Corrected vignettes to reflect changes in n_detect_lm outputs.

vignettes/Antimalarial_Resistance.Rmd

@@ -107,9 +107,9 @@ With the outputs from the `run_simulation()` function, we can visualise the effe
 ```{r, fig.align = 'center', eval = TRUE}
 
 # In each timestep, calculate PfPR_2-10 and add it to as a new column for each simulation output:
-sim_out_baseline$pfpr210 = sim_out_baseline$n_detect_730_3650/sim_out_baseline$n_730_3650
-sim_out_clin_treatment$pfpr210 = sim_out_clin_treatment$n_detect_730_3650/sim_out_clin_treatment$n_730_3650
-sim_out_resistance$pfpr210 = sim_out_resistance$n_detect_730_3650/sim_out_resistance$n_730_3650
+sim_out_baseline$pfpr210 = sim_out_baseline$n_detect_lm_730_3650/sim_out_baseline$n_730_3650
+sim_out_clin_treatment$pfpr210 = sim_out_clin_treatment$n_detect_lm_730_3650/sim_out_clin_treatment$n_730_3650
+sim_out_resistance$pfpr210 = sim_out_resistance$n_detect_lm_730_3650/sim_out_resistance$n_730_3650
 
 # Plot the prevalence through time in each of the three simulated scenarios:
 plot.new(); par(mar = c(4, 4, 1, 1), new = TRUE)
@@ -225,7 +225,7 @@ We can visualise the effect of increasing resistance through time by plotting th
 ```{r, fig.align = 'center', eval = TRUE}
 
 # Calculate the prevalence (PfPR210) through time:
-dynamic_resistance_output$pfpr210 <- dynamic_resistance_output$n_detect_730_3650/dynamic_resistance_output$n_730_3650
+dynamic_resistance_output$pfpr210 <- dynamic_resistance_output$n_detect_lm_730_3650/dynamic_resistance_output$n_730_3650
 
 # Open a new plotting window and add a grid:
 plot.new(); par(mar = c(4, 4, 1, 1), new = TRUE)
@@ -365,27 +365,27 @@ plot.new(); par(mar = c(4, 4, 1, 1), new = TRUE)
 
 # Plot malaria prevalence in 2-10 years through time:
 plot(x = simulation_outputs$base$timestep,
-     y = simulation_outputs$base$n_detect_730_3650/simulation_outputs$base$n_730_3650,
+     y = simulation_outputs$base$n_detect_lm_730_3650/simulation_outputs$base$n_730_3650,
      xlab = "Time (days)",
      ylab = expression(paste(italic(Pf),"PR"[2-10])), cex = 0.8,
      ylim = c(0, 1), type = "l", lwd = 2, xaxs = "i", yaxs = "i",
      col = cols[3])
 
 # Add the dynamics for no-intervention simulation
 lines(x = simulation_outputs$treatment$timestep,
-      y = simulation_outputs$treatment$n_detect_730_3650/simulation_outputs$treatment$n_730_3650,
+      y = simulation_outputs$treatment$n_detect_lm_730_3650/simulation_outputs$treatment$n_730_3650,
       col = cols[4])
 
 lines(x = simulation_outputs$etf$timestep,
-      y = simulation_outputs$etf$n_detect_730_3650/simulation_outputs$etf$n_730_3650,
+      y = simulation_outputs$etf$n_detect_lm_730_3650/simulation_outputs$etf$n_730_3650,
       col = cols[5])
 
 lines(x = simulation_outputs$spc$timestep,
-      y = simulation_outputs$spc$n_detect_730_3650/simulation_outputs$spc$n_730_3650,
+      y = simulation_outputs$spc$n_detect_lm_730_3650/simulation_outputs$spc$n_730_3650,
       col = cols[6])
 
 lines(x = simulation_outputs$etf_spc$timestep,
-      y = simulation_outputs$etf_spc$n_detect_730_3650/simulation_outputs$etf_spc$n_730_3650,
+      y = simulation_outputs$etf_spc$n_detect_lm_730_3650/simulation_outputs$etf_spc$n_730_3650,
       col = cols[7])
 
