Epidemiology
46 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
These leaderboards are used to track progress in Epidemiology
PHM2017
Most implemented papers
Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
The first, critical, task for these applications is classifying whether a personal health event was mentioned, which we call the (PHM) problem.
Correspondence Analysis Using Neural Networks
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies.
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.
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.
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
Epidemiologists model the dynamics of epidemics in order to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccination, etc).
Accelerating Simulation-based Inference with Emerging AI Hardware
graphcore/demos
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As a proof-of-concept, we demonstrate inference over a probabilistic epidemiology model used to predict the spread of COVID-19.
Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19
graphcore/demos
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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.
Guided Deep List: Automating the Generation of Epidemiological Line Lists from Open Sources
Specifically, we focus on deriving epidemiological characteristics of an emerging disease and the affected population from reports of illness.
Three faces of node importance in network epidemiology: Exact results for small graphs
We investigate three aspects of the importance of nodes with respect to Susceptible-Infectious-Removed (SIR) disease dynamics: influence maximization (the expected outbreak size given a set of seed nodes), the effect of vaccination (how much deleting nodes would reduce the expected outbreak size) and sentinel surveillance (how early an outbreak could be detected with sensors at a set of nodes).
