Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case

Manríquez, Ronald; Guerrero-Nancuante, Camilo; Taramasco, Carla

doi:10.3390/biology10070667

Open AccessArticle

Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case

by

Ronald Manríquez

^1,*

,

Camilo Guerrero-Nancuante

^2,*

and

Carla Taramasco

^3,4,*

¹

Laboratorio de Investigación Lab[e]saM, Departamento de Matemática y Estadística, Universidad de Playa Ancha, Valparaíso 2340000, Chile

²

Escuela de Enfermería, Universidad de Valparaíso, Viña del Mar 2520000, Chile

³

Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, Valparaíso 2340000, Chile

⁴

Centro Nacional de Sistemas de Información en Salud, Santiago 8320000, Chile

^*

Authors to whom correspondence should be addressed.

Biology 2021, 10(7), 667; https://doi.org/10.3390/biology10070667

Submission received: 17 May 2021 / Revised: 9 June 2021 / Accepted: 25 June 2021 / Published: 15 July 2021

(This article belongs to the Special Issue Coronavirus Disease 2019 (COVID-19))

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Simple Summary

Infectious diseases have been part of human history. Countless epidemics have produced high mortality rates in vulnerable populations. With the understanding of the spread of these types of diseases, population groups have been able to adapt and better cope with infections. Given the COVID-19 pandemic, one of the strategies used is the modeling of infectious diseases with the aim of establishing protection measures for people and stopping the spread of the epidemic. Our study evaluates protection strategies through infectious disease modeling with COVID-19 data in a commune in Chile. The results of the simulations indicate that the model generates important protection for the population by recognizing the super-propagating people (bridge nodes). This type of protection can be key in the fight against COVID-19.

Abstract

Among the diverse and important applications that networks currently have is the modeling of infectious diseases. Immunization, or the process of protecting nodes in the network, plays a key role in stopping diseases from spreading. Hence the importance of having tools or strategies that allow the solving of this challenge. In this paper, we evaluate the effectiveness of the DIL-W

^{α}

ranking in immunizing nodes in an edge-weighted network with 3866 nodes and 6,841,470 edges. The network is obtained from a real database and the spread of COVID-19 was modeled with the classic SIR model. We apply the protection to the network, according to the importance ranking list produced by DIL-W

^{α}

, considering different protection budgets. Furthermore, we consider three different values for

α

; in this way, we compare how the protection performs according to the value of

α

.

Keywords: edge-weighted graph; SIR model; graph protection; COVID-19

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MDPI and ACS Style

Manríquez, R.; Guerrero-Nancuante, C.; Taramasco, C. Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case. Biology 2021, 10, 667. https://doi.org/10.3390/biology10070667

AMA Style

Manríquez R, Guerrero-Nancuante C, Taramasco C. Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case. Biology. 2021; 10(7):667. https://doi.org/10.3390/biology10070667

Chicago/Turabian Style

Manríquez, Ronald, Camilo Guerrero-Nancuante, and Carla Taramasco. 2021. "Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case" Biology 10, no. 7: 667. https://doi.org/10.3390/biology10070667

APA Style

Manríquez, R., Guerrero-Nancuante, C., & Taramasco, C. (2021). Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case. Biology, 10(7), 667. https://doi.org/10.3390/biology10070667

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Protection Strategy against an Epidemic Disease on Edge-Weighted Graphs Applied to a COVID-19 Case

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