Zika

R/zika_maps.R

@@ -1,3 +1,5 @@
+# moved to an orderly task within priority pathogens project
+
 library(ggplot2)
 library(sf)
 library(rnaturalearth)

orderly_config.yml

@@ -1,4 +1,4 @@
-minimum_orderly_version: "1.99.0"
+minimum_orderly_version: "2.0.2"
 plugins:
   orderly.sharedfile:
     lassasingledb: "Z:/Lassa/Databases/Single Extractions"

shared/lassa_functions.R

@@ -534,6 +534,8 @@ metaprop_wrap <- function(dataframe, subgroup,
                      method.tau = "ML")
 
     png(file = "temp.png", width = width, height = height, res = resolution)
+    # postscript("CZS_metaanalysis_trimester.eps", width = 15, height = 37, horizontal = FALSE)
+    # pdf("CZS_metaanalysis_trimester.pdf", width = 10, height = 15)
     forest(mtan, layout = "RevMan5",
            overall = plot_pooled, pooled.events = TRUE,
            print.subgroup.name = FALSE, sort.subgroup = sort_by_subg,
@@ -543,9 +545,12 @@ metaprop_wrap <- function(dataframe, subgroup,
            col.diamond.lines = "black",col.diamond.common = colour, col.diamond.random = colour,
            col.subgroup = "black", col.inside = "black",
            weight.study = "same", #col.square.lines = "green", col.square = "blue", #not working
-           at = at, xlim = xlim, xlab=xlabel, fontsize=11)
+           at = at, xlim = xlim, xlab=xlabel, fontsize = 11)
+           # fontsize=9,  # Reduce font size
+           # spacing = 0.7,  # Reduce spacing between studies
+           # squaresize = 0.5)
     dev.off()
-  } else {
+    } else {
     mtan <- metaprop(data = dataframe,
                      studlab = dataframe[[studylabels]],
                      event = cfr_ifr_numerator,

src/zika_compilation/zika_cleaning.R

@@ -164,6 +164,7 @@ zika_clean_articles <- function(df, pathogen){
   df <- df %>%
     mutate(# Fix issues with dois
       doi = str_remove_all(doi, 'doi:'),
+      doi = str_remove_all(doi, 'DOI: '),
       doi = str_remove_all(doi, 'http://dx.doi.org/'),
       doi = str_remove_all(doi, 'https://doi.org/'),
       doi = str_remove(doi, "^/"),
@@ -173,7 +174,17 @@ zika_clean_articles <- function(df, pathogen){
         covidence_id %in% 1154 ~ "10.1136/bmjgh-2017-000309",
         covidence_id %in% 1993 ~ "10.1080/00034983.1983.11811687",
         covidence_id %in% 3042 ~ "10.1371/journal.pntd.0004726",
+        covidence_id %in% 5695 ~ '10.3390/ijerph18041831',
+        covidence_id %in% 8141 ~ '10.1016/j.ijid.2023.01.033',
+        covidence_id %in% 11565 ~ '10.3390/v13030523',
+        covidence_id %in% 31965 ~ '10.1001/jamanetworkopen.2019.8124',
         TRUE ~ doi
+      ),
+      journal = case_when(
+        covidence_id %in% 5695 ~ 'INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH',
+        covidence_id %in% 8141 ~ 'INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES',
+        covidence_id %in% 11565 ~ 'Viruses',
+        TRUE ~ journal
       )) #%>%
   # # Update article label
   #   # for different articles by the same author in the same year

src/zika_compilation/zika_compilation.R

@@ -3,7 +3,7 @@
 # install.packages('rio')
 library(dplyr)
 library(janitor)
-library(orderly2)
+library(orderly)
 library(readr)
 library(stringr)
 library(stringi)
@@ -237,12 +237,12 @@ saveRDS(params_clean, 'parameters.rds')
 params_clean_epireview <- params_clean %>%
   select(-pathogen, -starts_with('parameter_2'), -delay_start, -access_param_id,
          -other_delay_start, -other_delay_end, -method_2_from_supplement,
-         -starts_with('distribution_2'), -exponent_2, inverse_param_2, name_data_entry,
+         -starts_with('distribution_2'), -exponent_2, -inverse_param_2, -name_data_entry,
          -other_delay) %>%
   mutate(prnt_on_elisa = ifelse(prnt_on_elisa == 'V', TRUE, ifelse(prnt_on_elisa == 'F', FALSE, NA)))
 
 articles_qa_epireview <- articles_qa %>%
-  select(-name_data_entry, -covidence_id_text, -qa_denominator, -qa_numerator, -qa_score)
+  select(-name_data_entry, -notes, -covidence_id_text, -qa_denominator, -qa_numerator, -qa_score)
 
 write_csv(articles_qa_epireview, 'zika_articles.csv')
 write_csv(models_clean, 'zika_models.csv')

src/zika_delays/zika_delays.R

@@ -14,7 +14,7 @@ library(png)
 library(grid)
 library(patchwork)
 library(gridExtra)
-library(orderly2)
+library(orderly)
 library(countrycode)
 
