Nonparametric Tests for Comparing COVID-19 Machine Learning Forecasting Models
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-69744-0_9
Reference39 articles.
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2. Anand, N., Sabarinath, A., Geetha, S., Somanath, S.: Predicting the spread of COVID-19 using SIR model augmented to incorporate quarantine and testing. Trans. Indian Natl. Acad. Eng. 5, 141–148 (2020). https://doi.org/10.1007/s41403-020-00151-5
3. Ardabili, S.F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A.R., Reuter, U., Rabczuk, T., Atkinson, P.M.: COVID-19 Outbreak Prediction with Machine Learning. Preprints 2020040311. doi:10.20944/preprints202004.0311.v1 (2020)
4. Aron, J.L., Schwartz, I.B.: Seasonality and period-doubling bifurcations in an epidemic model J. Theor. Biol. 110, 665–679. https://doi.org/10.1016/s0022-5193(84)80150-2
5. Babu, M., Marimuthu, S., Joy, M., Nadaraj, A., Asirvatham, E., Jeyaseelan, L.: Forecasting COVID-19 epidemic in India and high incidence states using SIR and logistic growth models. Clin. Epidemiol. Glob. Health (2020). https://doi.org/10.1016/j.cegh.2020.06.006
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