tail(xcells)# 25% 50% 75% day
-# V55 65 65 65 55
-# V56 62 62 62 56
-# V57 64 64 64 57
-# V58 71 71 71 58
-# V59 77 77 77 59
-# V60 78 78 78 603.4 Estimates of mosquito dispersal spread (in km2 )
diff --git a/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-30-1.png b/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-30-1.png
index 7f7a285..64c93ef 100644
Binary files a/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-30-1.png and b/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-30-1.png differ
diff --git a/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-32-1.png b/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-32-1.png
index a366e7a..4b9c674 100644
Binary files a/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-32-1.png and b/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-32-1.png differ
diff --git a/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-33-1.png b/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-33-1.png
index 73d765d..33dac3b 100644
Binary files a/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-33-1.png and b/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-33-1.png differ
diff --git a/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-35-1.png b/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-35-1.png
index 5cb340a..9dad587 100644
Binary files a/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-35-1.png and b/articles/dynamAedes_02_local_files/figure-html/unnamed-chunk-35-1.png differ
diff --git a/articles/dynamAedes_04_uncompModel.html b/articles/dynamAedes_04_uncompModel.html
index ce87a7f..10e1bd0 100644
--- a/articles/dynamAedes_04_uncompModel.html
+++ b/articles/dynamAedes_04_uncompModel.html
@@ -127,17 +127,12 @@ 2024-01-08
both active and passive dispersal of the modelled mosquito species as
well as for the heterogeneity of input temperature data.
We will describe the uncompressed model application for -Ae. albopictus at the punctual scale by using a simulated -temperature dataset. The uncompressed model return the number -of simulated individuals not only for the three main compartments, -i.e. life stages (eggs, juveniles and adults), but also the number of -simulated individuals within each sub-compartments, such as 2-days old -eggs or host-seeking adult females.
-At the punctual scale, the model only requires a weather station -temperature time series provided as a numerical matrix (in degree -Celsius). For the purpose of this tutorial, we simulate a 1-year long -temperature time series and, for the sake of brevity, we will not -discuss the chunk of code already presented in other tutorials.
+Ae. albopictus at the punctual and regional scales by using a +simulated temperature dataset. The uncompressed model return +the number of simulated individuals not only for the three main +compartments, i.e. life stages (eggs, juveniles and adults), but also +the number of simulated individuals within each sub-compartments, such +as 2-days old eggs or host-seeking adult females.1. Sub-compartment structure
@@ -247,6 +242,9 @@1. Sub-compartment structure
For each species, the sub-compartments are arranges as follows.
+
+1.1 Aedes aegypti
+
+
+
+
1.1 Aedes aegypti +
2. Input data and punctual scale model settings
+2. Punctual scale model
-
-#Load packages
-# simulate temperatures
-library(eesim)
-
-# data manipulation and plotting
-library(ggplot2)
-Sys.setlocale("LC_TIME", "en_GB.UTF-8")
+
+2.1 Input data and model settings
+
+At the punctual scale, the model only requires a temperature time
+series, recorded by e.g. a weather station, provided as a numerical
+matrix (in degree Celsius). For the purpose of this tutorial, we
+simulate a 1-year long temperature time series and, for the sake of
+brevity, we will not discuss the chunks of code already presented in
+other tutorials.
# [1] ""
-
+
ndays <- 365*1 #length of the time series in days
set.seed(123)
sim_temp <- create_sims(n_reps = 1,
@@ -561,13 +569,14 @@ 2. Input data and punctual
## convert float temperatures to integer
df_temp <- data.frame("Date" = sim_temp[[1]]$date, "temp" = sim_temp[[1]]$x)
w <- t(as.integer(df_temp$temp*1000)[format(as.Date(str)+1,"%j"):format(as.Date(endr)+1,"%j")])
+
2.2 Run the model
-Running the model specifying compressed.output = FALSE,
-this will return the number of simulated individuals for each
-sub-compartments.
-
+It is crucial to run the model the model specifying the argument
+compressed.output = FALSE. This will return the number of
+simulated individuals for each sub-compartments.
