# Library ----
library(readxl)## Warning: package 'readxl' was built under R version 4.2.3library(dplyr)## Warning: package 'dplyr' was built under R version 4.2.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(lubridate)## Warning: package 'lubridate' was built under R version 4.2.3##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(leaflet)## Warning: package 'leaflet' was built under R version 4.2.3library(sf)## Warning: package 'sf' was built under R version 4.2.3## Linking to GEOS 3.9.3, GDAL 3.5.2, PROJ 8.2.1; sf_use_s2() is TRUEsetwd("C:/Users/alfredo.acosta/OneDrive - SVA/LORA/Draft/Scenario tree/")w <- read_xlsx("infur_20230407.xlsx")
# colnames(w)
# table(w$reporting_level)
# table(w$disease_eng)
# table(w$Species, w$is_wild == TRUE) #wild species
# table(w$Species[w$is_wild == FALSE]) #domestic species
#
# View(w[w$disease_eng == "Rabies virus (Inf. with)" | w$disease_esp == "Cattle",])
# View(w[w$disease_eng == "Bluetongue virus (Inf. with)" | w$disease_esp == "Cattle",])
# View(w[w$disease_eng == "Classical swine fever virus (Inf. with)" | w$disease_esp == "Cattle",])table(w$Species[w$is_wild == FALSE]) #domestic species TO MATCH##
## Al indeterminatum Aplodinotus grunniens
## 1 2
## Austropotamobius pallipes Bees
## 1 1099
## Birds Buffaloes
## 24705 302
## Camelidae (dom) Carassius auratus
## 92 3
## Cats Cattle
## 291 21493
## Cervidae (dom) Channa aurantimaculata
## 8 1
## Cherax quadricarinatus Clarias gariepinus
## 27 2
## Crassostrea gigas Crassostrea virginica
## 103 4
## Cyprinus carpio Cyprinus rubrofuscus
## 98 7
## Dogs Dorsoma cepedianum
## 1174 1
## Equidae (dom) Esox lucius
## 3244 1
## Esox masquinongy Goats
## 2 3600
## Haliotis discushannai Haliotis rubra
## 1 1
## Inimicus japonicus Labrus mixtus
## 1 6
## Lates calcarifer Lepomis macrochirus
## 1 1
## Litopenaeus vannamei Macrobrachium rosenbergii
## 17 2
## Meretrix lyrata Micropterus dolomieu
## 2 2
## Micropterus salmoides Mol indeterminatum
## 1 1
## Morone chrysops Moxostoma anisurum
## 1 1
## Moxostoma macrolepidotum Mustelidae (dom)
## 1 66
## Mytilus edulis Mytilus galloprovincialis
## 4 5
## Neogobius melanostomus Oncorhynchus mykiss
## 1 137
## Oreochromis niloticus Oreochromis spp
## 23 61
## Ostrea angasi Ostrea chilensis
## 5 2
## Ostrea edulis Other species
## 4 1
## Penaeus indicus Penaeus monodon
## 2 18
## Penaeus stylirostris Penaeus vannamei
## 4 21
## Perca flavescens Perna canaliculus
## 2 1
## Pis indeterminatum Pomoxis nigromaculatus
## 3 1
## Procambarus alleni Rabbits
## 1 1071
## Rabbits/hares (mixed group) Rachycentron canadum
## 27 1
## Rana catesbeiana Salmo marmoratus
## 4 1
## Salmo salar Salmo trutta
## 37 7
## Salmonidae (incognita) Seriola dumerili
## 2 1
## Sheep Sheep/goats (mixed herd)
## 15429 1855
## Stizostedion vitreum Swine
## 1 12484table(w$Species[w$is_wild == FALSE]) %>%
as.data.frame() %>%
arrange(desc(Freq))## Var1 Freq
## 1 Birds 24705
## 2 Cattle 21493
## 3 Sheep 15429
## 4 Swine 12484
## 5 Goats 3600
## 6 Equidae (dom) 3244
## 7 Sheep/goats (mixed herd) 1855
## 8 Dogs 1174
## 9 Bees 1099
## 10 Rabbits 1071
## 11 Buffaloes 302
## 12 Cats 291
## 13 Oncorhynchus mykiss 137
## 14 Crassostrea gigas 103
## 15 Cyprinus carpio 98
## 16 Camelidae (dom) 92
## 17 Mustelidae (dom) 66
## 18 Oreochromis spp 61
## 19 Salmo salar 37
## 20 Cherax quadricarinatus 27
## 21 Rabbits/hares (mixed group) 27
## 22 Oreochromis niloticus 23
## 23 Penaeus vannamei 21
## 24 Penaeus monodon 18
## 25 Litopenaeus vannamei 17
## 26 Cervidae (dom) 8
## 27 Cyprinus rubrofuscus 7
## 28 Salmo trutta 