| Title: | Individual-Based Simulation Model of Malaria Epidemiology and Genomics |
|---|---|
| Description: | Individual-based simulation model of malaria epidemiology and genomics. magenta extends the imperial malaria model by tracking the infection history of individuals. With this additional genetic characteristics of the parasite can be assessed, including resistance. |
| Authors: | OJ Watson [aut, cre] |
| Maintainer: | Oliver Watson <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.3.5 |
| Built: | 2024-06-18 05:35:27 UTC |
| Source: | https://github.com/OJWatson/magenta |
These datasets represent the data fitted within the Imperial College Malaria model for relating seasonal profiles to malaria transmission intensity at level 1 admin regions across Africa
admin_units_seasonaladmin_units_seasonal
A dataframe of 850 observations of 15 variables:
admin_units_seasonal: A dataframe of admin units and their seasonal parameters
country: Country string
admin1: Admin 1 string
map_prev_2010: 2010 Atlas map microscopy prevalence in 2-10 year olds
id: Numeric vector of 1:850
gaul_code: Numeric vector for gaul code
a0: Average value of fourier series
a1: First of partial series cos terms
b1: First of partial series sine terms
a2: Second of partial series cos terms
b2: Second of partial series sine terms
a3: Third of partial series cos terms
b3: Third of partial series sine terms
theta_c: Rainfall normalising constant
dide_code: DIDE geo plotting code
ft: Treatment Coverage
convert_barcode_vectors converts human barcode vectors to nums and
calculated COIs.
convert_barcode_vectors( sim_save, ID, sub_patents_included = TRUE, ibd = FALSE, nl = 24, COI_type = "pcr_imperial" )convert_barcode_vectors( sim_save, ID, sub_patents_included = TRUE, ibd = FALSE, nl = 24, COI_type = "pcr_imperial" )
sim_save |
Saved output of simulation |
ID |
Vector of detection immunities for the population |
sub_patents_included |
Boolean as to whether subpatents are included. Default = TRUE |
ibd |
Boolean for IBD simulations. Default = FALSE |
nl |
Numeric for number of loci. Default = 24 |
COI_type |
String for type of COI calculation. Default = "pcr_imperial"
|
Create list describing parameters for drug efficacy and prophlyaxis
drug_create( prob_of_lpf = c(1, 0.97, 0.8, 0.55), barcode_res_pos = c(0, 1), prophylactic_pos = 1, dur_P = 25, dur_SPC = 5, drug_clearance_max_time = 60, prophylactic_probability = 1 - pgamma(seq(0, drug_clearance_max_time, 0.2), shape = 16.8, rate = 16.8/17.9), prophylactic_resistant_probability = 1 - pgamma(seq(0, drug_clearance_max_time, 0.2), shape = 16.8, rate = 16.8/8.7) )drug_create( prob_of_lpf = c(1, 0.97, 0.8, 0.55), barcode_res_pos = c(0, 1), prophylactic_pos = 1, dur_P = 25, dur_SPC = 5, drug_clearance_max_time = 60, prophylactic_probability = 1 - pgamma(seq(0, drug_clearance_max_time, 0.2), shape = 16.8, rate = 16.8/17.9), prophylactic_resistant_probability = 1 - pgamma(seq(0, drug_clearance_max_time, 0.2), shape = 16.8, rate = 16.8/8.7) )
prob_of_lpf |
Vector of probabilities for the chance of late parasitological failure (lpf). The vector gives the prob of lpf for each relevent barcode combination for a given drug. E.g. The default is:
The vector is length 4, and so 2 barcode positions change the prob of lpf. If the parasite is 0,0 then the prob of lpf is 0 (1-1), but if it was 0,1 it would be 0.2. |
barcode_res_pos |
Vector for which barcode positions correspond to which drug resistance mechanism. E.g. the default is:
Resistance to drug is encoded at barcode position 0 and 1 |
prophylactic_pos |
Vector for which barcode positions determine the
impact of drug resistance in shortening the effective prophylactic period.
E.g. the default is |
dur_P |
Duration of prophylaxis in days. Default = 25. |
dur_SPC |
Duration of slow parasite clearance. Default = 5. |
drug_clearance_max_time |
Maximum number of days to which to consider waning prophylaxis. Default = 60 days. |
prophylactic_probability |
Vector of changing probability of reinfection
due to waning prophylaxis. The last element reflects the probability after
|
prophylactic_resistant_probability |
Vector of changing probability of
reinfection due to waning prophylaxis when challenged by a parasite that is
resistant to the partner drug. The last element reflects the probability after
|
AL Drug Create
drug_create_al()drug_create_al()
We have curves of the longest and shortest duration of AL prophylaxis from Bretscher et al. However, we have multiple types of partner drug resistance. At the moment we will say that any loci associated with lumefantrine resistance yield the resistant curve but it may be better to have a distinct curve for each lumefantrine resistant genotype with the T0.5 aligned to the prob of lpf.
