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Predicting the Epidemic Curve of the Coronavirus (SARS-CoV-2) Disease (COVID-19) Using Artificial Intelligence
26/26 checkable references clean · checked 2026-08-08
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
9 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 26 checked references that resolve
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Severe Acute Respiratory Syndrome Coronavirus as an Agent of Emerging and Reemerging Infection
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Coronavirus as a possible cause of severe acute respiratory syndrome
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Commentary: Middle East Respiratory Syndrome Coronavirus (MERS-CoV): Announcement of the Coronavirus Study Group
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The Battle Against COVID-19 in Jordan: An Early Overview of the Jordanian Experience
resolves10.1038/s41586-020-2012-7
A pneumonia outbreak associated with a new coronavirus of probable bat origin
resolves10.1016/S0140-6736(20)30211-7
Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study
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Response to: Sore throat in COVID‐19: comment on “Clinical characteristics of hospitalized patients with SARS‐CoV‐2 infection: A single arm meta‐analysis”
resolves10.20944/preprints202003.0246.v1
Association of COVID-19 Disease Severity with Transmission Routes and Suggested Changes to Community Guidelines
resolves10.1093/jtm/taaa021
The reproductive number of COVID-19 is higher compared to SARS coronavirus
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Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China
resolves10.1186/s12859-019-3131-8
Attention-based recurrent neural network for influenza epidemic prediction
resolves10.1007/978-3-030-35139-7_7
Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics
resolves10.1016/j.chaos.2020.110059
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
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Data-based analysis, modelling and forecasting of the COVID-19 outbreak
The 9 references without a DOI — listed, not checked
no DOI — not checkedWorld Health Organization MERS Situation Update Available online: http://www.emro.who.int/pandemic-epidemic-diseases/mers-cov/mers-situation-update-january-2020.html (accessed on Mar 29, 2020).
no DOI — not checkedWorld Health Organization Coronavirus disease (COVID-19) Weekly Epidemiological Update and Weekly Operational Update Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports (accessed on Apr 15, 2020).
no DOI — not checkedFerguson, N. , Laydon, D. , Nedjati Gilani, G. , Imai, N. , Ainslie, K. , Baguelin, M. , Bhatia, S. , Boonyasiri, A. , Cucunuba Perez, Z. , Cuomo-Dannenburg, G. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand Available online: https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf (accessed on Mar 16, 2020).
no DOI — not checkedA thorough review on the current advance of neural network structures
no DOI — not checkedJohns Hopkins University NEW CASES OF COVID-19 IN WORLD COUNTRIES Available online: https://coronavirus.jhu.edu/data/new-cases (accessed on Apr 12, 2020).
no DOI — not checkedYoon, S. , Yun, H. , Kim, Y. , Park, G. , Jung, K. Efficient transfer learning schemes for personalized language modeling using recurrent neural network. arXiv Prepr. 2017, doi:https://arxiv.org/abs/1701.03578.
no DOI — not checkedSutskever, I. , Vinyals, O. , Le, Q. V Sequence to Sequence Learning with Neural Networks Available online: https://arxiv.org/abs/1409.3215v3 (accessed on Apr 12, 2020).
no DOI — not checkedCenters for Disease Control and Prevention Using an Epi Curve to Determine Mode of Spread. Available online: https://www.cdc.gov/training/QuickLearns/epimode/6.html (accessed on Apr 12, 2020).
no DOI — not checkedWalker, P. , Whittaker, C. , Watson, O. , Baguelin, M. , Ainslie, K. , Bhatia, S. , Bhatt, S. , Boonyasiri, A. , Boyd, O. , Cattarino, L. Report 12: The global impact of COVID-19 and strategies for mitigation and suppression Available online: https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-12-global-impact-covid-19/ (accessed on Apr 12, 2020).
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