 #orderly preparation
@@ -391,7 +391,8 @@ CCDD
 EEEE"
 delays_plot <-  incp2 +  IP_forest  + lat_onset_serialplt + outcome_forest + eip_forest+
   plot_layout(design = layout) + plot_annotation(tag_levels = 'A')
-ggsave("delays.png", plot = delays_plot, width = 24, height = 25, bg = 'white')
+ggsave("delays.png", plot = delays_plot, width = 24, height = 25, bg = 'white', device = cairo_pdf)
+ggsave("delays.eps", plot = delays_plot, width = 24, height = 25, bg = 'white', device = cairo_ps)
 ggsave("delays.pdf", plot = delays_plot, width = 24, height = 25, bg = 'white')
 
 delays_plotSI <-  incp2_noqa + IP_forest_noqa + lat_onset_serialplt_noqa + outcome_forest_noqa + eip_forest_noqa +

src/zika_outbreaks/zika_outbreaks.R

@@ -1,4 +1,4 @@
-# Task to produce results for outbreaks 
+# Task to produce results for outbreaks
 
 library(tidyverse)
 library(ggplot2)
@@ -10,7 +10,7 @@ library(ggrepel)
 library(countrycode)
 library(ggpattern)
 
-#orderly preparation 
+#orderly preparation
 orderly_strict_mode()
 orderly_parameters(pathogen = NULL)
 orderly_dependency("zika_prep_data", "latest(parameter:pathogen == this:pathogen &&
@@ -24,26 +24,26 @@ outbreaks <- readRDS('outbreaks_curated.rds')
 articles <- readRDS('articles_curated.rds')
 world <- ne_countries(scale = 'medium', returnclass = 'sf')
 
-outbreaks <- merge(outbreaks, articles, by = "covidence_id") 
+outbreaks <- merge(outbreaks, articles, by = "covidence_id")
 outbreaks <-   filter(outbreaks, !qa_score < 0.5)
 
 outbreaks.WHO <- c(
   "Angola", "Burkina Faso", "Burundi", "Cabo Verde", "Cameroon", "Central African Republic",
   "Côte d’Ivoire", "Ethiopia", "Gabon", "Guinea-Bissau", "Kenya", "Nigeria", "Senegal", "Uganda",
   "Anguilla", "Antigua and Barbuda" , "Argentina", "Aruba", "Bahamas", "Barbados", "Belize", "Bolivia",
   "Bonaire", "Sint Eustatius", "Saba", "Brazil", "British Virgin Islands",
-  "Cayman Islands", "Colombia", "Costa Rica", "Cuba", "Curaçao", "Dominica", "Dominican Republic", 
+  "Cayman Islands", "Colombia", "Costa Rica", "Cuba", "Curaçao", "Dominica", "Dominican Republic",
   "Ecuador", "El Salvador", "French Guiana", "Grenada", "Guadeloupe", "Guatemala",
   "Guyana", "Haiti", "Honduras", "Easter Island– Chile", "Jamaica", "Martinique", "Mexico",
   "Montserrat", "Nicaragua", "Panama", "Paraguay", "Peru", "Puerto Rico", "Saint Barthélemy",
   "Saint Kitts", "Nevis", "Saint Lucia", "Saint Martin", "Saint Vincent", "Grenadines",
-  "Saint Maarten", "Suriname", "Trinidad", "Tobago", "Turks", "Caicos", 
-  "United States of America", "United States Virgin Islands", "Venezuela", 
+  "Saint Maarten", "Suriname", "Trinidad", "Tobago", "Turks", "Caicos",
+  "United States of America", "United States Virgin Islands", "Venezuela",
   "Bangladesh", "India", "Indonesia", "Maldives", "Myanmar", "Thailand",
-  "American Samoa", "Cambodia", "Cook Islands", "Fiji", "French Polynesia", 
+  "American Samoa", "Cambodia", "Cook Islands", "Fiji", "French Polynesia",
   "Lao People’s Democratic Republic","Marshall Islands", "Malaysia", "Micronesia" ,
    "New Caledonia", "Palau", "Papua New Guinea", "Philippines", "Samoa", "Singapore",
-  "Solomon Islands", "Tonga", "Vanuatu", "Vietnam", "France", "The Bahamas" 
+  "Solomon Islands", "Tonga", "Vanuatu", "Vietnam", "France", "The Bahamas"
 )
 