ASAQ Drug Create
drug_create_asaq()drug_create_asaq()
We have curves of the longest and shortest duration of ASAQ prophylaxis from Bretscher et al. However, we have multiple types of partner drug resistance. At the moment we will say that any loci associated with AQ resistance yield the resistant curve but it may be better to have a distinct curve for each AQ resistant genotype with the T0.5 aligned to the prob of lpf.
Perfect Drug Create
drug_create_default_no_resistance()drug_create_default_no_resistance()
Perfect Efficacy Drug. Used as default to match deterministic model easily
List for simulating drug usage for resistance/mft variables
drug_list_create( resistance_flag = FALSE, number_of_resistance_loci = 2, artemisinin_loci = c(0), cost_of_resistance = c(0.99, 0.99), absolute_fitness_cost_flag = FALSE, epistatic_logic = NULL, number_of_drugs = 1, drugs = list(drug_create_default_no_resistance()), mft_flag = FALSE, temporal_cycling = -1, sequential_cycling = -1, sequential_update = 3, drug_choice = 0, partner_drug_ratios = rep(1/number_of_drugs, number_of_drugs) )drug_list_create( resistance_flag = FALSE, number_of_resistance_loci = 2, artemisinin_loci = c(0), cost_of_resistance = c(0.99, 0.99), absolute_fitness_cost_flag = FALSE, epistatic_logic = NULL, number_of_drugs = 1, drugs = list(drug_create_default_no_resistance()), mft_flag = FALSE, temporal_cycling = -1, sequential_cycling = -1, sequential_update = 3, drug_choice = 0, partner_drug_ratios = rep(1/number_of_drugs, number_of_drugs) )
resistance_flag |
Boolean are we simulating resistance |
number_of_resistance_loci |
Numeric for number of res. loci |
artemisinin_loci |
Numerics for barcode positions that confer artemisinin resistance |
cost_of_resistance |
Numeric vector for fitness cost of each resistance loci. |
absolute_fitness_cost_flag |
Boolean are we simulating fitness costs as absolute (i.e. impactive onward transmission chance) or relative (resistant strains have decreases chance of being onwardly transmitted in mixed infections). Default = FALSE. |
epistatic_logic |
Is there compensatory relationships. i.e. what loci need to be true for resistance cost to exist. Default of NULL means that this becomes seq_len(number_of_resistance_loci), i.e. only dependent on their own loci. (TODO: Change this to be a list of length norl) |
number_of_drugs |
Numeric for number of drugs used |
drugs |
List of drugs that are being used, with each list element being
created by |
mft_flag |
Boolean are we doing mft |
temporal_cycling |
Numeric for when in years a drug switch occurs |
sequential_cycling |
Numeric for what perc. treatment failure before switch |
sequential_update |
How long does it take in years for sequential to be implemented |
drug_choice |
What's the default drug choice to begin. Default = 0 |
partner_drug_ratios |
Numeric vector for ratio of first line drugs used |
Table of probaility of late parasitological failure (28-day treatment failure) per genoytpe.
