After several weeks of "lockdown" as the sole answer to the COVID-19 pandemic, many countries are restarting their economic and social activities.
However, balancing the
re-opening of society against non-medical measures needed for minimizing interpersonal contacts requires a careful assessment of the risks of infection as a function of the
confinement relaxation strategies.
Here, we present a stochastic model that examines this problem.
In our model, people are allowed to move between discrete positions on a
one-dimensional grid with viral infection possible when two people are collocated at the same site.
Our model features three sets of adjustable parameters, which characterize (i)
viral transmission, (ii) viral detection, and (iii) degree of personal mobility, and as such, it is able to provide a qualitative assessment of the potential for second-wave infection
outbreaks based on the timing, extent, and pattern of the lockdown relaxation.
Note to the users:
- The simulation is performed using random values generated using Python's rand function. New random values are generated for each run of simulations, meaning that
the results will change from one run to the next. The users should average the results over multiple runs of simulation (with the same entry values) in order to obtain
reliable averaged values.
- The calculation time can vary from a few seconds to several minutes depending on the parameter used essentially depending on the grid number (M), person's number(N),
and their ratio (=population density=N/M). In order not to overload our server, ｗe set maximum values for N and M of 2000, and the program will stop when calculation
time reaches 3 minutes.
References : Ando, Matsuzawa et al., Stochastic Modelling of the effects of human mobility and viral transmission characters during the relaxation of COVID-19 lockdown restrictions