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This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google\u2019s Mobility Reports and climate variables acquired from the Weather Online API.\n The results suggest that the logistic growth model fits best the pandemic\u2019s behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.<\/jats:p>","DOI":"10.1007\/s12559-021-09885-y","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T22:03:00Z","timestamp":1622757780000},"page":"1794-1805","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models"],"prefix":"10.1007","volume":"16","author":[{"given":"Daniela A.","family":"Gomez-Cravioto","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7324-7205","authenticated-orcid":false,"given":"Ramon E.","family":"Diaz-Ramos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco J.","family":"Cantu-Ortiz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hector G.","family":"Ceballos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"9885_CR1","unstructured":"Organization WH. Pneumonia of unknown cause China. Emergencies preparedness, response, Disease outbreak news, World Health Organization (WHO).\u00a02020."},{"key":"9885_CR2","unstructured":"Home - Johns Hopkins Coronavirus Resource Center, 2020. https:\/\/coronavirus.jhu.edu\/"},{"issue":"1","key":"9885_CR3","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1186\/s12916-019-1406-6","volume":"17","author":"G Chowell","year":"2019","unstructured":"Chowell G, Tariq A, Hyman JM. A novel sub-epidemic modeling framework for short-term forecasting epidemic waves. BMC Med. 2019;17(1):164.","journal-title":"BMC Med"},{"issue":"8","key":"9885_CR4","doi-asserted-by":"publisher","first-page":"1596","DOI":"10.3390\/ijerph15081596","volume":"15","author":"S Chae","year":"2018","unstructured":"Chae S, Kwon S, Lee D. Predicting infectious disease using deep learning and big data. 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Diaz-Ramos declares that he has no conflict of interest. Daniela A. Gomez-Cravioto declares that she has no conflict of interest. Francisco J. Cantu-Ortiz declares that he has no conflict of interest. Hector G. Ceballos declares that he has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}