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Modeling COVID-19 epidemics in an Excel spreadsheet: Democratizing the access to first-hand accurate predictions of epidemic outbreaks

View ORCID ProfileMario Moisés Alvarez, Everardo González-González, View ORCID ProfileGrissel Trujillo-de Santiago
doi: https://doi.org/10.1101/2020.03.23.20041590
Mario Moisés Alvarez
1Centro de Biotecnología-FEMSA, Tecnologico de Monterrey, Monterrey 64849, NL, México
2Departamento de Bioingeniería, Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Monterrey 64849, NL, México
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  • For correspondence: mario.alvarez{at}tec.mx
Everardo González-González
1Centro de Biotecnología-FEMSA, Tecnologico de Monterrey, Monterrey 64849, NL, México
2Departamento de Bioingeniería, Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Monterrey 64849, NL, México
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Grissel Trujillo-de Santiago
1Centro de Biotecnología-FEMSA, Tecnologico de Monterrey, Monterrey 64849, NL, México
3Departamento de Ingeniería Mecatrónica y Eléctrica, Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Monterrey 64849, NL, México
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Abstract

COVID-19, the first pandemic of this decade and the second in less than 15 years, has harshly taught us that viral diseases do not recognize boundaries; however, they truly do discriminate between aggressive and mediocre containment responses.

We present a simple epidemiological model that is amenable to implementation in Excel spreadsheets and sufficiently accurate to reproduce observed data on the evolution of the COVID-19 pandemics in different regions (i.e., Italy, Spain, and New York City (NYC)). We also show that the model can be adapted to closely follow the evolution of COVID-19 in any large city by simply adjusting two parameters related to (a) population density and (b) aggressiveness of the response from a society/government to epidemics. Moreover, we show that this simple epidemiological simulator can be used to assess the efficacy of the response of a government/society to an outbreak.

The simplicity and accuracy of this model will greatly contribute to democratizing the availability of knowledge in societies regarding the extent of an epidemic event and the efficacy of a governmental response.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

MMA, EGG, and GTdS acknowledge the funding received from CONACyT (Consejo Nacional de Ciencia y Tecnología, México).

Author Declarations

All relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.

Yes

All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

All data required to (a) validate the information presented in the manuscript and use the model presented in the manuscript is available. Relevant links are included in the text. The Excel spreadsheet required to run the model is available as supplementary material.

https://elpais.com/sociedad/2020/03/16/actualidad/1584360628_538486.html

Copyright 
The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 31, 2020.
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Modeling COVID-19 epidemics in an Excel spreadsheet: Democratizing the access to first-hand accurate predictions of epidemic outbreaks
Mario Moisés Alvarez, Everardo González-González, Grissel Trujillo-de Santiago
medRxiv 2020.03.23.20041590; doi: https://doi.org/10.1101/2020.03.23.20041590
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Modeling COVID-19 epidemics in an Excel spreadsheet: Democratizing the access to first-hand accurate predictions of epidemic outbreaks
Mario Moisés Alvarez, Everardo González-González, Grissel Trujillo-de Santiago
medRxiv 2020.03.23.20041590; doi: https://doi.org/10.1101/2020.03.23.20041590

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