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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2021-01-01
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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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