scholarly journals Rational evaluation of various epidemic models based on the COVID-19 data of China

Epidemics ◽  
2021 ◽  
Vol 37 ◽  
pp. 100501
Author(s):  
Wuyue Yang ◽  
Dongyan Zhang ◽  
Liangrong Peng ◽  
Changjing Zhuge ◽  
Liu Hong
Keyword(s):  
Epidemic Models ◽  
Rational Evaluation

scholarly journals Rational evaluation of various epidemic models based on the COVID-19 data of China

2020 ◽  
Author(s):  
Wuyue Yang ◽  
Dongyan Zhang ◽  
Liangrong Peng ◽  
Changjing Zhuge ◽  
Liu Hong
Keyword(s):  
Inflection Point ◽  
Reproduction Number ◽  
Information Criterion ◽  
Logistic Function ◽  
Epidemic Models ◽  
Model Complexity ◽  
Dynamical Models ◽  
Epidemic Size ◽  
Mean Square Errors ◽  
Rational Evaluation

During the study of epidemics, one of the most significant and also challenging problems is to forecast the future trends, on which all follow-up actions of individuals and governments heavily rely. However, to pick out a reliable predictable model/method is far from simple, a rational evaluation of various possible choices is eagerly needed, especially under the severe threat of COVID-19 epidemics which is spreading worldwide right now. In this paper, based on the public COVID-19 data of seven provinces/cities in China reported during the spring of 2020, we make a systematical investigation on the forecast ability of eight widely used empirical functions, four statistical inference methods and five dynamical models widely used in the literature. We highlight the significance of a well balance between model complexity and accuracy, over-fitting and under-fitting, as well as model robustness and sensitivity. We further introduce the Akaike information criterion, root mean square errors and robustness index to quantify these three golden means and to evaluate various epidemic models/methods. Through extensive simulations, we find that the inflection point plays a crucial role in the choice of the size of dataset in forecasting. Before the inflection point, no model considered here could make a reliable prediction. We further notice the Logistic function steadily underestimate the final epidemic size, while the Gomertz's function makes an overestimation in all cases. Since the methods of sequential Bayesian and time-dependent reproduction number take the non-constant nature of the effective reproduction number with the progression of epidemics into consideration, we suggest to employ them especially in the late stage of an epidemic. The transition-like behavior of exponential growth method from underestimation to overestimation with respect to the inflection point might be useful for constructing a more reliable forecast. Towards the dynamical models based on ODEs, it is observed that the SEIR-QD and SEIR-PO models generally show a better performance than SIR, SEIR and SEIR-AHQ models on the COVID-19 epidemics, whose success could be attributed to the inclusion of self-protection and quarantine, and a proper trade-off between model complexity and fitting accuracy.

Thermodynamics of the interaction between the spike protein of severe acute respiratory syndrome- coronavirus-2 and the receptor of human angiotensin converting enzyme 2. Effects of possible ligands

2020 ◽  
Author(s):  
Cristina Garcia-Iriepa ◽  
Cecilia Hognon ◽  
Antonio Francés-Monerris ◽  
Isabel Iriepa ◽  
Tom Miclot ◽  
...  
Keyword(s):  
Angiotensin Converting Enzyme ◽  
Molecular Mechanisms ◽  
Molecular Design ◽  
Binding Free Energy ◽  
Transmembrane Protein ◽  
Spike Protein ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2 ◽  
Rational Molecular Design ◽  
Rational Evaluation

<div><p>Since the end of 2019, the coronavirus SARS-CoV-2 has caused more than 180,000 deaths all over the world, still lacking a medical treatment despite the concerns of the whole scientific community. Human Angiotensin-Converting Enzyme 2 (ACE2) was recently recognized as the transmembrane protein serving as SARS-CoV-2 entry point into cells, thus constituting the first biomolecular event leading to COVID-19 disease. Here, by means of a state-of-the-art computational approach, we propose a rational evaluation of the molecular mechanisms behind the formation of the complex and of the effects of possible ligands. Moreover, binding free energy between ACE2 and the active Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein is evaluated quantitatively, assessing the molecular mechanisms at the basis of the recognition and the ligand-induced decreased affinity. These results boost the knowledge on the molecular grounds of the SARS-CoV-2 infection and allow to suggest rationales useful for the subsequent rational molecular design to treat severe COVID-19 cases.</p></div>

scholarly journals Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

2021 ◽  
Vol 13 (3) ◽  
pp. 76
Author(s):  
Quintino Francesco Lotito ◽  
Davide Zanella ◽  
Paolo Casari
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Simulation Models ◽  
Epidemic Models ◽  
Time Varying ◽  
Fake News ◽  
Network Access ◽  
Spreading Process ◽  
Network Topologies ◽  
Access Patterns

The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

Runge–Kutta pairs suited for SIR‐type epidemic models

2020 ◽  
Author(s):  
Vladislav N. Kovalnogov ◽  
Theodore E. Simos ◽  
Charalampos Tsitouras
Keyword(s):  
Epidemic Models ◽  
Runge Kutta

scholarly journals Applications of the Poincare-Hopf Theorem: Epidemic Models and Lotka-Volterra Systems

2021 ◽  
pp. 1-1
Author(s):  
Mengbin Ye ◽  
Ji Liu ◽  
Brian D.O. Anderson ◽  
Ming Cao
Keyword(s):  
Epidemic Models ◽  
Volterra Systems ◽  
Hopf Theorem
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