CSIndicators (version 1.1.3)

CST_RNoughtIndices: R0 Index computation on s2dv_cube objects

Description

This function computes the environmental contribution to Aedes-borne disease transmissibility. Values higher than 1 imply that the environmental conditions allow for the disease to be transmitted to more than 1 person (the infection can spread) while R0 values lower than 1 imply that the environmental conditions are not suitable for the disease to spread.

This function utilizes four possible ento-epidemiological models, each of them stated in Caminade et al., 2015; Liu-Helmerssohn et al., 2014; and Mordecai et al., 2017 and Wesolowski et al., 2015 respectively. Additionally, an adjustment to real life data, using recorded DENV data recorded between 2014-2017 in the Caribbean, can also be performed.

Usage

CST_RNoughtIndices(temp, method, mm, lon_dim, lat_dim, ncores = NULL)

Value

An s2dv_cube object containing the R0 Indices (unitless).

Arguments

temp

An s2dv_cube object with temperature values, expressed in degrees Celsius. If "caminade" is selected in "method", named spatial dimensions are required in the s2dv_cube object, ordered as ("latitude", "longitude"). Valid dimension names are "lon", "longitude", "lat", "latitude".

method

a string indicating the R0 ento-epidemiological model used to obtain the R0 outputs. Methods include "caminade", "wesolowski", "liuhelmerssohn" and "mordecai". Alternatively, an extrapolation to real life data can be made by setting "method" to "empirical".

mm

A Kramer probability matrix of dimensions (2, longitude, latitude) to be passed if "caminade" is selected as the computation method. The spatial dimensions must match those of temp and be equal in length. Valid dimension names are "lon", "longitude", "lat", "latitude".

lon_dim

a character vector indicating the longitude dimension name in the element 'temp' if "caminade" is chosen in "method". Otherwise, can be set to NULL

lat_dim

a character vector indicating the latitude dimension name in the element 'temp' if "caminade" is chosen in "method". Otherwise, can be set to NULL

ncores

An integer indicating the number of cores to use in parallel computation for temporal subsetting.

Author

Javier Corvillo,

References

Caminade, Cyril et al. (Jan. 2017). ‘Global risk model for vector-borne transmission of Zika virus reveals the role of El Niño 2015’. In: Proceedings of the National Academy of Sciences 114.1. Publisher: Proceedings of the National Academy of Sciences, pp. 119–124. doi: 10.1073/pnas.1614303114. url: https://www.pnas.org/doi/full/10.1073/pnas.1614303114.

Liu-Helmerssohn, Jing et al. (Mar. 2014). ‘Vectorial Capacity of Aedes aegypti: Effects of Temperature and Implications for Global Dengue Epidemic Potential’. en. In: PLOS ONE 9.3. Publisher: Public Library of Science, e89783. issn: 1932-6203. doi: 10.1371/journal.pone.0089783. url: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0089783.

Mordecai, Erin A. et al. (Apr. 2017). ‘Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models’. en. In: PLOS Neglected Tropical Diseases 11.4. Publisher: Public Library of Science, e0005568. issn: 1935-2735. doi: 10.1371/journal.pntd.0005568. url: https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0005568.

Wesolowski, Amy et al. (Sept. 2015). ‘Impact of human mobility on the emergence of dengue epidemics in Pakistan’. In: Proceedings of the National Academy of Sciences 112.38. Publisher: Proceedings of the National Academy of Sciences, pp. 11887–11892. doi: 10.1073/pnas.1504964112. url: https://www.pnas.org/doi/full/10.1073/pnas.1504964112.

Examples

dims <- c(time = 100, lat = 5, lon = 4)
temp_cube <- list(
data = array(
    runif(prod(dims), min = 15, max = 35),
    dim = dims
  ),
  attrs = list(
    Variable = list(varName = '2m Temperature')
  )
)
class(temp_cube) <- 's2dv_cube'

R0 <- CST_RNoughtIndices(temp_cube, method = "mordecai", mm = NULL, 
lon_dim = NULL, lat_dim = NULL, ncores = NULL)

# Caminade method (requires Kramer matrix)
mm <- array(runif(2 * 5 * 4, 0.1, 1.0), 
            dim = c(2, 4, 5))
names(dim(mm)) <- c("", "lon", "lat")
R0 <- CST_RNoughtIndices(temp_cube, method = "caminade", mm = mm, 
lon_dim = "lon", lat_dim = "lat", ncores = NULL)