Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.
Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.
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DOI：
10.1155/2020/8857346
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发表时间：
2020
影响因子：
--
通讯作者：
Karime A
中科院分区：
文献类型：
--
作者：
Alanazi SA;Kamruzzaman MM;Alruwaili M;Alshammari N;Alqahtani SA;Karime A
COVID-19是一项紧迫的全球挑战，因为它具有传染性，特征经常变化，而且缺乏疫苗或有效药物。我们迫切需要一个衡量和预防COVID-19持续传播的模型，以提供智能医疗服务。这需要使用先进的智能计算，如人工智能，机器学习，深度学习，认知计算，云计算，雾计算和边缘计算。本文提出了一种使用SIR和机器学习预测COVID-19的模型，用于智能医疗保健和KSA公民的福祉。了解每天易感、感染和康复病例的数量对于数学建模至关重要，这样才能确定大流行的行为影响。它预测了未来700天的情况。拟议的系统预测COVID-19是否会在人群中传播或从长远来看灭绝。这里提供了数学分析和模拟结果，作为预测疫情进展及其可能结束的三种情况的手段：“不采取行动”，“封锁”和“新药”。将封锁和新药等干预措施的效果与“不采取行动”的情况进行比较。封锁病例通过减少感染来推迟峰值点，并影响感染曲线的面积相等规则。另一方面，新药通过减少感染人数对感染曲线产生重大影响。使用模拟的COVID-19现有预测数据预测，最高水平的病例可能发生在2020年11月15日至30日之间。模拟数据显示，病毒可能要到2021年6月以后才能完全得到控制。生育率表明，政府封锁和隔离个人等措施不足以阻止大流行。本研究建议当局应尽快采取严格的长期遏制策略，以成功地减少疫情规模。
COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently required to provide smart health care services. This requires using advanced intelligent computing such as artificial intelligence, machine learning, deep learning, cognitive computing, cloud computing, fog computing, and edge computing. This paper proposes a model for predicting COVID-19 using the SIR and machine learning for smart health care and the well-being of the citizens of KSA. Knowing the number of susceptible, infected, and recovered cases each day is critical for mathematical modeling to be able to identify the behavioral effects of the pandemic. It forecasts the situation for the upcoming 700 days. The proposed system predicts whether COVID-19 will spread in the population or die out in the long run. Mathematical analysis and simulation results are presented here as a means to forecast the progress of the outbreak and its possible end for three types of scenarios: “no actions,” “lockdown,” and “new medicines.” The effect of interventions like lockdown and new medicines is compared with the “no actions” scenario. The lockdown case delays the peak point by decreasing the infection and affects the area equality rule of the infected curves. On the other side, new medicines have a significant impact on infected curve by decreasing the number of infected people about time. Available forecast data on COVID-19 using simulations predict that the highest level of cases might occur between 15 and 30 November 2020. Simulation data suggest that the virus might be fully under control only after June 2021. The reproductive rate shows that measures such as government lockdowns and isolation of individuals are not enough to stop the pandemic. This study recommends that authorities should, as soon as possible, apply a strict long-term containment strategy to reduce the epidemic size successfully.
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