Sourced from:
Antimalarial mass drug administration in large populations and the evolution of drug resistance. Tran Dang Nguyen, Thu Nguyen-Anh Tran, Daniel M. Parker, Nicholas J White, Maciej F Boni. bioRxiv 2021.03.08.434496; doi: https://doi.org/10.1101/2021.03.08.434496
drug_tabledrug_table
A data.frame of drug efficacies by genotype
equilibrium_init_create creates an equilibrium initialisation state to be
used within later model runs
equilibrium_init_create( age_vector, het_brackets, ft, EIR, model_param_list, country = NULL, admin = NULL, quiet = FALSE )equilibrium_init_create( age_vector, het_brackets, ft, EIR, model_param_list, country = NULL, admin = NULL, quiet = FALSE )
age_vector |
Vector of age brackets. |
het_brackets |
Integer number of biting heteogenity compartments. |
ft |
Numeric for the frequency of people seeking treatment. |
EIR |
Numeric for desired annual EIR. |
model_param_list |
List of epidemiological parameters created by |
country |
String for country of interest. If NULL the seasonal parameters will attempt to be loaded using just the admin unit, however if there is ambiguity in the admin unit an error will be thrown. If both NULL then no seasonality is assumed. Default = NULL. |
admin |
String for admin unit with country for loading seasonal parameters. If country is NULL, the admin unit will attempt to be located,however if there is ambiguity in the admin unit an error will be thrown. If both country and admin are NULL then no seasonality is assumed. Default = NULL. |
quiet |
Whether function should be quiet. If FALSE (default) the returned country and admin are printed. |
List for simulation housekeeping vars, e.g. quiet prints,
housekeeping_list_create( quiet = TRUE, quiet_test = TRUE, cluster = FALSE, clear_up = TRUE )housekeeping_list_create( quiet = TRUE, quiet_test = TRUE, cluster = FALSE, clear_up = TRUE )
quiet |
Boolean for quiet simulation. Default = TRUE |
quiet_test |
Boolean for quiet testing statement. Default = TRUE |
cluster |
Boolean for simulation being on cluster. Default = TRUE |
clear_up |
Boolean for whether to clear up the memory used by the simulation. Default = TRUE |
Importation data for admin units in admin_units_seasonal
importationsimportations
A list of length 18
importations: A list of length 18, with each list representing a year. In each
year is then a further 2 lists which are:
incidence: Proportions of incidence that originated from other admin units, i.e. individuals in admin i that moved to admin j and then returned with an infection acquired while in admin j.
mosquitoFOI: Proportion of the force of infection towards mosquitoes that originated from outside admin units, i.e. the proportion of mosquitoes that are infected from infected individuals who travelled from admin j into admin i.
Intervention grab
intervention_grab( country, admin, year_range, final_itn_cov = NULL, final_irs_cov = NULL, final_ft = NULL )intervention_grab( country, admin, year_range, final_itn_cov = NULL, final_irs_cov = NULL, final_ft = NULL )
country |
Country string |
admin |
Admin string |
year_range |
Year range |
final_itn_cov |
Final ITN coverage |
final_irs_cov |
Final IRS coverage |
final_ft |
Final treatment coverage |
Grabs ITN, IRS ft from database
N.B. The admin region used for ft can be checked
IRS data for SSSA for 2000 to 2015
irs_2000_2015irs_2000_2015
A dataframe of 5 elements:
$irs_2000_2015: A dataframe of admin units and their seasonal parameters
intervention: String stating IRS
country: Country string
admin: Admin string
year: Numeric year
value: Value for IRS coverage
ITN data for SSSA for 2000 to 2015
itn_2000_2015itn_2000_2015
A dataframe of 5 elements:
itn_2000_2015: A dataframe of admin units and their seasonal parameters
intervention: String stating ITN
country: Country string
admin: Admin string
year: Numeric year
value: Value for ITN coverage
model_param_list_create creates list of model parameters to be used
within equilibrium_init_create
