Modified Bass Diffusion Model to Study Adoption of Covid–19 Vaccines in the Philippines: Input for Inoculation Rollout

Authors

  • Boyshin Balsa Rebalde Department of Education, Bitaogan NHS, Davao Oriental, 8207, Philippines
  • Christhoffer Paran Lelis University of Mindanao, Davao City, 8200, Philippines

DOI:

https://doi.org/10.54536/ajmri.v1i4.603

Keywords:

Bass Model, Covid-19 Vaccine Adoption, Forecasting, Philippines, Quantitative Research

Abstract

This study aimed to identify a model that better predicts future trends in adopting Covid-19 vaccines in the Philippines. A non-experimental quantitative research design using modeling techniques was applied. To that effect, this study implemented two diffusion models: the Bass model and the modified Bass diffusion model incorporating vaccine supply, and Google searches, analyzed their predictive abilities, and determined the model that fits better with the observed data. Metric criteria for data analysis include Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), R2, and the Akaike Information Criterion (AIC). The results revealed that when plotted over time, the cumulative number of adopters follows, but not substantially, an S-shaped curve. The modified Bass diffusion model incorporating vaccine supply and Google searches described the adoption of Covid–19 vaccines more accurately and improved the forecast accuracy of the benchmark model by approximately 10%. Moreover, the Philippines expected to reach herd immunity with 90% as a threshold on September 4 – 17, 2022, when the percent of change of the cumulative vaccine supply and Google searches is 0.5%. The policy recommendation is proposed based on the findings of the study. Furthermore, future researchers may utilize the proposed model using the data in another set to confirm its predictive ability.

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References

Al-Jayyousi, G.F., Sherbash, M.A.M., Ali, L.A.M., El-Heneidy, A., Alhussaini, N.W.Z., Elhassan, M.E.A., & Nazzal, M.A. (2021). Factors Influencing Public Attitudes towards Covid-19 Vaccination: A Scoping Review Informed by the Socio-Ecological Model. Vaccines, 9, 548. https://doi.org/10.3390/vaccines9060548

Ali, R. (2020, September 30). Predictive Modeling: Types, Benefits, and Algorithms. https://www.netsuite.com/portal/resource/articles/financial-management/predictive-modeling.shtml

Amit, A.M.L., Pepito, V.C.F., Sumpaico- Tanchanco, L., & Dayrit, M.M. (2022). Covid-19 vaccine brand hesitancy and other challenges to vaccination in the Philippines. PLOS Glob Public Health, 2(1), 1–23. https://doi.org/10.1371/journal.pgph.0000165

Arora, M., Goyal, L. M., Chintalapudi, N., & Mittal, M. (2020, October). Factors affecting digital education during Covid-19: A statistical modeling approach. In 2020 5th International Conference on Computing, Communication and Security (ICCCS), (pp. 1-5). IEEE.

Bass, F. (1969). A new product growth for model consumer durables. Management Science, 15, 215–227. http://www.uvm.edu/pdodds/research/papers/others/1969/bass1969a.pdf.

Bass, F.M., Krishnan, T.V. & Jain, D.C. (1994). Why the bass model fits without decision variables? Mark. Sci., 13, 203–223. http://dx.doi.org/10.1287/mksc.13.3.203

Cerda, A.A., & García, L.Y. (2021). Hesitation and Refusal Factors in Individuals’ Decision-Making Processes Regarding a Coronavirus Disease 2019 Vaccination. Front. Public Health, 9, 1–14.

Cihan, P. (2021). Forecasting fully vaccinated people against Covid-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe,Africa, South America, and the World. Applied Soft Computing, 111. https://doi.org/10.1016/j.asoc.2021.107708

Cheong, Q., Au-yeung, M., Quon, S., Concepcion, K., & Kong, J.D. (2021). Predictive modeling of vaccination uptake in U.S. Counties: A machine learning–based approach. Journal of Medical Internet Research, 23(11).

