Module: TimeSample

The TimeSample module chooses times at which a given individual in the ContactNetwork was sampled (i.e., sequenced). See the source code to see what is defined by the abstract class.

List of Implementations

TimeSample_End

• All individuals who are infected at the end of the transmission simulation are sampled (i.e., sequenced) once exactly at the end
• Requirements:
  • Must use NumTimeSample_Once module
• Config Parameters:
  • None

TimeSample_File

• Implements the TimeSample_File module such that the sample times are read from file
• Requirements:
  • Must use NumTimeSample_File module
• Config Parameters:
  • sample_time_file: Full path to the file storing the contact network, represented in the FAVITES Sample Time File Format.

TimeSample_Gamma

• Each sampling time of a given individual is chosen from a Gamma distribution of the individual's infection time window
  • If the individual has multiple infection time windows (i.e., from recovery and reinfection), a single time window is chosen randomly with equal probability
• Requirements:
  • NumPy
• Config Parameters:
  • ts_gamma_shape: The desired shape parameter of the Gamma distribution
  • ts_gamma_scale : The desired scale parameter of the Gamma distribution

TimeSample_GranichFirstART

• All individuals who ever receive ART in the Granich HIV model are sampled the first time they receive ART
• Requirements:
  • Must use NumTimeSample_Once module
  • Must use TransmissionTimeSample_HIVARTGranichGEMF module
• Config Parameters:
  • None

TimeSample_Infection

• All individuals are sampled (i.e., sequenced) once exactly at the moment they are first infected
• Requirements:
  • Must use NumTimeSample_Once module
• Config Parameters:
  • None

TimeSample_None

• No individuals are ever sampled
• Requirements:
  • Must use NodeAvailability_None module
  • Must use NumTimeSample_None module
• Config Parameters:
  • None

TimeSample_PANGEA

• Wrapper for PANGEA.HIV.sim
• This is not supported in the Docker/Singularity images from FAVITES 1.1.11 onward
• Requirements:
  • R
  • PANGEA.HIV.sim
  • Must use ContactNetwork_PANGEA module
  • Must use ContactNetworkGenerator_PANGEA module
  • Must use EndCriteria_Instant module
  • Must use NodeEvolution_PANGEA module
  • Must use NodeSample_PANGEA module
  • Must use NumBranchSample_All module
  • Must use NumTimeSample_PANGEA module
  • Must use PostValidation_Dummy module
  • Must use SeedSelection_PANGEA module
  • Must use SeedSequence_PANGEA module
  • Must use SequenceEvolution_PANGEA module
  • Must use SourceSample_PANGEA module
  • Must use TimeSample_PANGEA module
  • Must use TransmissionNodeSample_PANGEA module
  • Must use TransmissionTimeSample_PANGEA module
  • Must use TreeUnit_Same module
• Config Parameters
  • Rscript_path: The path to your Rscript executable (or simply "Rscript" if it is in your PATH variable)
  • All pangea_ parameters, which correspond to PANGEA.HIV.sim parameters (see entry in FAVITES_ModuleList.json for complete list, and see PANGEA.HIV.sim help for details)
    • Use "" for default

TimeSample_TruncatedExponential

• Each sampling time of a given individual is chosen from a Truncated Exponential distribution of the individual's infection time window
  • If the individual has multiple infection time windows (i.e., from recovery and reinfection), a single time window is chosen randomly with equal probability
• Requirements:
  • SciPy
• Config Parameters:
  • None

TimeSample_TruncatedNormal

• Each sampling time of a given individual is chosen from a Truncated Normal distribution of the individual's infection time window
  • If the individual has multiple infection time windows (i.e., from recovery and reinfection), a single time window is chosen randomly with equal probability
  • The Truncated Normal distribution is in the window [0,1], and the user can move the mode of the underlying Normal distribution via the ts_truncnorm_loc parameter, and the user can scale the underlying Normal distribution via the ts_truncnorm_scale parameter (larger scale = longer tails)
• Requirements:
  • SciPy
• Config Parameters:
  • ts_truncnorm_loc: The desired mode of the underlying Normal distribution
    • Note that the window is enforced at [0,1], so centering the underlying Normal distribution in the center of the window will require setting ts_truncnorm_loc to 0.5; centering the underlying Normal distribution at the far right of the window will require setting ts_truncnorm_loc to 1; etc.
  • ts_truncnorm_scale: The desired scaling factor of the underlying Normal distribution
    • Larger values of ts_truncnorm_scale result in longer tails (equivalent of zooming out the distribution)

TimeSample_Uniform

• Each sampling time of a given individual is chosen from a Uniform distribution of the individual's infection time window(s)
• Requirements:
  • None
• Config Parameters:
  • None