model_param_list_create( eta = 0.0001305, rho = 0.85, a0 = 2920, sigma2 = 1.67, max_age = 100 * 365, rA = 0.00512821, rT = 0.2, rD = 0.2, rU = 0.00906627, rP = 1/25, dE = 12, delayGam = 12.5, cD = 0.0676909, cT = 0.322 * cD, cU = 0.006203, gamma1 = 1.82425, d1 = 0.160527, dID = 3650, ID0 = 1.577533, kD = 0.476614, uD = 9.44512, aD = 8001.99, fD0 = 0.007055, gammaD = 4.8183, alphaA = 0.75735, alphaU = 0.185624, b0 = 0.590076, b1 = 0.5, dB = 3650, IB0 = 43.8787, kB = 2.15506, uB = 7.19919, phi0 = 0.791666, phi1 = 0.000737, dCA = 10950, IC0 = 18.02366, kC = 2.36949, uCA = 6.06349, PM = 0.774368, dCM = 67.6952, delayMos = 10, tau1 = 0.69, tau2 = 2.31, mu0 = 0.132, Q0 = 0.92, chi = 0.86, bites_Bed = 0.89, bites_Indoors = 0.97, muEL = 0.0338, muLL = 0.0348, muPL = 0.249, dEL = 6.64, dLL = 3.72, dPL = 0.643, gammaL = 13.25, km = 11, cm = 0.05, betaL = 21.2, num_int = 4, itn_cov = 0, irs_cov = 0, ITN_IRS_on = -1, DY = 365, d_ITN0 = 0.41, r_ITN0 = 0.56, r_ITN1 = 0.24, r_IRS0 = 0.6, d_IRS0 = 1, irs_half_life = 0.5 * DY, itn_half_life = 2.64 * DY, IRS_interval = 1 * DY, ITN_interval = 3 * DY )model_param_list_create( eta = 0.0001305, rho = 0.85, a0 = 2920, sigma2 = 1.67, max_age = 100 * 365, rA = 0.00512821, rT = 0.2, rD = 0.2, rU = 0.00906627, rP = 1/25, dE = 12, delayGam = 12.5, cD = 0.0676909, cT = 0.322 * cD, cU = 0.006203, gamma1 = 1.82425, d1 = 0.160527, dID = 3650, ID0 = 1.577533, kD = 0.476614, uD = 9.44512, aD = 8001.99, fD0 = 0.007055, gammaD = 4.8183, alphaA = 0.75735, alphaU = 0.185624, b0 = 0.590076, b1 = 0.5, dB = 3650, IB0 = 43.8787, kB = 2.15506, uB = 7.19919, phi0 = 0.791666, phi1 = 0.000737, dCA = 10950, IC0 = 18.02366, kC = 2.36949, uCA = 6.06349, PM = 0.774368, dCM = 67.6952, delayMos = 10, tau1 = 0.69, tau2 = 2.31, mu0 = 0.132, Q0 = 0.92, chi = 0.86, bites_Bed = 0.89, bites_Indoors = 0.97, muEL = 0.0338, muLL = 0.0348, muPL = 0.249, dEL = 6.64, dLL = 3.72, dPL = 0.643, gammaL = 13.25, km = 11, cm = 0.05, betaL = 21.2, num_int = 4, itn_cov = 0, irs_cov = 0, ITN_IRS_on = -1, DY = 365, d_ITN0 = 0.41, r_ITN0 = 0.56, r_ITN1 = 0.24, r_IRS0 = 0.6, d_IRS0 = 1, irs_half_life = 0.5 * DY, itn_half_life = 2.64 * DY, IRS_interval = 1 * DY, ITN_interval = 3 * DY )
eta |
Death rate for expoential population distribtuion, i.e. 1/Mean Population Age. Default = 0.0001305 |
rho |
Age-dependent biting parameter. Default = 0.85 |
a0 |
Age-dependent biting parameter. Default = 2920 |
sigma2 |
Variance of the log heterogeneity in biting rates. Default = 1.67 |
max_age |
Maximum age in days. Default = 100*365 |
rA |
Rate of leaving asymptomatic infection. Default = 0.00512821 |
rT |
Rate of leaving treatment. Default = 0.2 |
rD |
Rate of leaving clinical disease. Default = 0.2 |
rU |
Rate of recovering from subpatent infection. Default = 0.00906627 |
rP |
Rate of leaving prophylaxis. Default = 0.05 |
dE |
Latent period of human infection. Default = 12 |
delayGam |
Lag from parasites to infectious gametocytes. Default = 12.5 |
cD |
Untreated disease contribution to infectiousness. Default = 0.0676909 |
cT |
Treated disease contribution to infectiousness. Default = 0.322 * cD |
cU |
Subpatent disease contribution to infectiousness. Default = 0.006203 |
gamma1 |
Parameter for infectiousness of state A. Default = 1.82425 |
d1 |
Minimum probability due to maximum immunity. Default = 0.160527 |
dID |
Inverse of decay rate. Default = 3650 |
ID0 |
Scale parameter. Default = 1.577533 |
kD |
Shape parameter. Default = 0.476614 |
uD |
Duration in which immunity is not boosted. Default = 9.44512 |
aD |
Scale parameter relating age to immunity. Default = 8001.99 |
fD0 |
Time-scale at which immunity changes with age. Default = 0.007055 |
gammaD |
Shape parameter relating age to immunity. Default = 4.8183 |
alphaA |
PCR detection probability parameters state A. Default = 0.757 |
alphaU |
PCR detection probability parameters state U. Default = 0.186 |
b0 |
Maximum probability due to no immunity. Default = 0.590076 |
b1 |
Maximum relative reduction due to immunity. Default = 0.5 |
dB |
Inverse of decay rate. Default = 3650 |
IB0 |
Scale parameter. Default = 43.8787 |
kB |
Shape parameter. Default = 2.15506 |
uB |
Duration in which immunity is not boosted. Default = 7.19919 |
phi0 |
Maximum probability due to no immunity. Default = 0.791666 |
phi1 |
Maximum relative reduction due to immunity. Default = 0.000737 |
dCA |
Inverse of decay rate. Default = 10950 |
IC0 |
Scale parameter. Default = 18.02366 |
