Risk Assessment of COVID-19 Based on Multisource Data From a Geographical Viewpoint

Zhang, Yan; Li, Yingbing; Yang, Bo; Zheng, Xufei; Chen, Min

doi:10.1109/access.2020.3004933

Cited by 28 publications

(12 citation statements)

References 45 publications

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“…They enable computer applications to be extended to the fields of humanities, social sciences, and complex systems. Some other methods are listed as follows [103][104][105][106][107] :…”

Section: Many Other Methods Have Also Been Proposedmentioning

confidence: 99%

State-of-the-Art Development of Complex Systems and Their Simulation Methods

Tang

Liu

2021

Complex Syst. Model. Simul.

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The research on complex systems is different from that on general systems because the former must consider self-organization, emergence, uncertainty, predetermination, and evolution. As an important method to transform the world, a simulation is one of the most important skills to discover complex systems. In this study, we provide a survey on complex systems and their simulation methods. Initially, the development history of complex system research is summarized from two main lines. Then, the eight common characteristics of the most complex systems are presented. Furthermore, the simulation methods of complex systems are introduced in detail from four aspects, namely, meta-synthesis methods, complex networks, intelligent technologies, and other methods. From the overall point of view, intelligent technologies are the driving force, and complex networks are the advanced structure. Meta-synthesis methods are the integration strategy, and other methods are the supplements. In addition, we show three complex system simulation examples: digital reactor simulation, simulation of a logistics system in the industrial site, and crowd evacuation simulation. The examples show that a simulation is a useful means and an important method in complex system research.Finally, the future development prospects for complex systems and their simulation methods are suggested.

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“…They enable computer applications to be extended to the fields of humanities, social sciences, and complex systems. Some other methods are listed as follows [103][104][105][106][107] :…”

Section: Many Other Methods Have Also Been Proposedmentioning

confidence: 99%

State-of-the-Art Development of Complex Systems and Their Simulation Methods

Tang

Liu

2021

Complex Syst. Model. Simul.

View full text Add to dashboard Cite

show abstract

“…Based on mobile phone and confirmed patient data, Jia et al [24] developed a spatiotemporal “risk source” model to determine the geographic distribution and growth pattern of COVID-19 and quickly as well as accurately assess the related risk. Zhang et al [25] used the GeoDetector and the decision tree model to identify the main factors in low- and high-risk areas by combining multi-source data. Loske [26] explored the relationship between COVID-19 spread and transportation volume in food retail logistics by combining transport volume data and confirmed patient data.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Differences of COVID-19 Infection Among Healthcare Workers and Patients in China From January to March 2020

et al. 2021

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Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

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“…In [40] , the authors presented a basic SEIR model to model the COVID-19 spread in India and used it to simulate and evaluate different scenarios of the spread based on the transmission rate and finally, they proposed potential future evolution of the spread and possible mitigation. Along with mathematical modelling, in [41] , the authors employed a machine learning algorithm namely, the decision tree algorithm, to study the indicators responsible for the evolution of COVID-19 pandemic using multi-sourced data. The study identified the population density as one of the most important determinants of infection spread.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Mechanistic Model for Assessing and Forecasting the Spread of the COVID-19 Pandemic

et al. 2021

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Since early 2020, the world has been afflicted with an unprecedented global pandemic. The SARS-CoV-19 (COVID-19) has levied massive economic and public health costs across many countries. Due to its virulence, the pathogen is rapidly propagating throughout the world in such a way that makes it incredibly challenging for officials to contain its spread. Therefore, there is a pressing need for national and local authorities to have tools that aid in their ability to assess and extrapolate the future trends of the spread of COVID-19, so they may make rational and informed decisions that minimize public harm. Mechanistic models are prominent mathematical tools that are used to characterize epidemics. In this paper, we propose a generalized mechanistic model with eight states characterizing the COVID-19 pandemic evolution from a susceptible state to discharged states while passing by quarantined and hospitalized states. The parameters of the model are determined by solving a fitting optimization problem with three observed inputs: the number of infected, deceased, and reported cases. The model's objective function is weighted over the training days so as to guide the fitting algorithm towards the latest pandemic period and lead to more accurate trend predictions for a stronger forecast. We solve the fitting problem with the Levenberg-Marquardt algorithm; we compare the performance of the model generated from this algorithm to the one of another state-of-the-art fitting algorithm as well as to the one of another compartmental model widely used in literature. We test the model on the COVID-19 data from four highly afflicted countries. The fitting algorithm has been validated graphically and through numerical metrics, and results show significantly accurate results for most of the countries. Once the model's parameters are estimated, forecasting results are derived and uncertainty regions of the expected scenarios are provided. INDEX TERMS COVID-19, fitting optimization, mathematical modeling, mechanistic model, nonlinear differential equations, pandemic.

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Risk Assessment of COVID-19 Based on Multisource Data From a Geographical Viewpoint

Cited by 28 publications

References 45 publications

State-of-the-Art Development of Complex Systems and Their Simulation Methods

State-of-the-Art Development of Complex Systems and Their Simulation Methods

Spatiotemporal Differences of COVID-19 Infection Among Healthcare Workers and Patients in China From January to March 2020

A Generalized Mechanistic Model for Assessing and Forecasting the Spread of the COVID-19 Pandemic

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