,
Ruian Ke [aut] ,
Erik Lundgren [aut]| Title: | Sequence evolution and epidemiological process simulator |
|---|---|
| Description: | A modular, modern simulation suite and toolkit for simulating transmission networks, phylogenies, and evolutionary pairwise distance matrices under different models and assumptions for viral/sequence evolution. While intially developed for HIV, SEEPS offers modular utilities for custom workflows for extension beyond HIV. |
| Authors: | Michael Kupperman [aut, cre] ,
Ruian Ke [aut] ,
Erik Lundgren [aut] |
| Maintainer: | Michael D. Kupperman <[email protected]> |
| License: | file LICENSE |
| Version: | 0.2.0 |
| Built: | 2025-01-23 05:11:16 UTC |
| Source: | https://github.com/MolEvolEpid/SEEPS |
A utility function for placing an explicit "root" into a newick tree string. at the implicit root. This function performs: '(tree); -> (tree:0, root:0);'.
add_root_to_newick(tree, root_name = "root")add_root_to_newick(tree, root_name = "root")
tree |
A newick tree string to add a root node to. |
root_name |
The name of the root node to add. Default is "root". |
phylogeny_to_newick
Simple biphasic rate function used in [Kupperman et al.]
biphasic_HIV_rate(current_step, birth_step, params)biphasic_HIV_rate(current_step, birth_step, params)
current_step |
Current time step |
birth_step |
When the infection was generated |
params |
A list with one element 'R0' needed to evaluate the rate function. |
Use ape to compute a distance matrix using the specified distance model. Default is the "TN93" model.
build_distance_matrix_from_df(df, model = "TN93", keep_root = FALSE)build_distance_matrix_from_df(df, model = "TN93", keep_root = FALSE)
A helper function to check that a rate model paramterization is mathematically valid.
check_rate_model(rate_model)check_rate_model(rate_model)
rate_model |
A list of rate model parameters. |
Check that all rates are positive, that the fraction of bases add up to 1, the fraction of invariant sites is in [0, 1), and the discrete gamma model is defined for 'alpha' and 'ncat'.
generate_rate_model generate_sequences
Clean up the simulation state and return the relevant data.
clean_up(state)clean_up(state)
Perform iterative contact tracing on a simulated contact network. Randomness within contact tracing comes from the probability of discovering a new infection. This function assumes the infection rate is uniform across all possible contacts and individuals.
contact_traced_uniform_ids( active, parents, minimum_sample_size, p, max_attempts = 1 )contact_traced_uniform_ids( active, parents, minimum_sample_size, p, max_attempts = 1 )
active |
A vector of active individuals |
parents |
A matrix encoding the transmission history |
minimum_sample_size |
The minimum number of individuals to form a sample |
p |
The probability of discovering each contact during tracing |
max_attempts |
Maximum number of attempts to perform to obtain a sample. If no sample is found after this number of attempts, 'FALSE' is returned |
Contact tracing is terminated when 2 * min_sample_size active nodes are discovered, or when there are no more detections to trace. At least 'min_sample_size' individuals will always be returned.
To determine the initial detection, we loop over a the list of active nodes until we complete a successful contact tracing.
All contact tracing algorithms store both the ids of all discovered individuals and the ids of discovered active individuals.
A list with three fields: "status", "samples", and "found"
Perform iterative contact tracing on a simulated contact network. Randomness within contact tracing comes from the probability of discovering a new infection. This function assumes the infection rate is uniform across all possible contacts and individuals.
contact_traced_uniform_restarts_ids(active, parents, minimum_sample_size, p)contact_traced_uniform_restarts_ids(active, parents, minimum_sample_size, p)
active |
A vector of active individuals |
parents |
A matrix encoding the transmission history |
minimum_sample_size |
The minimum number of individuals to form a sample |
p |
The probability of discovering each contact during tracing |
Contact tracing is terminated when 2 * min_sample_size active nodes are discovered, or when there are no more detections to trace. At least 'min_sample_size' individuals will always be returned.
