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    Site built with pkgdown 2.1.0.

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    License

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    Site built with pkgdown 2.0.9.

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    MIT License

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    Site built with pkgdown 2.0.9.

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    Site built with pkgdown 2.1.0.

    - - + + diff --git a/articles/Antimalarial_Resistance.html b/articles/Antimalarial_Resistance.html new file mode 100644 index 00000000..717d5fa1 --- /dev/null +++ b/articles/Antimalarial_Resistance.html @@ -0,0 +1,693 @@ + + + + + + + +Antimalarial Resistance • malariasimulation + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    Antimalarial Resistance

    + + + + + +
    + + + +
    +
+# Load the requisite packages:
+library(malariasimulation)
+
+# Set colour palette:
+cols <- c("#E69F00", "#56B4E9", "#009E73", "#CC79A7","#F0E442", "#0072B2", "#D55E00")
    +
    +

    Introduction +

    +

    One of the major threats to the continued success of efforts to +reduce the burden of malaria is the evolution and spread of resistance +to the antimalarial drugs used to treat uncomplicated cases of malaria. +The most effective frontline antimalarials are a class of drugs called +artemisinin combination therapies (ACTs). ACTs combine a fast-acting, +short-lived artemisinin derivative (e.g. artemether), with a +slower-acting, longer-lasting partner drug (e.g. lumefantrine) that +clears the remaining parasites from the patient’s system. Efforts to +understand the effect of resistance to ACTs on malaria morbidity and +mortality, and to develop strategies to control or mitigate the spread +of resistance, would benefit from insights derived from mathematical +modelling. Building on the model developed by Slater et al. (2016), +malariasimulation provides the functionality to simulate +the effects of resistance to the artemisinin and/or partner drug +components to multiple, independent ACTs, on malaria transmission +dynamics.

    +

    Resistance to the artemisinin component of an ACT can result either +in slow parasite clearance (SPC), in which treatment with an ACT takes +longer than 3 days to fully clear patients with resistant parasites, or +early treatment failure (ETF), in which the ACT fails to clear the +infection and the individual develops a clinical infection. Resistance +to the partner drug, where the partner drug fails to clear the parasite +after the artemisinin derivative is depleted, results in infections +recrudescing to either clinical (D) or asymptomatic infections (A). +Resistance to the partner drug can also result in individuals developing +a novel, resistant infection following treatment, as the prophylaxis +provided by the ACT fails to protect the individual against reinfection +by a resistant strain. In the following vignette, we illustrate how to +parameterise and run malariasimulation simulations with +resistance to ACTs deployed as a clinical treatment

    +
    +
    +

    Using set_antimalarial_resistance() to parameterise resistance +

    +

    Simulations capturing the effects of resistance to clinical treatment +using antimalarial drugs are parameterised using the +set_antimalarial_resistance() function. This function +appends user-defined resistance parameters to a +malariasimulation parameter list and accepts ten inputs. +The first is a list of malariasimulation parameters to +append the resistance parameters to, and the second the index of the +drug for which resistance is being parameterised, as set +using the set_drugs() function. The +set_antimalarial_resistance() function requires the +timesteps, artemisinin_resistance_proportion, +partner_drug_resistance_proportion_proportion, +slow_parasite_clearance_probability, +early_treatment_failure_probability, +late_clinical_failure_probability, +late_parasitological_failure_prob, and +reinfection_during_prophylaxis_probability inputs to be of +equal length so that, for each time step in which an update occurs, a +value is available for each parameter. Finally, the +slow_parasite_clearance_time parameter represents the mean +residence time, in days, for artemisinin-resistant individuals +experiencing slow parasite clearance (SPC) in the Treated compartment, +and must be input as a single, positive value.

    +
    +
    +

    Simulating static resistance +

    +

    To illustrate how to parameterise resistance to an ACT using the +set_antimalarial_resistance() function, we’ll set-up and +run three simulations. The first simulates malaria transmission in the +absence of interventions or resistance. The second simulates a simple +regime of clinical treatment in which 80% of clinical cases are treated +with artemether lumefantrine (AL), beginning after one year, in the +absence of antimalarial resistance. The third simulates the same +clinical treatment programme but with resistance to the artemisinin +component of AL emerging after two years. For illustrative purposes, we +assume that the proportion of infections resistant to the artemisinin +component of AL increases from 0% to 80%, and that these infections have +a 90% chance of resulting in early treatment failure.

