get_p_LSHTM: Estimate Covid2019 outcome probabilities for a population...
In epicentre-msf/covidestim: Estimates Of Epidemiological Parameters For COVID19

Description Usage Arguments Value Author(s) Source Examples

Estimate Covid19 outcome probabilities including hospitalizion|infection, ICU|hospitalization, death|hospitalization, and death|infection, using age-specific outcomes estimates of Pr(Clinical|Infection) from Davies et al. (2020) (with confidence intervals) and point estimates of Pr(hospitalization|clinical), Pr(ICU|hospitalization), and Pr(dead|hospitalization) from Van Zandvoort et al. (2020).

Population age distributions can either be taken from the UN World Population Prospects 2019 (WPP2019), or directly supplied by the user.

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get_p_LSHTM(x, p_type = c("p_hosp_inf", "p_icu_hosp", "p_dead_hosp",
  "p_dead_inf"), p_stat = c("mean", "median", "low_95", "up_95",
  "low_50", "up_50"))

`x`	Either an ISO3 country code used to extract age-specific population estimates from the UN World Population Prospects 2019 dataset, or, a data.frame containing age categories in the first column and population counts (or proportions) in the second column
`p_type`	Outcome to estimate (either "p_hosp_inf", "p_icu_hosp", "p_dead_hosp", or "p_dead_inf")
`p_stat`	Statistic of the severity estimates to use (either "mean", "median", "low_95", "up_95", "low_50", or "up_50")

Estimated outcome probability (scalar)

Anton Camacho

Patrick Barks <patrick.barks@epicentre.msf.org>

van Zandvoort, K., Jarvis, C.I., Pearson, C., Davies, N.G., CMMID COVID-19 Working Group, Russell, T.W., Kucharski, A.J., Jit, M.J., Flasche, S., Eggo, R.M., and Checchi, F. (2020) Response strategies for COVID-19 epidemics in African settings: a mathematical modelling study. medRxiv preprint. https://doi.org/10.1101/2020.04.27.20081711

Davies, N.G., Klepac, P., Liu, Y., Prem, K., Jit, M., CMMID COVID-19 Working Group, and Eggo, R.M. (2020) Age-dependent effects in the transmission and control of COVID-19 epidemics. medRxiv preprint. https://doi.org/10.1101/2020.03.24.20043018

# mean Pr(hospitalization|infection) for Canada (ISO3 code "CAN"), taking age
# distribution from WPP2019
get_p_LSHTM(x = "CAN", p_type = "p_hosp_inf", p_stat = "mean")

# use custom age-distribution
age_df <- data.frame(
  age = c("0-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+"),
  pop = c(1023, 1720, 2422, 3456, 3866, 4104, 4003, 3576, 1210),
  stringsAsFactors = FALSE
)

get_p_LSHTM(x = age_df, p_type = "p_hosp_inf", p_stat = "mean")