DOI: 10.5281/zenodo.5786796
title: History of daily forecast of cumulative COVID-19 mortality in multiple geographic entities across the world

description: Forecasts are available from 2020-04-01 to 2021-10-20 for dozens to more than 200 geographic entities (GE) across the world (from 46 GE on 2020-04-01 to 246 GE on 2021-10-20). Each forecast of cumulative mortality is grounded on a probabilistic mixture of mortality trajectories of ahead-of-time geographic entities playing the role of real-life predictors eventually complemented by a parametric model based on a SIR representation. The methodology is presented in Soubeyrand, Ribaud et al. (2020, https://doi.org/10.1371/journal.pone.0238410) and Soubeyrand, Demongeot et al. (2020, https://doi.org/10.1016/j.onehlt.2020.100187). The forecast are daily implemented by a web application entitled "COVID-19 Visualization" available at https://shiny.biosp.inrae.fr/app_direct/mapCovid19/ The original code is available here: https://gitlab.paca.inrae.fr/biosp/shinyMapCovid19 Raw data for drawing the forecast are provided by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE; https://systems.jhu.edu/) available at https://github.com/CSSEGISandData/COVID-19/ (Dong et al., 2020, https://doi.org/10.1016/S1473-3099(20)30120-1). Information about the data set and the code are provided in the readme.txt file. Data are provided in the forecast_data.rds file produced originally with the saveRDS() function of the R Statistical Software (https://cran.r-project.org/). A code for loading the data set and extracting some data corresponding to specific dates and geographic entities with the R Statistical Software is provided in the read_data.R file.
description: The data file contains a list of lists of forecasts. The first list-level corresponds to dates up to which data are used for drawing forecasts (from 2020-04-01 to 2021-10-20). The second list-level corresponds to geographic entities (GE) for which forecasts are drawn (from 46 GE on 2020-04-01 to 246 GE on 2021-10-20). Each item of the list of lists is a matrix with 14 columns providing forecasts for a given target GE from data available up to a given date. Four forecasts corresponding to four model specifications are provided: - M0: Model consisting of a mixture of 20 predictors built from mortality dynamics of 20 GEs that are ahead of time with respect to the target GE, using the total population sizes for scaling mortality in the predicting GEs with respect to the target GE; - M1: Model consisting of a mixture of one parametric predictor and 20 predictors built from mortality dynamics of 20 GEs that are ahead of time with respect to the target GE, using the total population sizes for scaling mortality in the predicting GEs with respect to the target GE; - M0_65: Same specification as M0, but the scaling is made using the population sizes of people over 65 years old; - M1_65: Same specification as M1, but the scaling is made using the population sizes of people over 65 years old. Col. 1 [time]. Number of days since 2020-01-01. The first number (at row 1) coincides with the date up to which data are used for drawing the forecast. Col. 2 [death]. Registered cumulative number of deaths. Col. 3 [M0.forecast]. Forecast of the cumulative number of deaths under model M0. Col. 4 [M0.Qinf]. Lower confidence bound for the forecast of the cumulative number of deaths under model M0. Col. 5 [M0.Qsup]. Upper confidence bound for the forecast of the cumulative number of deaths under model M0. Col. 6 [M1.forecast]. Forecast of the cumulative number of deaths under model M1. Col. 7 [M1.Qinf]. Lower confidence bound for the forecast of the cumulative number of deaths under model M1. Col. 8 [M1.Qsup]. Upper confidence bound for the forecast of the cumulative number of deaths under model M1. Col. 9 [M0_65.forecast]. Forecast of the cumulative number of deaths under model M0_65. Col. 10 [M0_65.Qinf]. Lower confidence bound for the forecast of the cumulative number of deaths under model M0_65. Col. 11 [M0_65.Qsup]. Upper confidence bound for the forecast of the cumulative number of deaths under model M0_65. Col. 12 [M1_65.forecast]. Forecast of the cumulative number of deaths under model M1_65. Col. 13 [M1_65.Qinf]. Lower confidence bound for the forecast of the cumulative number of deaths under model M1_65. Col. 14 [M1_65.Qsup]. Upper confidence bound for the forecast of the cumulative number of deaths under model M1_65. The forecast horizon is typically 30 days. However, for target GEs whose best predictors are only slightly ahead of time, the forecast horizon may be lower. In such a case, NA are provided after the actual forecast horizon.
description: {"references": ["Soubeyrand S., Ribaud M., Baudrot V., Allard D., Pommeret D., Roques L. (2020). COVID-19 mortality dynamics: The future modelled as a (mixture of) past(s). PLoS ONE 15(9): e0238410.\u00a0https://doi.org/10.1371/journal.pone.0238410", "Soubeyrand S., Demongeot J., Roques L. (2020). Towards unified and real-time analyses of outbreaks at country-level during pandemics. One Health 11: 100187. https://doi.org/10.1016/j.onehlt.2020.100187"]}