    +
    +

    Parameterisation +

    +

    First, we load the default malariasimulation model +parameters, using the overrides argument to increase the +human population. The human and mosquito population parameters are then +calibrated to a user-specified initial EIR using the +set_equilibrium() function. Next, we load the in-built +parameters for the antimalarial drug AL and append them to the parameter +list using set_drugs(). We can then use +set_clinical_treatment() to specify a clinical treatment +regime, beginning after one year, that treats, on average, 80% of the +clinical cases of malaria with AL (AL_params). The +set_antimalarial_resistance() function is then used to +specify a scenario in which resistance is initially absent from the +population, but after two years the proportion of malaria infections +that are resistant to the artemisinin component of AL rises to 80%. We +also set the probability that artemisinin-resistant infections result in +early treatment failure to 0.9. In the current instance, we’ve set the +proportion of infections resistant to the AL partner drug to 0% and the +probabilities of other resistant infection outcomes to zero for +simplicity.

    +
    +
+# Specify the time steps over which to simulate:
+timesteps <- 365 * 3
+
+# Specify an initial EIR to calibrate to:
+init_EIR <- 8
+
+# Specify a time step for treatment to begin:
+treatment_start <- (1 * 365) + 1
+
+# Specify a time step  for resistance to emerge:
+resistance_start <- (2 * 365) + 1
+
+# Load the base malariasimulation parameter set:
+simparams <- get_parameters(
+  overrides = list(
+    human_population = 10000)
+)
+
+# Calibrate to the initial EIR:
+simparams <- set_equilibrium(parameters = simparams, init_EIR = init_EIR)
+
+# Append the parameters for artemether lumefantrine (AL) to the parameter list:
+simparams_clin_treatment <- set_drugs(parameters = simparams, drugs = list(AL_params))
+
+# Use the set_clinical_treatment() function to specify a treatment regime for AL:
+simparams_clin_treatment <- set_clinical_treatment(parameters = simparams_clin_treatment, 
+                                                   drug = 1,
+                                                   timesteps = treatment_start,
+                                                   coverages = 0.8)
+
+# Use the set_antimalarial_resistance() function to specify resistance to AL:
+simparams_resistance <- set_antimalarial_resistance(parameters = simparams_clin_treatment,
+                                                    drug = 1,
+                                                    timesteps = c(0, resistance_start),
+                                                    artemisinin_resistance_proportion = c(0, 0.8),
+                                                    partner_drug_resistance_proportion = rep(0, 2),
+                                                    slow_parasite_clearance_probability = rep(0, 2),
+                                                    early_treatment_failure_probability = c(0, 0.9),
+                                                    late_clinical_failure_probability = rep(0, 2),
+                                                    late_parasitological_failure_probability = rep(0, 2),
+                                                    reinfection_during_prophylaxis_probability = rep(0, 2), 
+                                                    slow_parasite_clearance_time = 10)
    +
    +
    +

    Simulation +

    +

    We can now use the run_simulation() to simulate the +three scenarios for which we have established parameter lists above.

    +
    +
+# Baseline: No-intervention, no resistance simulation:
+sim_out_baseline <- run_simulation(timesteps = timesteps, parameters = simparams)
+
+# Clinical treatment with no antimalarial drug resistance:
+sim_out_clin_treatment <- run_simulation(timesteps = timesteps, parameters = simparams_clin_treatment)
+
+# Clinical treatment with antimalarial drug resistance:
+sim_out_resistance <- run_simulation(timesteps = timesteps, parameters = simparams_resistance)
    +
    +
    +

    Visualisation +

    +

    With the outputs from the run_simulation() function, we +can visualise the effect of resistance on malaria transmission by +plotting the prevalence of malaria in children aged 2-10 years old +(PfPR2-10) over time.

    +
    +
+# 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_lm_730_3650/sim_out_baseline$n_age_730_3650
+sim_out_clin_treatment$pfpr210 = sim_out_clin_treatment$n_detect_lm_730_3650/sim_out_clin_treatment$n_age_730_3650
+sim_out_resistance$pfpr210 = sim_out_resistance$n_detect_lm_730_3650/sim_out_resistance$n_age_730_3650
+
+# Plot the prevalence through time in each of the three simulated scenarios:
+plot.new(); par(mar = c(4, 4, 1, 1), new = TRUE)
+plot(x = sim_out_baseline$timestep, y = sim_out_baseline$pfpr210,
+     xlab = "Time (days)",
+     ylab = expression(paste(italic(Pf),"PR"[2-10])), cex = 0.7,
+     ylim = c(0, 1), type = "l", lwd = 2, xaxs = "i", yaxs = "i",
+     col = cols[3])
+
+# Add a line for the clinical treatment scenario:
+lines(x = sim_out_clin_treatment$timestep,
+      y = sim_out_clin_treatment$pfpr210,
+      col = cols[4])
+
+# Add a line for the clinical treatment with resistance scenario:
+lines(x = sim_out_resistance$timestep,
+      y = sim_out_resistance$pfpr210,
+      col = cols[7])
+
+# Add lines to indicate the initiation of treatment and resistance:
+abline(v = treatment_start, lty = "dashed")
+abline(v = resistance_start, lty = "dashed")
+
+# Annotate the added vlines:
+text(x = treatment_start + 10, y = 0.9, labels = "Start of\nTreatment", adj = 0, cex = 0.7)
+text(x = resistance_start + 10, y = 0.9, labels = "Start of\nResistance", adj = 0, cex = 0.7)
+
+# Add gridlines:
+grid(lty = 2, col = "grey80", nx = NULL, ny = NULL, lwd = 0.5); box()
+
+# Add a legend:
+legend(x = 20, y = 0.99, legend = c("Baseline", "Treatment", "Resistance"),
+       col= c(cols[3:4], cols[7]), box.col = "white",
+       lwd = 1, lty = c(1, 1), cex = 0.7)
    +