7
## 29 Labrus mixtus 6
## 30 Mytilus galloprovincialis 5
## 31 Ostrea angasi 5
## 32 Crassostrea virginica 4
## 33 Mytilus edulis 4
## 34 Ostrea edulis 4
## 35 Penaeus stylirostris 4
## 36 Rana catesbeiana 4
## 37 Carassius auratus 3
## 38 Pis indeterminatum 3
## 39 Aplodinotus grunniens 2
## 40 Clarias gariepinus 2
## 41 Esox masquinongy 2
## 42 Macrobrachium rosenbergii 2
## 43 Meretrix lyrata 2
## 44 Micropterus dolomieu 2
## 45 Ostrea chilensis 2
## 46 Penaeus indicus 2
## 47 Perca flavescens 2
## 48 Salmonidae (incognita) 2
## 49 Al indeterminatum 1
## 50 Austropotamobius pallipes 1
## 51 Channa aurantimaculata 1
## 52 Dorsoma cepedianum 1
## 53 Esox lucius 1
## 54 Haliotis discushannai 1
## 55 Haliotis rubra 1
## 56 Inimicus japonicus 1
## 57 Lates calcarifer 1
## 58 Lepomis macrochirus 1
## 59 Micropterus salmoides 1
## 60 Mol indeterminatum 1
## 61 Morone chrysops 1
## 62 Moxostoma anisurum 1
## 63 Moxostoma macrolepidotum 1
## 64 Neogobius melanostomus 1
## 65 Other species 1
## 66 Perna canaliculus 1
## 67 Pomoxis nigromaculatus 1
## 68 Procambarus alleni 1
## 69 Rachycentron canadum 1
## 70 Salmo marmoratus 1
## 71 Seriola dumerili 1
## 72 Stizostedion vitreum 1w$Species_fg <- w$Species
w$Species_fg <- gsub("Swine", "Swine / pigs", w$Species_fg) #swine
w$Species_fg <- gsub("Equidae (dom)", "Horses", w$Species_fg) #Horses
w$Species_fg <- gsub("Birds", "Chickens", w$Species_fg) #Birds
table(w$Species_fg[w$is_wild == FALSE]) %>%
as.data.frame() %>%
arrange(desc(Freq))## Var1 Freq
## 1 Chickens 24705
## 2 Cattle 21493
## 3 Sheep 15429
## 4 Swine / pigs 12484
## 5 Goats 3600
## 6 Equidae (dom) 3244
## 7 Sheep/goats (mixed herd) 1855
## 8 Dogs 1174
## 9 Bees 1099
## 10 Rabbits 1071
## 11 Buffaloes 302
## 12 Cats 291
## 13 Oncorhynchus mykiss 137
## 14 Crassostrea gigas 103
## 15 Cyprinus carpio 98
## 16 Camelidae (dom) 92
## 17 Mustelidae (dom) 66
## 18 Oreochromis spp 61
## 19 Salmo salar 37
## 20 Cherax quadricarinatus 27
## 21 Rabbits/hares (mixed group) 27
## 22 Oreochromis niloticus 23
## 23 Penaeus vannamei 21
## 24 Penaeus monodon 18
## 25 Litopenaeus vannamei 17
## 26 Cervidae (dom) 8
## 27 Cyprinus rubrofuscus 7
## 28 Salmo trutta 7
## 29 Labrus mixtus 6
## 30 Mytilus galloprovincialis 5
## 31 Ostrea angasi 5
## 32 Crassostrea virginica 4
## 33 Mytilus edulis 4
## 34 Ostrea edulis 4
## 35 Penaeus stylirostris 4
## 36 Rana catesbeiana 4
## 37 Carassius auratus 3
## 38 Pis indeterminatum 3
## 39 Aplodinotus grunniens 2
## 40 Clarias gariepinus 2
## 41 Esox masquinongy 2
## 42 Macrobrachium rosenbergii 2
## 43 Meretrix lyrata 2
## 44 Micropterus dolomieu 2
## 45 Ostrea chilensis 2
## 46 Penaeus indicus 2
## 47 Perca flavescens 2
## 48 Salmonidae (incognita) 2
## 49 Al indeterminatum 1
## 50 Austropotamobius pallipes 1
## 51 Channa aurantimaculata 1
## 52 Dorsoma cepedianum 1
## 53 Esox lucius 1
## 54 Haliotis discushannai 1
## 55 Haliotis rubra 1
## 56 Inimicus japonicus 1
## 57 Lates calcarifer 1
## 58 Lepomis macrochirus 1
## 59 Micropterus salmoides 1
## 60 Mol indeterminatum 1
## 61 Morone chrysops 1
## 62 Moxostoma anisurum 1
## 63 Moxostoma macrolepidotum 1
## 64 Neogobius melanostomus 1
## 65 Other species 1
## 66 Perna canaliculus 1
## 67 Pomoxis nigromaculatus 1
## 68 Procambarus alleni 1
## 69 Rachycentron canadum 1
## 70 Salmo marmoratus 1
## 71 Seriola dumerili 1
## 72 Stizostedion vitreum 1wm <- st_read("TM_WORLD_BORDERS_SIMPL-0.3.shp")## Reading layer `TM_WORLD_BORDERS_SIMPL-0.3' from data source
## `C:\Users\alfredo.acosta\OneDrive - SVA\LORA\Draft\Scenario tree\TM_WORLD_BORDERS_SIMPL-0.3.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 246 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -180 ymin: -90 xmax: 180 ymax: 83.57027
## Geodetic CRS: WGS 84date_to_analyse <- ymd("2021-08-01") #ASF HPAI
disease_to_analyse <- "African swine fever virus (Inf. with)"{
