Bayesian calibration of stochastic agent based model via random forest

• URL: http://arxiv.org/abs/2406.19524v1
• Date: Thu, 27 Jun 2024 20:50:06 GMT
• Title: Bayesian calibration of stochastic agent based model via random forest
• Authors: Connor Robertson, Cosmin Safta, Nicholson Collier, Jonathan Ozik, Jaideep Ray,
• Abstract summary: Agent-based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology. These models are usually and highly parametrized, requiring precise calibration for predictive performance. This paper presents a random forest based surrogate modeling technique to accelerate the evaluation of ABMs.
• Score: 1.4447019135112433
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agent-based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology by explicitly accounting for diverse individual interactions and environments. However, these models are usually stochastic and highly parametrized, requiring precise calibration for predictive performance. When considering realistic numbers of agents and properly accounting for stochasticity, this high dimensional calibration can be computationally prohibitive. This paper presents a random forest based surrogate modeling technique to accelerate the evaluation of ABMs and demonstrates its use to calibrate an epidemiological ABM named CityCOVID via Markov chain Monte Carlo (MCMC). The technique is first outlined in the context of CityCOVID's quantities of interest, namely hospitalizations and deaths, by exploring dimensionality reduction via temporal decomposition with principal component analysis (PCA) and via sensitivity analysis. The calibration problem is then presented and samples are generated to best match COVID-19 hospitalization and death numbers in Chicago from March to June in 2020. These results are compared with previous approximate Bayesian calibration (IMABC) results and their predictive performance is analyzed showing improved performance with a reduction in computation.

Related papers

• Development and Comparative Analysis of Machine Learning Models for Hypoxemia Severity Triage in CBRNE Emergency Scenarios Using Physiological and Demographic Data from Medical-Grade Devices [0.0]
  Gradient Boosting Models (GBMs) outperformed sequential models in terms of training speed, interpretability, and reliability. A 5-minute prediction window was chosen for timely intervention, with minute-levels standardizing the data. This study highlights ML's potential to improve triage and reduce alarm fatigue.
  arXiv  Detail & Related papers  (2024-10-30T23:24:28Z)
• Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models [4.619907534483781]
  computational models that predict drug-target interactions are valuable tools to accelerate the development of new therapeutic agents. However, such models can be poorly calibrated, which results in unreliable uncertainty estimates. We show that combining post hoc calibration method with well-performing uncertainty quantification approaches can boost model accuracy and calibration.
  arXiv  Detail & Related papers  (2024-07-19T10:29:00Z)
• Predictive Analytics of Varieties of Potatoes [2.336821989135698]
  We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato clones in breeding trials. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties.
  arXiv  Detail & Related papers  (2024-04-04T00:49:05Z)
• Forecast reconciliation for vaccine supply chain optimization [61.13962963550403]
  Vaccine supply chain optimization can benefit from hierarchical time series forecasting. Forecasts of different hierarchy levels become incoherent when higher levels do not match the sum of the lower levels forecasts. We tackle the vaccine sale forecasting problem by modeling sales data from GSK between 2010 and 2021 as a hierarchical time series.
  arXiv  Detail & Related papers  (2023-05-02T14:34:34Z)
• Evaluating Probabilistic Classifiers: The Triptych [62.997667081978825]
  We propose and study a triptych of diagnostic graphics that focus on distinct and complementary aspects of forecast performance. The reliability diagram addresses calibration, the receiver operating characteristic (ROC) curve diagnoses discrimination ability, and the Murphy diagram visualizes overall predictive performance and value.
  arXiv  Detail & Related papers  (2023-01-25T19:35:23Z)
• Clinical Deterioration Prediction in Brazilian Hospitals Based on Artificial Neural Networks and Tree Decision Models [56.93322937189087]
  An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD) The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
  arXiv  Detail & Related papers  (2022-12-17T23:29:14Z)
• BSM loss: A superior way in modeling aleatory uncertainty of fine_grained classification [0.0]
  We propose a modified Bootstrapping loss(BS loss) function with Mixup data augmentation strategy. Our experiments indicated that BS loss with Mixup(BSM) model can halve the Expected Error(ECE) compared to standard data augmentation. BSM model is able to perceive the semantic distance of out-of-domain data, demonstrating high potential in real-world clinical practice.
  arXiv  Detail & Related papers  (2022-06-09T13:06:51Z)
• On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency [47.90235939359225]
  We propose a novel mixup strategy for pre-trained language models that improves model calibration further. Our method achieves the lowest expected calibration error compared to strong baselines on both in-domain and out-of-domain test samples.
  arXiv  Detail & Related papers  (2022-03-14T23:45:08Z)
• Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak [10.307715136465056]
  We present a comparative analysis of various machine learning (ML) approaches in predicting the COVID-19 outbreak in the epidemiological domain. The results reveal the advantages of ML algorithms for supporting decision making of evolving short term policies.
  arXiv  Detail & Related papers  (2021-02-11T11:57:33Z)
• Bayesian prognostic covariate adjustment [59.75318183140857]
  Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates.
  arXiv  Detail & Related papers  (2020-12-24T05:19:03Z)
• Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score [59.75318183140857]
  Estimating causal effects from randomized experiments is central to clinical research. Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
  arXiv  Detail & Related papers  (2020-12-17T21:10:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.