Example correlation diagnostics:

[giovannic]

I've made a test script which outputs some diagnostics for the cases you mentioned. Feel free to copy and paste/adapt to diagnose what you need.

Once you have something that might be generally useful, we could turn this into a PR to merge into dev :)

Here's an example of use:

library(malariasimulation)

month <- 30
year <- 365

# =========================================================
# testing that pev targets different people for inter round
# =========================================================

params <- get_parameters(list(
  human_population = 10000
))

params <- set_mass_pev(
  params,
  profile = rtss_profile,
  timesteps = c(5, 10),
  coverages = c(.5, .5),
  min_wait = 0,
  min_ages = 5 * month,
  max_ages = 10 * year,
  booster_spacing = c(1),
  booster_coverage = matrix(0., nrow=2), 
  booster_profile = list(rtss_booster_profile)
)

set.seed(123)

correlations <- get_correlation_parameters(params)

correlations$inter_round_rho('pev', 0)

output <- run_simulation(
  timesteps = 100,
  parameters = params,
  correlations = correlations
)

set.seed(123)

correlations <- get_correlation_parameters(params)

correlations$inter_round_rho('pev', 1)

output_2 <- run_simulation(
  timesteps = 100,
  parameters = params,
  correlations = correlations
)

print(output$n_new_vaccinations)
print(output_2$n_new_vaccinations)

# ===========================================
# testing that bednets and pev can be negatively correlated
# ===========================================

params <- get_parameters(list(
  human_population = 10000
))

params <- set_bednets(
  params,
  c(5, 10),
  coverages = c(.5, .5),
  retention = 5 * 365,
  dn0 = matrix(c(.533, .533), nrow = 2, ncol = 1),
  rn = matrix(c(.56, .56), nrow = 2, ncol = 1),
  rnm = matrix(c(.24, .24), nrow = 2, ncol = 1),
  gamman = rep(2.64 * 365, 2)
)

params <- set_mass_pev(
  params,
  profile = rtss_profile,
  timesteps = c(5, 10),
  coverages = c(.5, .5),
  min_wait = 0,
  min_ages = 5 * month,
  max_ages = 10 * year,
  booster_spacing = c(1),
  booster_coverage = matrix(0., nrow=2), 
  booster_profile = list(rtss_booster_profile)
)

set.seed(123)

correlations <- get_correlation_parameters(params)

correlations$inter_round_rho('pev', 1)
correlations$inter_round_rho('bednets', 1)
correlations$inter_intervention_rho('pev', 'bednets', 1.)

output <- run_simulation(
  timesteps = 100,
  parameters = params,
  correlations = correlations
)

set.seed(123)

correlations <- get_correlation_parameters(params)

correlations$inter_round_rho('pev', 1)
correlations$inter_round_rho('bednets', 1)
correlations$inter_intervention_rho('pev', 'bednets', -1.)

output_2 <- run_simulation(
  timesteps = 100,
  parameters = params,
  correlations = correlations
)

print(output$n_combined_pev_bednets)
print(output_2$n_combined_pev_bednets)

The outputs all look good!

  [1]   0   0   0   0   0   0   0   0   0 926   0   0   0   0   0   0   0   0
 [19]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
 [37]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
 [55]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
 [73]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
 [91]   0   0   0   0   0   0   0   0   0   0
  [1]    0    0    0    0    0    0    0    0    0 1789    0    0    0    0    0
 [16]    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
 [31]    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
 [46]    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
 [61]    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
 [76]    0    0    0    0    0    0    0    0    0    0    0    0    0    0    0
 [91]    0    0    0    0    0    0    0    0    0    0
  [1]    0    0    0    0    0 1830 1830 1830 1830 1827 1833 1833 1832 1832 1830
 [16] 1826 1824 1823 1823 1820 1820 1820 1819 1817 1815 1814 1812 1811 1811 1811
 [31] 1808 1806 1804 1802 1800 1798 1798 1796 1796 1794 1793 1792 1790 1788 1786
 [46] 1783 1782 1781 1778 1778 1778 1776 1775 1772 1772 1770 1769 1768 1767 1766
 [61] 1765 1765 1764 1762 1762 1762 1761 1760 1758 1758 1756 1755 1754 1753 1751
 [76] 1751 1749 1747 1746 1745 1742 1742 1741 1740 1740 1739 1739 1737 1737 1736
 [91] 1734 1734 1732 1730 1730 1729 1726 1725 1724 1722
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Changes to malariasimulation:

• Some utility functions for outputting combined intervention counts
• Some outputs for diagnosing inter round correlation issues

[giovannic]

Closing due to inactivity