Deep learning forecasting using time-varying parameters of the SIRD model for Covid-19

Cited 20 time in webofscience Cited 0 time in scopus
  • Hit : 128
  • Download : 0
Accurate epidemiological models are necessary for governments, organizations, and individuals to respond appropriately to the ongoing novel coronavirus pandemic. One informative metric epidemiological models provide is the basic reproduction number (R-0), which can describe if the infected population is growing (R-0 > 1) or shrinking (R-0 < 1). We introduce a novel algorithm that incorporates the susceptible-infected-recovered-dead model (SIRD model) with the long short-term memory (LSTM) neural network that allows for real-time forecasting and time-dependent parameter estimates, including the contact rate, beta, and deceased rate, mu. With an accurate prediction of beta and mu, we can directly derive R-0, and find a numerical solution of compartmental models, such as the SIR-type models. Incorporating the epidemiological model dynamics of the SIRD model into the LSTM network, the new algorithm improves forecasting accuracy. Furthermore, we utilize mobility data from cellphones and positive test rate in our prediction model, and we also present a vaccination model. Leveraging mobility and vaccination schedule is important for capturing behavioral changes by individuals in response to the pandemic as well as policymakers.
Publisher
NATURE PORTFOLIO
Issue Date
2022-02
Language
English
Article Type
Article
Citation

SCIENTIFIC REPORTS, v.12, no.1

ISSN
2045-2322
DOI
10.1038/s41598-022-06992-0
URI
http://hdl.handle.net/10203/311014
Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 20 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0