The forecast of COVID-19 spread risk at the county level.
The forecast of COVID-19 spread risk at the county level.
复制标题
DOI：
10.1186/s40537-021-00491-1
复制
发表时间：
2021
影响因子：
8.1
通讯作者：
Ghoraani B
中科院分区：
文献类型：
--
作者：
Hssayeni MD;Chala A;Dev R;Xu L;Shaw J;Furht B;Ghoraani B
及早发现2019冠状病毒病（COVID-19）爆发对于挽救人们的生命和快速安全地重启经济至关重要。人们的社会行为（反映在他们的流动数据中）在疾病的传播中发挥着重要作用。因此，我们使用县级汇总的每日流动性数据以及COVID-19统计数据和人口统计信息，对美国COVID-19疫情进行短期预测。每天的数据被输入到基于长短期记忆（LSTM）的深度学习模型中，以预测未来两周内COVID-19病例的累积数量。在2020年8月1日至2021年1月22日期间，模型预测和实际累积病例之间实现了显著的平均相关性（r=0.83（p = 0.005））。该模型对美国87%的县的预测r > 0.7。在测试期间，总病例数<1000的县报告的相关性较低。平均绝对误差（MAE）为605.4，并随着检测间隔期间病例总数的减少而减少。该模型能够捕捉政府对COVID-19病例的反应。此外，它能够捕捉年龄人口统计对COVID-19传播的影响。结果表明，平均每日病例数随着退休人员百分比的减少而减少，随着年轻人百分比的增加而增加。从这项研究中吸取的经验教训不仅有助于管理COVID-19大流行，而且有助于早期有效地管理未来可能的大流行。本研究使用的代码可在https://github.com/Murtadha44/covid-19-spread-risk上公开获取。在线版本包含补充材料，可通过10.1186/s40537-021-00491-1获得。
The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people’s lives and restart the economy quickly and safely. People’s 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 (r=0.83 (p = 0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <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 https://github.com/Murtadha44/covid-19-spread-risk. The online version contains supplementary material available at 10.1186/s40537-021-00491-1.
登录
查看更多内容
DOI：
10.1016/j.chaos.2021.110708
发表时间：
2021-03
期刊：
Chaos, solitons, and fractals
影响因子：
--
作者：
Gupta V;Jain N;Katariya P;Kumar A;Mohan S;Ahmadian A;Ferrara M
通讯作者：
Ferrara M
影响因子：
7.8
作者：
Kirbas, Ismail;Sozen, Adnan;Kazancioglu, Fikret Sinasi
通讯作者：
Kazancioglu, Fikret Sinasi
影响因子：
3.9
作者：
Hssayeni, Murtadha D.;Jimenez-Shahed, Joohi;Ghoraani, Behnaz
通讯作者：
Ghoraani, Behnaz
DOI：
10.1098/rspa.1927.0118
发表时间：
1927-08-01
期刊：
PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES A-CONTAINING PAPERS OF A MATHEMATICAL AND PHYSICAL CHARACTER
影响因子：
--
作者：
Kermack, WO;McKendrick, AG
通讯作者：
McKendrick, AG
影响因子：
7.7
作者：
Garvin, Michael R.;Alvarez, Christiane;Jacobson, Daniel
通讯作者：
Jacobson, Daniel