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Keeping track of secondary infections #262

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mghosh00 opened this issue Mar 6, 2024 · 4 comments · Fixed by #263 · May be fixed by #267
Open

Keeping track of secondary infections #262

mghosh00 opened this issue Mar 6, 2024 · 4 comments · Fixed by #263 · May be fixed by #267
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mghosh00 commented Mar 6, 2024 •
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Is your feature request related to a problem? Please describe.
We wish to record the number of people each person infects over the course of the epidemic (each time they are re-infected) so that we can find the R_t value at each time step. The dataframes produced will be further used in the sampling package EpiOS.

Describe the solution you'd like
I will change the Person class to record a list of secondary infections each person has during the epidemic. I will also update the simulation to record and output a dataframe containing this information.
@mghosh00 mghosh00 self-assigned this Mar 6, 2024
@mghosh00 mghosh00 added the enhancement New feature or request label Mar 6, 2024
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mghosh00 commented Mar 6, 2024 •
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Classes and functions to update:

core

  • Person.__init__ (infection_start_time, secondary_infections)
  • Person.increment_secondary_infections new
  • tests

output

  • AgeStratifiedNewCasesWriter.write
  • NewCasesWriter.write
  • tests

property

  • PersonalFOI.person_susc
  • tests

routine

  • Simulation.configure (add boolean to inf_history_params)
  • Simulation.run_sweeps
  • Simulation.write_to_rt_file new
  • tests

sweep

  • PlaceSweep.__call__
  • HouseholdSweep.__call__
  • SpatialSweep.__call__
  • HostProgressionSweep.set_infectiousness
  • HostProgressionSweep._updates_infectiousness
  • tests

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mghosh00 commented Mar 6, 2024 •
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Structure of output dataframe

Here is a template for what the output dataframe will look like (maybe not including R_t but is there to illustrate):

time person A person B person C person D ... person Z R_t
0.0 nan nan 5 nan ... 0 2.5
1.0 3 nan nan nan ... nan 3.0
2.0 nan nan nan nan ... nan 0.0
3.0 nan nan nan 3 ... nan 3.0
4.0 nan 6 nan nan ... 2 4.0

In this scenario, here are the lists of days in which each person becomes infected:
A: 1.0 - goes on to infect 3 others
B: 4.0 - goes on to infect 6 others
C: 0.0 - goes on to infect 5 others
D: 3.0 - goes on to infect 3 others
E: 0.0 - goes on to infect 0 others, reinfected at 4.0 goes on to infect 2 others

@mghosh00 mghosh00 mentioned this issue Mar 6, 2024
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@mghosh00 mghosh00 linked a pull request Mar 6, 2024 that will close this issue
Secondary infections #263
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@mghosh00 mghosh00 reopened this Jun 19, 2024
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mghosh00 commented Jun 19, 2024 •
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Tracking the serial interval of the epidemic

We wish to also take into account the serial interval. For every infection event, the time between the infector's infection_time and the infectee's infection_time will be recorded, and back-tracked to the timestep for which the infector was infected. Then we record a histogram of the these times for each timestep.

Example:

time person A person B person C person D person E person F
0.0 gets infected
1.0 infects B, C gets infected by A gets infected by A
2.0 infects D gets infected by B
3.0 infects E infects F gets infected by A gets infected by C

In this scenario, the serial interval vectors look like:
0.0: [1, 1, 3] - A infects B (1), C (1) and E (3)
1.0: [1, 2] - B infects D (1), C infects F (2)
2.0: [] - D infects no-one
3.0: [] - E and F infect no-one

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mghosh00 commented Jun 19, 2024 •
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Classes and functions to update:

core

  • Person.__init__ (serial_intervals)
  • Person.set_exposure_period new
  • Person.store_serial_interval new
  • tests

routine

  • Simulation.configure (add boolean to inf_history_params)
  • Simulation.run_sweeps
  • Simulation.write_to_serial_file new
  • tests

sweep

  • PlaceSweep.__call__
  • HouseholdSweep.__call__
  • SpatialSweep.__call__
  • HostProgressionSweep.update_time_status_change
  • tests

@mghosh00 mghosh00 linked a pull request Jun 20, 2024 that will close this issue
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