kC |
Shape parameter. Default = 2.36949 |
uCA |
Duration in which immunity is not boosted. Default = 6.06349 |
PM |
New-born immunity relative to mother. Default = 0.774368 |
dCM |
Inverse of decay rate of maternal immunity. Default = 67.6952 |
delayMos |
Extrinsic incubation period. Default = 10 |
tau1 |
Duration of host seeking, assumed to be constant between species. Default = 0.69 |
tau2 |
Duration of mosquito resting after feed. Default = 2.31 |
mu0 |
Daily mortality of adult mosquitos. Default = 0.132 |
Q0 |
Anthrophagy probability. Default = 0.92 |
chi |
Endophily probability. Default = 0.86 |
bites_Bed |
Percentage of bites indoors and in bed. Default = 0.89 |
bites_Indoors |
Percentage of bites indoors . Default = 0.97 |
muEL |
Per capita daily mortality rate of early stage larvae (low density). Default = 0.0338 |
muLL |
Per capita daily mortality rate of late stage larvae (low density). Default = 0.0348 |
muPL |
Per capita daily mortality rate of pupae. Default = 0.249 |
dEL |
Development time of early stage larvae. Default = 6.64 |
dLL |
Development time of late stage larvae. Default = 3.72 |
dPL |
Development time of pupae. Default = 0.643 |
gammaL |
Relative effect of density dependence on late instars relative to early instars. Default = 13.25 |
km |
Seasonal carrying capacity. Default = 11 |
cm |
Seasonal birth rate. Default = 0.05 |
betaL |
Number of eggs laid per day per mosquito. Default = 21.2 |
num_int |
Number of possible interventions. Default = 4 |
itn_cov |
The proportion of people that use an ITN. Default = 0 |
irs_cov |
The proportion of people living in houses that have been sprayed. Default = 0 |
ITN_IRS_on |
Time of ITN and IRS to be activated. Default = -1, i.e. never. |
DY |
Duration of year (days). Default = 365 |
d_ITN0 |
Probability of dying with an encounter with ITN (max). Default = 0.41 |
r_ITN0 |
Probability of repeating behaviour with ITN (max). Default = 0.56 |
r_ITN1 |
Probability of repeating behaviour with ITN (min). Default = 0.24 |
r_IRS0 |
Probability of repeating behaviour with IRS (min). Default = 0.6 |
d_IRS0 |
Probability of dying with an encounter with IRS (max). Default = 1 |
irs_half_life |
IRS half life. Default = 0.5 * DY |
itn_half_life |
ITN half life. Default = 2.64 * DY |
IRS_interval |
How long before IRS is repeated, i.e. when IRS decay = 1. Default = 1 * DY |
ITN_interval |
How long before ITN is repeated, i.e. when IRS decay = 1. Default = 3 * DY |
List for simulating non malarial fever
nmf_list_create( nmf_flag = FALSE, mean_nmf_frequency = c(148.578, 139.578, 141.564, 155.874, 179.364, 216.192, 233.478, 268.056, 312.858, 315.564, 285.156, 255.246, 238.302, 216.618), nmf_age_brackets = c(-0.1, 365, 730, 1095, 1460, 1825, 2555, 3285, 4015, 4745, 5475, 7300, 9125, 10950, 36850), prob_of_testing_nmf = 0.5 )nmf_list_create( nmf_flag = FALSE, mean_nmf_frequency = c(148.578, 139.578, 141.564, 155.874, 179.364, 216.192, 233.478, 268.056, 312.858, 315.564, 285.156, 255.246, 238.302, 216.618), nmf_age_brackets = c(-0.1, 365, 730, 1095, 1460, 1825, 2555, 3285, 4015, 4745, 5475, 7300, 9125, 10950, 36850), prob_of_testing_nmf = 0.5 )
nmf_flag |
Boolean are we doing non malarial fevers |
mean_nmf_frequency |
Vector for mean number of days between fevers for the age bracket considered |
nmf_age_brackets |
Vector for age brackets |
prob_of_testing_nmf |
Numeric for probability that a NMF is tested by RDT before being treated with antimalarials. |
param_list_simulation_finalizer_create creates suitable parameter list for
simulation_R for free memory used by a simulation
param_list_simulation_finalizer_create(statePtr)param_list_simulation_finalizer_create(statePtr)
statePtr |
Pointer for current model state as return by |
param_list_simulation_get_create creates suitable parameter list for
simulation_R for continuing a simulation from memory within the active
session.
param_list_simulation_get_create(statePtr)param_list_simulation_get_create(statePtr)
statePtr |
Pointer for current model state as return by |
param_list_simulation_init_create creates suitable parameter list for
simulation_R for the beginning of a simulation. Also takes an argument
for feeding in spatial parameters/data.