 # Add vlines to indicate when SP-AQ were administered:
@@ -407,4 +407,4 @@ legend(x = 3000, y = 0.99, legend = c("Baseline", "Treatment", "ETF-only", "SPC-
 ```
 
 ## References
-Slater, H.C., Griffin, J.T., Ghani, A.C. and Okell, L.C., 2016. Assessing the potential impact of artemisinin and partner drug resistance in sub-Saharan Africa. Malaria journal, 15(1), pp.1-11.
+Slater, H.C., Griffin, J.T., Ghani, A.C. and Okell, L.C., 2016. Assessing the potential impact of artemisinin and partner drug resistance in sub-Saharan Africa. Malaria journal, 15(1), pp.1-11.

vignettes/Carrying-capacity.Rmd

@@ -66,10 +66,10 @@ and can run the simulations and compare the outputs
 ```{r, fig.width = 7, fig.height = 4, out.width='100%'}
 set.seed(123)
 s <- run_simulation(timesteps = timesteps, parameters = p)
-s$pfpr <- s$n_detect_730_3650 / s$n_730_3650
+s$pfpr <- s$n_detect_lm_730_3650 / s$n_730_3650
 set.seed(123)
 s_seasonal <- run_simulation(timesteps = timesteps, parameters = p_seasonal)
-s_seasonal$pfpr <- s_seasonal$n_detect_730_3650 / s_seasonal$n_730_3650
+s_seasonal$pfpr <- s_seasonal$n_detect_lm_730_3650 / s_seasonal$n_730_3650
 
 par(mfrow = c(1, 2))
 plot(s$EIR_gamb ~ s$timestep, t = "l", ylim = c(0, 200), xlab = "Time", ylab = "EIR")
@@ -106,7 +106,7 @@ p_lsm <- p |>
   )
 set.seed(123)
 s_lsm <- run_simulation(timesteps = timesteps, parameters = p_lsm)
-s_lsm$pfpr <- s_lsm$n_detect_730_3650 / s_lsm$n_730_3650
+s_lsm$pfpr <- s_lsm$n_detect_lm_730_3650 / s_lsm$n_730_3650
 
 par(mfrow = c(1, 2))
 plot(s$EIR_gamb ~ s$timestep, t = "l", ylim = c(0, 150), xlab = "Time", ylab = "EIR")
@@ -144,7 +144,7 @@ p_stephensi <- p_stephensi |>
 
 set.seed(123)
 s_stephensi <- run_simulation(timesteps = timesteps, parameters = p_stephensi)
-s_stephensi$pfpr <- s_stephensi$n_detect_730_3650 / s_stephensi$n_730_3650
+s_stephensi$pfpr <- s_stephensi$n_detect_lm_730_3650 / s_stephensi$n_730_3650
 
 par(mfrow = c(1, 2))
 plot(s_stephensi$EIR_gamb ~ s_stephensi$timestep,
@@ -171,7 +171,7 @@ p_flexible <- p |>
 
 set.seed(123)
 s_flexible <- run_simulation(timesteps = timesteps, parameters = p_flexible)
-s_flexible$pfpr <- s_flexible$n_detect_730_3650 / s_flexible$n_730_3650
+s_flexible$pfpr <- s_flexible$n_detect_lm_730_3650 / s_flexible$n_730_3650
 
 par(mfrow = c(1, 2))
 plot(s$EIR_gamb ~ s$timestep, t = "l", ylim = c(0, 150), xlab = "Time", ylab = "EIR")

vignettes/EIRprevmatch.Rmd

@@ -117,7 +117,7 @@ simparams <- set_clinical_treatment(parameters = simparams,
 
 Having established a set of `malariasimulation` parameters, we are now ready to run simulations. In the following code chunk, we'll run the `run_simulation()` function across a range of initial EIR values to generate sufficient points to fit a curve matching *Pf*PR~2-10~ to the initial EIR. For each initial EIR, we first use the `set_equilibrium()` to update the model parameter list with the human and vector population parameter values required to achieve the specified EIR at equilibrium. This updated parameter list is then used to run the simulation.
 
