Similarities between pandemics and cancer in growth and risk models

Abstract Cancer and pandemics are leading causes of death globally, with severe socioeconomic repercussions. To better understand these repercussions, we investigate similarities between pandemics and cancer and describe the limited growth in number of infections or cancer cells, using mathematical...

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Main Authors: Lode K. J. Vandamme, Ignace H. J. T. de Hingh, Jorge Fonseca, Paulo R. F. Rocha
Format: Article
Language:English
Published: Nature Publishing Group 2021-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-020-79458-w
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spelling doaj-c07ca5e556de405c883168235bf9ff612021-01-17T12:35:06ZengNature Publishing GroupScientific Reports2045-23222021-01-0111111010.1038/s41598-020-79458-wSimilarities between pandemics and cancer in growth and risk modelsLode K. J. Vandamme0Ignace H. J. T. de Hingh1Jorge Fonseca2Paulo R. F. Rocha3Faculty of Electrical Engineering, Eindhoven University of TechnologyCatharina Cancer InstituteUrology Service, Champalimaud FoundationDepartment of Electronic and Electrical Engineering, Centre for Biosensors, Bioelectronics and Biodevices (C3Bio), University of BathAbstract Cancer and pandemics are leading causes of death globally, with severe socioeconomic repercussions. To better understand these repercussions, we investigate similarities between pandemics and cancer and describe the limited growth in number of infections or cancer cells, using mathematical models. For a pandemic, the analysis shows that in most cases, the initial fast growth is followed by a slower decay in the recovery phase. The risk of infection increases due to the airborne virus contact crossing a risk-threshold. For cancers caused by carcinogens, the increasing risk with age and absorbed dose of toxins that cross a risk-threshold, may lead to the disease onset. The time scales are different for both causes of death: years for cancer development and days to weeks for contact with airborne viruses. Contamination by viruses is on a time scale of seconds or minutes. The risk-threshold to get ill and the number-threshold in cancer cells or viruses, may explain the large variability in the outcome. The number of infected persons per day is better represented in log–lin plots instead of the conventional lin–lin plots. Differences in therapies are discussed. Our mathematical investigation between cancer and pandemics reveals a multifactorial correlation between both fragilities and brings us one step closer to understand, timely predict and ultimately diminish the socioeconomic hurdle of both cancer and pandemics.https://doi.org/10.1038/s41598-020-79458-w
collection DOAJ
language English
format Article
sources DOAJ
author Lode K. J. Vandamme
Ignace H. J. T. de Hingh
Jorge Fonseca
Paulo R. F. Rocha
spellingShingle Lode K. J. Vandamme
Ignace H. J. T. de Hingh
Jorge Fonseca
Paulo R. F. Rocha
Similarities between pandemics and cancer in growth and risk models
Scientific Reports
author_facet Lode K. J. Vandamme
Ignace H. J. T. de Hingh
Jorge Fonseca
Paulo R. F. Rocha
author_sort Lode K. J. Vandamme
title Similarities between pandemics and cancer in growth and risk models
title_short Similarities between pandemics and cancer in growth and risk models
title_full Similarities between pandemics and cancer in growth and risk models
title_fullStr Similarities between pandemics and cancer in growth and risk models
title_full_unstemmed Similarities between pandemics and cancer in growth and risk models
title_sort similarities between pandemics and cancer in growth and risk models
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-01-01
description Abstract Cancer and pandemics are leading causes of death globally, with severe socioeconomic repercussions. To better understand these repercussions, we investigate similarities between pandemics and cancer and describe the limited growth in number of infections or cancer cells, using mathematical models. For a pandemic, the analysis shows that in most cases, the initial fast growth is followed by a slower decay in the recovery phase. The risk of infection increases due to the airborne virus contact crossing a risk-threshold. For cancers caused by carcinogens, the increasing risk with age and absorbed dose of toxins that cross a risk-threshold, may lead to the disease onset. The time scales are different for both causes of death: years for cancer development and days to weeks for contact with airborne viruses. Contamination by viruses is on a time scale of seconds or minutes. The risk-threshold to get ill and the number-threshold in cancer cells or viruses, may explain the large variability in the outcome. The number of infected persons per day is better represented in log–lin plots instead of the conventional lin–lin plots. Differences in therapies are discussed. Our mathematical investigation between cancer and pandemics reveals a multifactorial correlation between both fragilities and brings us one step closer to understand, timely predict and ultimately diminish the socioeconomic hurdle of both cancer and pandemics.
url https://doi.org/10.1038/s41598-020-79458-w
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