param_list_simulation_init_create( N = 10000, eqSS, barcode_list, spatial_list, housekeeping_list, drug_list, nmf_list, vector_adaptation_list, mpl )param_list_simulation_init_create( N = 10000, eqSS, barcode_list, spatial_list, housekeeping_list, drug_list, nmf_list, vector_adaptation_list, mpl )
N |
Population size. Default = 1e4 |
eqSS |
Output of |
barcode_list |
List of barcode/genetic parameters |
spatial_list |
Spatial parmeters to come in |
housekeeping_list |
Housekeeping parameter list
from |
drug_list |
Drug parameter list
from |
nmf_list |
Non malarial fever parameter list
from |
vector_adaptation_list |
vector adaptation list
from |
mpl |
model parameter list from |
param_list_simulation_saved_init_create creates
suitable parameter list for simulation_R for continuing a
simulation from memory within the active session.
param_list_simulation_saved_init_create(savedState)param_list_simulation_saved_init_create(savedState)
savedState |
Saved state generated by
|
param_list_simulation_update_create creates suitable parameter list for
simulation_R for continuing a simulation from memory within the active
session.
param_list_simulation_update_create( years = 1, ft = 0.4, mu_vec = NULL, fv_vec = NULL, statePtr, spatial_list, drug_list, barcode_list )param_list_simulation_update_create( years = 1, ft = 0.4, mu_vec = NULL, fv_vec = NULL, statePtr, spatial_list, drug_list, barcode_list )
years |
Length of simulation. Default = 1 |
ft |
Treatments seeking value |
mu_vec |
Vector of mosquito mortalities for each day with years. Default = NULL, which will result in rep(0.132,floor(years*365)) |
fv_vec |
Vector of mosquito bitings for each day with years. Default = NULL, which will result in rep(1/3,floor(years*365)) |
statePtr |
Pointer for current model state as return by |
spatial_list |
Spatial list |
drug_list |
Drug list |
barcode_list |
Barcode parameter list |
pipeline steps through creating the parameter list, the equilibrium
initialisation and steady state creation before checking and passing suitable
parameters to the simulation. This is then saved. If a path to a
savedState is provided then this state is loaded and continued.
pipeline( EIR = 120, ft = 0.4, itn_cov = 0, irs_cov = 0, use_historic_interventions = FALSE, survival_percentage = 0.2, oocyst_mean = 2.5, oocyst_shape = 1, N = 1e+05, years = 20, update_length = 365, country = NULL, admin = NULL, spatial_type = NULL, spatial_incidence_matrix = NULL, spatial_mosquitoFOI_matrix = NULL, island_imports_plaf_linked_flag = FALSE, num_loci = 24, ibd_length = 1, plaf = rep(0.5, num_loci), prob_crossover = rep(0.5, num_loci), starting_ibd = 0, mutation_rate = rep(1e-07, num_loci), mutation_flag = FALSE, mutation_treated_modifier = 1, full_save = FALSE, full_update_save = FALSE, human_only_full_save = FALSE, update_save = FALSE, update_save_func = NULL, human_update_save = FALSE, genetics_df_without_summarising = FALSE, summary_saves_only = FALSE, set_up_only = FALSE, mean_only = TRUE, save_lineages = FALSE, saved_state_path = NULL, seed = as.integer(runif(1, 1, 1e+09)), sample_size = Inf, sample_states = 0:5, age_breaks = c(-0.001, 5, 15, 100.1), sample_reps = 1, housekeeping_list = housekeeping_list_create(), drug_list = drug_list_create(), vector_adaptation_list = vector_adaptation_list_create(num_loci), only_allele_freqs = TRUE, nmf_list = nmf_list_create(), ... )pipeline( EIR = 120, ft = 0.4, itn_cov = 0, irs_cov = 0, use_historic_interventions = FALSE, survival_percentage = 0.2, oocyst_mean = 2.5, oocyst_shape = 1, N = 1e+05, years = 20, update_length = 365, country = NULL, admin = NULL, spatial_type = NULL, spatial_incidence_matrix = NULL, spatial_mosquitoFOI_matrix = NULL, island_imports_plaf_linked_flag = FALSE, num_loci = 24, ibd_length = 1, plaf = rep(0.5, num_loci), prob_crossover = rep(0.5, num_loci), starting_ibd = 0, mutation_rate = rep(1e-07, num_loci), mutation_flag = FALSE, mutation_treated_modifier = 1, full_save = FALSE, full_update_save = FALSE, human_only_full_save = FALSE, update_save = FALSE, update_save_func = NULL, human_update_save = FALSE, genetics_df_without_summarising = FALSE, summary_saves_only = FALSE, set_up_only = FALSE, mean_only = TRUE, save_lineages = FALSE, saved_state_path = NULL, seed = as.integer(runif(1, 1, 1e+09)), sample_size = Inf, sample_states = 0:5, age_breaks = c(-0.001, 5, 15, 100.1), sample_reps = 1, housekeeping_list = housekeeping_list_create(), drug_list = drug_list_create(), vector_adaptation_list = vector_adaptation_list_create(num_loci), only_allele_freqs = TRUE, nmf_list = nmf_list_create(), ... )