Clapano, J.R. (2021, December 25). Government vax target: 77 million Pinoys by Q1. The Philippine Star. https://www.philstar.com/headlines/2021/12/25/2150005/government-vax-target-77-million-pinoys-q1

Department of Health. (2021). DOH, NTF grateful to hospitals and vaccinees as Ph inoculates 756 on first day of Covid – 19 vaccine rollout. https://doh.gov.ph/doh-press-release

Department of Health. (2022). National Covid 19 vaccination dashboard. https://doh.gov.ph/Covid19-vaccination-dashboard

Gholizadeh, P., Esmaeili, B., & Goodrum, P. (2018). Diffusion of building information modeling functions in the construction industry. Journal of Management in Engineering, 34(2), 04017060.

Google (2022). Google trends. https://trends.google.com/trends/explore?geo=PH&gpro=news&q=Covid%20vaccine,sinovac,astrazeneca,modernafize

Harapan, H., Wagner, A. L., Yufika, A., Winardi, W., Anwar, S., Gan, A. K., ... & Mudatsir, M. (2020). Acceptance of a Covid-19 vaccine in Southeast Asia: a cross-sectional study in Indonesia. Frontiers in public health, 8, 381.

Hsu, L.C., & Wang, C.H. (2008). The development and testing of a modified diffusion model for predicting tourism demand. International Journal of Management, 25(3), 439–445. https://www.proquest.com/openview/78cc4940541262bbd188bc85872f1171/1?pq-origsite=gscholar&cbl=5703

Hyder, A. A., Hyder, M. A., Nasir, K., & Ndebele, P. (2021). Inequitable Covid-19 vaccine distribution and its effects. Bulletin of the World Health Organization, 99(6), 406–406. https://doi.org/10.2471/BLT.21.285616

Jha, A., & Saha, D. (2018). Diffusion and forecast of mobile service generations in Germany, UK, France, and Italy – A comparative analysis bases on Bass, Gompertz and Simple Logistic Growth models. Research Papers, 17. https://aisel.aisnet.org/ecis2018_rp/17

Jiang, Y. P. (2017). Study on the Diffusion of the Instant Messaging Market Based on the Modified Bass Model. In 2017 International Conference on Economics and Management Engineering (ICEME 2017).

Jomnonkwao, S., Uttra, S., & Ratanavaraha, V. (2020). Forecasting road traffic deaths in Thailand: Applications of time-series, curve estimation, multiple linear regression, and path analysis models. Sustainability, 12(1), 395.

Kang, D. (2021). Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model. Electron Commer Res, 21, 41–72. https://doi.org/10.1007/s10660-020-09456-7

Kim, H. & Rao V.R. (2021). Vaccination Diffusion and Incentive: Empirical Analysis of the U.S. State of Michigan. Front. Public Health, 9, 1–9.

Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer New York Heidelberg Dordrecht London. https://vuquangnguyen2016.files.wordpress.com/2018/03/applied-predictive-modeling-max-kuhn-kjell-johnson_1518.pdf

Lartey, F. (2020). Predicting Product uptake using Bass, Gompertz, and Logistic diffusion models: Application to a broadband product. Journal of Business Administration Research. 9, 5-18.

Li, S., Chen, H., & Zhang, G. (2017). Comparison of the short-term forecasting accuracy on battery electric vehicle between modified Bass and Lotka-Volterra model: A case study of China. Journal of Advanced Transportation 1-6. https://doi.org/10.1155/2017/7801837

Marzo, R.R., Sami, W., Alam, Z., Acharya, S., Jermsittiparsert, K., Songwathana, K., Pham, N.T., Respati, T., Faller, E.M., Baldonado, A.M., Aung, Y., Borkar, S.M., Essar, M.Y., Shrestha, S., & Siyan Yi, S. (2022). Hesitancy in Covid‑19 vaccine uptake and its associated factors among the general adult population: a cross‑sectional study in six Southeast Asian countries. Tropical Medicine and Health 50(4), 1– 10. https://doi.org/10.1186/s41182-021-00393-1

Massiani, J. & Gohs, A. (2015). The choice of Bass model coefficients to forecast diffusionfor innovative products: An empirical investigation for new automotive technologies. Research in Transportation Economics, 50, 17-28. https://doi.org/10.1016/j.retrec.2015.06.003.

