & Kos, Nenad, 2023. "Epidemics with behavior," Journal of Economic Theory, Elsevier, vol. 207(C).
    9. Christine Jacob, 2010. "Branching Processes: Their Role in Epidemiology," IJERPH, MDPI, vol. 7(3), pages 1-19, March.
    10. Fabricius, Gabriel & Maltz, Alberto, 2020. "Exploring the threshold of epidemic spreading for a stochastic SIR model with local and global contacts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    11. András Schubert & Wolfgang Glänzel & Gábor Schubert, 2022. "Eponyms in science: famed or framed?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1199-1207, March.
    12. Ioannis Kioutsioukis & Nikolaos I. Stilianakis, 2021. "On the Transmission Dynamics of SARS-CoV-2 in a Temperate Climate," IJERPH, MDPI, vol. 18(4), pages 1-17, February.
    13. Gong, Jiangyue & Gujjula, Krishna Reddy & Ntaimo, Lewis, 2023. "An integrated chance constraints approach for optimal vaccination strategies under uncertainty for COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).

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