Volume 11, Issue 2 (Spring 2022)                   J Occup Health Epidemiol 2022, 11(2): 91-98 | Back to browse issues page


XML Print


Senior Lecturer in Statistics, Dept. of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Kelaniya, Sri Lanka. , succ@kln.ac.lk
Abstract:   (446 Views)
Background: Vaccination against COVID-19 is as a key solution to interrupt its spread. This study aimed to describe the vaccination coverage required to stop the spread of COVID-19 in Sri Lanka using a mathematical modeling strategy.
Materials & Methods: This longitudinal study used age-stratified and unstratified Susceptible-Infectious-Recovered (SIR) models. Data on the population's age distribution were acquired from the census report of the Census and Statistics Center of Sri Lanka, consisting of groups: below 30, between 30-59, and over 60. Models with differential equations forecasted the spread of COVID-19 with vaccination based on parameter estimates and numerical simulation, assuming fixed population, infection, and recovery rates.
Results: Simulations investigated how the susceptible, infected, and recovered populations varied according to the different vaccination coverages. According to the results, 75% vaccination coverage was required in the entire population of Sri Lanka to interrupt the transmission of COVID-19 completely. The age-stratified SIR model showed that over 90% of vaccination coverage in each age group (below 30, between 30-59, and over 60) was required to interrupt the transmission of COVID-19 in the country altogether.
Conclusions: The number of COVID-19 infections in each age group of Sri Lanka reduces with the increase in vaccination coverage. As 75% vaccination coverage is required in Sri Lanka to interrupt the transmission of the disease, precise vaccination coverage measurement is essential to assess the successfulness of a vaccine campaign and control COVID-19.
 
Article number: 1
Full-Text [PDF 522 kb]   (130 Downloads)    
Short Report: Original Article | Subject: Epidemiology
Received: 2021/12/3 | Accepted: 2022/07/19 | ePublished: 2022/08/22

