In annecori/mRIIDSprocessData: Epidemiological forcasting using real-time data

library(dplyr)
library(ggplot2)

library(sf)
drc <- here::here("data/Geography/drc_moritz/shp",
                  "congo_angola.shp") %>%
    st_read() 
drc <- filter(drc, ISO == "COD")

cols <- rep("#FFFFFF", 38)
names(cols) <- unique(drc$NAME_2)
cols["Équateur"] <- "#F8766D" 
cols["Mbandaka"] <- "#00BFC4"

incid <- here::here("data/CaseCounts/drc", "21-May-2018_ADM2.csv") %>%
    readr::read_csv()
simean <- 15.3
sisd <- 9.1
R <- 1.03
si <- EpiEstim::DiscrSI(k = 0:60,
                        mu = simean,
                        sigma  = sisd) %>% round(3)
eq_infectivity <- R * EpiEstim::overall_infectivity(incid$eq, si)
mb_infectivity <- R * EpiEstim::overall_infectivity(incid$mb, si)

Serial Interval.

p1 <- ggplot(NULL, aes(0:60, si)) +
    geom_col() +
    theme_classic() +
    xlab("") +
    ylab("") 
    ##ggtitle("Serial Interval Distribution")

Take a lot at infectivity.

infectivity <- data.frame(Date = incid$date_onset,
                          Équateur = eq_infectivity[1:nrow(incid)],
                          Mbandaka = mb_infectivity[1:nrow(incid)]) %>%
    tidyr::gather(Location, Infectivity, -Date)

p2 <- ggplot(infectivity, aes(Date, Infectivity, col = Location)) +
    geom_line() +
    ylim(0, 1.2) +
    theme_classic() +
    xlab("") + 
    theme(legend.title = element_blank()) +
    scale_colour_manual(values=cols)

Incidence

incid_wide <- select(incid, Date = date_onset, Équateur = eq, Mbandaka = mb) %>%
    tidyr::gather(location, incid, -Date) 

p3 <- ggplot(incid_wide, aes(Date, incid, fill = location)) +
    geom_col(position = "stack")
p3 <- p3 + xlab("") + ylab("Incidence")
p3 <- p3 + theme_classic() + theme(legend.title = element_blank())
##p3 <- p3 + ggtitle("Incidence")
p3 <- p3 + scale_fill_manual(values=cols)

And finally map.

library(ggrepel)
drc_metadata <- here::here("data/Geography/GravityModel/processed",
                           "drc_metadata.csv") %>%
    readr::read_csv() %>%
    filter(adm2 %in% c("Équateur", "Mbandaka")) %>%
    select(adm2, Centroid_Lon, Centroid_Lat)
drc_metadata[drc_metadata$adm2 == "Équateur", "Centroid_Lat"] <- 0.75
drc_metadata[drc_metadata$adm2 == "Mbandaka", "Centroid_Lon"] <- 17
p4 <- ggplot(drc) + geom_sf(aes(fill = NAME_2), lwd = 0.1)
p4 <- p4 + theme(
               panel.ontop = FALSE,
               panel.grid = element_blank(), 
               line = element_blank(), 
               rect = element_blank())
p4 <- p4 + coord_sf(datum = NA)
p4 <- p4 + scale_fill_manual(values = cols)
p4 <- p4 + geom_text_repel(data = drc_metadata,
                           aes(label = adm2,
                               x = Centroid_Lon,
                               y = Centroid_Lat),
                           size = 3)
p4 <- p4 + theme_void() + xlab("") + ylab("")
p4 <- p4 + theme(legend.position = "none")
p4 <- p4 + geom_path(data = data.frame(x = c(17, 18.3), y = c(-0.15, -0.08)),
                     aes(x = x, y = y))
ggsave(here::here("reports","mbandka.png"), p4)

Finally

ggpubr::ggarrange(p3, p2, p1,
                  nrow = 2,
                  ncol = 2,
                  common.legend = TRUE,
                  legend = "bottom",
                  align = "hv") %>%
    ggpubr::ggexport(filename = "who_report_fig1_withR.png",
                     width = 960,
                     height = 960,
                     pointsize = 8,
                     res = 200)