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CASE STUDY

Predicting Pandemic Evolution

Cerge
Predicting Pandemic Evolution

Client

Cerge

Segment

Healthcare

Solutions

Floxgen

Date

2024

↳

How technology has enabled realistic pandemic simulations, providing valuable insights for informed prevention measures.

01 —

The Task

The COVID-19 pandemic has highlighted the need for powerful tools to simulate the spread of disease as closely as possible to reality. A solution that would model contact networks between individual agents, ready for eventual simulation of other population diseases in the future.

We have set 3 main goals.

  1. Realistic Simulation: Base the prediction on a synthetic population reflecting the detailed demographic, social, and economic characteristics of various regions.
  2. Simple Deployment: Create a system that can be easily used in computing centers or the cloud despite processing computationally demanding tasks.
  3. User-friendly Interface: Enable easy manipulation of data and definition of parameters.

02 —

The Reaction

In the solution, we used the existing Covasim agent-based model and the Synthpops synthetic population generator developed by the Institute for Disease Modeling (IDM). These tools offered extensive simulation capabilities but lacked a user-friendly interface and support for new features.

We have enhanced Covasim and Synthpops with a user-friendly interface for importing data in different formats, enabling seamless integration. The system excels in simulating the dynamics of interregional travel and the emergence of new disease variants which offers valuable insights.

Extensive documentation is available to guide users through system operation and data entry. The ability to run components individually or chained together for a comprehensive assessment brought great flexibility in the use of the tool. Finally, the system generates customizable tabular reports that give users precise control over the information presented.

‍

03 —

The Effect

We have developed a comprehensive system for realistic simulation of the spread of diseases with a user-friendly interface and extended functions. It offers valuable tools for research institutions, healthcare organizations and government agencies to fight pandemics and plan smart preventive measures.

We faced some limitations with IDM modules, such as simulating traveling across regions or adding new disease variants. Still, the new features can simulate other illnesses with basic parameters like transmissibility and severity while the interface and detailed documentation made it much easier to operate the system and enter data. To refine the input parameters and improve efficiency and functionality, we further calibrate, test, and develop the model.

‍

NEXT CASE
Simplifying Genetic Research
Healthcare

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IMTM
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