-The `run_simulation()` outputs an EIR per time step, per species, across the entire human population. We first convert these to get the number of infectious bites experienced, on average, by each individual across the final year across all vector species. Next, the average *Pf*PR~2-10~ across the final year of the simulation is calculated by dividing the total number of individuals aged 2-10 by the number (`n_730_3650`) of detectable cases of malaria in individuals aged 2-10 (`n_detect_730_3650`) on each day and calculating the mean of these values. Finally, initial EIR, output EIR, and *Pf*PR~2-10~ are stored in a data frame.
+The `run_simulation()` outputs an EIR per time step, per species, across the entire human population. We first convert these to get the number of infectious bites experienced, on average, by each individual across the final year across all vector species. Next, the average *Pf*PR~2-10~ across the final year of the simulation is calculated by dividing the total number of individuals aged 2-10 by the number (`n_730_3650`) of detectable cases of malaria in individuals aged 2-10 (`n_detect_lm_730_3650`) on each day and calculating the mean of these values. Finally, initial EIR, output EIR, and *Pf*PR~2-10~ are stored in a data frame.
 
 ```{r}
 # Establish a vector of initial EIR values to simulate over and generate matching
@@ -158,7 +158,7 @@ malSim_prev <- lapply(
     mean(
       output[
         output$timestep %in% seq(4 * 365, 5 * 365),
-        'n_detect_730_3650'
+        'n_detect_lm_730_3650'
       ] / output[
         output$timestep %in% seq(4 * 365, 5 * 365),
         'n_730_3650'
@@ -276,7 +276,7 @@ library(cali)
 summary_mean_pfpr_2_10 <- function (x) {
  
  # Calculate the PfPR2-10:
- prev_2_10 <- mean(x$n_detect_730_3650/x$n_730_3650)
+ prev_2_10 <- mean(x$n_detect_lm_730_3650/x$n_730_3650)
  
  # Return the calculated PfPR2-10:
  return(prev_2_10)
@@ -325,8 +325,8 @@ malsim_sim <- run_simulation(timesteps = (simparams_malsim$timesteps),
                              parameters = simparams_malsim)
 
 # Extract the PfPR2-10 values for the cali and malsim simulation outputs:
-cali_pfpr2_10 <- cali_sim$n_detect_730_3650 / cali_sim$n_730_3650
-malsim_pfpr2_10 <- malsim_sim$n_detect_730_3650 / malsim_sim$n_730_3650
+cali_pfpr2_10 <- cali_sim$n_detect_lm_730_3650 / cali_sim$n_730_3650
+malsim_pfpr2_10 <- malsim_sim$n_detect_lm_730_3650 / malsim_sim$n_730_3650
 
 # Store the PfPR2-10 in each time step for the two methods:
 df <- data.frame(timestep = seq(1, length(cali_pfpr2_10)),

vignettes/MDA.Rmd

@@ -143,15 +143,15 @@ plot.new(); par(mar = c(4, 4, 1, 1), new = TRUE)
 
 # Plot malaria prevalence in 2-10 years through time:
 plot(x = mda_output$timestep,
-     y = mda_output$n_detect_730_3650/mda_output$n_730_3650,
+     y = mda_output$n_detect_lm_730_3650/mda_output$n_730_3650,
      xlab = "Time (days)",
      ylab = expression(paste(italic(Pf),"PR"[2-10])), cex = 0.8,
      ylim = c(0, 1), type = "l", lwd = 2, xaxs = "i", yaxs = "i",
      col = cols[3])
 
 # Add the dynamics for no-intervention simulation
 lines(x = noint_output$timestep,
-     y = noint_output$n_detect_730_3650/noint_output$n_730_3650,
+     y = noint_output$n_detect_lm_730_3650/noint_output$n_730_3650,
      col = cols[4])
 
