Applications to Epidemic Science

We will build cohesive teams to actively engage on problems of societal importance.

Key objectives:

  • Understand the impact of climate change, zoonosis, urbanization and increased global connectivity, anti-microbial-resistance, and individual and collective behavior on epidemic dynamics.
  • Develop multi-scale multi-layer (MSML) network models of co-evolving epidemic dynamics that incorporate new emerging components.
  • Establish fundamental limits on forecasting epidemic dynamics.
  • Understand when and how statistical, causal, and crowdsourced methods can be used and combined for improved forecasts.
  • Develop new methods to compare forecasting algorithms.
  • Understand how to communicate forecasts to policymakers and the public.
  • Design adaptive intervention strategies that optimize multiple cost/benefit objectives and satisfy real-world constraints.
  • Assess whether interventions are likely to have unintended consequences.
  • Deploy CERTES and demonstrate its use in real-world settings for planning and response in the event of an epidemic (such as COVID-19).

Expected outcomes and computational/epidemiological advances:

  • Novel interventions that are economically efficient and operationally realizable.
  • New scalable, data-driven MSML network models of co-evolving epidemic dynamics and behavioral adaptations.
  • Advances in real-time epidemic science leading to improved planning, detection, and response.
  • Novel methods to prevent or delay zoonosis and anti-microbial resistance, and to reduce the impact of climate change and dense urbanization.
  • New data-driven causal, ML-based ensemble forecasting methods.
  • New data integration techniques that yield real-time data for forecasting methods.
  • Improved forecast accuracy and lead time by 50% or more for seasonal and emerging diseases.
  • Methods for fundamentally new resource allocation problems, and robust optimization techniques for handling the inherent stochasticity of the models.
  • Novel cost-effective intervention strategies that reduce the overall burden due to an outbreak.

Examples of table-top exercises and epidemic outbreak response planning contributions of the team:

  • Pilot 1: Engaging national and international agencies in pandemic events
    • Spatio-temporal forecasting
    • Situation assessment
    • Mechanisms of spread
    • Interventions: efficacy, cost, allocation
    • Examples: H1N1, Ebola 2014, Zika, and Ebola 2018 outbreaks
  • Pilot 2: Participating in CDC Influenza Forecasting Challenge
    • County/city-level forecasting incidence rates for social and demographic groups
    • Include vaccine availability, uptake, efficacy, and timing
Global reach compass