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

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

Read in the populations

pops <- here::here("data/Geography/GravityModel/processed",
                   "drc_neighbors.csv") %>%
    readr::read_csv()

wtd_risk <- here::here("output",
                       "flow_from_équateur_1_1_1_flow_from_mbandaka_1_1_1.csv") %>%
    readr::read_csv()

Fix one row where adm0 is NA.

idx <- grep("ombellam", wtd_risk$flow_to)
wtd_risk$flow_to[idx] <- "ombellam'poko"

What if we add the relative risk across the subdivisions?

cum_risk <- group_by(wtd_risk, adm0) %>%
    summarise(cum_risk = sum(wtd_rel_risk, na.rm = TRUE)) %>%
    arrange(desc(cum_risk))

wtd_risk <- left_join(wtd_risk, cum_risk)

ADM2 Level

DRC

drc <- here::here("data/Geography/drc_moritz/shp",
                  "congo_angola.shp") %>%
    st_read() 

drc <- filter(drc, ISO == "COD")    
drc$NAME_2 <- clean_names(drc$NAME_2)
drc <- left_join(drc, wtd_risk, by = c("NAME_2" = "flow_to"))

drc2 <- split(drc, drc$NAME_1) %>% lapply(st_union)
drc2 <- purrr::reduce(drc2, c)

ADM1 Level

CAR

car <- here::here("data/Geography/gadm36_CAF_shp/gadm36_CAF_1.shp") %>%
    st_read()
car$NAME_1 <- clean_names(car$NAME_1)
car <- left_join(car, wtd_risk, by = c("NAME_1" = "flow_to"))

Rwanda

rwanda <- here::here("data/Geography/gadm36_RWA_shp",
                     "gadm36_RWA_1.shp") %>%
    st_read()
rwanda$NAME_1 <- clean_names(rwanda$NAME_1)
rwanda <- left_join(rwanda, wtd_risk, by = c("NAME_1" = "flow_to"))

South Sudan

ssudan <- here::here("data/Geography/gadm36_SSD_shp",
                     "gadm36_SSD_1.shp") %>%
    st_read()
ssudan$NAME_1 <- clean_names(ssudan$NAME_1)
ssudan <- left_join(ssudan, wtd_risk, by = c("NAME_1" = "flow_to"))

Zambia

zambia <- here::here("data/Geography/gadm36_ZMB_shp",
                     "gadm36_ZMB_1.shp") %>%
    st_read()
zambia$NAME_1 <- clean_names(zambia$NAME_1)
zambia <- left_join(zambia, wtd_risk, by = c("NAME_1" = "flow_to"))

Gabon

gabon <- here::here("data/Geography/gadm36_GAB_shp",
                    "gadm36_GAB_1.shp") %>%
    st_read()

gabon <- left_join(gabon, wtd_risk, by = c("NAME_1" = "flow_to"))

Angola

angola <- here::here("data/Geography/gadm36_AGO_shp",
                     "gadm36_AGO_1.shp") %>%
    st_read()
angola$NAME_1 <- clean_names(angola$NAME_1)
angola <- left_join(angola, wtd_risk, by = c("NAME_1" = "flow_to"))

Burundi

burundi <- here::here("data/Geography/gadm36_BDI_shp",
                      "gadm36_BDI_1.shp") %>%
    st_read()
burundi$NAME_1 <- clean_names(burundi$NAME_1)
burundi <- left_join(burundi, wtd_risk, by = c("NAME_1" = "flow_to"))

Republic of Congo

roc <- here::here("data/Geography/gadm36_COG_shp",
                  "gadm36_COG_1.shp") %>%
    st_read()
roc$NAME_1 <- clean_names(roc$NAME_1)
roc <- left_join(roc, wtd_risk, by = c("NAME_1" = "flow_to"))

Tanzania

tnzna <- here::here("data/Geography/gadm36_TZA_shp",
                    "gadm36_TZA_2.shp") %>%
    st_read()
tnzna$NAME_1 <- clean_names(tnzna$NAME_1)
tnzna <- left_join(tnzna, wtd_risk, by = c("NAME_1" = "flow_to"))

Uganda

uganda <- here::here("data/Geography/gadm36_UGA_shp",
                     "gadm36_UGA_1.shp") %>%
    st_read()
uganda$NAME_1 <- clean_names(uganda$NAME_1)
uganda <- left_join(uganda, wtd_risk, by = c("NAME_1" = "flow_to"))

Plot

drc_nghbrs <- list(car, uganda, tnzna, roc, burundi, angola,
                   ssudan, rwanda, zambia)

p <- ggplot()
p <- p + geom_sf(aes(geometry = st_union(car)), col = "red") 
p <- p + geom_sf(aes(geometry = st_union(drc)), col = "red")
p <- p + geom_sf(aes(geometry = st_union(uganda)), col = "red")
p <- p + geom_sf(aes(geometry = st_union(tnzna)), col = "red")
p <- p + geom_sf(aes(geometry = st_union(roc)), col = "red")
p <- p + geom_sf(aes(geometry = st_union(burundi)), col = "red") 
p <- p + geom_sf(aes(geometry = st_union(angola)), col = "red")
p <- p + geom_sf(aes(geometry = st_union(ssudan)), col = "red")
p <- p + geom_sf(aes(geometry = st_union(rwanda)), col = "red")
p <- p + geom_sf(aes(geometry = st_union(zambia)), col = "red")

for (df in drc_nghbrs) {
    p <- p + geom_sf(data = df, aes(fill = wtd_rel_risk),
                     lwd = 0.1,
                     alpha = 0.8)
}


p <- p + geom_sf(aes(geometry = drc2))
p <- p + geom_sf(data = drc, aes(fill = wtd_rel_risk),
                 lwd = 0,
                 alpha = 0.8)
p <- p + coord_sf(datum = NA)
p    

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