 # Add vlines to indicate when SP-AQ were administered:
@@ -282,15 +282,15 @@ legend("topleft",
 plot.new(); par(new = TRUE, mar = c(4, 4, 1, 1))
 
 # Plot malaria prevalence in 2-10 years through time:
-plot(x = smc_output$timestep, y = smc_output$n_detect_730_3650/smc_output$n_730_3650,
+plot(x = smc_output$timestep, y = smc_output$n_detect_lm_730_3650/smc_output$n_730_3650,
      xlab = "Time (days)", 
      ylab = expression(paste(italic(Pf),"PR"[2-10])), cex = 0.8,
      ylim = c(0, 1), type = "l", lwd = 2, xaxs = "i", yaxs = "i",
      col = cols[3])
 
 # Add the dynamics for no-intervention simulation
 lines(x = noint_output$timestep,
-     y = noint_output$n_detect_730_3650/noint_output$n_730_3650,
+     y = noint_output$n_detect_lm_730_3650/noint_output$n_730_3650,
      col = cols[4])
 
 # Add lines to indicate SMC events:
@@ -305,4 +305,4 @@ legend("topleft",
        legend = c("SMC", "No-Int"),
        col= c(cols[3:4]), box.col = "white",
        lwd = 1, lty = c(1, 1), cex = 0.8)
-```
+```

vignettes/Metapopulation.Rmd

@@ -107,7 +107,7 @@ output3$mix <- 'perfectly-mixed'
 output <- rbind(output1, output2, output3)
 
 # get mean PfPR 2-10 by year
-output$prev2to10 = output$p_detect_730_3650 / output$n_730_3650
+output$prev2to10 = output$p_detect_lm_730_3650 / output$n_730_3650
 output$year = ceiling(output$timestep / 365)
 output$mix = factor(output$mix, levels = c('isolated', 'semi-mixed', 'perfectly-mixed'))
 output <- aggregate(prev2to10 ~ mix + EIR + year, data = output, FUN = mean)

vignettes/Model.Rmd

@@ -154,8 +154,8 @@ The `run_simulation()` function then simulates malaria transmission dynamics and
 -   `iva_mean`: mean acquired immunity to severe infection
 -   `ivm_mean`: mean maternal immunity to severe infection
 -   `n_730_3650`: population size of an age group of interest (where the default is set to 730-3650 days old, or 2-10 years, but which may be adjusted (see [Demography](https://mrc-ide.github.io/malariasimulation/articles/Demography.html) vignette for more details)
--   `n_detect_730_3650`: number with possible detection through microscopy of a given age group
--   `p_detect_730_3650`: the sum of probabilities of detection through microscopy of a given age group
+-   `n_detect_lm_730_3650`: number with possible detection through microscopy of a given age group
+-   `p_detect_lm_730_3650`: the sum of probabilities of detection through microscopy of a given age group
 -   `E_gamb_count`, `L_gamb_count`, `P_gamb_count`, `Sm_gamb_count`, `Pm_gamb_count`, `Im_gamb_count`: species-specific mosquito population sizes in each state (default set to *An. gambiae*)
 -   `total_M_gamb`: species-specific number of adult mosquitoes (default set to *An. gambiae*)
 
@@ -173,7 +173,7 @@ Where **treatments** are specified, `n_treated` will report the number that have
 
 ### Output visualisation
 
-These outputs can then be visualised, such as the population changes in states. Another key output is the prevalence of detectable infections between the ages of 2-10 (*Pf*PR~2-10~), which can be obtained by dividing `n_detect_730_3650` by `n_730_3650`.
+These outputs can then be visualised, such as the population changes in states. Another key output is the prevalence of detectable infections between the ages of 2-10 (*Pf*PR~2-10~), which can be obtained by dividing `n_detect_lm_730_3650` by `n_730_3650`.
 