w$date <- floor_date(w$event_confirmation_date, unit="month")
# Number of outbreaks
outb <- w %>%
filter(date == date_to_analyse) %>%
filter(disease_eng == disease_to_analyse) %>%
group_by(Species, iso_code, country, reporting_level, date) %>%
summarize(outbreak=n())
outb
# Number of Cases ----
# Species is for domestic and have the same ID as in faostats
cases <- w %>%
filter(date == date_to_analyse) %>%
filter(disease_eng == disease_to_analyse) %>%
group_by(Species_fg, iso_code, country, reporting_level, date) %>%
summarize(cases=sum(cases, na.rm = TRUE))
cases
# DB FAOSTATS ----
# p=population of animals
p <- read.csv(file="FAOSTAT_data_en_10-25-2023.csv")
colnames(p)
head(p)
# Species
table(p$Item)
cases$Species_fg
colnames(p)
pop <- p %>%
filter(Item %in% cases$Species_fg) %>%
filter(Area.Code..ISO3. %in% cases$iso_code) %>%
filter(Year %in% year(date_to_analyse))
# WORLD MAP ----
# Update the number of outbreaks on the map
wm$outbreak <- outb$outbreak[match(wm$ISO3, outb$iso_code)]
# Update the number of cases on the map
wm$cases <- cases$cases[match(wm$ISO3, outb$iso_code)]
# Update the population on the map
wm$pop <- pop$Value[match(wm$ISO3, pop$Area.Code..ISO3.)]
# Prevalence ----
wm$prev <- wm$cases/wm$pop
# Maps of outbreaks
pal <- colorNumeric(
palette = "Spectral",
domain = wm$outbreak,
na.color = "white"
)
map <- leaflet(wm) %>%
addProviderTiles(providers$Esri.WorldStreetMap)
mo <- map %>%
setView(lng = 25, lat = 10, zoom = 2) %>%
addPolygons(weight = 0.5,
color = ~pal(outbreak),
popup = ~paste(NAME, outbreak)) %>%
addLegend("bottomright", pal = pal, values = ~outbreak,
title = paste("Outbreaks", "<br>", date_to_analyse, "<br>", disease_to_analyse),
opacity = 1)
# Maps of cases ----
library(leaflet)
pal <- colorNumeric(
palette = "Spectral",
domain = wm$cases,
na.color = "white")
map <- leaflet(wm) %>%
addProviderTiles(providers$Esri.WorldStreetMap)
mc <- map %>%
setView(lng = 25, lat = 10, zoom = 2) %>%
addPolygons(weight = 0.5,
color = ~pal(cases),
popup = ~paste(NAME, cases)) %>%
addLegend("bottomright", pal = pal, values = ~cases,
title = paste("Cases",
"<br>",
date_to_analyse,
"<br>",
disease_to_analyse),
opacity = 1)
# Map of prevalence ----
pal <- colorNumeric(
palette = "Spectral",
domain = wm$prev,
na.color = "white")
map <- leaflet(wm) %>%
addProviderTiles(providers$Esri.WorldStreetMap)
mp <- map %>%
setView(lng = 25, lat = 10, zoom = 2) %>%
addPolygons(weight = 0.5,
color = ~pal(prev),
popup = ~paste(NAME, prev)) %>%
addLegend("bottomright", pal = pal, values = ~prev,
title = paste("prev",
"<br>",
date_to_analyse,
"<br>",
disease_to_analyse),
opacity = 1)
head(wm %>%
as.data.frame() %>%
arrange(desc(prev)) %>%
select(NAME, pop, cases, prev))
}## `summarise()` has grouped output by 'Species', 'iso_code', 'country',
## 'reporting_level'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'Species_fg', 'iso_code', 'country',
## 'reporting_level'. You can override using the `.groups` argument.## NAME pop cases prev
## 1 Bulgaria 694660 100 1.439553e-04
## 2 Hungary 2725900 42 1.540776e-05
## 3 Republic of Moldova 339500 1 2.945508e-06
## 4 Ukraine 5876200 3 5.105340e-07
## 5 Russia 25850147 13 5.028985e-07
## 6 Antigua and Barbuda NA NA NAmomcmpAcosta, Alfredo PhD1. SVA1: FMVZ http://www.sva.se/.