+
simout <- dynamAedes.m(species="albopictus",
scale="ws",
jhwv=habitat_liters,
@@ -583,37 +592,42 @@ 2.2 Run the model verbose=FALSE,
seeding=TRUE)
-
-
-3. Analyse the results
-
+
+2.3 Analyse the results
+
A first summary of simulations can be obtained with:
-
+
summary(simout)
# Length Class Mode
# 1 dynamAedesClass S4
-The simout object is a S4 object where simulation outputs
-and related details are saved in different slot:
-For example, the number of model iterations is saved in:
-
+The simout object is a S4 object where the outputs of the
+model and related details are saved in different slot. For example, the
+number of model iterations is saved in:
+
simout@n_iterations
# [1] 1
-The simulation output is stored for each sub-compartments.
-simout@simulation is a list where the the
-first level stores simulation of different iteration,
-while the secondcorresponds to the simulated days in
-the corresponding iteration. If we inspect the first iteration, we
-observe that the model has computed length(simout[[1]])
-days, since we have started the simulation on the 1st of July and ended
-on the 1st of August.
-
+The model output, i.e. the number of simulated individuals, is stored
+in simout@simulation. For the uncompressed model,
+simout@simulation is a list where the
+first level stores the simulation of different
+iteration, while the second corresponds to the
+simulated days in the corresponding iteration. If we inspect the first
+iteration, we observe that the model has computed
+length(simout[[1]]) days, since we have started the
+simulation on the 1st of July and ended on the 1st of August.
+
length(simout@simulation[[1]])
# [1] 30
-The third level corresponds to an array with the
-quantity of individuals for each compartment (rows) in each day for each
-sub- compartments If we inspect the 1st and the 15th day within the
+
The third level corresponds to an array reporting,
+for a given iteration and a given day, the number of individuals
+belonging to each compartment (rows) for each sub-compartment (the third
+dimension of the array, noted as sc1-…-scN in the
+print). As example, if we inspect the 1st and the 15th day within the
first iteration, we obtain a matrix having:
-
+
+class(simout@simulation[[1]][[1]])
+# [1] "array"
+
simout@simulation[[1]][[1]]
# , , sc1
#
@@ -662,7 +676,7 @@ 3. Analyse the results# juvenile 0
# adult 0
# diapause_egg 0
-
+
simout@simulation[[1]][[15]]
# , , sc1
#
@@ -711,30 +725,42 @@ 3. Analyse the results# juvenile 1
# adult 0
# diapause_egg 0
-We can use the auxiliary functions of the package to analyse the
-results.
+
-3.1 Derive abundance 95% CI for a specific sub-compartment
+2.4 Derive abundance 95% CI for a specific sub-compartment
-
We now compute the interquantile range abundance for the host-seeking
-sub-compartment of the simulated population using the function
-adci.
-
+We can use the auxiliary functions of the package to analyse the
+results. We now compute the interquantile range abundance for the
+host-seeking sub-compartment of the simulated population using the
+function adci.
+
# Retrieve the maximum number of simulated days
dd <- max(simout)
-
# Compute the inter-quartile of abundances along the iterations
-breaks=c(0.25,0.50,0.75)
-ed=1:dd
-
+breaks <- c(0.25,0.50,0.75)
+ed <- 1:dd
hs <- adci(simout, eval_date=ed, breaks=breaks,
stage="Adults",
sub_stage = "Host-seeking" )
-
-head(hs)
-tail(hs)
+head(hs)
+# X25. X50. X75. day stage sub_stage
+# 1 0 0 0 1 Adult Host-seeking
+# 2 0 0 0 2 Adult Host-seeking
+# 3 0 0 0 3 Adult Host-seeking
+# 4 0 0 0 4 Adult Host-seeking
+# 5 0 0 0 5 Adult Host-seeking
+# 6 0 0 0 6 Adult Host-seeking
+
+tail(hs)
+# X25. X50. X75. day stage sub_stage
+# 25 1 1 1 25 Adult Host-seeking
+# 26 1 1 1 26 Adult Host-seeking
+# 27 4 4 4 27 Adult Host-seeking
+# 28 1 1 1 28 Adult Host-seeking
+# 29 1 1 1 29 Adult Host-seeking
+# 30 1 1 1 30 Adult Host-seeking
We can now simply plot it.
