Predicting the Size and Duration of the COVID-19 Pandemic
This article explores the ongoing COVID-19 pandemic, asking how long it might last. Focusing on Bahrain, which has a finite population of 1.7M, it aimed to predict the size and duration of the pandemic, which is key information for administering public health policy. We compare the predictions made...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-03-01
|
| Series: | Frontiers in Applied Mathematics and Statistics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2020.611854/full |
| _version_ | 1818847700866564096 |
|---|---|
| author | Ted G. Lewis Waleed I. Al Mannai |
| author_facet | Ted G. Lewis Waleed I. Al Mannai |
| author_sort | Ted G. Lewis |
| collection | DOAJ |
| description | This article explores the ongoing COVID-19 pandemic, asking how long it might last. Focusing on Bahrain, which has a finite population of 1.7M, it aimed to predict the size and duration of the pandemic, which is key information for administering public health policy. We compare the predictions made by numerical solutions of variations of the Kermack-McKendrick SIR epidemic model and Tsallis-Tirnakli model with the curve-fitting solution of the Bass model of product adoption. The results reiterate the complex and difficult nature of estimating parameters, and how this can lead to initial predictions that are far from reality. The Tsallis-Tirnakli and Bass models yield more realistic results using data-driven approaches but greatly differ in their predictions. The study discusses possible sources for predictive inaccuracies, as identified during our predictions for Bahrain, the United States, and the world. We conclude that additional factors such as variations in social network structure, public health policy, and differences in population and population density are major sources of inaccuracies in estimating size and duration. |
| first_indexed | 2024-12-19T06:05:37Z |
| format | Article |
| id | doaj.art-d1bec8bb927a450098ee6f162290ead5 |
| institution | Directory Open Access Journal |
| issn | 2297-4687 |
| language | English |
| last_indexed | 2024-12-19T06:05:37Z |
| publishDate | 2021-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj.art-d1bec8bb927a450098ee6f162290ead52022-12-21T20:33:10ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872021-03-01610.3389/fams.2020.611854611854Predicting the Size and Duration of the COVID-19 PandemicTed G. Lewis0Waleed I. Al Mannai1Naval Postgraduate School, Monterey, CA, United StatesBahrain Defence Ministry, Manama, BahrainThis article explores the ongoing COVID-19 pandemic, asking how long it might last. Focusing on Bahrain, which has a finite population of 1.7M, it aimed to predict the size and duration of the pandemic, which is key information for administering public health policy. We compare the predictions made by numerical solutions of variations of the Kermack-McKendrick SIR epidemic model and Tsallis-Tirnakli model with the curve-fitting solution of the Bass model of product adoption. The results reiterate the complex and difficult nature of estimating parameters, and how this can lead to initial predictions that are far from reality. The Tsallis-Tirnakli and Bass models yield more realistic results using data-driven approaches but greatly differ in their predictions. The study discusses possible sources for predictive inaccuracies, as identified during our predictions for Bahrain, the United States, and the world. We conclude that additional factors such as variations in social network structure, public health policy, and differences in population and population density are major sources of inaccuracies in estimating size and duration.https://www.frontiersin.org/articles/10.3389/fams.2020.611854/fullCOVID-19estimating epidemicsKermack-MckendrickTsallis-TirnakliBass modelpredicting cases |
| spellingShingle | Ted G. Lewis Waleed I. Al Mannai Predicting the Size and Duration of the COVID-19 Pandemic Frontiers in Applied Mathematics and Statistics COVID-19 estimating epidemics Kermack-Mckendrick Tsallis-Tirnakli Bass model predicting cases |
| title | Predicting the Size and Duration of the COVID-19 Pandemic |
| title_full | Predicting the Size and Duration of the COVID-19 Pandemic |
| title_fullStr | Predicting the Size and Duration of the COVID-19 Pandemic |
| title_full_unstemmed | Predicting the Size and Duration of the COVID-19 Pandemic |
| title_short | Predicting the Size and Duration of the COVID-19 Pandemic |
| title_sort | predicting the size and duration of the covid 19 pandemic |
| topic | COVID-19 estimating epidemics Kermack-Mckendrick Tsallis-Tirnakli Bass model predicting cases |
| url | https://www.frontiersin.org/articles/10.3389/fams.2020.611854/full |
| work_keys_str_mv | AT tedglewis predictingthesizeanddurationofthecovid19pandemic AT waleedialmannai predictingthesizeanddurationofthecovid19pandemic |