EIR |
Numeric for desired annual EIR. Default = 120 |
ft |
Vector of treatment frequency. Default = 0.4 |
itn_cov |
Vector for ITN coverages that change at update_length intervals. Default = 0 |
irs_cov |
Vector for IRS coverages that change at update_length intervals. Default = 0 |
use_historic_interventions |
Boolean as to whether to use interventions on file for the admin and country specified. If TRUE then provide the years as a year range, e.g. 2000:2015. WARNING - Best to have this as FALSE and manually specify the itn_cov, irs_cov and ft. |
survival_percentage |
Mumeric for Default = 0.2 |
oocyst_mean |
Mean for number of oocysts formed from a bite. Default=2.5 |
oocyst_shape |
Shape parameter for oocysts formed. Default=1 # Spatial |
N |
Population Size. Default = 100000 |
years |
Lenth of simulation. Default = 20 |
update_length |
How long each update is run for in days. Default = 365 |
country |
Character for country within which admin2 is in. Default = NULL |
admin |
Character for admin region. Some fuzzy logic will be used to match. If not provided then no seasonality is introduced. Default = NULL |
spatial_type |
Default = NULL. If spatial is wanted then provide a character describing the type of spatial simulation, which must be one of "island" or "metapop". |
spatial_incidence_matrix |
Spatial incidence for humans, i.e. importation vector |
spatial_mosquitoFOI_matrix |
Spatial mosquio FOI, i.e. importation to mosquitoes vector # Genetic Params |
island_imports_plaf_linked_flag |
Boolean. Whether imported barcodes for
island model are drawn dependent on other sites. Currently, if TRUE, imported
barcodes will either be all 0 or will be 1 at all loci where plaf > 0,
if |
num_loci |
Number of loci. Default = 24 |
ibd_length |
If we are simulating IBD dynamics, each loci is now represented by a bitset of ibd_length. Thus ibd_length needs to be long enough to ensure that as new identity relationships occur, i.e. an importation barcode will be a new identity. e.g. If are population is 1000, we may expect at 80 2400 different identities, i.e. 2^ibd_length > 2400. However, keep in mind importations as these need to be continually new, i.e. if we are simulating for 30 years, with 3 importations a day, then we will need at least length to ensure that 2^ibd_length > 2400 + (30*365*3). This will probably be automatically calculated in the future. If we are not ding IBD, then this should be 1, which is the defalt. |
plaf |
Vector of population level allele frequencies for the barcode. Default = rep(0.5, num_loci) |
prob_crossover |
Vector of probabilities for crossover events for the barcode. Default = rep(0.5, num_loci) |
starting_ibd |
Starting IBD. Default = 0, which means that each infected individual at initialisation is given a unique ID for their parasites. |
mutation_rate |
Probability of mutation occuring and fixing |
mutation_flag |
Boolean for simulating mutations # Saving Params |
mutation_treated_modifier |
Multiplier for how much more likely mutations are to occur in treated individuals with respect to resistance. Default = 1, i.e no difference |
full_save |
Boolean detailing whether the entire simulation is saved. Default = FALSE |
full_update_save |
Boolean to save entire simualation at each update save. Default = FALSE |
human_only_full_save |
Boolean detailing whether just the human component of the simulation is saved within full_save. Default = FALSE |
update_save |
Boolean detailing whether the logging output is saved each update_length up to years. Default = FALSE |
update_save_func |
As opposed to having to provide arguments for the
update behaviour, you can pass in a function. See |
human_update_save |
Boolean detailing if the human state is also saved during each update_length. Default = FALSE |
genetics_df_without_summarising |
Boolean for returning just the
genetics data frame without summarising with |
summary_saves_only |
Boolean if summary tables about COI are saved within human yearly save only. Dataframes of age, clinical status binned COI. |
set_up_only |
Boolean for whether to return just the initialised simulation. Default = FALSE |
mean_only |
Boolean for returning only the mean when summarising the population COI, COU etc. Default = TRUE |
save_lineages |
Boolean for whether we save the frequency of each strain
when summarising with |
saved_state_path |
