Literature DB >> 33431944

Similarities between pandemics and cancer in growth and risk models.

Lode K J Vandamme1, Ignace H J T de Hingh2,3, Jorge Fonseca4, Paulo R F Rocha5,6.   

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.

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Year:  2021        PMID: 33431944      PMCID: PMC7801496          DOI: 10.1038/s41598-020-79458-w

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.996


  19 in total

1.  Quantitative laws in metabolism and growth.

Authors:  L VON BERTALANFFY
Journal:  Q Rev Biol       Date:  1957-09       Impact factor: 4.875

2.  Growth, innovation, scaling, and the pace of life in cities.

Authors:  Luís M A Bettencourt; José Lobo; Dirk Helbing; Christian Kühnert; Geoffrey B West
Journal:  Proc Natl Acad Sci U S A       Date:  2007-04-16       Impact factor: 11.205

3.  Mutational signature in colorectal cancer caused by genotoxic pks+ E. coli.

Authors:  Cayetano Pleguezuelos-Manzano; Jens Puschhof; Axel Rosendahl Huber; Arne van Hoeck; Henry M Wood; Jason Nomburg; Carino Gurjao; Freek Manders; Guillaume Dalmasso; Paul B Stege; Fernanda L Paganelli; Maarten H Geurts; Joep Beumer; Tomohiro Mizutani; Yi Miao; Reinier van der Linden; Stefan van der Elst; K Christopher Garcia; Janetta Top; Rob J L Willems; Marios Giannakis; Richard Bonnet; Phil Quirke; Matthew Meyerson; Edwin Cuppen; Ruben van Boxtel; Hans Clevers
Journal:  Nature       Date:  2020-02-27       Impact factor: 49.962

4.  Inference of the generalized-growth model via maximum likelihood estimation: A reflection on the impact of overdispersion.

Authors:  Tapiwa Ganyani; Christel Faes; Niel Hens
Journal:  J Theor Biol       Date:  2019-09-27       Impact factor: 2.691

Review 5.  Mathematical models to characterize early epidemic growth: A review.

Authors:  Gerardo Chowell; Lisa Sattenspiel; Shweta Bansal; Cécile Viboud
Journal:  Phys Life Rev       Date:  2016-07-11       Impact factor: 11.025

Review 6.  Tumor growth dynamics: insights into evolutionary processes.

Authors:  Ignacio A Rodriguez-Brenes; Natalia L Komarova; Dominik Wodarz
Journal:  Trends Ecol Evol       Date:  2013-06-28       Impact factor: 17.712

Review 7.  Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors: a review.

Authors:  P Vaupel; F Kallinowski; P Okunieff
Journal:  Cancer Res       Date:  1989-12-01       Impact factor: 12.701

8.  Dual Challenge of Cancer and COVID-19: Impact on Health Care and Socioeconomic Systems in Asia Pacific.

Authors:  Roselle De Guzman; Monica Malik
Journal:  JCO Glob Oncol       Date:  2020-06

9.  Classical mathematical models for description and prediction of experimental tumor growth.

Authors:  Sébastien Benzekry; Clare Lamont; Afshin Beheshti; Amanda Tracz; John M L Ebos; Lynn Hlatky; Philip Hahnfeldt
Journal:  PLoS Comput Biol       Date:  2014-08-28       Impact factor: 4.475

10.  Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis.

Authors:  Lode K J Vandamme; Peter A A F Wouters; Gerrit D Slooter; Ignace H J T de Hingh
Journal:  Healthcare (Basel)       Date:  2019-10-28
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  1 in total

1.  Where Enhanced Recovery after Surgery (ERAS) Protocols Meet the Three Major Current Pandemics: COVID-19, Obesity and Malignancy.

Authors:  Anastasia Prodromidou; Aristotelis-Marios Koulakmanidis; Dimitrios Haidopoulos; Gregg Nelson; Alexandros Rodolakis; Nikolaos Thomakos
Journal:  Cancers (Basel)       Date:  2022-03-25       Impact factor: 6.639

  1 in total

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