Fix calls in RSV and time varying vax examples

Author: smjenness

examples/rsv/index.qmd

@@ -223,7 +223,7 @@ Three custom modules: `init_attrs` (one-shot setup of per-node intervention stat
 
 ```r
 for (k in 1:2) {                                         # <1>
-  el <- dat$run$el[[k]]
+  el <- get_edgelist(dat, network = k)
   if (is.null(el) || nrow(el) == 0) next
 
   head <- el[, 1]; tail <- el[, 2]
@@ -251,7 +251,7 @@ for (k in 1:2) {                                         # <1>
 
 Per-step transmission probability per discordant edge is built up from:
 
-1. **Per-layer iteration.** With `tergmLite = TRUE`, each network layer's current edgelist is in `dat$run$el[[k]]`. We walk each layer separately rather than calling `discord_edgelist()`, because we want different transmission rates on the family vs community layer.
+1. **Per-layer iteration.** `get_edgelist(dat, network = k)` returns the current edgelist for layer `k`. We walk each layer separately rather than calling `discord_edgelist()`, because we want different transmission rates on the family vs community layer.
 2. **NPI community-edge thinning.** When NPIs are active, we drop a fraction of community edges on the fly (representing reduced contact intensity).
 3. **Asymptomatic infectiousness multiplier.** Each row's transmission probability is halved if the infectious partner is in state `"ia"`.
 4. **Vaccine / prophylaxis efficacy on the susceptible side.** Looking up the susceptible's `vax_status` lets us apply leaky reductions for the two age-targeted interventions.

examples/rsv/module-fx.R

@@ -107,7 +107,7 @@ infect <- function(dat, at) {
   all_new <- integer(0)
 
   for (k in 1:2) {
-    el <- dat$run$el[[k]]
+    el <- get_edgelist(dat, network = k)
     if (is.null(el) || nrow(el) == 0) next
 
     head <- el[, 1]

examples/sir-time-varying-vaccination/README.md

@@ -62,7 +62,7 @@ The same pattern generalizes to any time-varying or state-dependent intervention
 
 ### Infection Module (`infect`)
 
-Standard S to I transmission along discordant edges, plus an optional `import.rate` parameter for exogenous (external) introductions. Vaccine-immune individuals are excluded automatically because `discord_edgelist()` only pairs `"s"` with `"i"`. Records `si.flow` and `im.flow`.
+Standard S to I transmission along discordant edges, plus an optional `exog.inf.prob` parameter that adds an external (exogenous) force of infection: each active susceptible has that per-timestep probability of being infected from outside the modeled network (spillover from an unmodeled reservoir, environmental exposure, off-network contacts). This is distinct from importation, which describes already-infected individuals arriving in the population and is properly modeled as an arrivals process in a vital-dynamics module. Vaccine-immune individuals are excluded automatically because `discord_edgelist()` only pairs `"s"` with `"i"`. Records `si.flow` and `exog.flow`.
 
 ### Recovery Module (`recov`)
 
@@ -88,7 +88,7 @@ Records `vax.active` (0/1 indicator), `vax.flow`, `sv.flow`, `vs.flow`, and `v.n
 | `act.rate` | Acts per partnership per timestep | 1 |
 | `rec.rate` | Per-timestep recovery rate | 0.04 |
 | `rs.rate` | R-to-S waning rate (`0` = SIR; `> 0` = SIRS) | 0 |
-| `import.rate` | Per-S probability of exogenous import per step | 0 |
+| `exog.inf.prob` | Per-S probability of exogenous infection per step | 0 |
 
 ### Vaccination
 

examples/sir-time-varying-vaccination/index.qmd

@@ -82,7 +82,7 @@ nsteps_endemic <- 1000
 
 ### Infection Module
 
-Standard S to I transmission along discordant edges, plus an optional `import.rate` parameter that lets the user model exogenous (external) introductions of the disease. Vaccine-immune individuals (`status == "v"`) are excluded automatically because `discord_edgelist()` only pairs `"s"` with `"i"`.
+Standard S to I transmission along discordant edges, plus an optional `exog.inf.prob` parameter that adds an **external (exogenous) force of infection**. At each timestep every active susceptible has probability `exog.inf.prob` of being infected by a source outside the modeled network: spillover from an unmodeled reservoir, environmental exposure, or contacts with individuals not represented in the simulated network. This is distinct from *importation*, which describes already-infected individuals arriving in the population and is properly modeled as an arrivals process in a vital-dynamics module. Here the population is closed and existing susceptibles transition to infectious at a constant background hazard, independent of in-population prevalence. Vaccine-immune individuals (`status == "v"`) are excluded automatically because `discord_edgelist()` only pairs `"s"` with `"i"`.
 