Mo, P.K., Luo, S., Wang, S., Zhao, J., Zhang, G., Li, L., Li, L., Xie, L., Lau, J.T.F. (2021). Intention to receive the Covid-19 vaccination in China: Application of the Diffusion of Innovations Theory and the moderating role of openness to experience. Vaccines, 9(1290). https://doi.org/10.3390/vaccines9020129

Naseri, M. & Elliott, G. (2013). The diffusion of online shopping in Australia: Comparing the Bass, Logistic and Gompertz growth models. Journal of Marketing Analytics, 1, 49–60. https://doi.org/10.1057/jma.2013.2

Our World in Data. (2022a). Coronavirus (Covid-19) vaccinations. Retrieved February 01, 2022, from https://ourworldindata.org/Covid-vaccinations

Our World in Data. (2022b). Coronavirus (Covid-19) vaccinations. Retrieved April 01, 2022 from https://ourworldindata.org/Covid-vaccinations

Parvin, A.J., & Beruvides, M.G. (2021). Macro patterns and trends of U.S. consumer technological innovation diffusion rates. Systems, 9(16). https://doi.org/10.3390/systems9010016

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York: Free Press. http://www.lamolina.edu.pe/postgrado/pmdas/cursos/innovacion/lecturas/Obligatoria/17%20-%20Rogers%201995%20cap%206.pdf

Su, Z., McDonnell, D., Cheshmehzangi, A., Li, X., Maestro, D., Šegalo, S., ... & Hao, X. (2021). With great hopes come great expectations: Access and adoption issues associated with Covid-19 vaccines. JMIR Public Health and Surveillance, 7(8), e26111.

Tagoe, E.T., Sheikh, N., Morton A., Nonvignon J., Sarker A.R., Williams L., & Megiddo I. Covid-19 Vaccination in Lower-Middle Income Countries: National Stakeholder Views on Challenges, Barriers, and Potential Solutions. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.709127

Tao, Y. (2020). Innovative approaches for short-term vehicular volume prediction in Intelligent Transportation System [Master’s Thesis, University of Ottawa]. https://ruor.uottawa.ca/bitstream/10393/40441/1/Tao_Yanjie_2020_thesis.pdf

Valente, C. (2021, September 9). P.H. raises jab target to 90% of population. The Manila Times. https://www.manilatimes.net/2021/09/08/news/ph-raises-herd-immunity-target-to-90m/1813989

Xin, M., Luo, S., She, R., Chen, X., Li, L., Li, L., Chen, X., & Lau, J.T.F. (2021). The Impact of Social Media Exposure and Interpersonal Discussion on Intention of Covid-19 Vaccination among Nurses. Vaccines, 9(10), 1204. https://doi.org/10.3390/vaccines9101204

Yang, J., Min, D., & Kim, J. (2020). The Use of Big Data and Its Effects in a Diffusion Forecasting Model for Korean Reverse Mortgage Subscribers. Sustainability, 12(3), 979. https://www.mdpi.com/2071-1050/12/3/979

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Published

2022-09-22

How to Cite

Boyshin Balsa, R., & Christhoffer , P. L. (2022). Modified Bass Diffusion Model to Study Adoption of Covid–19 Vaccines in the Philippines: Input for Inoculation Rollout . American Journal of Multidisciplinary Research and Innovation, 1(4), 76–85. https://doi.org/10.54536/ajmri.v1i4.603