tetano
Editor, Senior Moderator
Sci Rep
. 2022 Feb 22;12(1):3030.
doi: 10.1038/s41598-022-06992-0.
Deep learning forecasting using time-varying parameters of the SIRD model for Covid-19
Arthur Bousquet[SUP] 1 [/SUP], William H Conrad[SUP] 2 [/SUP], Said Omer Sadat[SUP] 1 [/SUP], Nelli Vardanyan[SUP] 1 [/SUP], Youngjoon Hong[SUP] 3 [/SUP]
Affiliations
Abstract
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 ([Formula: see text]), which can describe if the infected population is growing ([Formula: see text]) or shrinking ([Formula: see text]). 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, [Formula: see text], and deceased rate, [Formula: see text]. With an accurate prediction of [Formula: see text] and [Formula: see text], we can directly derive [Formula: see text], 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.
. 2022 Feb 22;12(1):3030.
doi: 10.1038/s41598-022-06992-0.
Deep learning forecasting using time-varying parameters of the SIRD model for Covid-19
Arthur Bousquet[SUP] 1 [/SUP], William H Conrad[SUP] 2 [/SUP], Said Omer Sadat[SUP] 1 [/SUP], Nelli Vardanyan[SUP] 1 [/SUP], Youngjoon Hong[SUP] 3 [/SUP]
Affiliations
- PMID: 35194090
- DOI: 10.1038/s41598-022-06992-0
Abstract
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 ([Formula: see text]), which can describe if the infected population is growing ([Formula: see text]) or shrinking ([Formula: see text]). 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, [Formula: see text], and deceased rate, [Formula: see text]. With an accurate prediction of [Formula: see text] and [Formula: see text], we can directly derive [Formula: see text], 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.