To determine the initial detection, we loop over a the list of active nodes until we complete a successful contact tracing.
All contact tracing algorithms store both the ids of all discovered individuals and the ids of discovered active individuals.
A list with four fields: "status", "samples", "success", and "found" if the algorithm fails to find a sample, FALSE is returned instead
Given a set of active nodes and the transmision history (parents), sample a set of nodes that are contact traced using the 'discovery_function' parameter to determine the transmission rate.
contact_tracing_core( detected_id, active, parents, discovery_function, termination_function )contact_tracing_core( detected_id, active, parents, discovery_function, termination_function )
detected_id |
The id of the node used to start the tracing |
active |
A vector of active individuals |
parents |
A matrix of transmission history |
discovery_function |
A function that takes a list of nodes and relative transmission times and determines which will be included |
The engine for simulating contact tracing. Provide network information ('active', 'parents'), a list or vector of individuals to begin tracing with ('detected_id'), functions to control the discovery and termination of tracing ('discovery_function', 'termination_function'), and parameters about iteration ('max_attempts') and fallback termination conditions ('minimum_size').
contact_tracing_engine( detected_id, active, parents, discovery_function, max_attempts, termination_function, minimum_size = 3 )contact_tracing_engine( detected_id, active, parents, discovery_function, max_attempts, termination_function, minimum_size = 3 )
detected_id |
A vector of individuals to begin tracing with |
active |
A vector of active individuals |
parents |
A matrix encoding the transmission history |
discovery_function |
A function to determine which individuals to trace |
termination_function |
A function to determine when to terminate tracing a contact tracing. |
minimum_size |
The minimum number of individuals to form a sample |
A list with three fields: "status", "samples", and "found"
A utility function to build a dataframe from a fasta string.
fasta_string_to_dataframe(fasta_string, trim = TRUE, include_root = FALSE)fasta_string_to_dataframe(fasta_string, trim = TRUE, include_root = FALSE)
@param fasta_string A string containing the fasta output from seq-gen
A dataframe with the "seq" and "name" columns
Usually, we want to simulate a fixed number of steps after the exponential growth phase. This function performs that step.
gen_const_phase(state, parameters, num_steps, verbose = FALSE)gen_const_phase(state, parameters, num_steps, verbose = FALSE)
Simulate an exponential growth.
gen_exp_phase(state, parameters)gen_exp_phase(state, parameters)
Simulate a balanced transmission tree with a given number of individuals. If the 'population_size' is a power of 2, then the tree will be unique up to relabeling the tips. If the 'population_size' is not a power of 2, then the tree that minimizes Sackin's index is not unique. To constrain the solution, we build the tree programmatically. We convert each leaf node to a cherry in the initial layer, to create a new layer of leaves. We repeat until we have a 'population_size' number of leaves. As a result, we will always find the shortest tree with the prescribed number of leaves that is perfectly balanced.
gen_transmission_history_balanced_tree(population_size, spike_root = FALSE)gen_transmission_history_balanced_tree(population_size, spike_root = FALSE)
n |
number of individuals to have in the final layer |
Forward stochastic simulation to generate transmission history for a complete population with an exponential growth then constant population structure.
gen_transmission_history_exponential_constant( minimum_population, offspring_rate_fn, maximum_population_target, total_steps, spike_root = FALSE )gen_transmission_history_exponential_constant( minimum_population, offspring_rate_fn, maximum_population_target, total_steps, spike_root = FALSE )
This simulation is intended for HIV, but may be broadly applicable if the lifespan distribution is adjusted to other distributions/parameters.