 outbreaks_agg <- outbreaks %>%
@@ -70,7 +70,7 @@ out_sf <- out_sf %>%
 out_plt <- ggplot() +
   # Layer based on n outbreaks
   geom_sf(data = out_sf, aes(fill = as.factor(n)), color = "gray50", lwd = 0.3) +
-  
+
   # Layer for outbreak.WHO
   geom_sf_pattern(
     data = filter(out_sf, is_outbreak_who),
@@ -85,26 +85,46 @@ out_plt <- ggplot() +
     pattern_size = 0.01,
     color = NA
   ) +
-  
+
   # Viridis scale
-  scale_fill_viridis_d(na.value = 'grey90', guide = guide_legend(na.translate = FALSE)) +
+  scale_fill_viridis_d(na.value = 'grey90',  guide = guide_legend(
+    na.translate = FALSE,
+    keywidth = unit(0.3, "cm"),
+    keyheight = unit(0.3, "cm")
+  )) +
   theme_void() +
   theme(axis.text = element_blank(),
         text = element_text(size = 14)) +
   labs(fill = 'Number of papers\nreporting outbreaks',
        x = '',
        y = '') +
-  
+
   # labels
   geom_label_repel(data = out_sf %>% filter(!is.na(n)),
-                   aes(x = label_x, y = label_y, label = paste0(admin, ": ", n)), 
+                   aes(x = label_x, y = label_y, label = paste0(admin, ": ", n)),
                    fontface = "bold", size = 3,
                    max.overlaps = 50,
                    label.padding = 0.2) +
-
+  theme(legend.position = c(0.1,0.2),
+        legend.text = element_text(size = 6),
+        legend.title = element_text(size = 6))+
+
   coord_sf(xlim = c(-180, 180), ylim = c(-60, 90))
 
+ggsave(
+       "outbreaks_QA2.pdf",
+       device = cairo_pdf,
+      plot   = out_plt,
+      width  = 180,
+      height = 100,
+     scale = 1.2,
+       units  = "mm",
+       bg     = "white"
+   )
+
 ggsave("outbreaks_QA.png", out_plt, bg = 'white', height = 8, width = 14, dpi =300)
+ggsave("outbreaks_QA.svg", out_plt, bg = 'white', height = 8, width = 14, dpi =300)
+ggsave("outbreaks_QA.eps", out_plt, device = eps, bg = 'white', height = 8, width = 14, dpi =300)
 # ggsave("outbreaks.png", out_plt, bg = 'white', height = 8, width = 14, dpi =300)
 # ggsave("sero_all.png", sero_all, bg = 'white')
 # ggsave("sero_general.png", sero_gen, bg = 'white')
@@ -114,13 +134,13 @@ ggsave("outbreaks_QA.png", out_plt, bg = 'white', height = 8, width = 14, dpi =3
 # nrow(outbreaks)
 # length(unique(outbreaks$covidence_id))
 # length(unique(outbreaks$outbreak_country))
-# 
+#
 # table(outbreaks$outbreak_country)
 # outsumm <- as.data.frame(table(outbreaks$outbreak_country, outbreaks$covidence_id)) %>%
 #   filter(Freq > 0) %>%
 #   group_by(Var1) %>%
 #   count()
-# 
+#
 # outbreaks <- outbreaks %>%
 #   mutate(continent = countrycode(sourcevar = outbreak_country,
 #                                  origin = "country.name",

src/zika_prep_data/zika_prep_data.R

@@ -5,6 +5,7 @@
 # as well as the ad-hoc data cleaning in each of the analysis orderly tasks
 library(dplyr)
 library(stringr)
+library(readr)
 
 #orderly preparation
 orderly_strict_mode()

src/zika_reproduction_numbers/zika_reproduction_numbers.R

@@ -14,7 +14,7 @@ library(png)
 library(grid)
 library(patchwork)
 library(gridExtra)
-library(orderly2)
+library(orderly)
 library(countrycode)
 library(janitor)
 
@@ -320,13 +320,21 @@ re_mosquito <- forest_plot_zika(parameters %>% filter(parameter_type == 'Reprodu
 
