Presently, novel coronavirus outbreak 2019 (COVID-19) is a major threat to public health. Mathematical epidemic models can be utilized to forecast the course of an epidemic and cultivate approaches for controlling it. This paper utilizes the real data of spreading COVID-19 in Saudi Arabia for mathematical modeling and complex analyses. This paper introduces the Susceptible, Exposed, Infectious, Recovered, Undetectable, and Deceased (SEIRUD) and Machine learning algorithm to predict and control COVID-19 in Saudi Arabia.This COVID-19 has initiated many methods, such as cloud computing, edge-computing, IoT, artificial intelligence. The use of sensor devices has increased enormously. Similarly, several developments in solving the COVID-19 crisis have been used by IoT applications. The new technology relies on IoT variables and the roles of symptoms using wearable sensors to forecast cases of COVID-19. The working model involves wearable devices, occupational therapy, condition control, testing of cases, suspicious and IoT elements. Mathematical modeling is useful for understanding the fundamental principle of the transmission of COVID-19 and providing guidance for possible predictions. The method suggested predicts whether COVID-19 would expand or die in the long term in the population. The mathematical study results and related simulation are described here as a way of forecasting the progress and the possible end of the epidemic with three forms of scenarios: 'No Action,' 'Lockdowns and New Medicine.' The lock case slows it down the peak by minimizing infection and impacts area equality of the infected deformation. This study familiarizes the ideal protocol, which can support the Saudi population to breakdown spreading COVID-19 in an accurate and timely way. The simulation findings have been executed, and the suggested model enhances the accuracy ratio of 89.3%, prediction ratio of 88.7%, the precision ratio of 87.7%, recall ratio of 86.4%, and F1 score of 90.9% compared to other existing methods.

中文翻译：

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基于物联网的分析，用于控制和预测沙特阿拉伯 COVID-19 的传播。

目前，2019 年新型冠状病毒爆发 (COVID-19) 是对公共卫生的主要威胁。数学流行病模型可用于预测流行病的进程并培养控制它的方法。本文利用在沙特阿拉伯传播 COVID-19 的真实数据进行数学建模和复杂分析。本文介绍了在沙特阿拉伯预测和控制 COVID-19 的易感、暴露、传染、恢复、不可检测和死亡 (SEIRUD) 和机器学习算法。这个 COVID-19 已经启动了许多方法，例如云计算、边缘计算、物联网、人工智能。传感器设备的使用已大大增加。同样，物联网应用程序也使用了解决 COVID-19 危机的一些进展。新技术依赖于物联网变量和症状的作用，使用可穿戴传感器来预测 COVID-19 病例。工作模式涉及可穿戴设备、职业治疗、状态控制、病例测试、可疑和物联网元素。数学建模有助于理解 COVID-19 传播的基本原理，并为可能的预测提供指导。建议的方法可以预测 COVID-19 是否会在人群中长期扩张或死亡。数学研究结果和相关模拟在这里被描述为一种预测流行病进展和可能结束的方式，具有三种形式的情景：“不采取行动”、“封锁和新药”。锁盒通过最大限度地减少感染和影响受感染变形的面积均等来减慢峰值。这项研究熟悉了理想的协议，该协议可以支持沙特人口以准确和及时的方式分解传播 COVID-19。仿真结果已被执行，与其他现有方法相比，该模型提高了 89.3% 的准确率、88.7% 的预测率、87.7% 的准确率、86.4% 的召回率和 90.9% 的 F1 分数。