A New Algorithm for Identifying Possible Epidemic Sources with Application to the German Escherichia coli Outbreak

Buscema, Massimo; Grossi, Enzo; Bronstein, Alvin C.; Lodwick, Weldon A.; Asadi–Zeydabadi, Masoud; Benzi, Roberto; Newman, Francis

doi:10.3390/ijgi2010155

Cited by 22 publications

(22 citation statements)

References 21 publications

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“…monitoring data), there simultaneously emerges a strategic area by which those events acquire an intrinsic meaning emerges simultaneously. This is the area from which all of the events of the process minimize the entropy of their mutual distances [44].…”

Section: Discussionmentioning

confidence: 99%

Pattern Recognition for Flank Eruption Forecasting: An Application at Mount Etna Volcano (Sicily, Italy)

Brancato¹,

Buscema²,

Massini³

et al. 2016

OJG

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The analysis involved successive steps. First, the TWIST algorithm was used to select the most predictive attributes associated with the flank eruption target. During his work, the algorithm TWIST selected 11 characteristics of the input vector: among them SO2 and CO 2 emissions, and also many other attributes whose linear correlation with the target was very low. A 5 × 2 Cross Validation protocol estimated the sensitivity and specificity of pattern recognition algorithms. Finally, different classification algorithms have been compared to understand if this pattern recognition task may have suitable results and which algorithm performs best. Best results (higher than 97% accuracy) have been obtained after performing advanced Artificial Neural Networks, with a sensitivity and specificity estimates over 97% and 98%, respectively. The present analysis highlights that a suitable monitoring dataset inferred hidden information about volcanic phenomena, whose highly non-linear processes are enhanced.

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Section: Discussionmentioning

confidence: 99%

Pattern Recognition for Flank Eruption Forecasting: An Application at Mount Etna Volcano (Sicily, Italy)

Brancato¹,

Buscema²,

Massini³

et al. 2016

OJG

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“…Moreover, as in diffusion processes, each single crime location influences the future local activity of the criminal, e.g., as each visit to a place reveals details and characteristics that may encourage or inhibit further initiatives in same or nearby locations. There is today an established corpus of mathematical techniques for geographic profiling (Rossmo, 2000 [82]; O'Leary, 2006 [83]), and it has been clarified that the problems of identifying outbreak sources for infective processes and finding out the location of criminals on the basis of their crimes present interesting analogies-and can therefore be both tackled through hybridization and selective improvement of existing techniques (Buscema et al, 2009 [84]; 2013 [85]). One could however object that, whereas epidemic outbreaks are not apparently subject to theory absorption phenomena, geographic profiling clearly is: the decision of the criminal agent as to where to commit new crimes is clearly influenced by the theories on its actual location, and there is a very high degree of strategic interaction around this-which also explains why such theories are usually kept confidential as soon as the chase for the criminal is still ongoing.…”

Section: Social Spatial Processes: Levels Of Complexitymentioning

confidence: 99%

Understanding Cultural Geography as a Pseudo-Diffusion Process: The Case of the Veneto Region

et al. 2015

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Abstract:In this paper, we study the cultural geography of the Veneto Region on the basis of a pseudo-diffusion approach to the analysis of the inherent semantic spatial data. We find somewhat surprising results, and, in particular, that Venice, indisputably the Region's cultural hub in terms of concentration of activities and facilities, global visibility and attraction of resources, plays a marginal role in determining the momentum of cultural initiative at the regional level as of 2007 data. The areas with the greater momentum are relatively marginal ones but characterized by a strong presence of design-oriented companies that are actively engaging in culture-driven innovation in a context of gradually horizontally-integrated clusters. Our findings call for a revision of the traditional policy approaches that identify centralities in terms of concentration of activities and facilities based on past dynamics, and to design policies accordingly. We argue in favour of a more forward-looking, evidence-based approach.

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“…The algorithm used is based on geographic profiling using a topological approach. One of the advantages of this algorithm, called Topological Weighted Centroid (TWC) [3] , [4] , [5] , [6] , [7] , [8] , [9] , compared to standard methods of analysis of diffusion processes, lies in the fact that it requires very simple data: the coordinates of the places where the events of the process took place without any kind of other assumptions. For this reason, in conditions of poor data availability, it is particularly useful.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the data used correspond to all places (by means of longitude and latitude) of Italy where at least one case of COVID-19 was detected until February 26th, 2020. The TWC has already been successfully applied several times to epidemic cases, in particular to Escherichia coli (Germany 2011), Chikungunya Fever (Italy 2007), Foot and Mouth Disease (UK 1968–1969), Food epidemic (Oahu 2010), Dengue fever (Brazil 2001), Listeria (US 2011) and Ebola (West Africa and Congo 2014–2018) epidemics [3] , [4] , [5] , [8] , [9] .…”

Section: Introductionmentioning

confidence: 99%

COVID-19 in Italy and extreme data mining

Buscema

Torre

Breda

et al. 2020

Physica A: Statistical Mechanics and its Applications

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In this article we want to show the potential of an evolutionary algorithm called Topological Weighted Centroid (TWC). This algorithm can obtain new and relevant information from extremely limited and poor datasets. In a world dominated by the concept of big (fat?) data we want to show that it is possible, by necessity or choice, to work profitably even on small data. This peculiarity of the algorithm means that even in the early stages of an epidemic process, when the data are too few to have sufficient statistics, it is possible to obtain important information. To prove our theory, we addressed one of the most central issues at the moment: the COVID-19 epidemic. In particular, the cases recorded in Italy have been selected. Italy seems to have a central role in this epidemic because of the high number of measured infections. Through this innovative artificial intelligence algorithm, we have tried to analyze the evolution of the phenomenon and to predict its future steps using a dataset that contained only geospatial coordinates (longitude and latitude) of the first recorded cases. Once the coordinates of the places where at least one case of contagion had been officially diagnosed until February 26th, 2020 had been collected, research and analysis was carried out on: outbreak point and related heat map (TWC alpha); probability distribution of the contagion on February 26th (TWC beta); possible spread of the phenomenon in the immediate future and then in the future of the future (TWC gamma and TWC theta); how this passage occurred in terms of paths and mutual influence (Theta paths and Markov Machine). Finally, a heat map of the possible situation towards the end of the epidemic in terms of infectiousness of the areas was drawn up. The analyses with TWC confirm the assumptions made at the beginning.

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A New Algorithm for Identifying Possible Epidemic Sources with Application to the German Escherichia coli Outbreak

Abstract: Abstract:In this paper we describe a recently developed algorithm called Topological

Cited by 22 publications

References 21 publications

Pattern Recognition for Flank Eruption Forecasting: An Application at Mount Etna Volcano (Sicily, Italy)

Pattern Recognition for Flank Eruption Forecasting: An Application at Mount Etna Volcano (Sicily, Italy)

Understanding Cultural Geography as a Pseudo-Diffusion Process: The Case of the Veneto Region

COVID-19 in Italy and extreme data mining

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