 HEIGHT = 25
 WIDTH = 20
-ggsave("r0_pc.pdf", r0_pc, height = HEIGHT, width = WIDTH, bg = 'white')
-ggsave("r0_sampletype.pdf", r0_sampletype, height = 24, width = WIDTH, bg = 'white')
-ggsave("r0_human.pdf", r0_human, height = 10, width = WIDTH, bg = 'white')
-ggsave("r0_mosquito.pdf", r0_mosquito, height = 8, width = WIDTH, bg = 'white')
-ggsave("re.pdf", re, height = 14, width = WIDTH, bg = 'white')
-ggsave("re_human.pdf", re_human, height = 12, width = 12, bg = 'white')
-ggsave("re_mosquito.pdf", re_mosquito, height = 10, width = 12, bg = 'white')
+ggsave("r0_pc.pdf", r0_pc, height = HEIGHT, width = WIDTH, bg = 'white', device = cairo_pdf)
+ggsave("r0_sampletype.pdf", r0_sampletype, height = 24, width = WIDTH, bg = 'white', device = cairo_pdf)
+ggsave("r0_human.pdf", r0_human, height = 10, width = WIDTH, bg = 'white', device = cairo_pdf)
+ggsave("r0_mosquito.pdf", r0_mosquito, height = 8, width = WIDTH, bg = 'white', device = cairo_pdf)
+ggsave("re.pdf", re, height = 14, width = WIDTH, bg = 'white', device = cairo_pdf)
+ggsave("re_human.pdf", re_human, height = 12, width = 12, bg = 'white', device = cairo_pdf)
+ggsave("re_mosquito.pdf", re_mosquito, height = 10, width = 12, bg = 'white', device = cairo_pdf)
+
+ggsave("r0_pc.eps", r0_pc, height = HEIGHT, width = WIDTH, bg = 'white', device = cairo_ps)
+ggsave("r0_sampletype.eps", r0_sampletype, height = 24, width = WIDTH, bg = 'white', device = cairo_ps)
+ggsave("r0_human.eps", r0_human, height = 10, width = WIDTH, bg = 'white', device = cairo_ps)
+ggsave("r0_mosquito.eps", r0_mosquito, height = 8, width = WIDTH, bg = 'white', device = cairo_ps)
+ggsave("re.eps", re, height = 14, width = WIDTH, bg = 'white', device = cairo_ps)
+ggsave("re_human.eps", re_human, height = 12, width = 12, bg = 'white', device = cairo_ps)
+ggsave("re_mosquito.eps", re_mosquito, height = 10, width = 12, bg = 'white', device = cairo_ps)
 
 ggsave("r0_pc.png", r0_pc, height = HEIGHT, width = WIDTH, bg = 'white')
 ggsave("r0_sampletype.png", r0_sampletype, height = 24, width = WIDTH, bg = 'white')
@@ -375,6 +383,11 @@ ggsave("r0_plnoqa.pdf", r0__plnoqa, height = 25, width = 20, bg = 'white')
 ggsave("re_pl.pdf", re_pl, height = 12, width = 20, bg = 'white')
 ggsave("re_plnoqa.pdf", re_plnoqa, height = 12, width = 20, bg = 'white')
 
+ggsave("r0_pl.eps", r0_pl, height = 25, width = 20, bg = 'white', device = cairo_ps)
+ggsave("r0_plnoqa.eps", r0__plnoqa, height = 25, width = 20, bg = 'white', device = cairo_ps)
+ggsave("re_pl.eps", re_pl, height = 12, width = 20, bg = 'white', device = cairo_ps)
+ggsave("re_plnoqa.eps", re_plnoqa, height = 12, width = 20, bg = 'white', device = cairo_ps)
+
 ggsave("r0_pl.png", r0_pl, height = 25, width = 20, bg = 'white', dpi = 400)
 ggsave("r0_plnoqa.png", r0__plnoqa, height = 25, width = 20, bg = 'white', dpi = 400)
 ggsave("re_pl.png", re_pl, height = 12, width = 20, bg = 'white', dpi = 400)

src/zika_serop/zika_serop.R

@@ -307,8 +307,8 @@ central_africa <- ggplot() +
 central_africa <- central_africa+
   labs(fill = NULL, col = NULL) +
   theme(legend.position = c(0.2,0.3),
-        legend.text = element_text(size = 6),
-        legend.title = element_text(size = 6))+
+        legend.text = element_text(size = 12),
+        legend.title = element_text(size = 12))+
   # theme(legend.position = "none")+
         # legend.background = element_rect(fill = "transparent"))+
   #theme(legend.position = c(0.1,0.2))+
@@ -334,9 +334,22 @@ central_asia <- central_asia +
     axis.title = element_blank()      # Remove axis titles
   )
 
