Models to Predict the Number of Infected Cases and Deaths from COVID-19 in Chile and Its Most Affected Regions
This paper designs and implements a methodology to model the evolution of the COVID-19 pandemic, produced by the SARS-CoV-2 virus, in what was called the first wave in Chile, which lasted from March 2 to 31 October 2020. The models are based on sigmoidal growth curves and can be used to predict the...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/1906435 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This paper designs and implements a methodology to model the evolution of the COVID-19 pandemic, produced by the SARS-CoV-2 virus, in what was called the first wave in Chile, which lasted from March 2 to 31 October 2020. The models are based on sigmoidal growth curves and can be used to predict the number of daily infections and deaths in future days, making them a useful tool for sanitary authorities to manage an epidemic. The methodology is applied to the entire country and to each of its most affected regions. In addition, the dynamics of these models allow it to be nurtured with the new information that is being produced and forecast a tentative date on which there would be some control over the pandemic. Moreover, these models allow for predicting the total number of infected and deceased people at the time the pandemic is under control. However, the simplicity of these models, which consider only the accumulated data of those infected and deceased, does not contemplate an intervention analysis such as vaccinations, which, as is known, are being effective in controlling the pandemic. |
|---|---|
| ISSN: | 1607-887X |