Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy

Abstract The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilarities of the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way toward a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

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Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy

10.21203/rs.3.rs-42771/v1 ◽

2020 ◽

Author(s):

Giuseppe Quaranta ◽

Giovanni Formica ◽

J. Terneiro Machado ◽

Walter Lacarbonara ◽

Sami F. Masri

Keyword(s):

Differential Evolution Algorithm ◽

Northern Region ◽

Multidimensional Analysis ◽

Data Driven ◽

Type Model ◽

Epidemic Spreading ◽

Epidemic Dynamics ◽

Multi Scale ◽

Evolution Algorithm ◽

Scale Data

Abstract The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of an SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm. Hierarchical clustering and multidimensional analysis are further exploited to reveal the similarities/dissimilaritiesof the remarkably different geographical epidemic developments. The combination of parametric identifications and multi-scale data-driven analyses paves the way towards a closer understanding of the nonlinear, spatially nonuniform epidemic spreading in Italy.

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Multi-scale data-driven engineering for biosynthetic titer improvement

Current Opinion in Biotechnology ◽

10.1016/j.copbio.2020.04.002 ◽

2020 ◽

Vol 65 ◽

pp. 205-212 ◽

Cited By ~ 1

Author(s):

Zhixing Cao ◽

Jiaming Yu ◽

Weishan Wang ◽

Hongzhong Lu ◽

Xuekui Xia ◽

...

Keyword(s):

Data Driven ◽

Multi Scale ◽

Scale Data

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Data driven safe vehicle routing analytics: a differential evolution algorithm to reduce CO $$_{2}$$ 2 emissions and hazardous risks

Annals of Operations Research ◽

10.1007/s10479-016-2343-9 ◽

2016 ◽

Vol 270 (1-2) ◽

pp. 515-538

Author(s):

Boon Ean Teoh ◽

S. G. Ponnambalam ◽

Nachiappan Subramanian

Keyword(s):

Differential Evolution ◽

Vehicle Routing ◽

Differential Evolution Algorithm ◽

Data Driven ◽

Evolution Algorithm

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Atomistic-to-meso multi-scale data-driven graph surrogate modeling of dislocation glide

Computational Materials Science ◽

10.1016/j.commatsci.2021.110569 ◽

2021 ◽

Vol 197 ◽

pp. 110569

Author(s):

Eduardo A. Barros de Moraes ◽

Jorge L. Suzuki ◽

Mohsen Zayernouri

Keyword(s):

Surrogate Modeling ◽

Data Driven ◽

Dislocation Glide ◽

Multi Scale ◽

Scale Data

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Fabric Defect Detection and Classifier via Multi-Scale Dictionary Learning and an Adaptive Differential Evolution Optimized Regularization Extreme Learning Machine

Fibres and Textiles in Eastern Europe ◽

10.5604/01.3001.0012.7510 ◽

2019 ◽

Vol 27 (1(133)) ◽

pp. 67-77 ◽

Cited By ~ 5

Author(s):

Zhiyu Zhou ◽

Chao Wang ◽

Xu Gao ◽

Zefei Zhu ◽

Xudong Hu ◽

...

Keyword(s):

Differential Evolution ◽

Extreme Learning Machine ◽

Dictionary Learning ◽

Gabor Filter ◽

Differential Evolution Algorithm ◽

Adaptive Mutation ◽

Multi Scale ◽

Adaptive Differential Evolution ◽

Evolution Algorithm ◽

Learning Machine

To develop an automatic detection and classifier model for fabric defects, a novel detection and classifier technique based on multi-scale dictionary learning and the adaptive differential evolution algorithm optimised regularisation extreme learning machine (ADE-RELM) is proposed. Firstly in order to speed up dictionary updating under the condition of guaranteeing dictionary sparseness, k-means singular value decomposition (KSVD) dictionary learning is used. Then multi-scale KSVD dictionary learning is presented to extract texture features of textile images more accurately. Finally a unique ADE-RELM is designed to build a defect classifier model. In the training ADE-RELM classifier stage, a self-adaptive mutation operator is used to solve the parameter setting problem of the original differential evolution algorithm, then the adaptive differential evolution algorithm is utilised to calculate the optimal input weights and hidden bias of RELM. The method proposed is committed to detecting common defects like broken warp, broken weft, oil, and the declining warp of grey-level and pure colour fabrics. Experimental results show that compared with the traditional Gabor filter method, morphological operation and local binary pattern, the method proposed in this paper can locate defects precisely and achieve high detection efficiency.

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