Reduce a geneology to a pairwise distance matrix.
geneology_to_distance_matrix(geneology, mode = "mu", spike_root = FALSE)geneology_to_distance_matrix(geneology, mode = "mu", spike_root = FALSE)
Provided for backwards compatability with [Kupperman et al 2022]. New code should use '[geneology_to_distance_matrix]'.
geneology_to_distance_matrix_classic(geneology, spike_root = FALSE)geneology_to_distance_matrix_classic(geneology, spike_root = FALSE)
geneology_to_distance_matrix
Run a backwards simulation using the transmission history to generate a phylogeny. See 'geneology_to_phylogeny_bpb' for details on how the input should be structured. This function assumes that all coalescent events occur at or after the initial infection, that there is a single introduction and ancestral sequence.
geneology_to_phylogeny_bpb( transmission_history, infection_times, leaf_sample_ids, sample_times, a = 5, b = 5, make_plot = FALSE )geneology_to_phylogeny_bpb( transmission_history, infection_times, leaf_sample_ids, sample_times, a = 5, b = 5, make_plot = FALSE )
transmission_history |
A transmission history matrix |
infection_times |
A vector of infection times |
reduce_transmission_history_bpb
generate_sequences
Accept a set of parameters for GTR+I+G model. See below for details of the parameterization. To disable I, set the parameter i to 0. To disable G, set alpha=1 and ncat=1 for an exponential distribution.
generate_rate_model( a2c, a2g, a2t, c2g, c2t, g2t, fa, fc, fg, ft, i, alpha, ncat )generate_rate_model( a2c, a2g, a2t, c2g, c2t, g2t, fa, fc, fg, ft, i, alpha, ncat )
a2c |
The rate of adenosine to cytosine transversions. |
a2g |
The rate of adenosine to cytosine transitions. |
a2t |
The rate of adenosine to thymine transversions. |
c2g |
The rate of cytosine to guanine transversions. |
c2t |
The rate of cytosine to thymine transitions. |
g2t |
The rate of guanine to thymine transversions. |
fa |
The fraction of adenosine at equilibrum. |
fc |
The fraction of cytosine at equilibrum. |
fg |
The fraction of guanine at equilibrum. |
ft |
The fraction of thymine at equilibrum. |
i |
The proportion of invariant sites. Traditionally estimated by the proportion of sites with no observed mutations. |
alpha |
The shape parameter for the discrete gamma distribution. |
ncat |
The number of categories in the discrete gamma distribution. |
For an overview of GTR+I+G and substitition models, see [here](https://www.ccg.unam.mx/~vinuesa/Model_fitting_in_phylogenetics.html). For a more detailed construction, see [Yang 1994](https://doi.org/10.1007/BF00178256), and [Yang 1996](https://doi.org/10.1093/oxfordjournals.molbev.a025625).
A list of rate model parameters.
generate_sequences get_V3_rate_model get_p17_rate_model check_rate_model
rate_model <- generate_rate_model( a2c = 1, a2g = 1, a2t = 1, c2g = 1, c2t = 1, g2t = 2, fa = 0.25, fc = 0.25, fg = 0.25, ft = 0.25, i = 0.1, alpha = 0.25, ncat = 8 )rate_model <- generate_rate_model( a2c = 1, a2g = 1, a2t = 1, c2g = 1, c2t = 1, g2t = 2, fa = 0.25, fc = 0.25, fg = 0.25, ft = 0.25, i = 0.1, alpha = 0.25, ncat = 8 )
Given a phylogeny, generate possible sequences using Seq-Gen [Rambaut & Grassley, 1997]. A root sequence for the simulation is required. The root sequence is placed at the MRCA of the phylogeny unless 'spike_root = TRUE' is specified when the phylogeny is constructed.