    +

    In the absence of clinical treatment or resistance, prevalence is +comparable between all three scenarios for the first year. Following the +initiation of clinical treatment at the beginning of the second year, +PfPR2-10 approximately halves relative to the +no-intervention baseline. However, following the introduction of +artemisinin resistance at the beginning of the third year, early +treatment failure causes the PfPR2-10 to increase to +an intermediate level in the resistance scenario.

    +
    +
    +
    +

    Simulating dynamic resistance +

    +

    We can also capture scenarios in which resistance to a drug changes +through time. To illustrate, we’ll establish and simulate a scenario in +which resistance to sulfadoxine-pyrimethamine amodiaquine (SP-AQ) is +absent from the population in the first year, but emerges in the third +year and doubles in proportion each year thereafter until 100% of +infections are artemisinin resistant. For simplicity, we’ll assume only +artemisinin resistance is present in the population, and resistance to +artemisinin results only, and always, in early treatment failure.

    +
    +

    Parameterisation +

    +

    First, we store in vectors the artemisinin resistance proportions and +the time steps on which they will be updated in the simulation. We also +create a vector of early treatment failure probabilities which, for +simplicity, we assume remain at 1 for each update. Next, we load the +default malariasimulation parameter set, specifying a +larger population size and seasonal transmission, and append the +parameters for SP-AQ (SP_AQ_params) using the +set_drugs() function. We’ll specify a simple treatment +regimen using set_clinical_treatment() where, on average, +80% of clinical cases are treated with SP-AQ, beginning after one year. +We then specify a resistance schedule in which artemisinin resistance is +introduced at a proportion of 0.2 after 3 years, and doubles each year +thereafter until all infections are artemisinin resistant. Finally, we +calibrate the human and mosquito population parameters to a defined +entomological inoculation rate (EIR) and are ready to run the +simulation.

    +
    +
+# Specify the time steps over which to simulate:
+timesteps <- 365 * 8
+
+# Set an initial EIR value:
+initial_eir <- 8
+
+# Specify the proportion of infections that are artemisinin resistant:
+resistance_updates <- c(0, 0.2, 0.4, 0.8, 1)
+
+# Specify the time steps in which to update the resistance parameters:
+resistance_update_timesteps <- c(0, seq(3*365, 6*365, by = 365))
+
+# Specify the probability artemisinin resistance infections result in early treatment failure:
+early_treatment_failure_updates <- rep(1, length(resistance_update_timesteps))
+
+# Load the base malariasimulation parameter set, with seasonal transmission:
+simparams <- get_parameters(
+  overrides = list(
+    human_population = 1000,
+    model_seasonality = TRUE,
+    g0 =  0.284596,
+    g = c(-0.317878,-0.0017527,0.116455),
+    h = c(-0.331361,0.293128,-0.0617547)
+))
+
+# Append the parameters for sulfadomamine pyrimethaine (SP-AQ) to the parameter list:
+simparams <- set_drugs(parameters = simparams, drugs = list(SP_AQ_params))
+
+# Use the set_clinical_treatment() function to specify a treatment regime for SP-AQ:
+simparams <- set_clinical_treatment(parameters = simparams,
+                                    drug = 1,
+                                    timesteps = 365 * 1,
+                                    coverages = 0.8)
+
+# Use the set_antimalarial_resistance() function to specify resistance to SP-AQ:
+simparams <- set_antimalarial_resistance(parameters = simparams,
+                                         drug = 1,
+                                         timesteps = resistance_update_timesteps,
+                                         artemisinin_resistance_proportion = resistance_updates,
+                                         partner_drug_resistance_proportion = rep(0, length(resistance_update_timesteps)), 
+                                         slow_parasite_clearance_probability = rep(0, length(resistance_update_timesteps)),
+                                         early_treatment_failure_probability = early_treatment_failure_updates,
+                                         late_clinical_failure_probability = rep(0, length(resistance_update_timesteps)),
+                                         late_parasitological_failure_probability = rep(0, length(resistance_update_timesteps)),
+                                         reinfection_during_prophylaxis_probability = rep(0, length(resistance_update_timesteps)), 
+                                         slow_parasite_clearance_time = 10)
+
+# Calibrate the parameters to an initial EIR:
+simparams <- set_equilibrium(parameters = simparams, init_EIR = initial_eir)
    +
    +
    +

    Simulation +

    +

    We can now use our parameter list to run the simulation using the the +run_simulation() function.

