updated README and figure scripts.

README.md

@@ -1,10 +1,10 @@
 # [AnophelesModel](https://swisstph.github.io/AnophelesModel/) 
-An R package to quantify the impact of vector control interventions according to mosquito species characteristics 
+AnophelesModel is an R package to quantify the impact of vector control interventions according to mosquito species characteristics. It can be used to parameterize a [dynamical model of the mosquito feeding cycle](https://www.tandfonline.com/doi/full/10.1080/17513750701769857) using data about mosquito bionomics (entomological characteristics) and biting patterns, as well as human activity and intervention effects. The different types of data have been extracted from field studies and are included in the package. The model infers the species-specific impact of various vector control interventions on the vectorial capacity. The package can be used to compare the impact of interventions for different mosquito species and to generate geographic-specific parameterizations for the entomological and vector control components of more complex models of malaria transmission dynamics.
 
-The AnophelesModel package can be used to parameterize a [dynamical model of the mosquito feeding cycle](https://www.tandfonline.com/doi/full/10.1080/17513750701769857) using data about mosquito bionomics (entomological characteristics) and biting patterns, as well as human activity and intervention effects. The different types of data have been extracted from field studies and are included in the package. The model infers the species-specific impact of various vector control interventions on the vectorial capacity. The package can be used to compare the impact of interventions for different mosquito species and to generate parameterizations for the entomological and vector control components of more complex models of malaria transmission dynamics.
+This package is associated with the following manuscript:
+M. Golumbeanu, O. Briët, C. Champagne, J. Lemant, M. Winkel, B. Zogo, M. Gerhards, M. Sinka, N. Chitnis, M. Penny, E. Pothin, T. Smith. "**AnophelesModel: An R package to quantify the effect of vector bionomics on the impact of vector control interventions against malaria transmitted by *Anopheles* mosquitoes**".
 
-This package is associated with the following manuscript in preparation:
-M. Golumbeanu, O. Briët, C. Champagne, J. Lemant, B. Zogo, M. Gerhards, M. Sinka, N. Chitnis, M. Penny, E. Pothin, T. Smith. "**AnophelesModel: An R package to quantify the effect of vector bionomics on the impact of vector control interventions against malaria transmitted by *Anopheles* mosquitoes**".
+The scripts and data used for generating figures 2-4 in the manuscript are provided at https://github.com/SwissTPH/AnophelesModel/tree/main/extdata (scripts example_fig2.R, etc.).
 
