ArticleScientific reports2022
Deep learning forecasting using time-varying parameters of the SIRD model for Covid-19.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review.Systematic reviews · 2024Pooled it
- Comparative estimation of the spread of acute diarrhea and dengue in India using statistical mathematical and deep learning models.Scientific reports · 2025Article
- Analysis of a mathematical model for malaria using data-driven approach.Scientific reports · 2025Article
- A novel framework for inferring dynamic infectious disease transmission with graph attention: a COVID-19 case study in Korea.BMC public health · 2025Article
- Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges.Nature communications · 2025Article
- Time-varying reproduction number estimation: fusing compartmental models with generalized additive models.Journal of the Royal Society, Interface · 2025Article
- Deep neural networks for endemic measles dynamics: Comparative analysis and integration with mechanistic models.PLoS computational biology · 2024Article
- Long COVID diagnostic with differentiation from chronic lyme disease using machine learning and cytokine hubs.Scientific reports · 2024Article
- Analysis of the impact of COVID-19 variants and vaccination on the time-varying reproduction number: statistical methods.Frontiers in public health · 2024Article
- Article
- On fast simulation of dynamical system with neural vector enhanced numerical solver.Scientific reports · 2023Article
- Space-Distributed Traffic-Enhanced LSTM-Based Machine Learning Model for COVID-19 Incidence Forecasting.Computational intelligence and neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.