 ```{r}
 #| label: infection-module
@@ -94,13 +94,13 @@ infect <- function(dat, at) {
 
   inf.prob <- get_param(dat, "inf.prob")
   act.rate <- get_param(dat, "act.rate")
-  import.rate <- get_param(dat, "import.rate")
+  exog.inf.prob <- get_param(dat, "exog.inf.prob")
 
   idsInf <- which(active == 1 & status == "i")
   nActive <- sum(active == 1)
   nElig <- length(idsInf)
   nInf <- 0
-  nImp <- 0
+  nExog <- 0
 
   ## Endogenous network transmission ##
   if (nElig > 0 && nElig < nActive) {
@@ -120,32 +120,32 @@ infect <- function(dat, at) {
     }
   }
 
-  ## Exogenous (imported) introductions ##
-  if (import.rate > 0) { # <2>
+  ## Exogenous force of infection ##
+  if (exog.inf.prob > 0) { # <2>
     idsSus <- which(active == 1 & status == "s")
     if (length(idsSus) > 0) {
-      vecImp <- which(rbinom(length(idsSus), 1, import.rate) == 1)
-      nImp <- length(vecImp)
-      if (nImp > 0) {
-        idsImp <- idsSus[vecImp]
-        status[idsImp] <- "i"
-        infTime[idsImp] <- at
+      vecExog <- which(rbinom(length(idsSus), 1, exog.inf.prob) == 1)
+      nExog <- length(vecExog)
+      if (nExog > 0) {
+        idsExog <- idsSus[vecExog]
+        status[idsExog] <- "i"
+        infTime[idsExog] <- at
       }
     }
   }
 
-  if (nInf + nImp > 0) {
+  if (nInf + nExog > 0) {
     dat <- set_attr(dat, "status", status)
     dat <- set_attr(dat, "infTime", infTime)
   }
 