    +
    +
+# Run the simulation:
+dynamic_resistance_output <- run_simulation(timesteps = timesteps, parameters = simparams)
    +
    +
    +

    Visualisation +

    +

    We can visualise the effect of increasing resistance through time by +plotting the PfPR2-10. We’ve added vertical lines to +indicate when clinical treatment begins, and when the proportion of +infections resistant to artemisinin is updated.

    +
    +
+# Calculate the prevalence (PfPR210) through time:
+dynamic_resistance_output$pfpr210 <- dynamic_resistance_output$n_detect_lm_730_3650/dynamic_resistance_output$n_age_730_3650
+
+# Open a new plotting window and add a grid:
+plot.new(); par(mar = c(4, 4, 1, 1), new = TRUE)
+plot(x = dynamic_resistance_output$timestep,
+     y = dynamic_resistance_output$pfpr210,
+     xaxt = "n",
+     xlab = "Time (years)",
+     ylab = expression(paste(italic(Pf),"PR"[2-10])), cex = 0.8,
+     ylim = c(0, 1), type = "l", lwd = 2, xaxs = "i", yaxs = "i",
+     col = cols[3])
+
+# Specify x-axis ticks and labels:
+axis(1, at = seq(0, 8 * 365, by = 365), labels = seq(0, 8 * 365, by = 365)/365)
+
+# Add a line indicating the start of the clinical treatment:
+abline(v = 365, lty = "dotted")
+
+# Add lines indicating when resistance is updated:
+abline(v = resistance_update_timesteps, lty = "dashed")
+
+# Add a line highlighting the maximum PfPR_2-10 value prior to treatment or resistance:
+abline(h = max(dynamic_resistance_output$pfpr210[1:365]), col = "red")
+
+# Add annotations for the vlines:
+text(x = 365 + 30, y = 0.6, labels = "Clin. treat. begins", adj = 0, cex = 0.8, srt = 90)
+text(x = resistance_update_timesteps[2:5] + 30, y = 0.6, labels = paste0("Art. Res. = ", resistance_updates[2:5]),
+     adj = 0, cex = 0.8, srt = 90)
    +

    +

    Looking at the figure, we can see that the +PfPR2-10 decreases over the two years following the +onset of clinical treatment in the absence of artemisinin resistance. +However, as resistance is introduced and increases through time, the +PfPR2-10 increases towards the pre-intervention +seasonal peak as SP-AQ becomes increasingly ineffective in the treatment +of clinical cases of malaria.

    +
    +
    +
    +

    Simulating antimalarial resistance with multiple resistance +outcomes +

    +

    As we’ve discussed, resistance to an ACT can manifest in multiple +ways. For instance, resistance to the artemisinin component of an ACT +can result in either early treatment failure or slow parasite +clearance.

    +

    Using malariasimulation, we can simulate the effects of +multiple potential outcomes of resistance on malaria transmission +dynamics. To illustrate, we’ll parameterise and run a series of +simulations in is i) no clinical treatment, ii) no resistance, iii) +resistance with early treatment failure, iv) resistance with slow +parasite clearance, and v) resistance with early treatment failure and +slow parasite clearance.

