model_influeza_seasonal_dynamics

stochastic modeling | HMM

https://github.com/ecoronado92/model_influeza_seasonal_dynamics

Science Score: 31.0%

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CITATION.cff file
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Low similarity (6.1%) to scientific vocabulary

Keywords

baum-welch hmm influenza stochastic-processes viterb

Last synced: 6 months ago · JSON representation ·

Repository

stochastic modeling | HMM

Basic Info

Host: GitHub
Owner: ecoronado92
Language: HTML
Default Branch: master
Homepage: https://ecoronado.github.io/
Size: 2.39 MB

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baum-welch hmm influenza stochastic-processes viterb

Created over 6 years ago · Last pushed about 6 years ago

Metadata Files

Readme Citation

Hidden Markov Models (HMM) and Autoregressive Processes (AR)

Summary

Early detection of the influenza outbreaks is one of the biggest challenges of outbreak surveillance systems. In this paper, a finite, homogeneous two-state Hidden Markov Model (HMM) was developed to determine the epidemic and non-epidemic dynamics influenza-like illnesses (ILI) in a differenced time series. These dynamics were further modeled via a first-order auto-regressive process (AR1) based on the state, and parameters estimated via the Baum-Welch algorithm. The model was evaluated with US ILI data from 1998-2018.

Files

*.html final document
*.Rmd generates final document
BW_fcns.R is a script containing the Baum-Welch functions to train the model
viterbi.R is a script containing the decoding algorithm to determin epidemic vs non-epidemic weeks based on training data
flu_paper_model.R is a JAGS model from a similar paper run as a comparison

Owner

Name: Eduardo Coronado
Login: ecoronado92
Kind: user
Location: New York, NY

Website: https://ecoronado92.github.io/
Repositories: 2
Profile: https://github.com/ecoronado92

Citation (citations.bib)

@book{bishop_2016, title={PATTERN RECOGNITION AND MACHINE LEARNING}, publisher={SPRINGER-VERLAG NEW YORK}, author={BISHOP, CHRISTOPHER M.}, year={2016}}
@article{conesa_martínez-beneito_amorós_lópez-quílez_2011, title={Bayesian hierarchical Poisson models with a hidden Markov structure for the detection of influenza epidemic outbreaks}, volume={24}, DOI={10.1177/0962280211414853}, number={2}, journal={Statistical Methods in Medical Research}, author={Conesa, D and Martínez-Beneito, Ma and Amorós, R and López-Quílez, A}, year={2011}, pages={206–223}}
@misc{centers for disease control and prevention_2019, title={Influenza (Flu)}, url={https://www.cdc.gov/flu/index.htm}, journal={Centers for Disease Control and Prevention}, publisher={Centers for Disease Control and Prevention}, year={2019}, month={Apr}}
@misc{jurafsky_ martin_2018, title={Speech and Language Processing (3rd ed. draft) Dan Jurafsky and James H. Martin}, url={https://web.stanford.edu/~jurafsky/slp3/}, journal={Speech and Language Processing}, author={Jurafsky, Daniel and Martin, James H.}, year={2018}, month={Sep}}
@article{nunes_natário_carvalho_2012, title={Nowcasting influenza epidemics using non-homogeneous hidden Markov models}, volume={32}, DOI={10.1002/sim.5670}, number={15}, journal={Statistics in Medicine}, author={Nunes, Baltazar and Natário, Isabel and Carvalho, M. Lucília}, year={2012}, pages={2643–2660}}
@article{rath_carreras_sebastiani_2003, title={Automated Detection of Influenza Epidemics with Hidden Markov Models}, DOI={10.1007/978-3-540-45231-7_48}, journal={Advances in Intelligent Data Analysis V Lecture Notes in Computer Science}, author={Rath, Toni M. and Carreras, Maximo and Sebastiani, Paola}, year={2003}, pages={521–532}}
@article{strat_carrat_1999, title={Monitoring epidemiologic surveillance data using hidden Markov models}, volume={18}, DOI={10.1002/(sici)1097-0258(19991230)18:24<3463::aid-sim409>3.3.co;2-9}, number={24}, journal={Statistics in Medicine}, author={Strat, Yann Le and Carrat, Fabrice}, year={1999}, pages={3463–3478}}
@misc{zhai_2003, title={A Brief Note on the Hidden Markov Models (HMMs)}, url={https://pdfs.semanticscholar.org/54dc/c2a758e7fa34b8c2ef19826f39f16c4d1731.pdf}, publisher={University of Illinois at Urbana-Champaign}, author={Zhai, ChengXiang}, year={2003}, month={Mar}}

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