UMMS Affiliation

Department of Emergency Medicine at UMMS-Baystate

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Article Preprint


Analytical, Diagnostic and Therapeutic Techniques and Equipment | Clinical Epidemiology | Environmental Public Health | Epidemiology | Immunology and Infectious Disease | Infectious Disease | Respiratory Tract Diseases | Virus Diseases


Coronavirus disease 2019 (COVID-19) is a novel human respiratory disease caused by the SARS-CoV-2 virus. Asymptomatic carriers of the virus display no clinical symptoms but are known to be contagious. Recent evidence reveals that this sub-population, as well as persons with mild, represent a major contributor in the propagation of COVID-19. The asymptomatic sub-population frequently escapes detection by public health surveillance systems. Because of this, the currently accepted estimates of the basic reproduction number (Ro) of the virus are inaccurate. It is unlikely that a pathogen can blanket the planet in three months with an Ro in the vicinity of 3, as reported in the literature. In this manuscript, we present a mathematical model taking into account asymptomatic carriers. Our results indicate that an initial value of the effective reproduction number could range from 5.5 to 25.4, with a point estimate of 15.4, assuming mean parameters. The first three weeks of the model exhibit exponential growth, which is in agreement with average case data collected from thirteen countries with universal health care and robust communicable disease surveillance systems; the average rate of growth in the number of reported cases is 23.3% per day during this period.


epidemiology, coronavirus disease 2019 (COVID-19), SARS-CoV-2 virus, transmission, asymptomatic carriers, pandemic, basic reproduction number

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The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.

DOI of Published Version



medRxiv 2020.03.18.20037994; doi: Link to preprint on medRxiv service

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Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.