    +
    +

    Parameterisation +

    +
    +
+# Determine the number of timesteps to run for:
+timesteps <- 365 * 6
+
+# Set up a list to store the simulation parameter lists in:
+simulation_parameters <- list()
+
+# Establish a list of the base parameters with no clinical treatment or antimalarial resistance:
+get_parameters(overrides = list(human_population = 1000)) -> simulation_parameters$base
+
+# Establish a parameter list with clinical treatment starting after one year:
+simulation_parameters$base |>
+  set_drugs(drugs = list(AL_params)) |>
+  set_clinical_treatment(drug = 1, timesteps = (365 * 1) + 1, coverages = c(0.8)) |>
+  set_equilibrium(init_EIR = 16) -> simulation_parameters$treatment
+
+# Set the equilibrium for the base parameters:
+simulation_parameters$base |>
+  set_equilibrium(init_EIR = 16) -> simulation_parameters$base
+
+# Establish a parameter list with clinical treatment and early treatment failure
+simulation_parameters$treatment |>
+  set_antimalarial_resistance(drug = 1,
+                              timesteps = c(0, (365 * 3) + 1),
+                              artemisinin_resistance_proportion = c(0, 0.8), 
+                              partner_drug_resistance_proportion = c(0, 0), 
+                              slow_parasite_clearance_probability = c(0, 0), 
+                              early_treatment_failure_probability = c(0, 0.8), 
+                              late_clinical_failure_probability = c(0, 0), 
+                              late_parasitological_failure_probability = c(0, 0), 
+                              reinfection_during_prophylaxis_probability = c(0, 0), 
+                              slow_parasite_clearance_time = 10) -> simulation_parameters$etf
+
+# Establish a parameter list with clinical treatment and slow parasite clearance
+simulation_parameters$treatment |>
+  set_antimalarial_resistance(drug = 1,
+                              timesteps = c(0, (365 * 3) + 1),
+                              artemisinin_resistance_proportion = c(0, 0.8), 
+                              partner_drug_resistance_proportion = c(0, 0), 
+                              slow_parasite_clearance_probability = c(0, 0.8), 
+                              early_treatment_failure_probability = c(0, 0), 
+                              late_clinical_failure_probability = c(0, 0), 
+                              late_parasitological_failure_probability = c(0, 0), 
+                              reinfection_during_prophylaxis_probability = c(0, 0), 
+                              slow_parasite_clearance_time = 10) -> simulation_parameters$spc
+
+# Establish a parameter list with clinical treatment, early treatment failure and slow parasite clearance:
+simulation_parameters$treatment |>
+  set_antimalarial_resistance(drug = 1,
+                              timesteps = c(0, (365 * 3) + 1),
+                              artemisinin_resistance_proportion = c(0, 0.8), 
+                              partner_drug_resistance_proportion = c(0, 0), 
+                              slow_parasite_clearance_probability = c(0, 0.8), 
+                              early_treatment_failure_probability = c(0, 0.8), 
+                              late_clinical_failure_probability = c(0, 0), 
+                              late_parasitological_failure_probability = c(0, 0), 
+                              reinfection_during_prophylaxis_probability = c(0, 0), 
+                              slow_parasite_clearance_time = 10) -> simulation_parameters$etf_spc
    +
    +
    +

    Simulation +

    +

    We can now use our lists of malariasimulation parameters +to run the simulations.

    +
    +
+# Open a list to store the simulation outputs in:
+simulation_outputs <- list()
+
+# Run the simulations
+for(i in seq(length(simulation_parameters))) {
+  
+  # Run the i-th simulation
+  simulation_temp <- run_simulation(timesteps = timesteps, 
+                                    parameters = simulation_parameters[[i]])
+  
+  # Append the simulation identifier:
+  simulation_temp$identifier <- names(simulation_parameters)[i]
+  
+  # Append the ith simulation outputs to the combined simulation dataframe:
+  simulation_outputs[[names(simulation_parameters)[i]]] <- simulation_temp
+  
+  # Print the number of columns in the i-th simulation outputs dataframe:                            
+  print(ncol(simulation_temp))
+}
+#> [1] 31
+#> [1] 35
+#> [1] 37
+#> [1] 37
+#> [1] 37
    +
    +
    +

    Visualisation +

    +

    We can compare the effects of independent resistance outcomes with +combined resistance outcomes visually. In the following plot, we compare +the PfPR2-10 between a baseline without any clinical +treatment or antimalarial resistance, a clinical-treatment only run, a +clinical treatment with early treatment failure run, a clinical +treatment with slow parasite clearance run, and a clinical treatment +with both early treatment failure and slow parasite clearance run.

    +
    +
+# Open a new plotting window and add a grid:
+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_lm_730_3650/simulation_outputs$base$n_age_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_lm_730_3650/simulation_outputs$treatment$n_age_730_3650,
+      col = cols[4])
+
+lines(x = simulation_outputs$etf$timestep,
+      y = simulation_outputs$etf$n_detect_lm_730_3650/simulation_outputs$etf$n_age_730_3650,
+      col = cols[5])
+
+lines(x = simulation_outputs$spc$timestep,
+      y = simulation_outputs$spc$n_detect_lm_730_3650/simulation_outputs$spc$n_age_730_3650,
+      col = cols[6])
+
+lines(x = simulation_outputs$etf_spc$timestep,
+      y = simulation_outputs$etf_spc$n_detect_lm_730_3650/simulation_outputs$etf_spc$n_age_730_3650,
+      col = cols[7])
+
+# Add vlines to indicate when SP-AQ were administered:
+abline(v = 365 + 1, lty = "dashed", lwd = 1)
+text(x = (365 * 1) - 40, y = 0.6, labels = "Treatment Introduced", adj = 0, cex = 0.8, srt = 90)
+
+abline(v = (365 * 3) + 1, lty = "dashed", lwd = 1)
+text(x = (365 * 3) - 40, y = 0.6, labels = "Resistance Introduced", adj = 0, cex = 0.8, srt = 90)
+
+# Add gridlines:
+grid(lty = 2, col = "grey80", nx = NULL, ny = NULL, lwd = 0.5); box()
+
+# Add a legend:
+legend(x = 3000, y = 0.99, legend = c("Baseline", "Treatment", "ETF-only", "SPC-only", "ETF and SPC"),
+       col= c(cols[3:7]), box.col = "white",
+       lwd = 1, lty = c(1, 1), cex = 0.8)
    +

