EpiPolicy is a population-based epidemic simulator and policy aid.
With EpiPolicy you can define compartmental models
to capture different disease scenarios including Covid-19.
It offers transparent ways to define various interventions such as
social distancing, school closures, and vaccinations.
EpiPolicy aids policy makers by constructing intervention schedules across locales that
not only reduce disease burden, but also minimize social and
economic costs.
EpiPolicy provides a population-based model so disease dynamics are specified through
a series of ordinary differential equations.
Here an epidemiologist has created a COVID-19 model
that includes compartments for isolation, vaccination, and hospitalization.
As the epidemiologist builds the model, EpiPolicy automatically generates a visual
graph summary of the model.
Policy makers can specify the geographic and population properties of the administrative
regions or locales they wish to simulate disease spread
and the effect of interventions within.
EpiPolicy provides a plugin to automatically load this information
from NASA’s Gridded Population of the World dataset.
In EpiPolicy, one can define groups for which the disease behaves differently
or that require unique interventions.
With a disease like Covid-19, older patients are more
likely to develop more severe complications when compared to younger patients.
In the parameters page you can specify this difference
by customizing the relevant parameters in the disease model for each population group.
For example, you can set a higher probability of getting severe or critical
complications for seniors.
Within each locale, children, adults, and seniors
will travel to different facilities for their daily activities.
Each population group spends a proportion of its time in a facility.
Using standard algorithms such as impedance or radiation,
you can describe the movement of individuals across
borders between neighboring locales.
In EpiPolicy, interventions are specified as python classes.
The API for an intervention is simple:
It has two methods: effect and cost.
The effect and the cost methods are called at every time step of the simulation.
Consider a COVID-19 intervention plan that requires 100% mask compliance throughout
close schools, enforce workplace closures, social distancing and border restrictions
temporarily for a few months and then relax these interventions.
You can see the overall effect of this plan in the results page,
which provides an overview of the number of deaths and infections,
the economic costs, and other metrics such as
the effective viral reproduction number.
You can even focus on the impact of a policy within a specific locale.
Given this intervention plan as a baseline, we will explore how slight modifications to
the plan can adversely affect outcomes.
Suppose we reduced mask compliance from 100% to only 25%.
Unlike the base plan, the effective viral reproduction
number increases above 1showing uncontrolled disease spread
as soon as the other interventions are relaxed.
This leads to more deaths, infections and an overall higher economic cost.
We then allow the EpiPolicy optimizer to search for an alternative more cost-effective
intervention plan when vaccinations are available.
EpiPolicy selects the following plan:
It maintains high mask-wearing compliance for the entire duration of the plan.
It favors this intervention over other more costly ones,
such as workplace closures or border restrictions.
This illustrates EpiPolicy’s ability to select plans
that reduce overall disease burden and costs.