Tutsoy Onder, Polat Adem, Colak Sule, Balikci Kemal
Department of Electrical-Electronics EngineeringAdana Alparslan Türkeş Science and Technology University 01250 Adana Turkey.
Department of Electrical and Electronics EngineeringOsmaniye Korkut Ata University 80000 Osmaniye Turkey.
IEEE Access. 2020 Dec 15;8:225272-225283. doi: 10.1109/ACCESS.2020.3044929. eCollection 2020.
Coronavirus Disease 2019 (COVID-19) has spread the world resulting in detrimental effects on human health, lives, societies, and economies. The state authorities mostly take non-pharmacological actions against the outbreak since there are no confirmed vaccines or treatments yet. In this paper, we developed Suspicious-Infected-Death with Non-Pharmacological policies (SpID-N) model to analyze the properties of the COVID-19 casualties and also estimate the future behavior of the outbreak. We can state the key contributions of the paper with three folds. Firstly, we propose the SpID-N model covering the higher-order internal dynamics which cause the peaks in the casualties. Secondly, we parametrize the non-pharmacological policies such as the curfews on people with chronic disease, people age over 65, people age under 20, restrictions on the weekends and holidays, and closure of the schools and universities. Thirdly, we explicitly incorporate the internal and coupled dynamics of the model with these multi-dimensional non-pharmacological policies. The corresponding higher-order and strongly coupled model has utterly unknown parameters and we construct a batch type Least Square (LS) based optimization algorithm to learn these unknown parameters from the available data. The parametric model and the predicted future casualties are analyzed extensively.
2019冠状病毒病（COVID-19）已在全球蔓延，对人类健康、生命、社会和经济造成了不利影响。由于尚未有经证实的疫苗或治疗方法，国家当局大多采取非药物措施应对疫情爆发。在本文中，我们开发了带有非药物政策的可疑感染死亡模型（SpID-N），以分析COVID-19伤亡情况的特征，并预测疫情未来的发展趋势。我们可以从三个方面阐述本文的关键贡献。首先，我们提出了SpID-N模型，该模型涵盖了导致伤亡人数峰值的高阶内部动态。其次，我们对非药物政策进行了参数化，例如对慢性病患者、65岁以上人群、20岁以下人群的宵禁，对周末和节假日的限制，以及学校和大学的关闭。第三，我们明确将模型的内部动态和耦合动态与这些多维非药物政策相结合。相应的高阶强耦合模型具有完全未知的参数，我们构建了一种基于批量最小二乘法（LS）的优化算法，从可用数据中学习这些未知参数。我们对参数模型和预测的未来伤亡情况进行了广泛分析。