    +
    +
    +
    +

    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.

    +
    +
    + + + +
    + + + + +
    + + + + + + + + diff --git a/articles/Antimalarial_Resistance_files/figure-html/unnamed-chunk-10-1.png b/articles/Antimalarial_Resistance_files/figure-html/unnamed-chunk-10-1.png new file mode 100644 index 00000000..858ba862 Binary files /dev/null and b/articles/Antimalarial_Resistance_files/figure-html/unnamed-chunk-10-1.png differ diff --git a/articles/Antimalarial_Resistance_files/figure-html/unnamed-chunk-4-1.png b/articles/Antimalarial_Resistance_files/figure-html/unnamed-chunk-4-1.png new file mode 100644 index 00000000..0bdbad79 Binary files /dev/null and b/articles/Antimalarial_Resistance_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/articles/Antimalarial_Resistance_files/figure-html/unnamed-chunk-7-1.png b/articles/Antimalarial_Resistance_files/figure-html/unnamed-chunk-7-1.png new file mode 100644 index 00000000..e08d5cac Binary files /dev/null and b/articles/Antimalarial_Resistance_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/articles/Carrying-capacity.html b/articles/Carrying-capacity.html index fc844964..f268a7dd 100644 --- a/articles/Carrying-capacity.html +++ b/articles/Carrying-capacity.html @@ -12,14 +12,13 @@ - - +
    @@ -42,7 +41,7 @@
  • @@ -109,7 +108,7 @@
    • - +
    @@ -117,7 +116,7 @@

    Modifying the carrying capacity

    - +
    @@ -162,10 +161,10 @@

    Seasonality
     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_age_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_age_730_3650
 
 par(mfrow = c(1, 2))
 plot(s$EIR_gamb ~ s$timestep, t = "l", ylim = c(0, 200), xlab = "Time", ylab = "EIR")
@@ -178,18 +177,14 @@ 
    SeasonalityCustom modification of the mosquito carrying capacity
 
    
 
    In malariasimulation, we can also modify the carrying capacity over
-time in a more bespoke manner. Firstly it is helpful to start with our
-baseline (equilibrium) carrying capacity. We can access this for our
-baseline parameters with:
    
-
    -cc <- get_init_carrying_capacity(p)
-cc
-#>     gamb 
-#> 65585.97
    
-
    We can then use this in combination with
-set_carrying_capacity() to provide modified,
-species-specific carrying capacity values at given timepoints. This
-allows us to model a number of useful things:
    
+time in a more bespoke manner. We can use the
+set_carrying_capacity() helper function to specify scalars
+that modify the species-specific carrying capacity through time,
+relative to the baseline carrying capacity, at defined time steps. The
+baseline carrying capacity is generated when calling
+set_equilibrium(), to match the specified EIR and
+population size.
    
+
    This functionality allows us to model a number of useful things:
    
 
    
 
    Larval source management
 
    
@@ -197,18 +192,18 @@ 
    Larval source managementset_carrying_capacity() can be used to specify this
 intervention
    
-
    +
     # Specify the LSM coverage
 lsm_coverage <- 0.8
-# Set LSM by reducing the carrying capacity by (1 - coverage)
+# Set LSM by scaling the carrying capacity by (1 - coverage)
 p_lsm <- p |>
   set_carrying_capacity(
-    carrying_capacity = matrix(cc * (1 - lsm_coverage), ncol = 1),
+    carrying_capacity_scalers = matrix(1 - lsm_coverage, ncol = 1),
     timesteps = 365
   )
 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_age_730_3650
 
 par(mfrow = c(1, 2))
 plot(s$EIR_gamb ~ s$timestep, t = "l", ylim = c(0, 150), xlab = "Time", ylab = "EIR")
@@ -217,36 +212,36 @@ 
    Larval source managementplot(s$pfpr ~ s$timestep, t = "l", ylim = c(0, 1), xlab = "Time", ylab = "PfPr")
 lines(s_lsm$pfpr ~ s_lsm$timestep, col = "darkorchid3")
 abline(v = 365, lty = 2, col = "grey60")
    