-
+
ggplot(hs, aes(x=day, y=X50., group=factor(stage), col=factor(stage))) +
ggtitle("Host-seeking Ae. albopictus Interquantile range abundance") +
geom_ribbon(aes(ymin=X25., ymax=X75., fill=factor(stage)),
@@ -744,7 +770,6 @@ 3.1 Derive abunda
geom_line(linewidth=0.8) +
ylim(0,10)+
labs(x="Date", y="Interquantile range abundance", col="Stage", fill="Stage") +
-# facet_wrap(~stage, scales = "free_y") +
theme_classic() +
theme(legend.pos="bottom",
text = element_text(size=16),
@@ -753,14 +778,14 @@ 3.1 Derive abunda
-4. Regional model
+3. Regional scale model
We can now repeat the exercise for a regional scale model.
-
+
# terra 1.7.65
-
+
gridDim <- 20 # 5000m/250 m = 20 columns and rows
xy <- expand.grid(x=1:gridDim, y=1:gridDim)
varioMod <- vgm(psill=0.5, range=100, model='Exp') # psill = partial sill = (sill-nugget)
@@ -800,7 +825,7 @@ 4. Regional modelcc <- df_temp[,c("x","y")]
We run now the regional model keeping the same settings defined for
the punctual scale model.
-
+
simout <- dynamAedes.m(species="albopictus",
scale="rg",
jhwv=habitat_liters,
@@ -815,7 +840,7 @@ 4. Regional model compressed.output=FALSE,
seeding=TRUE,
verbose=FALSE)
-
+
summary(simout)
# Summary of dynamAedes simulations:
# ----------------------------------
@@ -830,27 +855,22 @@ 4. Regional model# Water in the System: 1 L
# Min days with population: 30
# Max days with population: 30
-
-# Retrieve the maximum number of simulated days
-dd <- max(simout)
-
-# Compute a raster with the median of the iterations
-breaks <- c(0.50)
-ed <- 1:dd
-hs.r <- adci(simout, eval_date=ed, breaks=breaks,
- stage="Adults",
- sub_stage = "Host-seeking", type="N")
-# REGIONAL scale input simulation provided
-# Uncompressed simulation provided
-# Computing spatio-temporal abundance estimate at substage level.
-# Computing sub-compartment Host-seeking Adults abundance...
-
+
# inspect the raster
-hs.r$`Host-seeking_q_0.5`
-
-# plot the raster with the median host-seeking abundace
+hs.r$`Host-seeking_q_0.5`
+# class : SpatRaster
+# dimensions : 20, 20, 30 (nrow, ncol, nlyr)
+# resolution : 250, 250 (x, y)
+# extent : 1220000, 1225000, 5700000, 5705000 (xmin, xmax, ymin, ymax)
+# coord. ref. :
+# source(s) : memory
+# names : day1, day2, day3, day4, day5, day6, ...
+# min values : 0, 0, 0, 0, 0, 0, ...
+# max values : 0, 0, 0, 0, 0, 0, ...
+
+# plot the raster with the median host-seeking abundace
plot(hs.r$`Host-seeking_q_0.5`$day30)
-
+
diff --git a/articles/dynamAedes_04_uncompModel_files/figure-html/unnamed-chunk-18-1.png b/articles/dynamAedes_04_uncompModel_files/figure-html/unnamed-chunk-18-1.png
new file mode 100644
index 0000000..e8519b9
Binary files /dev/null and b/articles/dynamAedes_04_uncompModel_files/figure-html/unnamed-chunk-18-1.png differ
diff --git a/articles/dynamAedes_05_spreader.html b/articles/dynamAedes_05_spreader.html
index e16d195..fb73a70 100644
--- a/articles/dynamAedes_05_spreader.html
+++ b/articles/dynamAedes_05_spreader.html
@@ -359,7 +359,7 @@ 2. Pre-processing
#add temporal ID to the observational dataset and merge the missing dates
tmp <- templatedf %>%
- mutate(temporalID=paste0(year, "_", stringr::str_pad(week(sampling_date), 2, pad="0"))) %>%
+ mutate(temporalID=paste0(year, "_", stringr::str_pad(week(sampling_date), 2, pad="0"))) %>%
select(temporalID, value, delta_week) %>%
full_join(mySeq, by="temporalID" ) %>%
arrange(date)
@@ -500,7 +500,7 @@ 3.1 User-defined sampling period
cols <- c("Observed" = "#E1BE6A", "Post-processed" = "#40B0A6" )
-bind_rows(templatedf %>%
+dplyr::bind_rows(templatedf %>%
mutate(week = lubridate::week(sampling_date),
field = "Observed") %>%
select(week, value, field),
diff --git a/news/index.html b/news/index.html
index cc33cb5..dcc6090 100644
--- a/news/index.html
+++ b/news/index.html
@@ -72,9 +72,10 @@ Changelog
dynamAedes 2.2.8
- Adds reference file for life history traits
AedeslifeHistoryList.
