library(dplyr)
library(magrittr)
library(stringr)
library(ggplot2)
devtools::load_all()

This file outputs an incidence time series from cumulative case count.

Case Counts

Read in the case counts from the sitrep and calculate overall infectivity at the current time.

cum_cases <- here::here("data/CaseCounts/drc",
                        params$cases) %>%
    readr::read_csv()

cum_cases$date <- lubridate::dmy(cum_cases$date)

Fixing data

According to the report on 17th May, the cumulative case count in Bikoro has reduced from 36 to 29. Ignore this point.

cum_cases <- cum_cases[ -nrow(cum_cases), ]

Assume a starting point.

cum_cases <- rbind(data.frame(date = lubridate::dmy("01/05/20180"),
                              Bikoro = NA,
                              Iboko = NA,
                              Wangata = NA),
                   cum_cases)

Interpolation

Fit a line to fill in missing data.

linear_fit <- select_if(cum_cases, is.numeric) %>%
    purrr:: map_dfr(function(x) {
        interpolate_missing_data(cum_incidence = data.frame(date = cum_cases$date,
                                                            cases = x),
                                 method = "linear")},
        .id = "district")

linear_fit <- select(linear_fit,
                     district,
                     interpolated_date,
                     interpolated_cases) %>%
    tidyr::spread(district, interpolated_cases)

BIKORO and Iboko are in Equateur province while Wangata is in Mbandaka province.

linear_fit$eq_cases <- linear_fit$Bikoro + linear_fit$Iboko
linear_fit$mb_cases <- linear_fit$Wangata

And the incidence curve from the cumulative case count.

eq_incid <- c(linear_fit$eq_cases[1], diff(linear_fit$eq_cases))
mb_incid <- c(linear_fit$mb_cases[1], diff(linear_fit$mb_cases))

Write out incidence curve

here::here("data/CaseCounts/drc",
           paste0("interpolated_", params$cases)) %>%
    readr::read_csv(x = linear_fit, path = .)


annecori/mRIIDSprocessData documentation built on May 29, 2019, 1:16 p.m.