-
    
+
    
 
    
 
    
 
    Invasive species
 
    
 
    An invasive species may exploit a new niche, increase the carrying
-capacity at the point of invasion. The functions
+capacity at the point of invasion. The function
 set_carrying_capacity() gives complete freedom to allow
 changes to the carrying capacity to be made. Here we will demonstrate
 using carrying capacity rescaling to capture the invasion of
 Anopheles stephensi.
    
-
    +
     # First initialise the mosquito species so that the invasive species starts with
 # a negligible, but non-zero proportion.
 p_stephensi <- p |>
   set_species(list(gamb_params, steph_params), c(0.995, 1 - 0.995)) |>
   set_equilibrium(init_EIR = 5)
-cc_invasive <- get_init_carrying_capacity(p_stephensi)
+
 # Next, at the time point of invasion, we scale up the carrying capacity for
-# the invasive species by a factor that will be dependent on the proporties of
+# the invasive species by a factor that will be dependent on the properties of
 # the invasion.
 p_stephensi <- p_stephensi |>
   set_carrying_capacity(
-    carrying_capacity = matrix(cc_invasive * c(1, 2000), ncol = 2),
+    carrying_capacity_scalers = matrix(c(1, 2000), ncol = 2),
     timesteps = 365
   )
 
 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_age_730_3650
 
 par(mfrow = c(1, 2))
 plot(s_stephensi$EIR_gamb ~ s_stephensi$timestep,
@@ -255,7 +250,7 @@ 
    Invasive speciesabline(v = 365, lty = 2, col = "grey60")
 plot(s_stephensi$pfpr ~ s_stephensi$timestep, t = "l", ylim = c(0, 1), xlab = "Time", ylab = "PfPr")
 abline(v = 365, lty = 2, col = "grey60")
    
-
    
+
    
 
    
 
    
 
    Full flexibility
@@ -264,16 +259,16 @@ 
    Full flexibility
-
    +
     p_flexible <- p |>
   set_carrying_capacity(
-    carrying_capacity = cc * matrix(c(0.1, 2, 0.1, 0.5, 0.9), ncol = 1),
+    carrying_capacity = matrix(c(0.1, 2, 0.1, 0.5, 0.9), ncol = 1),
     timesteps = seq(100, 500, 100)
   )
 
 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_age_730_3650
 
 par(mfrow = c(1, 2))
 plot(s$EIR_gamb ~ s$timestep, t = "l", ylim = c(0, 150), xlab = "Time", ylab = "EIR")
@@ -282,16 +277,14 @@ 
    Full flexibilityplot(s$pfpr ~ s$timestep, t = "l", ylim = c(0, 1), xlab = "Time", ylab = "PfPr")
 lines(s_flexible$pfpr ~ s_flexible$timestep, col = "darkorchid3")
 abline(v = 365, lty = 2, col = "grey60")
    
-
    
+
    
 
    
 
    
   
    
 
   
+

    @@ -304,16 +297,16 @@

    Full flexibility

    -

    Site built with pkgdown 2.0.9.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/articles/Carrying-capacity_files/figure-html/unnamed-chunk-4-1.png b/articles/Carrying-capacity_files/figure-html/unnamed-chunk-4-1.png index 95367ed0..8ec75117 100644 Binary files a/articles/Carrying-capacity_files/figure-html/unnamed-chunk-4-1.png and b/articles/Carrying-capacity_files/figure-html/unnamed-chunk-4-1.png differ diff --git a/articles/Carrying-capacity_files/figure-html/unnamed-chunk-5-1.png b/articles/Carrying-capacity_files/figure-html/unnamed-chunk-5-1.png new file mode 100644 index 00000000..1060ac8f Binary files /dev/null and b/articles/Carrying-capacity_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/articles/Carrying-capacity_files/figure-html/unnamed-chunk-6-1.png b/articles/Carrying-capacity_files/figure-html/unnamed-chunk-6-1.png index 8b38f4aa..0375e5fc 100644 Binary files a/articles/Carrying-capacity_files/figure-html/unnamed-chunk-6-1.png and b/articles/Carrying-capacity_files/figure-html/unnamed-chunk-6-1.png differ diff --git a/articles/Carrying-capacity_files/figure-html/unnamed-chunk-7-1.png b/articles/Carrying-capacity_files/figure-html/unnamed-chunk-7-1.png index c807df15..c846fd73 100644 Binary files a/articles/Carrying-capacity_files/figure-html/unnamed-chunk-7-1.png and b/articles/Carrying-capacity_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/articles/Carrying-capacity_files/figure-html/unnamed-chunk-8-1.png b/articles/Carrying-capacity_files/figure-html/unnamed-chunk-8-1.png deleted file mode 100644 index c83cd40b..00000000 Binary files a/articles/Carrying-capacity_files/figure-html/unnamed-chunk-8-1.png and /dev/null differ diff --git a/articles/Demography.html b/articles/Demography.html index bd6af7a2..8bb36eac 100644 --- a/articles/Demography.html +++ b/articles/Demography.html @@ -12,14 +12,13 @@ - - +
    @@ -42,7 +41,7 @@
  • @@ -109,7 +108,7 @@
    • - +
    @@ -117,7 +116,7 @@

