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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Tutsoy, Onder Tanrikulu, Mahmud Yusuf |
| Abstract | Background There have been several destructive pandemic diseases in the human history. Since these pandemic diseases spread through human-to-human infection, a number of non-pharmacological policies has been enforced until an effective vaccine has been developed. In addition, even though a vaccine has been developed, due to the challenges in the production and distribution of the vaccine, the authorities have to optimize the vaccination policies based on the priorities. Considering all these facts, a comprehensive but simple parametric model enriched with the pharmacological and non-pharmacological policies has been proposed in this study to analyse and predict the future pandemic casualties. Method This paper develops a priority and age specific vaccination policy and modifies the non-pharmacological policies including the curfews, lockdowns, and restrictions. These policies are incorporated with the susceptible, suspicious, infected, hospitalized, intensive care, intubated, recovered, and death sub-models. The resulting model is parameterizable by the available data where a recursive least squares algorithm with the inequality constraints optimizes the unknown parameters. The inequality constraints ensure that the structural requirements are satisfied and the parameter weights are distributed proportionally. Results The results exhibit a distinctive third peak in the casualties occurring in 40 days and confirm that the intensive care, intubated, and death casualties converge to zero faster than the susceptible, suspicious, and infected casualties with the priority and age specific vaccination policy. The model also estimates that removing the curfews on the weekends and holidays cause more casualties than lifting the restrictions on the people with the chronic diseases and age over 65. Conclusion Sophisticated parametric models equipped with the pharmacological and non-pharmacological policies can predict the future pandemic casualties for various cases. |
| Related Links | https://bmcmedinformdecismak.biomedcentral.com/counter/pdf/10.1186/s12911-021-01720-6.pdf |
| Ending Page | 12 |
| Page Count | 12 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14726947 |
| DOI | 10.1186/s12911-021-01720-6 |
| Journal | BMC Medical Informatics and Decision Making |
| Issue Number | 1 |
| Volume Number | 22 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2022-01-06 |
| Access Restriction | Open |
| Subject Keyword | Health Informatics Information Systems and Communication Service Management of Computing and Information Systems COVID-19 Pandemic Priority and age specific vaccination policy Parametric model Prediction |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics Computer Science Applications Health Policy |
| Journal Impact Factor | 3.3/2023 |
| 5-Year Journal Impact Factor | 3.9/2023 |
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