LogoLogo
  • Mission and Strategy
  • Ernakulam District Emergency Response Plan
  • Load Balancing Corona Patients against healthcare facilities in a District
  • Go Live Check List
  • Checklist for setting up Digital War Rooms at Collectorates
  • Preparedness and Go Live Checklist for FLTC's.
  • Primary Treatment Centers for Corona
    • First Line (Primary)Treatment Center for Corona at Panchayat Level
    • Setting up the physical facility for Primary Treatment Center
    • Organisation Structure
    • Operating Model of a Primary Treatment Center
    • Patient Management
    • Capacity Modelling
    • Hygiene Management
    • Resource Coordination Team
  • Centralised Facilities
    • Ambulance Network
    • Community Pharmacy Network
    • Technology Center
    • SuperFab Labs
    • Training Academy
    • Inventory Supplies
    • Medical Oxygen Supply
    • Volunteer Registration
    • Food Logistics Network
    • Waste Disposal
    • TeleMedicine Network
    • Resource Mobilisation
    • Funeral Management
    • Covid 19 Spread Modelling
    • Emergency Evacuation Plan
    • Trucks Management System
    • Integrated Sample Management and diagnostic System
    • ARIKE: Palliative/Homecare System
    • Critical Care
  • Field Notes
    • Brainstorming Notes
    • Field Operational Notes
    • Things to Clarify
    • GoLive Checklist for BluePrint of First Line Treatment Center
  • Emergency Grants
    • ACT
  • District Administration
    • Phase wise Roll Out Plan for EKM
    • How to run a mock drill
    • SOP for Airport
  • Government Orders
    • Minutes
    • GO & Circulars
  • Training Documents
    • Introductory Training Documents
Powered by GitBook
On this page
  • Tentative Modelling Task List
  • Dataset Specs (In Progress)
  • For Modelling Epidemic Curve
  • Data Scientist Reading Resources (In Progress)
  • TODO
  • Suggestions:

Was this helpful?

  1. Centralised Facilities

Covid 19 Spread Modelling

This page contains details of how SDMA requires the support of data scientists to help in modeling the spread of Covid 19 in Kerala.

PreviousFuneral ManagementNextEmergency Evacuation Plan

Last updated 5 years ago

Was this helpful?

Goal: To create a data driven decision-making framework for public authorities to actively intervene and manage the spread of COVID19 outbreak.

COVID-19 cases will soon explode into unprecedented levels and we need to flatten the epidemic curve to reduce the burden on our current healthcare system. In order to facilitate effective response, we require our data-science volunteers to help us model the epidemic curve for COVID-19.

"To minimize the impact of the coming pandemic, we need to slow its spread and thereby keep our healthcare systems from being overwhelmed. Through aggressive measures we can flatten out the epidemic curve, keeping the number of people simultaneously infected at a low enough level to be manageable." - Carl Bergstorm, Professor of Biology, University of Washington

“If you look at the curves of outbreaks, they go big peaks, and then come down. What we need to do is flatten that down.” - Anthony Fauci, Director of the National Institute of Allergy and Infectious Diseases

Tentative Modelling Task List

Dataset Specs (In Progress)

Requirements for data collection from each location for modelling purpose

  1. Mandatory Patient Data to be collected while admitting

    1. Day of occurence of symptoms

    2. Admission date

    3. Dates of exposure / infection

    4. Severity of condition - Critical/Moderate/Mild

    5. Dates of outcome: death / recovery

    6. Metadata on patient: age, gender, location, occupation, etc

    7. Contact data: exposure (who infected you?) and Contact tracing (who could you have infected?)

For Modelling Epidemic Curve

  • Data collection need to be done in this format

  1. Current Hospital Capacity Data

    1. Require current available hostpital beds in each location

    2. Require current available ICU capacity for each location

  2. Estimate of supplies needed to treat one person in

    1. Critical condition who needs an ICU bed

    2. Moderate condition who needs a hospital bed

    3. Mild or Stay-at-home condition

    4. List of items require to treat a person

Data Scientist Reading Resources (In Progress)

TODO

Suggestions:

https://www.reconlearn.org
https://www.reconlearn.org/slides/projections/projections.pdf
http://hplgit.github.io/prog4comp/doc/pub/._p4c-solarized-Python021.html
https://www.kaggle.com/volpatto/covid-19-study-with-epidemiology-models/data
https://www.kaggle.com/lisphilar/covid-19-data-with-sir-model#Introduction
Source: Carl Bergstrom, UW & Esther Kim