    Demography

    - +
    @@ -149,7 +148,7 @@

    Age group renderingget_parameters() function and accept the default values. The run_simulation() function’s default behaviour is to output only the number of individuals aged 2-10 years old -(output$n_730_3650, where 730 and 3650 are the ages in +(output$n_age_730_3650, where 730 and 3650 are the ages in days). However, the user can instruct the model to output the number of individuals in age groups of their choosing using the age_group_rendering_min_ages and @@ -410,9 +409,7 @@

    Visualisation - -

    + @@ -425,16 +422,16 @@

    Visualisation

    -

    Site built with pkgdown 2.0.9.

    +

    Site built with pkgdown 2.1.0.

    - - + + diff --git a/articles/Demography_files/figure-html/unnamed-chunk-5-1.png b/articles/Demography_files/figure-html/unnamed-chunk-5-1.png index 4feeb4a3..1331c524 100644 Binary files a/articles/Demography_files/figure-html/unnamed-chunk-5-1.png and b/articles/Demography_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/articles/Demography_files/figure-html/unnamed-chunk-8-1.png b/articles/Demography_files/figure-html/unnamed-chunk-8-1.png index a6992b75..68ea5495 100644 Binary files a/articles/Demography_files/figure-html/unnamed-chunk-8-1.png and b/articles/Demography_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/articles/EIRprevmatch.html b/articles/EIRprevmatch.html index 5440c226..43b23471 100644 --- a/articles/EIRprevmatch.html +++ b/articles/EIRprevmatch.html @@ -12,14 +12,13 @@ - - +
    @@ -42,7 +41,7 @@
  • @@ -109,7 +108,7 @@
    • - +
    @@ -119,7 +118,7 @@

    Matching EIR to

    - +
    @@ -321,11 +320,11 @@

    Run the simulations and calc individual across the final year across all vector species. Next, the average PfPR2-10 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 PfPR2-10 are stored in a data -frame.

    +aged 2-10 by the number (n_age_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 +PfPR2-10 are stored in a data frame.

     # Establish a vector of initial EIR values to simulate over and generate matching
 # PfPR2-10 values for:
@@ -365,10 +364,10 @@ 
    Run the simulations and calc
     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'
+        'n_age_730_3650'
       ]
     )
   }
@@ -380,13 +379,13 @@ 
    Run the simulations and calc
 # View the dataframe containing the EIR and matching PfPR2-10 values:
 malSim_P2E
 #>   init_EIR         EIR        prev
-#> 1     0.01  0.02080912 0.002780211
-#> 2     0.10  0.12550912 0.012511817
-#> 3     1.00  1.20879350 0.088166918
-#> 4     5.00  5.49484775 0.235150800
-#> 5    10.00 10.86100433 0.341189809
-#> 6    25.00 26.83117129 0.485926742
-#> 7    50.00 52.64982501 0.604108088
    +#> 1 0.01 0.01865945 0.002635393 +#> 2 0.10 0.09282683 0.007495763 +#> 3 1.00 1.18501613 0.096890083 +#> 4 5.00 5.83869188 0.268115857 +#> 5 10.00 11.30439827 0.343008297 +#> 6 25.00 25.74267129 0.483218187 +#> 7 50.00 54.26493735 0.611062770

    Fit line of best fit relating initial EIR values to @@ -532,7 +531,7 @@

    Calibrating PfPRsummary_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_age_730_3650) # Return the calculated PfPR2-10: return(prev_2_10) @@ -591,8 +590,8 @@

    Calibrating PfPR 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_age_730_3650 +malsim_pfpr2_10 <- malsim_sim$n_detect_lm_730_3650 / malsim_sim$n_age_730_3650 # Store the PfPR2-10 in each time step for the two methods: df <- data.frame(timestep = seq(1, length(cali_pfpr2_10)), @@ -649,9 +648,7 @@

    Calibrating PfPR