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 (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.
Highlights
Accurate epidemiological models are necessary for governments, organizations, and individuals to respond appropriately to the ongoing novel coronavirus pandemic
The epidemiological parameters β, γ, and μ in the SIRD model can be esteimated from the number of the susceptible, infectious, recovered, and dead
Our main contributions consist of three key findings; (i) our SIRD–long short-term memory (LSTM) combined network outperforms classical prediction models; (ii) we incorporate the mobility and vaccination as inputs of our neural network to increase the accuracy of our parameters predictions; (iii) we forecast Covid-19 trends when mobility decreases or increases
Summary
Accurate epidemiological models are necessary for governments, organizations, and individuals to respond appropriately to the ongoing novel coronavirus pandemic. Another method to solve the equations is to use neural networks by considering the system as a time series[17] with a recurrent neural network[18] This approach does not ensure that the model follows the dynamics of compartmental models, and the neural network is required to predict twice as many variables. A metapopulation SEIR model was investigated in[21] that integrated fine-grained dynamic mobility from Safegraph data to simulate the spread of Covid-19 Each of these studies demonstrated that by integrating. These mobility data, the SEIR model can accurately fit the real case trajectory, despite substantial changes in the behavior of the population over time
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