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People\u2019s social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (\n                    <jats:italic>r<\/jats:italic>\n                    =0.83 (\n                    <jats:italic>p = 0.005<\/jats:italic>\n                    )) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had\n                    <jats:italic>r<\/jats:italic>\n                    &gt; 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of &lt;1000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also can help with early and effective management of possible future pandemics. The code used for this study was made publicly available on\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Murtadha44\/covid-19-spread-risk.\">https:\/\/github.com\/Murtadha44\/covid-19-spread-risk.<\/jats:ext-link>\n                  <\/jats:p>","DOI":"10.1186\/s40537-021-00491-1","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T05:19:55Z","timestamp":1625635195000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["The forecast of COVID-19 spread risk at the county level"],"prefix":"10.1186","volume":"8","author":[{"given":"Murtadha D.","family":"Hssayeni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arjuna","family":"Chala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roger","family":"Dev","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesse","family":"Shaw","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Borko","family":"Furht","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0075-7663","authenticated-orcid":false,"given":"Behnaz","family":"Ghoraani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"issue":"8","key":"491_CR1","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1016\/S2468-2667(20)30157-2","volume":"5","author":"ME Kretzschmar","year":"2020","unstructured":"Kretzschmar ME, Rozhnova G, Bootsma MC, van Boven M, van de Wijgert JH, Bonten MJ. 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