Epidemiology
71 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
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.
Inference for High Dimensional Censored Quantile Regression
To our knowledge, there is little work available to draw inference on the effects of high dimensional predictors for censored quantile regression.
TCube: Domain-Agnostic Neural Time-series Narration
We present TCube (Time-series-to-text), a domain-agnostic neural framework for time-series narration, that couples the representation of essential time-series elements in the form of a dense knowledge graph and the translation of said knowledge graph into rich and fluent narratives through the transfer-learning capabilities of PLMs (Pre-trained Language Models).
Detecting Anomalies within Time Series using Local Neural Transformations
boschresearch/local_neural_transformations
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We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.
Wastewater catchment areas in Great Britain
Wastewater catchment area data are essential for wastewater treatment capacity planning and have recently become critical for operationalising wastewater-based epidemiology (WBE) for COVID-19.
Enhancing crowd flow prediction in various spatial and temporal granularities
jonpappalord/crowd_flow_prediction
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We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks.
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based dynamics are core objects of study.
Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting
In this study, we introduce the Probabilistic AutoRegressive Neural Networks (PARNN), capable of handling complex time series data exhibiting non-stationarity, nonlinearity, non-seasonality, long-range dependence, and chaotic patterns.
COVID-19 epidemiology as emergent behavior on a dynamic transmission forest
In this paper we create a compartmental, stochastic process model of SARS-CoV-2 transmission, where the process's mean and variance have distinct dynamics.
LAPIS is a fast web API for massive open virus sequencing databases
Conclusions: Powered by an optimized database engine and available through a web API, LAPIS enhances the accessibility of genomic sequencing data.