Full file path to a saved model state to be loaded and continued. Default = NULL, which will trigger initialisation |
seed |
Random seed. Default is Random |
sample_size |
Numeric for number of individuals to be sampled at the end of each update. Default = Inf, which samples everyone. If you provide a vector of sample sizes it will sample at each specified sample size. |
sample_states |
Numeric for which sample infection states are to be included in sampling. Default = 0:5 (i.e. all states). 1:4 for example would ensure only infected individuals are included. |
age_breaks |
What age breaks are used when summarising the population. Default is 'c(-0.001, 5, 15, 100.1)' |
sample_reps |
Numeric for how many sample reps are done. Default = 1. # Parameter Lists |
housekeeping_list |
List created by |
drug_list |
List created by |
vector_adaptation_list |
List created by |
only_allele_freqs |
Boolean for returning the summarised genetics (allele
frequencies and maybe strain frequencies) or the whole data frame produced
by |
nmf_list |
List created by |
... |
Other parameters to model_param_list_create
|
# Main Params
Returns the population's parasite genetics summarised by coi for given sample size and state
population_get_genetics_df_n(param_list)population_get_genetics_df_n(param_list)
param_list |
param_list containing statePtr, sample_size, and sample_states |
list of population information
Returns the population's parasite genetics for ibd style summarised by pibd for given sample size and state
population_get_genetics_ibd_df_n(param_list)population_get_genetics_ibd_df_n(param_list)
param_list |
param_list containing statePtr, sample_size, and sample_states |
list of population information
Returns whole model to R in series of nested lists
Simulation_Finalizer_cpp(param_list)Simulation_Finalizer_cpp(param_list)
param_list |
parameter list generated with |
list of 1 confirming finalizer has finished
Returns whole model to R in series of nested lists
Simulation_Get_cpp(param_list)Simulation_Get_cpp(param_list)
param_list |
parameter list generated with |
list of 4 lists with the entire model state
Creates initial model simulation using paramter list provided
Simulation_Init_cpp(param_list)Simulation_Init_cpp(param_list)
param_list |
parameter list generated with |
list with ptr to model state and loggers describing the current model state
This function triggers the main magenta simulation from the R side
simulation_R(param_list, seed)simulation_R(param_list, seed)
param_list |
paramlist passed from
|
seed |
Seed for the simulation |
Creates initial model simulation using a saved model state
Simulation_Saved_Init_cpp(param_list)Simulation_Saved_Init_cpp(param_list)
param_list |
parameter list generated with |
list with ptr to model state and loggers describing the current model state
Continues simulation forward for as long as specified in param_list
Simulation_Update_cpp(param_list)Simulation_Update_cpp(param_list)
param_list |
parameter list generated with |
list with ptr to model state and loggers describing the current model state
Grabs spatial incidence and mosquitoFOI matrix from database
spl_grab(country, admin, year_range)spl_grab(country, admin, year_range)
country |
Country string |
admin |
Admin string |
year_range |
Year range |
List for vector adaptations relating to oocyst success
vector_adaptation_list_create( vector_adaptation_loci, vector_adaptation_flag = FALSE, local_oocyst_advantage = 0.5, gametocyte_sterilisation_flag = FALSE, gametocyte_sterilisation = 0.5, oocyst_reduction_by_artemisinin = 0.2 )vector_adaptation_list_create( vector_adaptation_loci, vector_adaptation_flag = FALSE, local_oocyst_advantage = 0.5, gametocyte_sterilisation_flag = FALSE, gametocyte_sterilisation = 0.5, oocyst_reduction_by_artemisinin = 0.2 )
vector_adaptation_loci |
Vector of integers detailing which loci in the barcode correspond to the vector adaptation phenotype |
vector_adaptation_flag |
Boolean are we doing vector adaptation. |
local_oocyst_advantage |
Numeric for probability that non adapated parasites will get through. Default = 0.5 |
gametocyte_sterilisation_flag |
Boolean for whether we are doing gametocye sterilisation as a result from artemisinin. Default = FALSE |
gametocyte_sterilisation |
Numeric for theimpact of artemisinin on male gametocytes. Default = 0.5, which causes the probability that a wild type male gametocyte will be chosen to be in oocysts is halved. |
oocyst_reduction_by_artemisinin |
Numeric for reduction in oocyst under artemisinin drug pressure. default = 0.2 |