 ```{r, fig.align = 'center', out.width='100%', fig.asp=0.55, }
 # Define vector of column names to plot
@@ -202,7 +202,7 @@ par(mfrow = c(1,2))
 states_plot(test_sim)
 
 # Calculate Pf PR 2-10
-test_sim$PfPR2_10 <- test_sim$n_detect_730_3650/test_sim$n_730_3650
+test_sim$PfPR2_10 <- test_sim$n_detect_lm_730_3650/test_sim$n_730_3650
 
 # Plot Pf PR 2-10
 plot(x = test_sim$timestep, y = test_sim$PfPR2_10, type = "l",
@@ -226,7 +226,7 @@ par(mfrow = c(1,2))
 states_plot(test_sim_eq)
 
 # Calculate Pf PR 2-10
-test_sim_eq$PfPR2_10 <- test_sim_eq$n_detect_730_3650/test_sim_eq$n_730_3650
+test_sim_eq$PfPR2_10 <- test_sim_eq$n_detect_lm_730_3650/test_sim_eq$n_730_3650
 
 # Plot Pf PR 2-10
 plot(x = test_sim_eq$timestep, y = test_sim_eq$PfPR2_10, type = "l",

vignettes/Parameter_variation.Rmd

@@ -40,7 +40,7 @@ sim <- run_simulation(timesteps = 1000, simparams)
 # plot the default median parameter
 plot(
   sim$timestep,
-  sim$n_detect_730_3650 / sim$n_730_3650,
+  sim$n_detect_lm_730_3650 / sim$n_730_3650,
   t = "l",
   ylim = c(0, 1),
   ylab = "PfPr",
@@ -60,7 +60,7 @@ Keep in mind that `set_parameter_draw` must be called prior to `set_equilibrium`
 # plot the default median parameter
 plot(
   sim$timestep[1:500],
-  sim$n_detect_730_3650[1:500] / sim$n_730_3650[1:500],
+  sim$n_detect_lm_730_3650[1:500] / sim$n_730_3650[1:500],
   t = "l",
   ylim = c(0, 1),
   ylab = "PfPr",
@@ -78,9 +78,9 @@ for (i in 1:7) {
   
   sim <- run_simulation(timesteps = 500, param_draw)
   
-  lines(sim$timestep, sim$n_detect_730_3650 / sim$n_730_3650, col = cols[i])
+  lines(sim$timestep, sim$n_detect_lm_730_3650 / sim$n_730_3650, col = cols[i])
 }
 ```
 
 
-For more information on uncertainty in parameters, please refer to [The US President's Malaria Initiative, Plasmodium falciparum transmission and mortality: A modelling study](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002448), Supplemental material, Section 4. 
+For more information on uncertainty in parameters, please refer to [The US President's Malaria Initiative, Plasmodium falciparum transmission and mortality: A modelling study](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002448), Supplemental material, Section 4. 

vignettes/SetSpecies.Rmd

@@ -40,11 +40,11 @@ We will create a plotting function to visualise the output.
 ```{r}
 # Plotting functions
 plot_prev <- function() {
-  plot(x = output_endophilic$timestep, y = output_endophilic$n_detect_730_3650 / output_endophilic$n_730_3650, 
+  plot(x = output_endophilic$timestep, y = output_endophilic$n_detect_lm_730_3650 / output_endophilic$n_730_3650, 
        type = "l", col = cols[3], lwd = 1,
        xlab = "Time (days)", ylab = expression(paste(italic(Pf),"PR"[2-10])),
        xaxs = "i", yaxs = "i", ylim = c(0,1))
-  lines(x = output_exophilic$timestep, y = output_exophilic$n_detect_730_3650 / output_exophilic$n_730_3650,
+  lines(x = output_exophilic$timestep, y = output_exophilic$n_detect_lm_730_3650 / output_exophilic$n_730_3650,
         col = cols[5], lwd = 1)
   abline(v = sprayingtimesteps, lty = 2, lwd = 2.5, col = "black")
   text(x = sprayingtimesteps + 10, y = 0.9, labels = "Spraying\nint.", adj = 0, cex = 0.8)

vignettes/Treatment.Rmd

@@ -124,20 +124,20 @@ output <- run_simulation(sim_length, treatment_params)
 
