New cholera branch. Started to make more cholera-like. 1) Removed EUL…

…A-ification; 2) Added waning immunity upon recovery (too short at first, to test); 3) Fixed some math bugs in enviro transmission.

nnmm/cholera.py

@@ -26,24 +26,24 @@ class Model:
 from idmlaser.utils import PropertySet
 
 meta_params = PropertySet({
- "ticks": int(365*10),
+ "ticks": int(365*2),
  "cbr": 40, # Nigeria 2015 according to (somewhat random internet source): https://fred.stlouisfed.org/series/SPDYNCBRTINNGA
  "output": Path.cwd() / "outputs",
- "eula_age": 5
+ #"eula_age": 5
 })
 # parameter?
 prevalence = 0.025 # 2.5% prevalence
 
 measles_params = PropertySet({
- "exp_mean": np.float32(7.0),
+ "exp_mean": np.float32(2.0),
  "exp_std": np.float32(1.0),
- "inf_mean": np.float32(7.0),
- "inf_std": np.float32(1.0),
+ "inf_mean": np.float32(6.0),
+ "inf_std": np.float32(2.0),
  "r_naught": np.float32(14.0),
  "seasonality_factor": np.float32(0.125),
  "seasonality_phase": np.float32(182),
  "ri_coverage": np.float32(0.75),
- "beta_env": np.float32(0.1), # beta(j,0) -- The baseline rate of environment-to-human transmission (all destinations)
+ "beta_env": np.float32(1.0), # beta(j,0) -- The baseline rate of environment-to-human transmission (all destinations)
  "kappa": np.float32(5e5), # The concentration (number of cells per mL) of V. cholerae required for a 50% probability of infection.
 })
 

nnmm/measles.py

@@ -1,15 +1,3 @@
-commit 2ce4764876daa91d69b2212c02d1bec3b5b8ec7e
-Author: Christopher Lorton <christopher.lorton@gatesfoundation.org>
-Date: Fri Aug 2 15:45:04 2024 -0700
-
- Add plain script of FUSION code.
-
-diff --git a/nnmm/measles.py b/nnmm/measles.py
-new file mode 100644
-index 0000000..3ec43ae
---- /dev/null
-++ b/nnmm/measles.py
-@@ -0,0 +1,1194 @@
 #!/usr/bin/env python
 # coding: utf-8
 

nnmm/measles_cache_input.py

@@ -754,9 +754,9 @@ def save_initial_pop():
  tfinish = datetime.now(tz=None) # noqa: DTZ005
  delta = tfinish - tstart
  metrics.append(delta.seconds * 1_000_000 + delta.microseconds) # delta is a timedelta object, let's convert it to microseconds
-  #if tick == 1824: # 365*5:
-  #model.population.save(filename="pop_5yrs.hd5", tail_number=total_births)
-  #model.population.save_npz(filename="pop_5yrs.npz", tail_number=total_births)
+ #if tick == 1824: # 365*5:
+ #model.population.save(filename="pop_5yrs.hd5", tail_number=total_births)
+ #model.population.save_npz(filename="pop_5yrs.npz", tail_number=total_births)
  model.metrics.append(metrics)
 
 

nnmm/mods/intrahost.py

@@ -29,17 +29,20 @@ def do_exposure_update(model, tick):
  return
 
 
-@nb.njit((nb.uint32, nb.uint8[:]), parallel=True)
-def _infection_update(count, itimers):
+@nb.njit((nb.uint32, nb.uint8[:], nb.uint16[:], nb.uint8[:]), parallel=True)
+def _infection_update(count, itimer, sus_timer, susceptibility):
  for i in nb.prange(count):
- if itimers[i] > 0:
- itimers[i] -= 1
+ if itimer[i] > 0:
+ itimer[i] -= 1
+ if itimer[i] <= 0:
+ susceptibility = 0
+ sus_timer[i] = 1*365
 
  return
 
 def do_infection_update(model, tick):
 
- _infection_update(nb.uint32(model.population.count), model.population.itimer)
+ _infection_update(nb.uint32(model.population.count), model.population.itimer, model.population.susceptibility_timer, model.population.susceptibility)
 
  return
 

nnmm/mods/maternal_immunity.py

@@ -13,7 +13,7 @@ def init( model, istart, iend ):
  model.population.susceptibility_timer[istart:iend] = int(0.5*365) # 6 months
 
 # Define the function to decrement susceptibility_timer and update susceptibility
-@nb.njit((nb.uint32, nb.uint8[:], nb.uint8[:]), parallel=True)
+@nb.njit((nb.uint32, nb.uint16[:], nb.uint8[:]), parallel=True)
 def _update_susceptibility_based_on_sus_timer_nb(count, susceptibility_timer, susceptibility):
  for i in nb.prange(count):
  if susceptibility_timer[i] > 0:

nnmm/mods/transmission.py

@@ -139,12 +139,14 @@ def do_transmission_update(model, tick) -> None:
  # This reduces the amount of environmental contagion each timestep
  # HARDCODE FOR NOW
  nodes.enviro_contagion *= (1 - 0.5)
- 
+
  # Add newly shed contagion to the environmental contagion
  # This accumulates the current infections into the environmental contagion
  # (Assuming `contagion` represents newly shed contagion at each node)
  # TBD: Use zeta to calculate environmentally shed contagion vs contact shed contagion
  nodes.enviro_contagion += contagion
+
+ nodes.enviro_contagion *= 1-model.nodes.WASH_fraction
 
  # Calculate the effective environmental transmission rate for all nodes at the current timestep
  beta_env_effective = get_enviro_beta_from_psi(model.params.beta_env, model.nodes.psi[:, tick])
@@ -155,17 +157,17 @@ def do_transmission_update(model, tick) -> None:
 
  # Normalize the environmental forces by dividing by the population at each node
  # This scales the environmental forces to be a probability per individual
- forces_environmental /= model.nodes.population[:, tick]
+ #forces_environmental /= model.nodes.population[:, tick]
 
  # Combine the contact transmission forces with the environmental transmission forces
  # `forces` are the contact transmission forces calculated elsewhere
  # `forces_environmental` are the environmental transmission forces computed in this section
- total_forces = forces + forces_environmental
+ total_forces = (forces + forces_environmental).astype(np.float32)
 
  tx_inner(
  population.susceptibility,
  population.nodeid,
- forces,
+ total_forces,
  population.etimer,
  population.count,
  model.params.exp_mean,

nnmm/schema.py

@@ -8,7 +8,7 @@
  "ri_timer": np.uint16,
  "etimer": np.uint8,
  "itimer": np.uint8,
- "susceptibility_timer": np.uint8,
+ "susceptibility_timer": np.uint16,
  "accessibility": np.uint8
 }