Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
June 27, 2024Open Access

Bayesian calibration of stochastic agent based model via random forest

View Full Paper
Ask AI
Bookmark
Share

Authors

CRConnor RobertsonCSCosmin SaftaNCNicholson Collier

Discussion

Loading...

Member takes

Overview

Computational study demonstrates efficient Bayesian calibration of an agent-based pandemic model via random forest surrogates, indicating faster epidemic forecasting.

Key Points

  • Surrogate modeling using random forest substantially accelerates the calibration of a stochastic agent based model while maintaining high predictive accuracy.
  • Simulation using Markov chain Monte Carlo and principal component analysis accurately matched Chicago COVID-19 hospitalizations from March to June in 2020.
  • Supports scalable Bayesian calibration of large-scale epidemiological simulations, enabling rapid computational evaluation of urban outbreak interventions.

Cite This Study

Robertson et al. (2024) studied this question.

synapsesocial.com/papers/68e6312bb6db6435875c3a7chttps://doi.org/10.48550/arxiv.2406.19524
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis2025
  2. 2ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis2025
  3. 3Country-Wide Agent-Based Epidemiological Modeling Using 17 Million Individual-Level Microdata2024
  4. 4Calibration of stochastic, agent-based neuron growth models with Approximate Bayesian Computation2024
  5. 5Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on SARS-CoV-22024 · 5 citations