https://github.com/sauravcsvt/KDD_linelisting
공식 코드Guided Deep List: Automating the Generation of Epidemiological Line Lists from Open Sources
Papers with Code 아카이브
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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/scc-usc/ReCOVER-COVID-19
공식 코드Data-driven Identification of Number of Unreported Cases for COVID-19: Bounds and Limitations
https://github.com/schwalger/refracdens_epidem
공식 코드A refractory density approach to a multi-scale SEIRS epidemic model
https://github.com/shahanaibrahimosu/undercounted-tensor-completion
공식 코드Under-Counted Tensor Completion with Neural Incorporation of Attributes
https://github.com/stefanradev93/AIAgainstCorona
공식 코드https://github.com/stefanradev93/cINN
공식 코드BayesFlow: Learning complex stochastic models with invertible neural networks
https://github.com/stevenkleinegesse/GradBED
공식 코드https://github.com/thgaskin/neuralabm
공식 코드Neural parameter calibration for large-scale multi-agent models
https://github.com/trevorreckell/numerical-comparison-of-pn-vs-ode-for-sir
공식 코드A Numerical Comparison of Petri Net and Ordinary Differential Equation SIR Component Models
https://github.com/tsinghua-fib-lab/Spatio-temporal-Diffusion-Point-Processes
공식 코드https://github.com/uu-sml/neurips2019-parameter-elimination
공식 코드https://github.com/viluiz/gan
공식 코드https://github.com/willtebbutt/PseudoPointStateSpace-UAI-2021
공식 코드Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes
https://github.com/xiaolin-cs/backtime
공식 코드BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting
https://github.com/ylab-open/mets-cov
공식 코드METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
https://github.com/youngseogchung/parity-calibration
공식 코드https://github.com/Raocp/PeRCNN
https://github.com/SourabhKul/ABC-COVID-19-GPU-TPU
Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19
https://github.com/aapeterson/powerlaw-figures
Heterogeneity in susceptibility dictates the order of epidemiological models
https://github.com/bayesflow-org/bayesflow
BayesFlow: Learning complex stochastic models with invertible neural networks
https://github.com/bcallaway11/pte
https://github.com/devbas/ovassistant-alpha
Spatio-Temporal Data Mining: A Survey of Problems and Methods
https://github.com/elizavetasemenova/priorCVAE_jax
Federated Learning for Non-factorizable Models using Deep Generative Prior Approximations
https://github.com/emiliasiv/flexible-parametric-inference-for-space-time-hawkes-processes
Flexible Parametric Inference for Space-Time Hawkes Processes
https://github.com/frb-yousefi/multitask-gp
Multi-task Learning for Aggregated Data using Gaussian Processes
https://github.com/frinve/tetys
Exploring the evolution of research topics during the COVID-19 pandemic
https://github.com/hyerinshelly/RL_COVID-19_Korea
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
https://github.com/jstrappa/jllcm
Efficient comparison of independence structures of log-linear models
https://github.com/oist/nextnet
https://github.com/p-karisani/FirstPHM
https://github.com/peck94/cann-detector
https://github.com/shirakawatoru/dltmle-icml-2024
https://github.com/stefanradev93/BayesFlow
BayesFlow: Learning complex stochastic models with invertible neural networks
https://github.com/yashlal/Microbial-Vector-Field-Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.