-- Adds function
spread.
-- Adds vignettes 4 (Uncompressed output) and 5 (spread).
+- Adds function
spreader.
+- Adds vignettes 4 (Uncompressed output) and 5 (spreader).
- Fixes minor issues in
adci.
+- check summary print for punctual and local scale: done
dynamAedes 2.2.7
diff --git a/pkgdown.yml b/pkgdown.yml
index 98b7903..1d4d102 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -7,5 +7,5 @@ articles:
dynamAedes_03_regional: dynamAedes_03_regional.html
dynamAedes_04_uncompModel: dynamAedes_04_uncompModel.html
dynamAedes_05_spreader: dynamAedes_05_spreader.html
-last_built: 2024-01-08T13:24Z
+last_built: 2024-01-08T14:55Z
-#Load packages
-# simulate temperatures
-library(eesim)
-
-# data manipulation and plotting
-library(ggplot2)
-Sys.setlocale("LC_TIME", "en_GB.UTF-8") 2.1 Input data and model settings +
+At the punctual scale, the model only requires a temperature time +series, recorded by e.g. a weather station, provided as a numerical +matrix (in degree Celsius). For the purpose of this tutorial, we +simulate a 1-year long temperature time series and, for the sake of +brevity, we will not discuss the chunks of code already presented in +other tutorials.
# [1] ""++ndays <- 365*1 #length of the time series in days set.seed(123) sim_temp <- create_sims(n_reps = 1, @@ -561,13 +569,14 @@2. Input data and punctual ## convert float temperatures to integer df_temp <- data.frame("Date" = sim_temp[[1]]$date, "temp" = sim_temp[[1]]$x) w <- t(as.integer(df_temp$temp*1000)[format(as.Date(str)+1,"%j"):format(as.Date(endr)+1,"%j")])
2.2 Run the model
-Running the model specifying compressed.output = FALSE,
-this will return the number of simulated individuals for each
-sub-compartments.
+It is crucial to run the model the model specifying the argument +
+compressed.output = FALSE. This will return the number of +simulated individuals for each sub-compartments.simout <- dynamAedes.m(species="albopictus", scale="ws", jhwv=habitat_liters, @@ -583,37 +592,42 @@2.2 Run the model verbose=FALSE, seeding=TRUE)
3. Analyse the results -
+2.3 Analyse the results +
A first summary of simulations can be obtained with:
-+summary(simout)-# Length Class Mode # 1 dynamAedesClass S4The simout object is a S4 object where simulation outputs -and related details are saved in different slot:
-For example, the number of model iterations is saved in:
-+The simout object is a S4 object where the outputs of the +model and related details are saved in different slot. For example, the +number of model iterations is saved in:
+simout@n_iterations-# [1] 1The simulation output is stored for each sub-compartments. -
-simout@simulationis a list where the the -first level stores simulation of different iteration, -while the secondcorresponds to the simulated days in -the corresponding iteration. If we inspect the first iteration, we -observe that the model has computedlength(simout[[1]])-days, since we have started the simulation on the 1st of July and ended -on the 1st of August.+The model output, i.e. the number of simulated individuals, is stored +in
+simout@simulation. For the uncompressed model, +simout@simulationis a list where the +first level stores the simulation of different +iteration, while the second corresponds to the +simulated days in the corresponding iteration. If we inspect the first +iteration, we observe that the model has computed +length(simout[[1]])days, since we have started the +simulation on the 1st of July and ended on the 1st of August.length(simout@simulation[[1]])-# [1] 30The third level corresponds to an array with the -quantity of individuals for each compartment (rows) in each day for each -sub- compartments If we inspect the 1st and the 15th day within the +
The third level corresponds to an array reporting, +for a given iteration and a given day, the number of individuals +belonging to each compartment (rows) for each sub-compartment (the third +dimension of the array, noted as sc1-…-scN in the +print). As example, if we inspect the 1st and the 15th day within the first iteration, we obtain a matrix having:
-+++class(simout@simulation[[1]][[1]])+# [1] "array"simout@simulation[[1]][[1]]-# , , sc1 # @@ -662,7 +676,7 @@3. Analyse the results# juvenile 0 # adult 0 # diapause_egg 0
+simout@simulation[[1]][[15]]-# , , sc1 # @@ -711,30 +725,42 @@3. Analyse the results# juvenile 1 # adult 0 # diapause_egg 0
We can use the auxiliary functions of the package to analyse the -results.