References
1. Worldometer, COVID-19 Coronaviru andemic, [Internet]. 2022 Apr 18. Available from: https://www.worldometers.info/coronavirus/ [Report]
2. Barber SJ, Kim H. COVID-19 Worries and Behavior Changes in Older and Younger Men and Women. J Gerontol B Psychol Sci Soc Sci. 2021;76(2):e17-23. [DOI] [PMID] [PMCID]
3. [3] Ritchie H, Mathieu E, Rodés-Guirao L, Appel C, Giattino C, Ortiz-Ospina E, et al. Coronavirus Pandemic (COVID-19) Vaccinations. World Data. 2021. [Report]
4. 4. Humanitarian Data Exchange. Novel Coronavirus (COVID-19) Cases Data. [Internet]. 2021 Aug 27. Available from: https://data.humdata.org/event/covid-19. [Report]
5. Agarwal P, Jhajharia K. Data analysis and modeling of COVID-19. J Stat Manage Syst. 2021;24(1):1-16. [DOI]
6. Alharbi Y, Alqahtani A, Albalawi O, Bakouri M. Epidemiological Modeling of COVID-19 in Saudi Arabia: Spread Projection, Awareness, and Impact of Treatment. Appl Sci. 2020;10(17):5895. [DOI]
7. Liu M, Thomadsen R, Yao S. Forecasting the spread of COVID-19 under different reopening strategies. Sci Rep. 2020;10(1):20367. [DOI] [PMID] [PMCID]
8. Yadav RSYRS. Mathematical Modeling and Simulation of SIR Model for COVID-2019 Epidemic Outbreak: A Case Study of India. INFOCOMP J Comput Sci. 2020;19(2):01-09. [DOI]
9. Haidere MF, Ratan ZA, Nowroz S, Zaman SB, Jung Y, Hosseinzadeh H, Cho JY. COVID-19 Vaccine: Critical Questions with Complicated Answers. Biomol Ther (Seoul). 2021;29(1):1-10. [DOI] [PMID] [PMCID]
10. Abdy M, Side S, Annas S, Wahyuddin Nur, Wahidah Sanusi. An SIR epidemic model for COVID-19 spread with fuzzy parameter: the case of Indonesia. Adv Differ Equ. 2021;2021(1):105 [DOI] [PMID] [PMCID]
11. [11] Wickramaarachchi, WPTM, Perera, SSN, Jayasinghe S. COVID-19 Epidemic in Sri Lanka: A Mathematical and Computational Modelling Approach to Control. Comput Math Methods Med. 2020;2020:4045064 [DOI] [PMID] [PMCID]
12. Population Tables. Census of Population and Housing – 2012 Sri Lanka. 14th ed. Battaramulla, Sri Lanka: Department of Census and Statistics; 2015. [Article]
13. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2018. [Report]
14. Kermack WO, McKendrick AG. A Contribution to the Mathematical Theory of Epidemics. Proc Math Phys Eng Sci. 1927;115(772):700-21. [DOI]
15. World Health Organization, 2020. “‘Immunity passports” in the context of COVID-19’. Geneva, Switzerland: World Health Organization; 2021. [DOI]
16. Puspitasari IM, Yusuf L, Sinuraya RK, Abdulah R, Koyama H. Knowledge, Attitude, and Practice During the COVID-19 Pandemic: A Review. J Multidiscip Healthc. 2020;13:727-33. [DOI] [PMID] [PMCID]
17. Udwadia ZF, Raju RS. How to protect the protectors: 10 lessons to learn for doctors fighting the COVID -19 coronavirus. Med J Armed Forces India. 2020;76(2):128-31. [DOI] [PMID] [PMCID]
18. de Gier B, de Oliveira BLP, van Gaalen RD, de Boer PT, Alblas J, Ruijten M, et al. Occupation- and age-associated risk of SARS-CoV-2 test positivity, the Netherlands, June to October 2020. Euro Surveill. 2020;25(50):2001884. [DOI]
19. Razzini K, Castrica M, Menchetti L, Maggi L, Negroni L, Orfeo NV, Pizzoccheri A, Stocco M, Muttini S, Balzaretti CM. SARS-CoV-2 RNA detection in the air and on surfaces in the COVID-19 ward of a hospital in Milan, Italy. Sci Total Environ. 2020;742:140540. [DOI] [PMID] [PMCID]
20. Riddell S, Goldie S, Hill A, Eagles D, Drew TW. The effect of temperature on persistence of SARS-CoV-2 on common surfaces. Virol J. 2020;17(1):145. [DOI] [PMID] [PMCID]
21. Zhou J, Otter JA, Price JR, Cimpeanu C, Garcia DM, Kinross J, Boshier PR, Mason S, Bolt F, Holmes AH, Barclay WS. Investigating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Surface and Air Contamination in an Acute Healthcare Setting During the Peak of the Coronavirus Disease 2019 (COVID-19) Pandemic in London. Clin Infect Dis. 2021;73(7):e1870-7. [DOI] [PubMed] [PMCID]
22. Kraft KB, Godoy AA, Vinjerui KH, Kour P, Kjollesdal MKR, Indseth T. COVID-19 vaccination by immigrant background. Tidsskr Nor Legeforen. 2022;141(2). [DOI] [PMID]
23. Ram K, Thakur RC, Singh DK, Kawamura K, Shimouchi A, Sekine Y, Nishimura H, Singh SK, Pavuluri CM, Singh RS, Tripathi SN. Why airborne transmission hasn't been conclusive in case of COVID-19? An atmospheric science perspective. Sci Total Environ. 2021;773:145525. [DOI] [PMID] [PMCID]
24. Firouzbakht M, Omidvar S, Firouzbakht S, Asadi-Amoli A. COVID-19 preventive behaviors and influencing factors in the Iranian population; a web-based survey. BMC Public Health. 2021;21(1):143. [DOI] [PMID] [PMCID]
25. Ridenhour B, Kowalik JM, Shay DK. Unraveling R0: Considerations for public health applications. Am J Public Health.2018; 108(Suppl 6):S445-54. [DOI] [PMCID]
26. Orevi M, Chicheportiche A, Ben Haim S. Lessons Learned from Post-COVID-19 Vaccination PET/CT Studies. J Nucl Med. 2022;63(3):453-60. [DOI] [PubMed] [PMCID]
27. Kaplan RM, Milstein A. Influence of a COVID-19 vaccine's effectiveness and safety profile on vaccination acceptance. Proc Natl Acad Sci U S A. 2021;118(10):e2021726118. [DOI] [PMID] [PMCID]
28. [28] Saxena R, Jadeja M, Bhateja V. Propagation Analysis of COVID-19: An SIR Model-Based Investigation of the Pandemic. Arab J Sci Eng. 2021;10:1-13. [DOI] [PubMed] [PMCID]
29. [29] Igor N. Detections and SIR simulations of the COVID-19 PANDEMIC WAVES IN Ukraine.Comput Math Biophys. 2021;9(1):46-65. [DOI]
30. [30] Ulrich N, Freeman M, Audric D, BrunoV. Simulating the progression of the COVID-19 disease in Cameroon using SIR models. PLoS One. 2020;15(8):e0237832. [DOI] [PMID] [PMCID]

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.