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
Accelerating Simulation-based Inference with Emerging AI Hardware
SourabhKul/ABC-COVID-19-GPU
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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
SourabhKul/ABC-COVID-19-GPU-TPU
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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.
Outcome-guided Sparse K-means for Disease Subtype Discovery via Integrating Phenotypic Data with High-dimensional Transcriptomic Data
We demonstrated the superior performance of the GuidedSparseKmeans by comparing with existing clustering methods in simulations and applications of high-dimensional transcriptomic data of breast cancer and Alzheimer's disease.
Cost Effective Reproduction Number Based Strategies for Reducing Deaths from COVID-19
The response of $R_e$ to two types of control measures (testing and distancing) applied to the two different subpopulations is characterized.
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
We introduce a framework for Bayesian experimental design (BED) with implicit models, where the data-generating distribution is intractable but sampling from it is still possible.
Policy Evaluation during a Pandemic
National and local governments have implemented a large number of policies in response to the Covid-19 pandemic.
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
Pseudo-point approximations, one of the gold-standard methods for scaling GPs to large data sets, are well suited for handling off-the-grid spatial data.
Mandoline: Model Evaluation under Distribution Shift
If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target.
Challenges for machine learning in clinical translation of big data imaging studies
nkdinsdale/challenges_review
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The combination of deep learning image analysis methods and large-scale imaging datasets offers many opportunities to imaging neuroscience and epidemiology.
Unifying incidence and prevalence under a time-varying general branching process
We also show that the incidence integral equations that arise from both of these specific models agree with the renewal equation used ubiquitously in infectious disease modelling.