+-3.1 Derive abundance 95% CI for a specific sub-compartment +
2.4 Derive abundance 95% CI for a specific sub-compartment
-We now compute the interquantile range abundance for the host-seeking -sub-compartment of the simulated population using the function -adci.
-++We can use the auxiliary functions of the package to analyse the +results. We now compute the interquantile range abundance for the +host-seeking sub-compartment of the simulated population using the +function adci.
++head(hs)# Retrieve the maximum number of simulated days dd <- max(simout) - # Compute the inter-quartile of abundances along the iterations -breaks=c(0.25,0.50,0.75) -ed=1:dd - +breaks <- c(0.25,0.50,0.75) +ed <- 1:dd hs <- adci(simout, eval_date=ed, breaks=breaks, stage="Adults", sub_stage = "Host-seeking" ) - -head(hs) -tail(hs)+# X25. X50. X75. day stage sub_stage +# 1 0 0 0 1 Adult Host-seeking +# 2 0 0 0 2 Adult Host-seeking +# 3 0 0 0 3 Adult Host-seeking +# 4 0 0 0 4 Adult Host-seeking +# 5 0 0 0 5 Adult Host-seeking +# 6 0 0 0 6 Adult Host-seeking++tail(hs)# X25. X50. X75. day stage sub_stage +# 25 1 1 1 25 Adult Host-seeking +# 26 1 1 1 26 Adult Host-seeking +# 27 4 4 4 27 Adult Host-seeking +# 28 1 1 1 28 Adult Host-seeking +# 29 1 1 1 29 Adult Host-seeking +# 30 1 1 1 30 Adult Host-seekingWe can now simply plot it.
-+ggplot(hs, aes(x=day, y=X50., group=factor(stage), col=factor(stage))) + ggtitle("Host-seeking Ae. albopictus Interquantile range abundance") + geom_ribbon(aes(ymin=X25., ymax=X75., fill=factor(stage)), @@ -744,7 +770,6 @@3.1 Derive abunda geom_line(linewidth=0.8) + ylim(0,10)+ labs(x="Date", y="Interquantile range abundance", col="Stage", fill="Stage") + -# facet_wrap(~stage, scales = "free_y") + theme_classic() + theme(legend.pos="bottom", text = element_text(size=16), @@ -753,14 +778,14 @@
3.1 Derive abunda
-4. Regional model +
3. Regional scale model
We can now repeat the exercise for a regional scale model.
-+-# terra 1.7.65+gridDim <- 20 # 5000m/250 m = 20 columns and rows xy <- expand.grid(x=1:gridDim, y=1:gridDim) varioMod <- vgm(psill=0.5, range=100, model='Exp') # psill = partial sill = (sill-nugget) @@ -800,7 +825,7 @@4. Regional modelcc <- df_temp[,c("x","y")]
We run now the regional model keeping the same settings defined for the punctual scale model.