 ### Visualisation
 
-Following simulation of malaria transmission under this treatment regimen, we can now visualise the effect of the regimen on the number of detectable cases through time using the `n_detect_730_3650`.
+Following simulation of malaria transmission under this treatment regimen, we can now visualise the effect of the regimen on the number of detectable cases through time using the `n_detect_lm_730_3650`.
 
 ```{r, fig.align = 'center', out.width='100%'}
 # Plot results
-plot(x = output$timestep, y = output$n_detect_730_3650, type = "l",
+plot(x = output$timestep, y = output$n_detect_lm_730_3650, type = "l",
      xlab = "Days", ylab = "Detectable cases", col = cols[1],
-     ylim = c(min(output$n_detect_730_3650)-1, max(output$n_detect_730_3650)+7),
+     ylim = c(min(output$n_detect_lm_730_3650)-1, max(output$n_detect_lm_730_3650)+7),
      xaxs = "i", yaxs = "i")
 
 # Show treatment times
 abline(v = 300, lty = 2)
-text(x = 310, y = max(output$n_detect_730_3650), labels = "Treatment\nbegins", adj = 0, cex = 0.8)
+text(x = 310, y = max(output$n_detect_lm_730_3650), labels = "Treatment\nbegins", adj = 0, cex = 0.8)
 abline(v = 600, lty = 2)
-text(x = 610, y = max(output$n_detect_730_3650), labels = "Treatment\nends", adj = 0, cex = 0.8)
+text(x = 610, y = max(output$n_detect_lm_730_3650), labels = "Treatment\nends", adj = 0, cex = 0.8)
 
 # Add grid lines
 grid(lty = 2, col = "grey80", nx = 11, ny = 10, lwd = 0.5)

vignettes/Vaccines.Rmd

@@ -77,12 +77,12 @@ plot_prev <- function(type = "not seasonal"){
   output$time_year <- output$timestep / year
   comparison$time_year <- comparison$timestep / year
 
-  plot(x = output$time_year, y = output$n_detect_730_3650/output$n_730_3650, 
+  plot(x = output$time_year, y = output$n_detect_lm_730_3650/output$n_730_3650, 
        type = "l", col = cols[3], ylim=c(0,1), lwd = 3,
        xlab = "Time (years)", ylab = expression(paste(italic(Pf),"PR"[2-10])),
        xaxs = "i", yaxs = "i")
   grid(lty = 2, col = "grey80", lwd = 0.5)
-  lines(x = comparison$time_year, y = comparison$n_detect_730_3650/comparison$n_730_3650,
+  lines(x = comparison$time_year, y = comparison$n_detect_lm_730_3650/comparison$n_730_3650,
         col = cols[6], lwd = 3)
   abline(v = vaccinetime, col = "black", lty = 2, lwd = 2.5)
   text(x = vaccinetime + 0.05, y = 0.9, labels = "Start of\nvaccination", adj = 0, cex = 0.8)
@@ -388,7 +388,7 @@ plot_prev()
 ```{r, fig.align = 'center', out.width='100%', message=FALSE}
 # Plot doses
 output$month <- ceiling(output$timestep / month)
-doses <- output[, c(2:6, 38)]
+doses <- output[, c(grep(pattern = "n_pev", x = names(output), value = T), "month")]
 doses <- aggregate(cbind(doses[1:5]),
                     by = list(doses$month), 
                     FUN = sum)

vignettes/Variation.Rmd

@@ -29,7 +29,7 @@ plot_prev <- function(output, ylab = TRUE, ylim = c(0,1)){
   if (ylab == TRUE) {
     ylab = "Prevalence in children aged 2-10 years"
     } else {ylab = ""}
-  plot(x = output$timestep, y = output$n_detect_730_3650 / output$n_730_3650, 
+  plot(x = output$timestep, y = output$n_detect_lm_730_3650 / output$n_730_3650, 
        type = "l", col = cols[3], ylim = ylim,
        xlab = "Time (days)", ylab = ylab, lwd = 1,
        xaxs = "i", yaxs = "i")
@@ -49,7 +49,7 @@ plot_inci <- function(output, ylab = TRUE, ylim){
 
 aggregate_function <- function(df){
   tmp <- aggregate(
-    df$n_detect_730_3650,
+    df$n_detect_lm_730_3650,
     by=list(df$timestep),
     FUN = function(x) {
       c(median = median(x),
@@ -108,7 +108,7 @@ simparams_big <- set_equilibrium(parameters = simparams_big, init_EIR = 50)
 
 ### Simulations
 
-The `n_detect_730_3650` output below shows the total number of individuals in the age group rendered (here, 730-3650 timesteps or 2-10 years) who have microscopy-detectable malaria. Notice that the output is smoother at a higher population.  
+The `n_detect_lm_730_3650` output below shows the total number of individuals in the age group rendered (here, 730-3650 timesteps or 2-10 years) who have microscopy-detectable malaria. Notice that the output is smoother at a higher population.  
 
 Some outcomes will be more sensitive than others to stochastic variation even with the same population size. In the plots below, prevalence is smoother than incidence even at the same population. This is because prevalence is a measure of existing infection, while incidence is recording new cases per timestep.