+# coord <- coord_sf(expand = FALSE)
+
+# south_america <- south_america + coord
+# central_africa <- central_africa + coord
+# central_asia <- central_asia + coord
+
+final_plot <- ggarrange(
+  south_america,
+  central_africa,
+  central_asia,
+  ncol = 3,
+  align = "hv"
+)
 
 # Final multipanel plot
-final_plot <- wrap_plots(south_america, central_africa, central_asia, ncol = 3)
+final_plot <- ggarrange(south_america, central_africa, central_asia, ncol = 3)
 # +
 #   plot_layout(guides = "none")
 # final_plot
@@ -348,8 +361,42 @@ ggsave(
   paste0("ZIKA_serop_map_QA.png"),
   plot = final_plot,
   device = png,
+
   scale = 1,
-  height = 50,
+  height = 100,
+  width = 200,
+  units = "mm",
+  dpi = 300)
+
+ggsave(
+  paste0("ZIKA_serop_map_QA_AF.pdf"),
+  plot = central_africa,
+  # device = pdf,
+  device = cairo_pdf,
+  scale = 1,
+  height = 200,
+  width = 200,
+  units = "mm",
+  dpi = 300)
+
+ggsave(
+  paste0("ZIKA_serop_map_QA_SA.pdf"),
+  plot = south_america,
+  # device = pdf,
+  device = cairo_pdf,
+  scale = 1,
+  height = 200,
+  width = 200,
+  units = "mm",
+  dpi = 300)
+
+ggsave(
+  paste0("ZIKA_serop_map_QA_AS.pdf"),
+  plot = central_asia,
+  device = cairo_pdf,
+  # device = pdf,
+  scale = 1,
+  height = 200,
   width = 200,
   units = "mm",
   dpi = 300)

src/zika_zcs_microcephaly/zika_zcs_microcephaly.R

@@ -14,7 +14,7 @@ library(png)
 library(grid)
 library(patchwork)
 library(gridExtra)
-library(orderly2)
+library(orderly)
 library(countrycode)
 
 #orderly preparation
@@ -181,7 +181,8 @@ CZSplot_noqa <- forest_plot_zika(CZS_rate %>% arrange(central) ,
                             custom_colours = cols) +
   theme(legend.position = 'inside', legend.position.inside =  c(.66, 0.36))
 # ggsave(filename = 'CZS_plot_loc_country.svg', CZSplot, height =26, width = 14, bg = 'white')
-ggsave(filename = 'CZS_plot_loc_country.pdf', CZSplot, height =24, width = 15, bg = 'white')
+ggsave(filename = 'CZS_plot_loc_country.pdf', CZSplot, height =24, width = 15, bg = 'white', device = cairo_pdf)
+ggsave(filename = 'CZS_plot_loc_country.eps', CZSplot, height =24, width = 15, bg = 'white', device = cairo_ps)
 # ggsave(filename = 'CZS_plot_loc_country_noqa.svg', CZSplot_noqa, height =32, width = 16, bg = 'white')
 ggsave(filename = 'CZS_plot_loc_country_noqa.pdf', CZSplot_noqa, height =32, width = 16, bg = 'white')
 
@@ -360,7 +361,8 @@ metaanalysis_CZS_trimester <- metaprop_wrap(dataframe = CZSqa ,
 CZS_meta_trimester <- metaanalysis_CZS_trimester$plot
 
 ggsave(filename = "CZS_metaanalysis_trimester.svg", plot = CZS_meta_trimester, width = 5, height = 9)
-ggsave(filename = "CZS_metaanalysis_trimester.pdf", plot = CZS_meta_trimester, width = 5, height = 9)
+ggsave(filename = "CZS_metaanalysis_trimester.pdf", plot = CZS_meta_trimester, width = 5, height = 9, device = cairo_pdf)
+ggsave(filename = "CZS_metaanalysis_trimester.eps", plot = CZS_meta_trimester, width = 9, height = 5, device = cairo_ps, dpi = 500)
 
 metaanalysis_CZS_trimester_noqa <- metaprop_wrap(dataframe = CZS_meta_noqa,
                                             plot_pooled = FALSE, subgroup = 'trimester_exposed',