Modeling the Spread and Control of COVID-19.
Age - An integer attribute sampled from a triangular distribution between 1 and 90 with a peak of 25.
Hygiene A value between 0 and 1, where 1 corresponds to perfect hygiene. High hygiene level indicate in real life washing hands, wearing masks etc., which leads to less exposure of virus.
Overall health - An integer value between 0 and 3, both inclusive. A higher value corresponds to strong health and a lower value cor-responds to poor health.
Comorbidity An integer value between 0 and -2, both inclusive. A co-morbidity value of zero corresponds to an agent with no co-morbid conditions, and a lower value corresponds to prevalence of more serious co-morbid conditions.
Immunity A value of either 0 or 1, where 0 corresponds to weak immunity and 1 corresponds to strong immunity.
Many policies can be applied at any time during the simulation. These policies change and control agent behavior and also update environmental resources.
Boarder Close This policy is applied to stop the incoming infectious agents in the environment.
Lockdown Lockdown is applied to control the movement of the agents in the simulation. When applied it can make some agents static in the environment.
Age Based Lockdown This lockdown is applied with a threshold age. All the agents above the threshold will be under lockdown.
Quarantine This policy, if applied -- can remove certain agents from the environment. Sick and positively tested agents can be quarantined.
Daily Testing When this policy is applied -- Daily, a random number of agents are being sampled and tested. There are parameters which can be modified to control capacity of daily testing and false negative percentage of test results.
Contact Tracing This policy trace back to a certain number of contacts of an agent when it is tested positive. If Quarantine is also invoked, the traced agents will be isolated from the environment.
Social Distancing This policy when applied -- It controls how close any one agent come to another one.
Hospitalization If this policy is invoked, very sick agents goes gets isolated and moved to hospital environment subject to availability of hospital beds.
Reinforcement Hospital beds and ICU beds can be always modified during the simulation.
- Compile all java classes.
cd src
javac abmforcovid/*.java
- Start the server in background.
nohup python server.py &
-
Assuming server running at
http://localhost:8080 -
Define an experiment in json file (you can see example experiment file in
experiments/dir) -
Check the
notebooks/dir for jupyter notebooks with different experiments. -
Call function with the json
run_exp -
Check the result
csvwith the same name as the experiment inresults/dir. -
Note - Have Java in your machine; Adding all the jar files here for quick setup.
- Compile all the java classes
- Run EnvUI class
- It will open all the views related to the simulation.
- Create environment and define mobility.
- Improve the code and suggestions to scale.
Using Multi agent simulator MASON