Epidemiology
25 papers with code • 0 benchmarks • 1 datasets
Epidemiology is a scientific discipline that provides reliable knowledge for clinical medicine focusing on prevention, diagnosis and treatment of diseases. Research in Epidemiology aims at characterizing risk factors for the outbreak of diseases and at evaluating the efficiency of certain treatment strategies, e.g., to compare a new treatment with an established gold standard. This research is strongly hypothesis-driven and statistical analysis is the major tool for epidemiologists so far. Correlations between genetic factors, environmental factors, life style-related parameters, age and diseases are analyzed.
Source: Visual Analytics of Image-Centric Cohort Studies in Epidemiology
Benchmarks
PHM2017
Greatest papers with code
Simulation-Based Inference for Global Health Decisions
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision makers about suitable prevention and containment strategies.
Neural Spatio-Temporal Point Processes
facebookresearch/neural_stpp
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ICLR 2021
We propose a new class of parameterizations for spatio-temporal point processes which leverage Neural ODEs as a computational method and enable flexible, high-fidelity models of discrete events that are localized in continuous time and space.
Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19
The statistical inference framework is implemented and compared on Intel Xeon CPU, NVIDIA Tesla V100 GPU and the Graphcore Mk1 IPU, and the results are discussed in the context of their computational architectures.
Accelerating Simulation-based Inference with Emerging AI Hardware
As a proof-of-concept, we demonstrate inference over a probabilistic epidemiology model used to predict the spread of COVID-19.
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
sjblim/rmsn_nips_2018
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NeurIPS 2018
Electronic health records provide a rich source of data for machine learning methods to learn dynamic treatment responses over time.
Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and Limitations
A critical factor that can hinder accurate long-term forecasts, is the number of unreported/asymptomatic cases.
BayesFlow: Learning complex stochastic models with invertible neural networks
In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics.
A Bayesian Monte Carlo approach for predicting the spread of infectious diseases
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.
Multi-task Learning for Aggregated Data using Gaussian Processes
Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task.
Total Variation Regularization for Compartmental Epidemic Models with Time-Varying Dynamics
Compartmental epidemic models are among the most popular ones in epidemiology.