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Spatiotemporal epidemic model introduced in the context of COVID-19, ACM TSAS, 2022

Home Page: https://dl.acm.org/doi/abs/10.1145/3530774

License: MIT License

Jupyter Notebook 42.35% Python 57.64% Shell 0.01%
epidemics covid-19 infection-hotspots hotspots epidemiology coronavirus coronavirus-analysis points-of-interest superspreaders superspreading-events

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dependabot[bot] avatar heinerkremer avatar larslorch avatar manuelgr avatar stsirtsis avatar

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simulator's Issues

concurrent.futures.process issue

Hi again! When running the experiments.ipynb using a mob_sitting that is tu_settings_10_10.pk, meaning that the population is scaled down to 10x factor and sites is scaled down to 10x factor, I ran into the below error:

concurrent.futures.process.BrokenProcessPool: A process in the process pool was terminated abruptly while the future was running or pending.

Such error did not occurs when scaling down the population to 20x and 15x, only when scaling down the population to 10x (the sites is kept scaled down to 10x in all cases). any idea about why it occurs and how to solve it? Thanks in advance.

'Plotter' object has no attribute 'plot_Rt_types'

It looks like the function plotter.plot_Rt_types() was removed by commit 88328a4 which adds functions for computing the daily Rts. There are still several places in sim/experiments.ipynb that call plotter.plot_Rt_types(). Is the new function plotter.plot_daily_rts() intended to replace plotter.plot_Rt_types()?

possible bug in defining tmax for pushing household exposures

Hi! I have a question about the tmax variable that is passed to the __push_household_exposure_infector_to_j function in dynamics.py:

tmax = (self.state_started_at['ipre'][infector] + self.delta_ipre_to_isym[infector] +
self.delta_isym_to_dead[infector] if self.bernoulli_is_fatal[infector] else self.delta_isym_to_resi[infector])

It seams to me that if self.bernoulli_is_fatal[infector] is False, then tmax can be smaller than the current time, and this actually occurs fairly often while running the code (making household infections much less likely). There seems to be a similar issue in lines 762 and 1027. Can you please clarify this for me?

Thanks in advance!

Regarding the exe-inference.ipynb example

First, I would like to thanks you for making this agent-based simulator available with its detailed documentation. Second, I just want ask if there is a missing parameter in the example exe-inference.ipynb when calling the make_loss_function. Shouldn't we also pass the num_sites parameter?

Thanks again for your great works

Generating town settings

hi,

There is an overflow issue when downscaling the population_per_age_group array which gives minus values to some of the age group. You might have to change the data type into int64 in town_settings_[town_name].py file.

missing (?) time adjustment in asym->resi transition time

Hi! I have a question about the following function, that samples transition times for asymptomatic nodes to recovery:

def sample_iasy_resi(self, size=1):
'''
Samples r.v. of iasy -> resi
'''
return self.__mean_distribution(self.median_asymp_to_resi, 1.0, size=size)

Is it possible that a multiplication factor from days to hours (self.tadj) is missing here?
Also because median_asymp_to_resi is expressed in days as well as all the other transition times parameters.

Thanks in advance!

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