ABSTRACT
The unprecedented outbreak of Corona Virus Disease 2019 (COVID-19) has resulted in extreme volatility in stock markets. This study mainly examines the predictive ability of the Internet concern about COVID-19 on stock index returns, based on the framework of GARCH type models. Instead of using the whole sample period, we divide the Internet concern about COVID-19 into high-concern and low-concern periods by breakpoint test method and then examine its predictive ability for stock returns in different periods, respectively. Using stock indexes of 10 countries and abnormal Google search volume of 'coronavirus' as study samples, the results reveal that (1) the Internet concern about COVID-19 has a negative impact on the stock index returns in the whole and high-concern periods, while its influence in the low-concern period is mixed;(2) the Internet concern about COVID-19 improves the prediction accuracy of stock index returns in the high-concern period, while seems to lose its powerful predictive ability in the whole and low-concern periods.
ABSTRACT
This paper deals with the prediction and analysis of COVID-19 epidemic situation based on a modified SEIR model with asymptomatic infection. First, by considering the self-isolation and asymptomatic infection, a modified SEIR model is proposed to predict and evaluate the epidemic situation of COVID-19 in Hubei Province, China. Then, based on the daily data reported by the Health Commission of Hubei Province, the modified SEIR model is solved numerically, and the parameters of the modified model are inverted by the least square method. Third, based on the modified model, the epidemic situation of COVID-19 in Hubei Province is predicted and verified. The simulation results show that the modified SEIR model is significant and reliable to describe the spread property of the COVID-19, thereby providing a potential theoretical support for the decision-making of epidemic prevention and control in the future. © 2021 IEEE.
ABSTRACT
The prevention and control of COVID-19 epidemic is a great challenge for human beings today. In the battle against COVID-19, the hierarchical treatment measures based on symptom classifications have proved to be a particularly effective way to deal with the large-scale epidemic in the absence of adequate medical resources. This paper deals with the epidemic dynamic analyses of the COVID-19 based on a modified SEIR model with different symptoms. First, by taking symptom classifications and hierarchical treatments of patients into account, a modified SEIR model is established. Then, the proposed differential equations model is solved by using Runge-Kutta methods, and the parameters herein are estimated by least square principle based on the data released by the National Health Commission. Simulation results of the model show that the introduction of symptom classifications in the SEIR model can not only improve the fitting accuracy, but also precisely describe the evolution rules and mutual transfer rules of patients with different symptoms. The model can provide theoretical support for decision-making of the corresponding government departments, especially for the construction of mobile cabin hospitals and the reasonable preparation of important epidemic prevention resources. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.
ABSTRACT
The pandemic of the coronavirus (COVID-19) has caused an unprecedented global public health crisis, and most countries in the world are running out of the healthcare resources. A fine-grained COVID-19 vulnerability map will be essential to track the number of people with covid-like symptoms, so that the the potential outbreak communities can be identified and the valuable healthcare resources can proactively and dynamically be allocated. Mobile crowdsourcing based symptom reporting is a promising and convenient option to construct such a map, while it may compromise the location privacy of crowdsourcing participants. In this work, we propose a novel approach to establish the COVID-19 vulnerability map based on the crowdsourced reporting without disclosing the participants' location privacy to a semi-honest crowdsourcing aggregator. Briefly, based on the differentially private geo-indistinguishability, the mobile participants are able to locally perturb their geographic data. With the masked geographic information, we employ the best linear unbiased prediction estimator with spatial smoothing to obtain the reliable vulnerability estimates in the areas of interest and construct the map. Given the fast spreading nature of coronavirus, we integrate the vulnerability estimates with a susceptible-exposed-infected-removed (SEIR) model to build up a future trend map. Extensive simulations based on real-world data verify the effectiveness of the proposed method.