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


Greatest papers with code

Simulation-Based Inference for Global Health Decisions

mrc-ide/covid-sim 14 May 2020

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.

Bayesian Inference Epidemiology

Neural Spatio-Temporal Point Processes

facebookresearch/neural_stpp 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.

Epidemiology Point Processes

Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19

graphcore/demos 23 Dec 2020

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

graphcore/demos 12 Dec 2020

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 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

scc-usc/ReCOVER-COVID-19 3 Jun 2020

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

stefanradev93/cINN 13 Mar 2020

In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics.

Bayesian Inference Epidemiology

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

ostojanovic/BSTIM biorxiv, PLOS ONE (under review) 2019

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases.

Disease Prediction Epidemiology +2

Multi-task Learning for Aggregated Data using Gaussian Processes

frb-yousefi/multitask-gp NeurIPS 2019

Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task.

Air Pollution Prediction Epidemiology +2

Total Variation Regularization for Compartmental Epidemic Models with Time-Varying Dynamics

WenjieZ/2019-nCoV 1 Apr 2020

Compartmental epidemic models are among the most popular ones in epidemiology.