ebola {pomp} | R Documentation

## Ebola outbreak, West Africa, 2014-2016

### Description

Data and models for the 2014–2016 outbreak of Ebola virus disease in West Africa.

### Usage

    ebolaModel(country = c("GIN", "LBR", "SLE"), data = NULL,
      timestep = 1/8, nstageE = 3L, R0 = 1.4, rho = 0.2, cfr = 0.7,
      k = 0, index_case = 10, incubation_period = 11.4,
      infectious_period = 7)

### Arguments

`country` | ISO symbol for the country (GIN=Guinea, LBR=Liberia, SLE=Sierra Leone).
`data` | if NULL, the situation report data (see `ebolaWHO`) for the appropriate country or region will be used. Providing a dataset here will override this behavior.
`timestep` | duration (in days) of Euler time-step for the simulations.
`nstageE` | integer; number of incubation stages.
`R0` | basic reproduction ratio
`rho` | case reporting efficiency
`cfr` | case fatality rate
`k` | dispersion parameter (negative binomial `size` parameter)
`index_case` | number of cases on day 0 (2014-04-01)
`incubation_period, infectious_period` | mean duration (in days) of the incubation and infectious periods.

### Details

The data include monthly case counts and death reports derived from WHO situation reports, as reported by the U.S. CDC. The models are described in King et al. (2015).

The data-cleaning script is included in the R source code file ‘ebola.R’.

### Model structure

The default incubation period is supposed to be Gamma distributed with shape parameter `nstageE` and mean 11.4 days and the case-fatality ratio ('cfr') is taken to be 0.7 (cf. WHO Ebola Response Team 2014). The discrete-time formula is used to calculate the corresponding `alpha` (cf. He et al. 2010).

The observation model is a hierarchical model for cases and deaths:

p(R_t, D_t| C_t) = p(R_t | C_t) p(D_t | C_t, R_t).

Here, p(R_t | C_t) is negative binomial with mean ρ C_t and dispersion parameter 1/k; p(D_t | C_t, R_t) is binomial with size R_t and probability equal to the case fatality rate `cfr`.

### References

A. A. King, M. Domenech de Cellès, F. M. G. Magpantay, and P. Rohani. Avoidable errors in the modelling of outbreaks of emerging pathogens, with special reference to Ebola. Proceedings of the Royal Society of London, Series B, 282: 20150347, 2015.

WHO Ebola Response Team. Ebola virus disease in West Africa—the first 9 months of the epidemic and forward projections. New England Journal of Medicine 371: 1481–1495, 2014.

D. He, E. L. Ionides, & A. A. King. Plug-and-play inference for disease dynamics: measles in large and small populations as a case study. J. R. Soc. Interface, 7: 271–283, 2010.

### See Also

Other datasets: `blowflies`, `bsflu`, `dacca`, `measles`, `parus`

Other pomp examples: `blowflies`, `dacca`, `gompertz`, `measles`, `ou2`, `ricker`, `rw2`, `sir_models`, `verhulst`

### Examples

    data(ebolaWA2014)

    library(ggplot2)
    library(tidyr)

    ebolaWA2014 %>%
      gather(variable,count,cases,deaths) %>%
      ggplot(aes(x=date,y=count,group=country,color=country))+
      geom_line()+
      facet_grid(variable~.,scales="free_y")+
      theme_bw()+
      theme(axis.text=element_text(angle=-90))

    ebolaWA2014 %>%
      gather(variable,count,cases,deaths) %>%
      ggplot(aes(x=date,y=count,group=variable,color=variable))+
      geom_line()+
      facet_grid(country~.,scales="free_y")+
      theme_bw()+
      theme(axis.text=element_text(angle=-90))

    plot(ebolaModel(country="SLE"))
    plot(ebolaModel(country="LBR"))
    plot(ebolaModel(country="GIN"))

[Package pomp version 2.3 Index]