Machine learning models for Prediction of the need for future Covid-19 vaccine booster
| dc.contributor.author | Marzook, Ahmad Al | |
| dc.contributor.author | Xu, Ge | |
| dc.contributor.author | Jagannath, Prajna Shetty | |
| dc.date.accessioned | 2022-01-20T01:26:57Z | |
| dc.date.available | 2022-01-20T01:26:57Z | |
| dc.date.issued | 2021-04 | |
| dc.description.abstract | About 4 million Virginia citizens are fully vaccinated against COVID-19. According to medical data from nations like Israel and the United Kingdom, all those people may require another shot in the near future, at least when keeping transmis- sible diseases at bay. Health officials are looking at whether a booster shot is needed to ensure the vaccine’s effectiveness. To prepare it for predictions, we will develop a new simple logistic regression model. Make prediction models using classification and probability. With the vaccine immune response lifetime taken into account, we will run our model to forecast the completely vaccinated number timeline and observe when it achieves the herd immunity percentage of the overall Virginia population. The percentage of people that need to be vaccinated to attain COVID- 19 herd immunity is still up for dispute. The goal of this study was to forecast when everyone would be fully immunized. COVID-19 vaccination loses nearly half of its defense antibodies every 108 days, according to Elie Dolgin. As a result, immunizations that initially provided 90% protection against mild episodes of the disease may only provide 70% protection after 6 or 7 months. The percentage of people that need to be vaccinated to attain COVID-19 herd immunity is still being disputed. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12230 | |
| dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/us/ | |
| dc.subject | Machine learning | |
| dc.subject | Vaccines | |
| dc.subject | COVID-19 | |
| dc.subject | Prediction | |
| dc.title | Machine learning models for Prediction of the need for future Covid-19 vaccine booster | |
| dc.type | Working Paper |