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UTLDR: an agent-based framework for modeling infectious diseases and public interventions.

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|June 22, 2021
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A new framework, UTLDR, enables "what if" epidemic scenario generation, incorporating public health interventions. This tool supports understanding intervention effects without specific epidemic forecasting.

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health

Background:

  • The SARS-CoV-2 pandemic heightened interest in epidemic modeling across diverse research fields.
  • Existing software tools for epidemic modeling often lack specific features for simulating public interventions.
  • There is a need for accessible resources to study the impact of public health measures like lockdowns and contact tracing.

Purpose of the Study:

  • To introduce UTLDR, a versatile framework for generating "what if" epidemic scenarios.
  • To provide a user-friendly tool for exploring the potential effects of various public health interventions.
  • To offer qualitative support for understanding the impact of restrictions on epidemic dynamics.

Main Methods:

  • Development of the UTLDR framework, designed for ease of use.
  • Incorporation of stratified population data (age, gender, geography, mobility).
  • Simulation of multiple public interventions and their combinations within epidemic models.

Main Results:

  • UTLDR facilitates the creation of customizable epidemic scenarios.
  • The framework allows for the qualitative assessment of different intervention strategies.
  • It supports the exploration of intervention effects on agent-based models with diverse population strata.

Conclusions:

  • UTLDR addresses the gap in accessible tools for simulating public health interventions in epidemic modeling.
  • The framework offers a generic approach to understanding the qualitative impact of restrictions.
  • It serves as a valuable resource for researchers seeking to explore policy implications without requiring specific data-driven forecasts.