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Epidemiology
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Simple Approximate Bayesian Inference Neural Surrogate for Stochastic Petri Net Models
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
Improving wastewater-based epidemiology through strategic placement of samplers
A note on metapopulation models
Mamba Integrated with Physics Principles Masters Long-term Chaotic System Forecasting
Symbolic Foundation Regressor on Complex Networks
Are Statistical Methods Obsolete in the Era of Deep Learning?
Bayesian ensemble learning for predicting health outcomes of multipollutant mixtures
Clustering and Pruning in Causal Data Fusion
Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer
Mathematical epidemiology of infectious diseases: an ongoing challenge
The two-clock problem in population dynamics
A Hamiltonian Higher-Order Elasticity Framework for Dynamic Diagnostics(2HOED)
New insights into population dynamics from the continuous McKendrick model
Simulating biochemical reactions: The Linear Noise Approximation can capture non-linear dynamics
Continuous and discrete compartmental models for infectious disease
Deep spatio-temporal point processes: Advances and new directions
A Behaviour and Disease Model of Testing and Isolation
Group centrality in optimal and suboptimal vaccination for epidemic models in contact networks
Dynamic Graph Structure Estimation for Learning Multivariate Point Process using Spiking Neural Networks
Topological Properties of the Effective Reproduction Number in an Heterogeneous SIS Model
Coupling plankton and cholera dynamics: insights into outbreak prediction and practical disease management
Bayesian Semi-Parametric Spatial Dispersed Count Model for Precipitation Analysis
Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting
Optimal virulence strategies in epidemiological models with asymptomatic transmission
Primer C-VAE: An interpretable deep learning primer design method to detect emerging virus variants
Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates
Filtered Markovian Projection: Dimensionality Reduction in Filtering for Stochastic Reaction Networks
Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association
Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs
An SIRS-model considering waning efficiency and periodic re-vaccination
Redefining Influenza Transmission Seasonality Using the Novel Seasonality Index
Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database
Recovering Unobserved Network Links from Aggregated Relational Data: Discussions on Bayesian Latent Surface Modeling and Penalized Regression
Modelling Activity Scheduling Behaviour with Deep Generative Machine Learning
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