Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting

• URL: http://arxiv.org/abs/2102.11249v1
• Date: Mon, 22 Feb 2021 18:32:44 GMT
• Title: Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
• Authors: Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra, Xenia Miscouridou, Ricardo P Schnekenberg, Charles Whittaker, Michaela Vollmer, Seth Flaxman, Samir Bhatt, Thomas A Mellan
• Abstract summary: Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing. We present a flexible approach using a latent Gaussian process that is capable of describing the changing auto-correlation structure present in the reporting time-delay surface. This approach also yields robust estimates of uncertainty for the estimated nowcasted numbers of deaths.
• Score: 2.8712862578745018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty? Without this correction, raw data will often mislead by suggesting an improving situation. We present a flexible approach using a latent Gaussian process that is capable of describing the changing auto-correlation structure present in the reporting time-delay surface. This approach also yields robust estimates of uncertainty for the estimated nowcasted numbers of deaths. We test assumptions in model specification such as the choice of kernel or hyper priors, and evaluate model performance on a challenging real dataset from Brazil. Our experiments show that Gaussian process nowcasting performs favourably against both comparable methods, and a small sample of expert human predictions. Our approach has substantial practical utility in disease modelling -- by applying our approach to COVID-19 mortality data from Brazil, where reporting delays are large, we can make informative predictions on important epidemiological quantities such as the current effective reproduction number.

Related papers

• Evidential time-to-event prediction model with well-calibrated uncertainty estimation [12.446406577462069]
  We introduce an evidential regression model designed especially for time-to-event prediction tasks. The most plausible event time is directly quantified by aggregated Gaussian random fuzzy numbers (GRFNs) Our model achieves both accurate and reliable performance, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-12T15:06:04Z)
• Risk and cross validation in ridge regression with correlated samples [72.59731158970894]
  We provide training examples for the in- and out-of-sample risks of ridge regression when the data points have arbitrary correlations. We further extend our analysis to the case where the test point has non-trivial correlations with the training set, setting often encountered in time series forecasting. We validate our theory across a variety of high dimensional data.
  arXiv  Detail & Related papers  (2024-08-08T17:27:29Z)
• Modeling Epidemic Spread: A Gaussian Process Regression Approach [0.7374726900469741]
  We present a new data-driven method based on Gaussian process regression (GPR) to model epidemic spread. We present examples that use GPR to model and predict epidemic spread by using real-world infection data gathered in the UK during the COVID-19 epidemic.
  arXiv  Detail & Related papers  (2023-12-14T22:45:01Z)
• Neural parameter calibration and uncertainty quantification for epidemic forecasting [0.0]
  We apply a novel and powerful computational method to the problem of learning probability densities on contagion parameters. Using a neural network, we calibrate an ODE model to data of the spread of COVID-19 in Berlin in 2020. We show convergence of our method to the true posterior on a simplified SIR model of epidemics, and also demonstrate our method's learning capabilities on a reduced dataset.
  arXiv  Detail & Related papers  (2023-12-05T21:34:59Z)
• Reconstructing Graph Diffusion History from a Single Snapshot [87.20550495678907]
  We propose a novel barycenter formulation for reconstructing Diffusion history from A single SnapsHot (DASH) We prove that estimation error of diffusion parameters is unavoidable due to NP-hardness of diffusion parameter estimation. We also develop an effective solver named DIffusion hiTting Times with Optimal proposal (DITTO)
  arXiv  Detail & Related papers  (2023-06-01T09:39:32Z)
• Predicting the impact of treatments over time with uncertainty aware neural differential equations [2.099922236065961]
  We propose Counterfactual ODE, a novel method to predict the impact of treatments continuously over time. We demonstrate over several longitudinal data sets that CF-ODE provides more accurate predictions and more reliable uncertainty estimates than previously available methods.
  arXiv  Detail & Related papers  (2022-02-24T09:50:02Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)
• 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)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• Tracking disease outbreaks from sparse data with Bayesian inference [55.82986443159948]
  The COVID-19 pandemic provides new motivation for estimating the empirical rate of transmission during an outbreak. Standard methods struggle to accommodate the partial observability and sparse data common at finer scales. We propose a Bayesian framework which accommodates partial observability in a principled manner.
  arXiv  Detail & Related papers  (2020-09-12T20:37:33Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)

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.