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Seasonality

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Epidemics

Part of the book series: Use R! ((USE R))

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Abstract

Host behavior and environmental factors influence disease dynamics in a variety of ways through affecting the pathogen such as the survival of infective stages outside the host and via host demographies from changing birth rates, carrying capacitities, social organization, etc. Sometimes such influences have relatively subtle consequences (e.g., slight changes in R 0) as is likely the effect of absolute humidity on influenza transmission (Lowen et al., 2007; Bjørnstad & Viboud, 2016).

The chapter uses the following R packages: deSolve and plotrix.

A five minute epidemics MOOC on seasonality can be seen on youtube:

https://www.youtube.com/watch?v=TDuuM-wm6nw .

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Notes

  1. 1.

    split creates a list from a vector and sapply applies a function to a list to return a new vector.

  2. 2.

    If the infection has a case fatality ratio of, say, 30%, the additional rate of removal is α = −log(1 − 0.3)∕ip, where ip is the duration of infection and the appropriate calculation would be R 0 = σβ∕(σ + μ)(γ + μ + α).

  3. 3.

    It is possible to analyze more realistic patterns of seasonality, such as a more explicit term-time forcing; see Keeling et al. (2001) and Chap. 8. The qualitative (but not detailed) results appear to be robust to the exact shape of the forcing function.

  4. 4.

    We discuss coexisting attractors in further detail in Sect. 11.4.

  5. 5.

    As for the nextgenR0 function introduced in Sect. 3.11, the internal syntax here involves quite a bit of acrobatics. Its construction involved a lot of trial and error on the authors part but it works.

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Authors and Affiliations

  1. Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA

    Ottar Bjørnstad

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  1. Ottar Bjørnstad
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Bjørnstad, O. (2023). Seasonality. In: Epidemics. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-031-12056-5_6

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