   dat <- set_epi(dat, "si.flow", at, nInf)
-  dat <- set_epi(dat, "im.flow", at, nImp)
+  dat <- set_epi(dat, "exog.flow", at, nExog)
   return(dat)
 }
 ```
 1. `discord_edgelist()` only considers nodes with status `"s"` or `"i"`. Vaccine-immune individuals (`"v"`) and recovered individuals (`"r"`) never appear and are therefore protected without any explicit exclusion logic.
-2. Imports model exogenous introductions of the disease (travel-related cases, spillover events, etc.). Setting `import.rate = 0` gives a purely closed population.
+2. The exogenous force of infection adds a constant per-step Bernoulli hazard to every susceptible (e.g. spillover from an unmodeled reservoir, environmental exposure, off-network contacts). Setting `exog.inf.prob = 0` gives a purely network-driven model. This is not importation, which would describe already-infected individuals arriving in the population and is properly handled by an arrivals module.
 
 ### Recovery Module
 
@@ -245,9 +245,10 @@ vaccinate <- function(dat, at) {
       sum(active == 1 & status == "i") / nAct
     } else 0
     prev.active <- 0
-    if (!is.null(dat$epi$vax.active) && at > 2) {
-      val <- dat$epi$vax.active[at - 1]
-      if (!is.na(val)) prev.active <- val
+    if (at > 2) {
+      val <- get_epi(dat, "vax.active", at = at - 1,
+                     override.null.error = TRUE)
+      if (!is.null(val) && !is.na(val)) prev.active <- val
     }
     if (prev.active == 1) {
       vax.active <- as.numeric(current.prev > vax.prev.off) # <4>
@@ -351,13 +352,13 @@ All scenarios share the same disease parameters and differ only in schedule and
 make_param <- function(vax.starts = -1, vax.ends = -1,
                        vax.prev.on = -1, vax.prev.off = -1,
                        vax.rate = 0.05, vax.wane = 0, rs.rate = 0,
-                       import.rate = 0) {
+                       exog.inf.prob = 0) {
   param.net(
     inf.prob = 0.10,
     act.rate = 1,
     rec.rate = 0.04,
     rs.rate = rs.rate, # <1>
-    import.rate = import.rate,
+    exog.inf.prob = exog.inf.prob,
     vax.rate = vax.rate,
     vax.efficacy = 0.9,
     vax.wane = vax.wane, # <2>
@@ -620,7 +621,7 @@ The sweep adds ~1 minute of runtime on a laptop (11 simulations at `nsteps = 400
 | `act.rate` | Acts per partnership per timestep | 1 |
 | `rec.rate` | Per-timestep recovery rate (mean infectious ~25) | 0.04 |
 | `rs.rate` | R-to-S waning rate (`0` = SIR; `> 0` = SIRS) | 0 |
-| `import.rate` | Per-S probability of exogenous import per step | 0 |
+| `exog.inf.prob` | Per-S probability of exogenous infection per step | 0 |
 
 ### Vaccination
 

examples/sir-time-varying-vaccination/model.R

@@ -90,13 +90,13 @@ control <- control.net(
 make_param <- function(vax.starts = -1, vax.ends = -1,
                        vax.prev.on = -1, vax.prev.off = -1,
                        vax.rate = 0.05, vax.wane = 0, rs.rate = 0,
-                       import.rate = 0) {
+                       exog.inf.prob = 0) {
   param.net(
     inf.prob = 0.10,
     act.rate = 1,
     rec.rate = 0.04,
     rs.rate = rs.rate,
-    import.rate = import.rate,
+    exog.inf.prob = exog.inf.prob,
     vax.rate = vax.rate,
     vax.efficacy = 0.9,
     vax.wane = vax.wane,

examples/sir-time-varying-vaccination/module-fx.R

@@ -17,10 +17,18 @@ infect <- function(dat, at) {
   # Vaccine-immune individuals (status = "v") are excluded automatically:
   # discord_edgelist() only pairs "s" with "i".
   #
-  # Optional import.rate adds a background rate of external introductions.
-  # When import.rate > 0, each susceptible has that per-timestep probability
-  # of becoming infected by an exogenous source (e.g., travel-related
-  # introductions). With import.rate = 0 this is a purely closed model.
+  # Optional exog.inf.prob adds an external (exogenous) force of infection.
+  # At each timestep every active susceptible has probability exog.inf.prob
+  # of being infected by a source outside the modeled network: spillover
+  # from an unmodeled reservoir, environmental exposure, or contacts with
+  # individuals not represented in the simulated network. This is *not*
+  # importation in the traditional epidemiological sense. Importation
+  # describes already-infected individuals arriving in the population,
+  # which is an arrivals process and belongs in a vital-dynamics module.
+  # Here the population is closed and existing susceptibles transition to
+  # infectious at a constant background hazard that is independent of
+  # in-population prevalence. Setting exog.inf.prob = 0 disables the
+  # exogenous channel and leaves a purely network-driven model.
 
   ## Attributes ##
   active <- get_attr(dat, "active")
@@ -30,14 +38,14 @@ infect <- function(dat, at) {
   ## Parameters ##
   inf.prob <- get_param(dat, "inf.prob")
   act.rate <- get_param(dat, "act.rate")
-  import.rate <- get_param(dat, "import.rate")
+  exog.inf.prob <- get_param(dat, "exog.inf.prob")
 
   ## Find infected nodes ##
   idsInf <- which(active == 1 & status == "i")
   nActive <- sum(active == 1)
   nElig <- length(idsInf)
   nInf <- 0
-  nImp <- 0
+  nExog <- 0
 
   ## Endogenous (network) transmission ##
   if (nElig > 0 && nElig < nActive) {
@@ -57,28 +65,28 @@ infect <- function(dat, at) {
     }
   }
 
-  ## Exogenous (imported) introductions ##
-  if (import.rate > 0) {
+  ## Exogenous force of infection ##
+  if (exog.inf.prob > 0) {
     idsSus <- which(active == 1 & status == "s")
     nSus <- length(idsSus)
     if (nSus > 0) {
-      vecImp <- which(rbinom(nSus, 1, import.rate) == 1)
-      nImp <- length(vecImp)
-      if (nImp > 0) {
-        idsImp <- idsSus[vecImp]
-        status[idsImp] <- "i"
-        infTime[idsImp] <- at
+      vecExog <- which(rbinom(nSus, 1, exog.inf.prob) == 1)
+      nExog <- length(vecExog)
+      if (nExog > 0) {
+        idsExog <- idsSus[vecExog]
+        status[idsExog] <- "i"
+        infTime[idsExog] <- at
       }
     }
   }
 
-  if (nInf + nImp > 0) {
+  if (nInf + nExog > 0) {
     dat <- set_attr(dat, "status", status)
     dat <- set_attr(dat, "infTime", infTime)
   }
 
   dat <- set_epi(dat, "si.flow", at, nInf)
-  dat <- set_epi(dat, "im.flow", at, nImp)
+  dat <- set_epi(dat, "exog.flow", at, nExog)
 
   return(dat)
 }
@@ -218,9 +226,10 @@ vaccinate <- function(dat, at) {
       0
     }
     prev.active <- 0
-    if (!is.null(dat$epi$vax.active) && at > 2) {
-      val <- dat$epi$vax.active[at - 1]
-      if (!is.na(val)) prev.active <- val
+    if (at > 2) {
+      val <- get_epi(dat, "vax.active", at = at - 1,
+                     override.null.error = TRUE)
+      if (!is.null(val) && !is.na(val)) prev.active <- val
     }
     if (prev.active == 1) {
       vax.active <- as.numeric(current.prev > vax.prev.off) # <1>