Epidemiology
70 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
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).
Spatio-Temporal Data Mining: A Survey of Problems and Methods
Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences.
Uncertainty-aware generative models for inferring document class prevalence
Prevalence estimation is the task of inferring the relative frequency of classes of unlabeled examples in a group{---}for example, the proportion of a document collection with positive sentiment.
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.
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.
Efficient comparison of independence structures of log-linear models
Our method relies only on the independence structure of the models, which is useful when the interest lies in obtaining knowledge from said structure, or when comparing the performance of structure learning algorithms, among other possible uses.
Distilling Importance Sampling for Likelihood Free Inference
dennisprangle/distillingimportancesampling
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The training data is "distilled" by using it to train an updated normalizing flow.
Parameter elimination in particle Gibbs sampling
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories.
Interplay between competitive and cooperative interactions in a three-player pathogen system
We studied this problem considering two cooperating pathogens, where one pathogen is further structured in two strains.
Total Variation Regularization for Compartmental Epidemic Models with Time-Varying Dynamics
Compartmental epidemic models are among the most popular ones in epidemiology.
