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基于边权重图的传染病传播：以 COVID-19 为例的研究

Spread of Epidemic Disease on Edge-Weighted Graphs from a Database: A Case Study of COVID-19.

机构信息

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

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

出版信息

Int J Environ Res Public Health. 2021 Apr 22;18(9):4432. doi: 10.3390/ijerph18094432.

DOI:10.3390/ijerph18094432
PMID:33921934
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8122399/
Abstract

The understanding of infectious diseases is a priority in the field of public health. This has generated the inclusion of several disciplines and tools that allow for analyzing the dissemination of infectious diseases. The aim of this manuscript is to model the spreading of a disease in a population that is registered in a database. From this database, we obtain an edge-weighted graph. The spreading was modeled with the classic SIR model. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics. Moreover, a deterministic approximation is provided. With database COVID-19 from a city in Chile, we analyzed our model with relationship variables between people. We obtained a graph with 3866 vertices and 6,841,470 edges. We fitted the curve of the real data and we have done some simulations on the obtained graph. Our model is adjusted to the spread of the disease. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics, in this case with real data of COVID-19. This valuable information allows us to also include/understand the networks of dissemination of epidemics diseases as well as the implementation of preventive measures of public health. These findings are important in COVID-19's pandemic context.

摘要

传染病的理解是公共卫生领域的重点。这引发了包括几个学科和工具的应用，这些学科和工具可以分析传染病的传播。本文的目的是在一个登记在数据库中的人群中模拟疾病的传播。从这个数据库中，我们得到了一个带权边的图。使用经典的 SIR 模型对传播进行建模。带权边图的模型允许识别传染病传播中最重要的变量。此外，还提供了确定性逼近。我们使用智利一个城市的 COVID-19 数据库，对人与人之间的关系变量进行了我们模型的分析。我们得到了一个包含 3866 个顶点和 6841470 条边的图。我们拟合了真实数据的曲线，并对得到的图进行了一些模拟。我们的模型与疾病的传播相适应。带权边图的模型允许识别传染病传播中最重要的变量，在这种情况下，我们使用 COVID-19 的真实数据。这些有价值的信息使我们能够了解传染病的传播网络以及公共卫生预防措施的实施。这些发现对 COVID-19 大流行的背景非常重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f9/8122399/a9c2a2034bd9/ijerph-18-04432-g026.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f9/8122399/7753e77425f9/ijerph-18-04432-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f9/8122399/9bf429cde652/ijerph-18-04432-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f9/8122399/ca3a8445795f/ijerph-18-04432-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f9/8122399/ad1084d5067d/ijerph-18-04432-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f9/8122399/bead89292d38/ijerph-18-04432-g019.jpg
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