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Epidemiology

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

Papers

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Showing 1–25 of 425 papers

TitleStatusHype
A Review of Graph Neural Networks in Epidemic ModelingCode2
TotalVibeSegmentator: Full Body MRI Segmentation for the NAKO and UK BiobankCode2
Epidemiology-Aware Neural ODE with Continuous Disease Transmission GraphCode2
Simulation-Based Inference for Global Health DecisionsCode2
All-in-one simulation-based inferenceCode2
Neural Spatio-Temporal Point ProcessesCode1
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related TweetsCode1
Policy Evaluation during a PandemicCode1
Epidemiological Agent-Based Modelling Software (Epiabm)Code1
Detecting Anomalies within Time Series using Local Neural TransformationsCode1
LAPIS is a fast web API for massive open virus sequencing databasesCode1
Mandoline: Model Evaluation under Distribution ShiftCode1
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in GermanyCode1
Neural parameter calibration for large-scale multi-agent modelsCode1
Differentiable Agent-based EpidemiologyCode1
Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment EffectsCode1
BayesFlow: Learning complex stochastic models with invertible neural networksCode1
Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and LimitationsCode1
A Categorical Framework for Modeling with Stock and Flow DiagramsCode1
BACKTIME: Backdoor Attacks on Multivariate Time Series ForecastingCode1
Encoding physics to learn reaction-diffusion processesCode1
Enhancing crowd flow prediction in various spatial and temporal granularitiesCode1
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty QuantificationCode1
Interpreting Temporal Graph Neural Networks with Koopman TheoryCode1
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control TheoryCode1
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