generate_sequences( phylogeny, branch_rate, root_sequence, rng_seed = -1, rate_model, rate_per_nt = FALSE )generate_sequences( phylogeny, branch_rate, root_sequence, rng_seed = -1, rate_model, rate_per_nt = FALSE )
phylogeny |
A phylogeny object |
root_sequence |
A root sequence |
rate_model |
A list of GTR+I+G model parameters. Expects a list of 13 parameters: (6 rate parameters) 'a2c', 'a2g', 'a2t', 'c2g', 'c2t', 'g2t', (nucleotide frequencies:) 'fa, fc, fg, ft', (proportion of sites with no variation:) 'i', (site specific heterogeneity shape parameter) 'alpha', and number of categories for discretized gamma heterogeneity ('ncat'). |
rate_per_nt |
Flag to indicate whether the mutation rate is per nucleotide (nt) or per sequence. If per-sequence ('rate_per_nt=FALSE'), the mutation rate is normalized by the length of the root sequence. |
If the root sequence is a character vector, it is flattened into a single string.
geneology_to_phylogeny_bpb
Biphasic rate function factory
get_biphasic_HIV_rate(params)get_biphasic_HIV_rate(params)
params |
list of parameters used. |
Factory function to generate biphasic rate functions
get_biphasic_HIV_rate_function( front_density_factor, front_cutoff, target_length )get_biphasic_HIV_rate_function( front_density_factor, front_cutoff, target_length )
front_density_factor |
How much relative significance to place in the first phase of the rate function. |
front_cutoff |
Length of first phase. An integer 1 or greater. |
target_length |
Expected length of an infection. Used for normalization to ensure an average of $R0$ secondary infections. |
Callable, rate function
Kphasic HIV rate function factory Provide a list of relative importance (multiples over a "base" rate) for
get_Kphasic_hiv_rate_function(rate_list, target_length, params)get_Kphasic_hiv_rate_function(rate_list, target_length, params)
rate_list |
# List of relative rates for each phase. |
target_length |
List of lengths of each phase. |
params |
list with element R0 |
Callable, rate function
Estimates for GTR+I+G rate model parameter from [Leitner et al. 1997] on the Swedish transmission chain on p17. Rate estimates for p17 follow from a secondary analysis.
get_p17_rate_model()get_p17_rate_model()
A list of rate model parameters.
generate_sequences generate_rate_model
rate_model <- get_p17_rate_model()rate_model <- get_p17_rate_model()
Estimates for GTR+I+G rate model parameter on the Swedish transmission chain on pol region. Follow up analysis by Lundgren et al. [2022] on the Swedish transmission chain on pol region.
get_pol_rate_model(nonzero_I = FALSE)get_pol_rate_model(nonzero_I = FALSE)
nonzero_I |
Boolean indicating whether to use the I parameter (invariant sites) or not. Default is 'TRUE'(0.255). If 'FALSE', the I parameter is set to 0. |
A list of rate model parameters.
generate_sequences generate_rate_model
Estimates for GTR+I+G rate model parameter from [Leitner et al. 1997] on the Swedish transmission chain on envelope V3 region.
get_V3_rate_model(nonzero_I = TRUE)get_V3_rate_model(nonzero_I = TRUE)
nonzero_I |
Boolean indicating whether to use the I parameter (invariant sites) or not. Default is 'TRUE'(0.68). If 'FALSE', the I parameter is set to 0. |
A list of rate model parameters.
generate_sequences generate_rate_model
rate_model <- get_V3_rate_model()rate_model <- get_V3_rate_model()
Utility function for initializing the simulation
initialize(parameters)initialize(parameters)
Remove all other samples from the simulation. Intended to be used to represent a masking event, Where only a subset of the population is propagated forward in time.
keep_samples(state, samples)keep_samples(state, samples)
Provide a state object and a vector of samples to keep. A new state object is returned with only the samples kept.
