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
Latest papers
All-in-one simulation-based inference
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data.
A Review of Graph Neural Networks in Epidemic Modeling
In this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic tasks and highlight potential future directions.
Forecasting and predicting stochastic agent-based model data with biologically-informed neural networks
johnnardini/forecasting_predicting_abms
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In this work, we describe how biologically-informed neural networks (BINNs) can be trained to learn BINN-guided PDE models to predict ABM behavior.
Learning and Generalizing Polynomials in Simulation Metamodeling
jesperhauch/polynomial_deep_learning
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The ability to learn polynomials and generalize out-of-distribution is essential for simulation metamodels in many disciplines of engineering, where the time step updates are described by polynomials.
Amortized Simulation-Based Frequentist Inference for Tractable and Intractable Likelihoods
The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology.
Under-Counted Tensor Completion with Neural Incorporation of Attributes
shahanaibrahimosu/undercounted-tensor-completion
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Systematic under-counting effects are observed in data collected across many disciplines, e. g., epidemiology and ecology.
Parity Calibration
In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation.
BAND: Biomedical Alert News Dataset
fuzihaofzh/band
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Infectious disease outbreaks continue to pose a significant threat to human health and well-being.
Spatio-temporal Diffusion Point Processes
facebookresearch/neural_stpp
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To enhance the learning of each step, an elaborated spatio-temporal co-attention module is proposed to capture the interdependence between the event time and space adaptively.
Using Geographic Location-based Public Health Features in Survival Analysis
Time elapsed till an event of interest is often modeled using the survival analysis methodology, which estimates a survival score based on the input features.
