In epiforecasts/covid-rt-brazil-subregional: Forecasting and nowcasting Covid-19 related metrics for Subregional Areas of Brazil.

knitr::opts_chunk$set(echo = FALSE)

library(RtD3)
library(here)
library(data.table)
library(sf)

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About

City level estimates of the time-varying reproduction number of Covid-19 produced using EpiNow2 based on data from brazil.io. Regional estimates for Brazil (along with a national estimate) are available here. Detail of the method used is given here, though the estimates shown here were derived using an approximate approach (direct de-convolution of observed cases) rather than the exact method used on epiforecasts.io and therefore should be considered indicative. The code and data supporting these estimates is available here. Estimates are available in a summarised form here.

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Data sources

• Case data is extracted from: brazil.io

• Estimates were only produced for cities with at least 1000 cases, and 90 days of data, since the start of the data extraction. All time series were truncated by 3 days to account for delayed reporting (the delay from original report to reporting in this dataset not the adjusted for delay from onset to original report). Estimates are only shown from the date when a given region reported at least 10 cases on a single day.

• Data on the delay from onset to report were extracted for Brazil from here: github.com/beoutbreakprepared/nCoV2019

• The delay from onset to report was assumed to be log normal and fit using a subsampled bootstrap in order to better represent the uncertainty in the distribution at any time point. See here for details.

• Estimates for the incubation period and the generation time were based on those used here: epiforecasts.io

today <- Sys.Date()

# dropped as RtD3 is not functional without a shapefile and with a shapefile makes this to big to add to github.
# Load shapefile
geoData <- readRDS(here::here("data", "shapefile", "brazil-admin-2.rds"))
# Load case data
cases <- data.table::fread(here::here("data", "cases", paste0(today, ".csv")))
cases <- cases[, .(sovereignt = city, code = city_ibge_code)][,
                 .SD[1,], by = code]
# Link shapefile with case data to get region
geoData <- merge(geoData, cases, by = "sovereignt", all.x = FALSE)
# Load estimates
rtData <- list("Cases" = RtD3::readInEpiNow2(path = here::here("data", "rt", today)))
# Make interactive widget
RtD3::summaryWidget(rtData = rtData,
                    activeArea = "São Paulo",
                    downloadUrl = "https://github.com/epiforecasts/covid-rt-brazil-subregional")

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Regional Breakdown

latest_date <- readRDS(here::here("data","rt", today, "latest_date.rds"))
case_def <- "case"
region_path <- NULL
standalone <- TRUE
interactive <- TRUE
report_forecast <- TRUE
summary_path <- file.path("data","rt", today)

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epiforecasts/covid-rt-brazil-subregional documentation built on Nov. 27, 2020, 12:27 a.m.