Provide an interval and a reference sequence name. Currently supported are: "HIV1" (HXB2 reference) and "toy" (a poly A/C/G/T example sequence for testing).
lookup_sequence_by_index(organism_name, start, stop)lookup_sequence_by_index(organism_name, start, stop)
start |
The start coordinate of the sequence |
end |
The end coordinate of the sequence |
Return a portion of a reference genome using a standard annotation name.
lookup_sequence_by_name(organism_name, region_name)lookup_sequence_by_name(organism_name, region_name)
Currently supported are: "HIV1" (HXB2 reference) and "toy" (a poly A/C/G/T example sequence for testing). Supported HIV1 regions include the short annotated list, see (here)[https://www.hiv.lanl.gov/components/sequence/HIV/search/help.html#region] for the full list. Clinical regions (p17, V3) are supported as "p17-clinical" and "v3-clinical".
For a more detailed lookup proceedure with the reference sequences using user-provided coordinates, see 'lookup_sequence_by_index'.
lookup_sequence_by_index
When contact tracing, we don't want to stop until we have traced all known. This is computationally more expensive, but better describes reality.
never_terminate_early_factory()never_terminate_early_factory()
Build a newick tree from a phylogeny or geneology array. Nodes are named "#_". Specify the mode argument to select the branch length mode. Mode "mu" denotes a (sampled) mutation count, while mode "mean" denotes expected distances.
phylogeny_to_newick(phylogeny, mode = "mu", label_mode = "local")phylogeny_to_newick(phylogeny, mode = "mu", label_mode = "local")
phylogeny |
A phylogeny or geneology array. |
mode |
String to determine reconstruction mode. Default is "mu", for mutation count. Alternative "mean" for expected distance. |
label_mode |
String to determine how to label nodes. Default is "local", for node index. Alternative "abs" for absolute index. |
A helper function for sampling a fixed number of individuals from the population. Select a fixed number of individuals from the active set. Does not use any information about the transmission history or the active population size to select the sample.
random_fixed_size_ids(active, minimum_size, spike_root = FALSE)random_fixed_size_ids(active, minimum_size, spike_root = FALSE)
active |
A vector of active individuals |
minimum_size |
The minimum number of individuals to sample. |
spike_root |
Whether to include the root in the sample. |
random_prop_ids
A helper function for sampling a proportion of the active individuals. Select a proportion of individuals from the active set. Does not use any information about the transmission history to select the sample.
random_prop_ids(active, proportion, minimum_size, spike_root = FALSE)random_prop_ids(active, proportion, minimum_size, spike_root = FALSE)
active |
A vector of active individuals |
proportion |
A float between 0 and 1. The proportion of active individuals to sample. |
minimum_size |
The minimum number of individuals to sample. If the proportional sample is smaller than the minimum size, the proprotional size is used. |
spike_root |
Whether to include the root in the sample. |
random_fixed_size_ids
An obtained sample (through contact tracing or random sampling) may be larger than needed. This function extracts a subset of closely related individuals with a randomly selected "center". This respects the 'spike_root' option, and if 'spike_root = TRUE', will return the root individual in the last column.
reduce_large_matrix( oversampled_matrix, subsample_size, spike_root = FALSE, root_position = 0, index_id = -1, sort_order = NULL )reduce_large_matrix( oversampled_matrix, subsample_size, spike_root = FALSE, root_position = 0, index_id = -1, sort_order = NULL )
Reduce a large simulation output to a smaller transmission history for a subset by tracing back the ancestory of each individual in the sample. If 'spike_root' is 'TRUE', then the root of the tree is included in the geneology. We call this a geneology, rather than a phylogeny as it assumes that the transmission history is the phylogeny and no within-host diversity occurs.
reduce_transmission_history(samples, parents, current_step, spike_root = FALSE)reduce_transmission_history(samples, parents, current_step, spike_root = FALSE)
samples |
A vector of individuals (integers) to include in the sample. |
parents |
A matrix of parental individuals that encodes the transmission history and sample times. |
current_step |
The current (absolute) time step in the simulation. |
spike_root |
A boolean indicating whether the geneology should include the root of the outbreak or not. Default is 'FALSE'. This should be specified even if the founding infection is sampled, as the root of the outbreak will have evolved since the founding event. |
To include within host diversity, use 'reduce_transmission_history_bpb' to extract a subst of the transmission history that we need for the phylogeny, and see 'geneology_to_phylogeny_bpb' to simulate within-host diversity and recover a true phylogeny.
