ARF synthesizes epidemiological data to match original findings.
problem Synthetic data quality and privacy in epidemiology.
method Adversarial Random Forests (ARF) for efficient data synthesis.
result ARF-generated synthetic data consistently matches original epidemiological findings.
Machine learning aids epidemiologists in analyzing big data.
problem Handling large, complex data in epidemiology.
method Explains principles and methods of supervised and unsupervised learning.
result Develops strategies for model evaluation and hyperparameter optimization.
The study examines methods to correct measurement error in nutritional epidemiology studies.
problem Measurement error in nutritional studies leads to biased and underconfident estimates.
method The article reviews various bias-correction models for exposure variables in nutritional epidemiology.
result Bias-correction methods are essential for accurate inference in nutritional studies.
A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.
problem Lack of reliable inference for ML-discovered nonlinearities and interactions in epidemiological data.
method Combines Bayesian sparse regression, tree ensembles, and Shapley values.
result Valid uncertainty quantification for feature effects at the individual level.
Epidemiological model updates infection rates in China, US, Italy.
problem Evaluate policy interventions for mitigating the 2019-nCov pandemic.
method Custom SITR model with variational data assimilation for real-time updates.
result Model robust to initial conditions, infers infection numbers and parameters.
Bayesian network modelling is a well adapted approach to study messy and highly correlated datasets which are very common in, e.g., systems epidemiology. A popular approach to learn a Bayesian network from an observational datasets is to identify the maximum a posteriori network in a search-and-score approach. Many sco…
Efficiently calibrates epidemiological models using Bayesian optimization.
problem Calibration of complex epidemiological models is computationally expensive and challenging.
method Graybox Bayesian optimization scheme leveraging Gaussian processes and functional structure of compartmental models.
result Proposed methods achieve efficient calibration and improved performance compared to existing schemes.
Novel method for estimating SIRD model parameters and forecasting COVID-19 deaths in Poland.
problem Estimating epidemiological parameters for SARS-CoV-2 spread in Poland.
method Modified SIRD model, GPU architecture for computation.
result Effectiveness of short-term forecasts (up to 2 weeks) and stability of the method.
ESOP uses Bayesian optimization to find optimal lock-down schedules.
problem Finding optimal lock-down schedules balancing health and economy.
method Bayesian optimization interacting with epidemiological models.
result ESOP schedules balance public health and economic impacts.
SVI and GP surrogates improve calibration of ABMs in epidemiology.
problem Calibrating stochastic ABMs in epidemiology is computationally expensive.
method Stein Variational Inference (SVI) with Gaussian process (GP) surrogates.
result SVI maintains comparable predictive accuracy and calibration effectiveness to MCMC.
TMLE improves unbiased estimation in public health studies.
problem Improving unbiased estimation in observational studies.
method Targeted Maximum Likelihood Estimation (TMLE) integrates machine learning and statistical theory.
result TMLE has been adopted by researchers worldwide, especially outside the US.
Automates infectious disease policy-making via inference in epidemiological models.
problem Improving policy-making for infectious diseases during pandemics.
method Performing inference in existing epidemiological models using a probabilistic programming language.
result Automated inference leads to better disease progression outcomes and policy prescriptions.
Unified Bayesian-AI framework improves epidemiological risk prediction and uncertainty quantification.
problem Lack of calibrated uncertainty in machine learning models for epidemiology.
method Combines Bayesian prediction with Bayesian hyperparameter optimization using logistic regression and Gaussian-process Bayesian optimization.
result Unified Bayesian-AI framework provides reliable coverage and improved calibration, enhancing epidemiological decision making.
TOO optimizes stochastic epidemiological models by finding both parameter settings and random seeds.
problem Calibrating stochastic epidemiological models to match empirical observations.
method Gaussian process surrogates and Thompson sampling for optimization.
result Produces actual trajectories consistent with ground truth.
Framework extracts symptoms from EHRs for rapid disease outbreak detection.
problem Extracting relevant data from unstructured medical texts.
method Conformal active learning for efficient data mining.
result Framework achieves strong performance with minimal manual labeling.
Italy's daily COVID-19 cases stratified by age groups.
problem Analyzing the spread and impact of COVID-19 across different age groups.
method Stratified daily data of COVID-19 cases by age classes.
result Detailed insights into how age affects COVID-19 outcomes.
The process of contagiousness spread modelling is well-known in epidemiology. However, the application of spread modelling to banking market is quite recent. In this work, we present a system of ordinary differential equations, simulating data from the largest European banks. Then, an optimal control problem is formula…
EGDL predicts TB outbreaks with deep learning, integrating epidemiological models.
problem Predicting TB outbreaks with complex spatiotemporal dynamics.
method Modified MN-SIR model with Bayesian inference, deep neural networks.
result EGDL delivers robust and accurate TB outbreak predictions.
Simulation-based inference aids in predicting disease dynamics for health policy.
problem Predicting disease dynamics to inform public health interventions.
method Simulation-based inference using machine learning.
result Potential for efficient interpretable Bayesian inference in epidemiological models.
Neural likelihood approximates integer time series data efficiently.
problem Inference of parameters for integer-valued stochastic processes is challenging.
method Constructs a neural likelihood approximation for inference of parameters from time series data.
result Accurately approximates the true posterior with significant computational speed-ups.
State-space models (SSMs) provide a flexible framework for modelling time-series data. Consequently, SSMs are ubiquitously applied in areas such as engineering, econometrics and epidemiology. In this paper we provide a fast approach for approximate Bayesian inference in SSMs using the tools of deep learning and variati…
Cost-aware SBI reduces expensive simulations in complex models.
problem High computational cost in simulating complex models.
method Combination of rejection and self-normalised importance sampling.
result Significant reduction in overall cost of inference.