 To install the package:
 ```{r}

extdata/example_fig2.R

@@ -1,5 +1,5 @@
 ###############################
-# Script for generating Fig. 1 which describes the heterogeneity of bionomics,
+# Script for generating Fig. 2 which describes the heterogeneity of bionomics,
 # mosquito biting and intervention effects and emphasizes the need of a
 # way to incorporate those when estimating the effect of vector control
 #
@@ -13,6 +13,7 @@ library(tidyr)
 library(reshape2)
 library(stringr)
 library(ggpubr)
+library(reporter)
 
 plot_activities_human = function(country, id_tab, act_file) {
 
@@ -200,8 +201,9 @@ nets_prop_Kenya = nets_prop_Kenya %>% filter(Parameter != "SD of log transformed
 nets_prop_Kenya = nets_prop_Kenya %>% filter(Net_Type != "PermaNet 3.0")
 nets_prop_Kenya$L95 = pmax(nets_prop_Kenya$L95, 0)
 nets_prop_Kenya = nets_prop_Kenya %>% filter(Semester < 7)
-nets_prop_Kenya[which(nets_prop_Kenya$Parameter == "Mean of log transformed holed area"), "Title"] = "Mean LLIN log holed area"
-nets_prop_Kenya[which(nets_prop_Kenya$Parameter == "Survival"), "Title"] = "LLIN survival"
+title_area = paste0("Mean LLIN log holed area (cm" %p% supsc("2"), ")")
+nets_prop_Kenya[which(nets_prop_Kenya$Parameter == "Mean of log transformed holed area"), "Title"] = title_area
+nets_prop_Kenya[which(nets_prop_Kenya$Parameter == "Survival"), "Title"] = "LLIN survival (% remaining nets)"
 
 p_net_decay_country = ggplot(nets_prop_Kenya, aes(x = Semester, y = Value, fill = Net_Type)) +
     geom_bar(stat = "identity", position = position_dodge(width=0.9)) +
@@ -216,4 +218,6 @@ p_net_decay_country = ggplot(nets_prop_Kenya, aes(x = Semester, y = Value, fill
     labs(fill = "LLIN type")
 
 p_fig2 = ggarrange(plotlist = list(p_bio, p_act, p_net_decay_country), ncol = 1, nrow = 3, labels = c("A", "B", "D"))
-ggsave("~/paper_AnophelesModel/Figures/Fig2.pdf", width = 11, height = 9)
+
+# To modify ouptut folder accordingly
+# ggsave("~/paper_AnophelesModel/Figures/Fig2.pdf", width = 11, height = 9)

extdata/example_fig3.R

@@ -1,6 +1,8 @@
 ###############################
-# Script for generating Fig. 3 which presents the impact on vectorial capacity
-# as well as integration with OpenMalaria
+# Script for generating Fig. 3 which presents the decay of intervention effects
+# for LLIN in PNG and KEN as well as corresponding reduction in vectorial capacity.
+# This script also generates the GVI snippets to be used for parameterising OpenMalaria.
+#
 # [email protected]
 # 22.08.2022
 ##############################
@@ -163,6 +165,6 @@ p_vc = ggplot(impact_df, aes(x = intervention_coverage, y = intervention_impact*
                 labs(x = "Coverage", y="Mean reduction in\nvectorial capacity (%)")
 
 p_fig3 = ggarrange(plotlist = list(p_effects, p_vc), ncol = 1, nrow = 2, labels = c("A", "B"))
-ggsave("~/paper_AnophelesModel/Figures/Fig3.pdf", width = 8, height = 7)
+# ggsave("~/paper_AnophelesModel/Figures/Fig3.pdf", width = 8, height = 7)
 
 

extdata/example_fig4.R

@@ -1,5 +1,6 @@
 #################################
 # Example script for downstream analysis using the postprocessed simulation outputs
+# This script generates Figure 4 from the paper.
 #
 # 10.11.2022
 # [email protected]
@@ -8,12 +9,22 @@ library(RSQLite)
 library(magrittr)
 library(dplyr)
 library(ggplot2)
+library(lubridate)
+####################
+# PART TO BE SPECIFIED BY THE USER
+# The file paths are relative to the "extdata/om_data/" directory of the AnophelesModel git repository
+# This working directory needs to be specified for each user as it depends on their system
+working_dir = "~/GitRepos/STPHrepos/AnophelesModel/extdata/om_data/"
+# Folder where the generated figure should be saved
+out_dir = "~/paper_AnophelesModel/Figures/"
+#####################
+
 
 # Define database file path
-db_exp_KEN = "/scicore/home/pothin/golmon00/OpenMalaria/AnophelesModel/KEN.sqlite"
-db_exp_PNG = "/scicore/home/pothin/golmon00/OpenMalaria/AnophelesModel/PNG.sqlite"
-scens_KEN = readRDS("/scicore/home/pothin/golmon00/OpenMalaria/AnophelesModel/KEN/cache/scenarios.rds")
-scens_PNG = readRDS("/scicore/home/pothin/golmon00/OpenMalaria/AnophelesModel/PNG/cache/scenarios.rds")
+db_exp_KEN = paste0(working_dir, "KEN.sqlite")
+db_exp_PNG = paste0(working_dir, "PNG.sqlite")
+scens_KEN = readRDS(paste0(working_dir, "scenarios_KEN.rds"))
+scens_PNG = readRDS(paste0(working_dir, "scenarios_PNG.rds"))
 
 # Read the results table from the database:
 # open database connection:
@@ -62,16 +73,20 @@ plot_df[which(plot_df$setting == "PNG"), "setting"] = "Papua New Guinea"
 
 plot_df$setting = factor(plot_df$setting, levels = c("Papua New Guinea", "Kenya"))
 
+# Make the dates relevant for the present time
+plot_df$date = as.Date(plot_df$date) %m+% years(23)
+
 ggplot(plot_df, aes(x = as.Date(date), y = mean_prev*100, color = setting)) +
   theme_light() + theme_linedraw() + theme_bw(base_size = 16) +
   geom_ribbon(aes(ymin = (mean_prev - sd_prev)*100, ymax = (mean_prev - sd_prev)*100 + 1,
                   fill = setting, alpha = 0.5, color = NULL), show.legend = FALSE) +
-    geom_vline(xintercept = as.Date("2000-01-01"), color = "black", linetype = "dashed") +
-  labs(x = "Time", y="Prevalence (%)") +
+    geom_vline(xintercept = as.Date("2023-01-01"), color = "black", linetype = "dashed") +
+  labs(x = "Time since LLIN deployment (years)", y="Prevalence (%)") +
   scale_color_manual(values = c("#74c476", "#c51b8a")) +
     scale_fill_manual(values = c("#74c476", "#c51b8a")) +
+    scale_x_date(date_breaks = "4 years",date_labels = "%Y")+
   labs(color = "Setting") + theme(legend.position = "top") +
   geom_line()
 
-ggsave("~/paper_AnophelesModel/Figures/Fig4.pdf", width = 8, height = 4)
+ggsave(paste0(out_dir, "Fig4.pdf"), width = 8, height = 4)