-+-simout <- dynamAedes.m(species="albopictus", scale="rg", jhwv=habitat_liters, @@ -815,7 +840,7 @@4. Regional model compressed.output=FALSE, seeding=TRUE, verbose=FALSE)
diff --git a/articles/dynamAedes_04_uncompModel_files/figure-html/unnamed-chunk-18-1.png b/articles/dynamAedes_04_uncompModel_files/figure-html/unnamed-chunk-18-1.png new file mode 100644 index 0000000..e8519b9 Binary files /dev/null and b/articles/dynamAedes_04_uncompModel_files/figure-html/unnamed-chunk-18-1.png differ diff --git a/articles/dynamAedes_05_spreader.html b/articles/dynamAedes_05_spreader.html index e16d195..fb73a70 100644 --- a/articles/dynamAedes_05_spreader.html +++ b/articles/dynamAedes_05_spreader.html @@ -359,7 +359,7 @@+summary(simout)-# Summary of dynamAedes simulations: # ---------------------------------- @@ -830,27 +855,22 @@4. Regional model# Water in the System: 1 L # Min days with population: 30 # Max days with population: 30
--# Retrieve the maximum number of simulated days -dd <- max(simout) - -# Compute a raster with the median of the iterations -breaks <- c(0.50) -ed <- 1:dd -hs.r <- adci(simout, eval_date=ed, breaks=breaks, - stage="Adults", - sub_stage = "Host-seeking", type="N")-# REGIONAL scale input simulation provided-# Uncompressed simulation provided-# Computing spatio-temporal abundance estimate at substage level.-# Computing sub-compartment Host-seeking Adults abundance...++# inspect the raster -hs.r$`Host-seeking_q_0.5` - -# plot the raster with the median host-seeking abundace +hs.r$`Host-seeking_q_0.5`+# class : SpatRaster +# dimensions : 20, 20, 30 (nrow, ncol, nlyr) +# resolution : 250, 250 (x, y) +# extent : 1220000, 1225000, 5700000, 5705000 (xmin, xmax, ymin, ymax) +# coord. ref. : +# source(s) : memory +# names : day1, day2, day3, day4, day5, day6, ... +# min values : 0, 0, 0, 0, 0, 0, ... +# max values : 0, 0, 0, 0, 0, 0, ...-+# plot the raster with the median host-seeking abundace plot(hs.r$`Host-seeking_q_0.5`$day30)+
2. Pre-processing #add temporal ID to the observational dataset and merge the missing dates tmp <- templatedf %>% - mutate(temporalID=paste0(year, "_", stringr::str_pad(week(sampling_date), 2, pad="0"))) %>% + mutate(temporalID=paste0(year, "_", stringr::str_pad(week(sampling_date), 2, pad="0"))) %>% select(temporalID, value, delta_week) %>% full_join(mySeq, by="temporalID" ) %>% arrange(date) @@ -500,7 +500,7 @@
3.1 User-defined sampling period
cols <- c("Observed" = "#E1BE6A", "Post-processed" = "#40B0A6" ) -bind_rows(templatedf %>% +dplyr::bind_rows(templatedf %>% mutate(week = lubridate::week(sampling_date), field = "Observed") %>% select(week, value, field), diff --git a/news/index.html b/news/index.html index cc33cb5..dcc6090 100644 --- a/news/index.html +++ b/news/index.html @@ -72,9 +72,10 @@Changelog
dynamAedes 2.2.8
- Adds reference file for life history traits
-AedeslifeHistoryList.- Adds function
-spread.- Adds vignettes 4 (Uncompressed output) and 5 (spread).
+- Adds function
+spreader.- Adds vignettes 4 (Uncompressed output) and 5 (spreader).
- Fixes minor issues in
+adci.- check summary print for punctual and local scale: done
dynamAedes 2.2.7
diff --git a/pkgdown.yml b/pkgdown.yml index 98b7903..1d4d102 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -7,5 +7,5 @@ articles: dynamAedes_03_regional: dynamAedes_03_regional.html dynamAedes_04_uncompModel: dynamAedes_04_uncompModel.html dynamAedes_05_spreader: dynamAedes_05_spreader.html -last_built: 2024-01-08T13:24Z +last_built: 2024-01-08T14:55Z