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Research Article
Gustavo Zampier Dos Santos Lima
Federal University of Rio Grande do Norte
Corresponding Author
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An important and elusive characteristic of the COVID-19 pandemic is the fatality rate which allows us to understand the severity of this disease, health care needs, and the impact on large populations. To address this and other questions we present a probabilistic model to study the evolution of the COVID-19 pandemic and to correct the case fatality rates. Our model employs probabilities to estimate the time evolution of infections, recoveries, and deaths. This model discriminates asymptomatic, mild/moderate, and severe cases and allows for the estimation of undiagnosed individuals. Furthermore, we compare the model curves to official data for medium-sized cities, world metropolises, and medium-sized countries, spanning a range of populations from a few million to several million individuals. Using the undiagnosed estimates we correct the case fatality rates and find that it ranges from 0.33% ± 0.02% to 1.14% ± 0.07%. Since we applied the method to cities and countries with different characteristics, the corrected case fatality rates indicate a universality that is independent of location and other social/demographic conditions. Our results agree with sample tests and seroprevalence studies, considerably changing our understanding of the COVID-19 fatality rates. Other applications include the estimate for severe cases, ICU needs, and undiagnosed cases.
Keywords
COVID-19 pandemic, Corrected Case Fatality Rate, Probabilistic Model, ICU needs, undiagnosed cases
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No competing interests reported.
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Gustavo Zampier Dos Santos Lima
Federal University of Rio Grande do Norte
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 10 Mar, 2021
No community comments so far
Review #1 received
Received 15 Apr, 2021
Reviewer #2 agreed
On 01 Apr, 2021
Reviewer #3 agreed
On 01 Apr, 2021
Reviewer #4 agreed
On 01 Apr, 2021
Reviewers invited
Invitations sent on 29 Mar, 2021
Editor assigned
On 24 Mar, 2021
Editor invited
On 04 Mar, 2021
Submission checks complete
On 04 Mar, 2021
First submitted
On 17 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
An important and elusive characteristic of the COVID-19 pandemic is the fatality rate which allows us to understand the severity of this disease, health care needs, and the impact on large populations. To address this and other questions we present a probabilistic model to study the evolution of the COVID-19 pandemic and to correct the case fatality rates. Our model employs probabilities to estimate the time evolution of infections, recoveries, and deaths. This model discriminates asymptomatic, mild/moderate, and severe cases and allows for the estimation of undiagnosed individuals. Furthermore, we compare the model curves to official data for medium-sized cities, world metropolises, and medium-sized countries, spanning a range of populations from a few million to several million individuals. Using the undiagnosed estimates we correct the case fatality rates and find that it ranges from 0.33% ± 0.02% to 1.14% ± 0.07%. Since we applied the method to cities and countries with different characteristics, the corrected case fatality rates indicate a universality that is independent of location and other social/demographic conditions. Our results agree with sample tests and seroprevalence studies, considerably changing our understanding of the COVID-19 fatality rates. Other applications include the estimate for severe cases, ICU needs, and undiagnosed cases.
Figure 1
Figure 2
Figure 3
Figure 4
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