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Epidemiological Modeling Analysis Reveals the Transmission Potential of COVID-19 Asymptomatic Patients:A Prospective Study of Epidemiological Transmission in America (preprint)
researchsquare; 2020.
Preprint
in English
| PREPRINT-RESEARCHSQUARE | ID: ppzbmed-10.21203.rs.3.rs-103012.v1
ABSTRACT
Background:
The asymptomatic of COVID-2019 are getting more and more attention from all walks of life. Now, America has become the epicenter of the pandemic, with more reported cases and deaths than other regions of the world. Studying the development asymptomatic populations may play a key role in managing the outbreak effectively.Methods:
We propose a new model to predict the course of the epidemic and simulate the transmission of the asymptomatic. The model considers seven stages of infection susceptible (S), exposed (E), infected (I), asymptomatic (A), confirmed (C), recovered (R), dead (D), we named it as SEIACRD. We used a model to study the interaction between asymptomatic patients and viral transmission.Result:
Our model confirms that about 12 million people will be infected with the virus. Changes in mortality rates will be volatile, first falling, then rising. Not only the number of patients, but also the spread of the epidemic will be affected by the ability to detect asymptomatic persons. Contact with asymptomatic infected patients also significantly promoted the spread of the virus. But these methods had no significant effect on changes in patient mortality.Conclusion:
American Asymptomatic patients have a strong interaction with epidemic transmission. They are no less at risk of transmission than symptomatic patients. In terms of controlling the number of infections, efforts to improve detection capacity are more effective than simply suppressing the spread of the virus. Extensive testing and effective social distancing measures should be taken to protect more people from the virus.
Full text:
Available
Collection:
Preprints
Database:
PREPRINT-RESEARCHSQUARE
Main subject:
COVID-19
/
Infections
Language:
English
Year:
2020
Document Type:
Preprint
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