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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 1–50 of 425 papers

TitleStatusHype
Epidemiology-Aware Neural ODE with Continuous Disease Transmission GraphCode2
TotalVibeSegmentator: Full Body MRI Segmentation for the NAKO and UK BiobankCode2
All-in-one simulation-based inferenceCode2
A Review of Graph Neural Networks in Epidemic ModelingCode2
Simulation-Based Inference for Global Health DecisionsCode2
Interpreting Temporal Graph Neural Networks with Koopman TheoryCode1
BACKTIME: Backdoor Attacks on Multivariate Time Series ForecastingCode1
Spatio-temporal Diffusion Point ProcessesCode1
Epidemiological Agent-Based Modelling Software (Epiabm)Code1
A Categorical Framework for Modeling with Stock and Flow DiagramsCode1
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related TweetsCode1
Neural parameter calibration for large-scale multi-agent modelsCode1
Differentiable Agent-based EpidemiologyCode1
LAPIS is a fast web API for massive open virus sequencing databasesCode1
Enhancing crowd flow prediction in various spatial and temporal granularitiesCode1
Wastewater catchment areas in Great BritainCode1
Detecting Anomalies within Time Series using Local Neural TransformationsCode1
TCube: Domain-Agnostic Neural Time-series NarrationCode1
Mandoline: Model Evaluation under Distribution ShiftCode1
Encoding physics to learn reaction-diffusion processesCode1
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty QuantificationCode1
Policy Evaluation during a PandemicCode1
Neural Spatio-Temporal Point ProcessesCode1
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in GermanyCode1
Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment EffectsCode1
Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and LimitationsCode1
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control TheoryCode1
BayesFlow: Learning complex stochastic models with invertible neural networksCode1
A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net ModelsCode0
Six Decades Post-Discovery of Taylor's Power Law: From Ecological and Statistical Universality, Through Prime Number Distributions and Tipping-Point Signals, to Heterogeneity and Stability of Complex Networks—0
Improving wastewater-based epidemiology through strategic placement of samplers—0
A note on metapopulation models—0
Mamba Integrated with Physics Principles Masters Long-term Chaotic System ForecastingCode0
Symbolic Foundation Regressor on Complex Networks—0
Are Statistical Methods Obsolete in the Era of Deep Learning?—0
Bayesian ensemble learning for predicting health outcomes of multipollutant mixtures—0
Clustering and Pruning in Causal Data FusionCode0
Structural-Temporal Coupling Anomaly Detection with Dynamic Graph TransformerCode0
Mathematical epidemiology of infectious diseases: an ongoing challenge—0
A Hamiltonian Higher-Order Elasticity Framework for Dynamic Diagnostics(2HOED)—0
New insights into population dynamics from the continuous McKendrick model—0
The two-clock problem in population dynamics—0
Simulating biochemical reactions: The Linear Noise Approximation can capture non-linear dynamics—0
Continuous and discrete compartmental models for infectious disease—0
Deep spatio-temporal point processes: Advances and new directions—0
A Behaviour and Disease Model of Testing and IsolationCode0
Identifying Macro Causal Effects in C-DMGs—0
Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks—0
Group centrality in optimal and suboptimal vaccination for epidemic models in contact networks—0
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