A list with 1 element: "geneology" a matrix of transmission history that encodes an evolutionary tree.
reduce_transmission_history_bpb
For a detailed explenation of inputs, see 'reduce_transmission_history', which is intended to reconstruct back only until the most recent common ancestor of the sample, and return a tree.
reduce_transmission_history_bpb(samples, parents, current_step)reduce_transmission_history_bpb(samples, parents, current_step)
samples |
A vector of individuals (integers) to include in the sample. |
parents |
A matrix of parental individuals that encodes the transmission history and sample times. |
current_step |
The current (absolute) time step in the simulation. |
spike_root |
A boolean indicating whether the geneology should include the root of the outbreak or not. Default is 'FALSE'. This should be specified even if the founding infection is sampled, as the root of the outbreak will have evolved since the founding event. |
To include within host diversity, use 'reduce_transmission_history_bpb' to extract a subset of the transmission history that we need for the phylogeny, and see 'geneology_to_phylogeny_bpb' to simulate within-host diversity and recover a true phylogeny.
A list with 4 elements: 'parents' A vector of parents of each infection in the sample until the root 'times' A vector of times of sampling times in the tree. Sample times for internal nodes are after the last offspring generation time needed to reconstruct the sample. 'transmission_times' A vector of transmission times of each infection in the tree. 'samples_available' A boolean vector (mask) of which samples are leaves in the tree. Used by the coalescent simulation to know which individuals should be assigned detected sequences.
reduce_transmission_history_bpb
Reduce a large simulation output to a smaller transmission history for a subset by tracing back the ancestory of each individual in the sample. If 'spike_root' is 'TRUE', then the root of the tree is included in the geneology. We call this a geneology, rather than a phylogeny as it assumes that the transmission history is the phylogeny and no within-host diversity occurs.
reduce_transmission_history_mt( samples, parents, current_step, spike_root = FALSE )reduce_transmission_history_mt( samples, parents, current_step, spike_root = FALSE )
samples |
A vector of individuals (integers) to include in the sample. |
parents |
A matrix of parental individuals that encodes the transmission history and sample times. |
current_step |
The current (absolute) time step in the simulation. |
spike_root |
A boolean indicating whether the geneology should include the root of the outbreak or not. Default is 'FALSE'. This should be specified even if the founding infection is sampled, as the root of the outbreak will have evolved since the founding event. |
To include within host diversity, use 'reduce_transmission_history_bpb' to extract a subst of the transmission history that we need for the phylogeny, and see 'geneology_to_phylogeny_bpb' to simulate within-host diversity and recover a true phylogeny.
A list with 1 element: "geneology" a matrix of transmission history that encodes an evolutionary tree.
reduce_transmission_history_bpb
Remove a set of samples from the simulation. Intended to be used with contact tracing, we expect individuals identified through contact tracing to be removed from the population of active (uncontrolled or undiagnosed) cases.
remove_samples(state, samples)remove_samples(state, samples)
Provide a state object and a vector of samples to remove. A new state object is returned with the samples removed.
Show the list of available regions for a provided reference sequence
show_available_regions(organism_name, chart = TRUE)show_available_regions(organism_name, chart = TRUE)
organism_name |
The name of the reference sequence |
chart |
bool (default TRUE). If TRUE, a chart of the available regions is printed to the screen. |
A list of available regions for the named reference sequence. If the organism_name is not found, an NULL is printed.
