Uncertainty in R0 and 1/ω will be modeled by simulating minimum and maximum values.
Remaining parameters are assumed to be accurately known and will remain fixed.
Uncertainty in R0 and 1/ω will be modeled by simulating minimum and maximum values.
Remaining parameters are assumed to be accurately known and will remain fixed.
Infection trajectories show a numerical solution to the SEIRS equations with 500 time steps and initial parameters
$$S(0) = 0.999$$ $$E(0) = 0.001$$ $$I(0) = 0$$ $$R(0) = 0$$ $$B(0) = 0.001$$ $$S + E + I + R = N = 1$$where p is the annual vaccination rate and B is the cumulative disease burden.
Ottar Bjørnstad1,2, Katriona Shea1, Martin Krzywinski3*, Naomi Altman4
1. Department of Biology, The Pennsylvania State University, State College, PA, USA.
2. Department of Entomology, The Pennsylvania State University, State College, PA, USA.
3. Canada’s Michael Smith Genome Sciences Center, Vancouver, British Columbia, Canada.
4. Department of Statistics, The Pennsylvania State University, State College, PA, USA.
https://github.com/martinkrz/posepi3
Shea, K., Bjørnstad, O., Krzywinski, M. & Altman, N. Points of Significance: Uncertainty and the modelling and management of infectious epidemics. (2020) Nature Methods 17:in press.
27 July 2020 v1.0.0 — initial public release
Bjørnstad, O., Shea, K., Krzywinski, M. & Altman, N. Points of Significance: Modelling infectious epidemics. (2020) Nature Methods 17:455–456. (interactive figures, download code)
Bjørnstad, O., Shea, K., Krzywinski, M. & Altman, N. Points of Significance: The SEIRS model for infectious disease dynamics. (2020) Nature Methods 17:557–558. (interactive figures, download code).