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

Author

Listed:
  • Ronald Manríquez

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

  • Camilo Guerrero-Nancuante

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

  • Felipe Martínez

    (Facultad de Medicina, Escuela de Medicina, Universidad Andrés Bello, 2520000 Viña del Mar, Chile)

  • Carla Taramasco

    (Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, 2340000 Valparaíso, Chile
    Centro Nacional de Sistemas de Información en Salud, 8320000 Santiago, Chile)

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.

Suggested Citation

  • Ronald Manríquez & Camilo Guerrero-Nancuante & Felipe Martínez & Carla Taramasco, 2021. "Spread of Epidemic Disease on Edge-Weighted Graphs from a Database: A Case Study of COVID-19," IJERPH, MDPI, vol. 18(9), pages 1-25, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:9:p:4432-:d:540897
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    References listed on IDEAS

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    1. Croccolo, Fabrizio & Roman, H. Eduardo, 2020. "Spreading of infections on random graphs: A percolation-type model for COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    Cited by:

    1. Pablo Ormeño & Gastón Márquez & Camilo Guerrero-Nancuante & Carla Taramasco, 2022. "Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study," IJERPH, MDPI, vol. 19(13), pages 1-15, June.

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