A list of all available organism/sequence names is printed to the screen.
show_available_sequences()show_available_sequences()
This simulation returns three distance matrices for the same set of individuals. The first matrix (matrix_trans) is derived from the transmission history. The second matrix (matrix_phylo) is derived from the phylogeny by simulating a coalescent process on the transmission history. The third matrix (matrix_seq) is derived from the sequences by computing the pairwise distances using the TN93 model.
simulate_all_paradigms_HIV_V3(params)simulate_all_paradigms_HIV_V3(params)
list with keys "matrix" and "input_params"
parameters <- list("rate_function_parameters" = list("R0"=5), "a"=5, "b"=5, "mutation_rate" = 0.0067, # units are per nt "contact_tracing_discovery_probability" = 0.9, "minimum_population"=15, "maximum_population_target"=500)parameters <- list("rate_function_parameters" = list("R0"=5), "a"=5, "b"=5, "mutation_rate" = 0.0067, # units are per nt "contact_tracing_discovery_probability" = 0.9, "minimum_population"=15, "maximum_population_target"=500)
A simple agent-based simulation for HIV. Returns pairwise distance matrix samples obtained from a simulated outbreak. This simulation models population level diversity, but does not explicitly consider within-host diversity. A mutation clock rate, R0, and several epidemiological parameters are required to describe the duration of infections.
simulate_classic_HIV(params)simulate_classic_HIV(params)
list with key "matrix"
parameters <- list("rate_function_parameters" <- list("R0"=5), "mutation_rate" = 0.0067 / 12 * 300, "minimum_population"=50, "maximum_population_target"=100)parameters <- list("rate_function_parameters" <- list("R0"=5), "mutation_rate" = 0.0067 / 12 * 300, "minimum_population"=50, "maximum_population_target"=100)
A modern agent-based simulation for HIV that explicitly models within-host simulations using biophybreak extensions.
simulate_modern_HIV(params)simulate_modern_HIV(params)
list with keys "matrix" and "input_params"
parameters <- list("rate_function_parameters" = list("R0"=5), "a"=5, "b"=5, "minimum_population"=15, "maximum_population_target"=500)parameters <- list("rate_function_parameters" = list("R0"=5), "a"=5, "b"=5, "minimum_population"=15, "maximum_population_target"=500)
A modern agent-based simulation for HIV that explicitly models within-host simulations using biophybreak extensions. This pre-build simulation returns a collection of sequences, rather than a matrix.
simulate_sequences_HIV_V3(params)simulate_sequences_HIV_V3(params)
list with keys "matrix" and "input_params"
parameters <- list("rate_function_parameters" = list("R0"=5), "a"=5, "b"=5, "mutation_rate" = 0.0067, # units are per nt "contact_tracing_discovery_probability" = 0.9, "minimum_population"=15, "maximum_population_target"=500)parameters <- list("rate_function_parameters" = list("R0"=5), "a"=5, "b"=5, "mutation_rate" = 0.0067, # units are per nt "contact_tracing_discovery_probability" = 0.9, "minimum_population"=15, "maximum_population_target"=500)
Perform a single step of the simulation
step(state, parameters)step(state, parameters)
Convert temporal distances for a geneology to an integer number of mutations. This step is extracted so a single geneology can be re-sampled and create multiple possible matrices.
stochastify_transmission_history(transmission_history, rate)stochastify_transmission_history(transmission_history, rate)
transmission_history |
A matrix output by 'reduce transmission history'. |
rate |
Clock rate for evolutionary process with units of substitions per sequence per year. |
Factory function for a detection to check termination condition based on A sufficient amount of individuals Terminate when we have found a 'minimum_size' number of active individuals
sufficient_data_data_factory(minimum_size)sufficient_data_data_factory(minimum_size)
minimum_size |
The minimum number of discovered active individuals to terminate |
Obtain a discovery function that determines which adjacent nodes in the contact network will be revealed with uniform probability.
uniform_discovery_factory(p)uniform_discovery_factory(p)
p |
The discovery probability. A float between 0 and 1. |
contact_traced_uniform_ids
Check that the state object is a valid and has the correct list elements.
validate_state(state)validate_state(state)