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The emergence of multiple strains of SARS-COV-2 has made it complicated to predict and control the COVID-19 pandemic. Although some vaccines have been effective in reducing the severity of the disease, these vaccines are designed for a specific strain of the virus and are usually less effective for other strains. In addition, the waning of vaccine-induced immunity, reinfection of recovered people, and incomplete vaccination are challenging to the vaccination program. In this study, we developed a detailed model to describe the multi-strain transmission dynamics of COVID-19 under vaccination. We implemented our model to examine the impact of inter-strain transmission competition under vaccination on the critical outbreak indicators: hospitalized cases, undiagnosed cases, basic reproduction numbers, and the overtake-time by a new strain to the existing strain. In particular, our results on the dependence of the overtake-time on vaccination rates, progression-to-infectious rate, and relative transmission rates provide helpful information for managing a pandemic with circulating two strains. Furthermore, our results suggest that a reduction in the relative transmission rates and a decrease in vaccination dropout rates or an increase in vaccination rates help keep the reproduction number of both strains below unity and keep the number of hospitalized cases and undiagnosed cases at their lowest levels. Moreover, our analysis shows that the second and booster-dose vaccinations are useful for further reducing the reproduction number.
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Simulating the Mitigating Effects of Social Distancing and Vaccination during a Pandemic
Understanding the spread of a virus through a large population has always been important for mitigation, treatment, and prevention purposes. In the unprecedented wake of the COVID-19 (SARS-CoV-2) pandemic, techniques to understand and test preventative measures—like vaccination and quarantining—have become increasingly necessary. The developed compartmental model simulates the interactions between (N) people, with variable infection rates for vaccinated (alpha = 0.05) and unvaccinated (Beta = 0.25) people, as well as a (t = 15) day recovery from infection. Both the range of their infectivity, controlled by the quarantine variable (q), and the vaccination rate (f) of the population were varied. The main tests were on the variables of vaccine distribution and effectiveness, as well as quarantine range and the combination between the two. The results suggest that vaccination has a negative linear effect on infection cases over the course of the simulation, while quarantine has a minimal effect until higher amounts (over 80% quarantine), with the inverse being true for duration (increasing with stricter measures). Vaccination and quarantining also have a negative linear and exponential effect respectively on peak case count, which would be helpful in managing the patient flow into hospitals. Reproduction number was also found to be limited below 1.0 by vaccination measures, bringing the outbreak to an end. From these findings, it would be reasonable to suggest that vaccination is nearly ubiquitously helpful, while much stricter quarantine restrictions must be put in place for substantial effect, with the goal to flatten peak cases and decrease reproduction number.
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- Award ID(s):
- 1603806
- PAR ID:
- 10558268
- Publisher / Repository:
- rScroll Publications
- Date Published:
- Journal Name:
- Journal of Student Research
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2167-1907
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.47611/jsrhs.v11i1.2490