Model predicts US COVID-19 deaths with quantile estimates.
problem Predicting US COVID-19 deaths at county level.
method Hybrid machine learning and epidemiological approach, minimizing pinball loss.
result Quantile estimates accurately forecast deaths for different forecast periods.
New bound for neural nets on non-iid data.
problem Generalization of deep nets for dependent data.
method Establishes a generalization bound for feed-forward neural networks on φ-mixing data. result Proves neural nets can generalize well on non-iid data.
SIGMA prior enables federated learning for non-factorizable models.
problem Current FL methods assume conditional independence, limiting applicability to non-factorizable models.
method SIGMA prior approximates deep generative model to induce conditional independence structure.
result SIGMA prior expands FL applicability to fields requiring modeling dependencies.
In this study, we propose an automatic learning method for variables selection based on Lasso in epidemiology context. One of the aim of this approach is to overcome the pretreatment of experts in medicine and epidemiology on collected data. These pretreatment consist in recoding some variables and to choose some inter…
WeatherFormer learns robust weather features from small datasets.
problem Modeling complex weather dynamics from limited data.
method Pretrained transformer encoder on large satellite dataset, with spatiotemporal encoding.
result State-of-the-art performance in county-level soybean yield prediction and influenza forecasting.
New method detects close contacts to prevent SARS-CoV-2 spread.
problem Detecting close contacts to prevent SARS-CoV-2 spread.
method Machine learning approach using BLE data.
result Reliable detection of close contacts reduces SARS-CoV-2 risk.
A framework for data-driven decision-making in infectious disease control.
problem Optimizing trade-offs between public health and economic impacts.
method Multi-objective model-based reinforcement learning.
result Pareto-optimal policies minimizing long-term costs.
Bayesian calibration speeds up ABM for pandemic modeling.
problem Calibrating stochastic ABMs for accurate pandemic predictions is computationally intensive.
method Random forest surrogate modeling for accelerated ABM evaluation.
result Improved predictive performance with random forest calibration compared to previous methods.
DA-PredGAN uses GANs for accurate COVID-19 spread predictions and data assimilation.
problem Predicting and understanding the spread of COVID-19.
method Generative adversarial network (GAN) for predictions and data assimilation.
result DA-PredGAN accurately predicts and assimilates COVID-19 spread data.
Study on financial impacts of zombie outbreak on economy.
problem Financial and economic consequences of a zombie epidemic.
method Epidemiological modeling and financial computation.
result GDP losses of 23.44% and financial market drop of 29.30% in a major industrialized nation.
Method estimates parameters for disease spread models robustly.
problem Estimating parameters for disease spread models.
method Statistical Learning applied to Approximate Bayesian Computation.
result Qualitative properties of disease evolution can be assessed.
New model captures long-term memory effects in epidemic dynamics.
problem Identifying memory effects in disease progression and recovery.
method Physics-informed neural networks (PINN) with fractional SEIRD model.
result Fractional memory order α improves predictive performance over classical models. This is a brief survey of the research performed by Grandata Labs in collaboration with numerous academic groups around the world on the topic of human mobility. A driving theme in these projects is to use and improve Data Science techniques to understand mobility, as it can be observed through the lens of mobile phone…
Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria. However, the complicated and stochastic nature of these simulators can mean their output is difficult to interpret, which reduces their usefulness to policymakers. In this pap…
MOAI evaluates indoor airflow's impact on COVID-19 transmission.
problem Understanding indoor airflow's role in COVID-19 transmission.
method Developed a privacy-preserving app and model to evaluate risk exposure.
result Quantified factors contributing to higher or lower contamination in settings.
Model for valuing options on epidemic spread.
problem Valuation of options on epidemic spread during an outbreak.
method Stochastic differential SIR model for epidemic dynamics.
result Parsimonious model for option valuation on epidemic spread.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation, but would leave open the problem of designing what kind of synthetic data. W…
Parity calibration aims to predict increase-decrease events, not values.
problem Forecasting future increase-decrease events rather than exact values.
method Online binary calibration method to achieve parity calibration.
result Online binary calibration achieves parity calibration effectively.
The global crisis of 2008 provoked a heightened interest among scientists to study the phenomenon, its propagation and negative consequences. The process of modelling the spread of a virus is commonly used in epidemiology. Conceptually, the spread of a disease among a population is similar to the contagion process in e…
Simplifies inference for simulators with or without tractable likelihoods.
problem Inference for models with intractable likelihoods.
method Amortized simulation-based frequentist inference.
result Valid confidence sets for parameter inference.
A new method combines ANN and Laplace for fast Bayesian inference in ODE models.
problem Bayesian inference for ODE systems with non-analytical solutions is computationally expensive.
method Hybrid approach using ANN for tractable likelihood and Laplace approximation.
result Effective posterior inference with improved computational cost compared to traditional methods.
We present a method for the reconstruction of networks, based on the order of nodes visited by a stochastic branching process. Our algorithm reconstructs a network of minimal size that ensures consistency with the data. Crucially, we show that global consistency with the data can be achieved through purely local consid…
Paper uses bond pricing and convexity adjustments to explain herd immunity paradox.
problem Early onset of herd immunity contradicts R value estimates from early stage growth.
method Utilizes Vasicek's bond pricing formula and de Finetti's Theorem approach.
result Reduces modeling discrepancy to simple convexity formulas.
DAMNETS generates complex network dynamics models.
problem Generating flexible and scalable models for network time series is challenging.
method Deep autoregressive model for Markovian network time series.
result DAMNETS outperforms other methods in sample quality.
One component of precision medicine is to construct prediction models with their predictive ability as high as possible, e.g. to enable individual risk prediction. In genetic epidemiology, complex diseases have a polygenic basis and a common assumption is that biological and genetic features affect the outcome under co…