2020
DOI: 10.1016/j.chaos.2020.110339
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Isfahan and Covid-19: Deep spatiotemporal representation

Abstract: The coronavirus COVID-19 is affecting 213 countries and territories around the world. Iran was one of the first affected countries by this virus. Isfahan, as the third most populated province of Iran, experienced a noticeable epidemic. The prediction of epidemic size, peak value, and peak time can help policymakers in correct decisions. In this study, deep learning is selected as a powerful tool for forecasting this epidemic in Isfahan. A combination of effective Social Determinant of Health (SDH) and the occu… Show more

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Cited by 6 publications
(6 citation statements)
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“…That study compared different types of LSTM and found that bi-directional LSTM performed better than other models. A study conducted using the cumulative number of confirmed cases in Isfahan, Iran was used to test different machine learning forecasting models [ 21 ]. The input data included DPC and social determinants of health.…”
Section: Related Studiesmentioning
confidence: 99%
See 1 more Smart Citation
“…That study compared different types of LSTM and found that bi-directional LSTM performed better than other models. A study conducted using the cumulative number of confirmed cases in Isfahan, Iran was used to test different machine learning forecasting models [ 21 ]. The input data included DPC and social determinants of health.…”
Section: Related Studiesmentioning
confidence: 99%
“…6 in Ref. [ 21 ]). Data from Russia, Peru, and Iran obtained from January to July 2020 were used to validate a standard LSTM network [ 22 ].…”
Section: Related Studiesmentioning
confidence: 99%
“…The root mean square error computed from a 10-fold cross-validation study was reported to be 27.187. A study conducted using the cumulative number of confirmed cases in Isfahan, Iran, between January and May 2020 was used to test different machine learning forecasting models [ 31 ]. The input data included DPC and social determinants of health (SDH).…”
Section: Related Workmentioning
confidence: 99%
“…This drawback is overcome by performing the data-training phase in a Bi-LSTM system in two sequential directions: forward and backward. This improves the performance of the model [71][72][73][74][75][76][77]. Bi-LSTM was used for forecasting along coastal areas of Queensland, Australia [78], and a developed Bi-LSTM model was used for COVID-19 cases in Japan [79].…”
Section: Bidirectional Long Short-term Memory Algorithm (Bi-lstm)mentioning
confidence: 99%