Modified Bass Diffusion Model to Study Adoption of Covid–19 Vaccines in the Philippines: Input for Inoculation Rollout
DOI:
https://doi.org/10.54536/ajmri.v1i4.603Keywords:
Bass Model, Covid-19 Vaccine Adoption, Forecasting, Philippines, Quantitative ResearchAbstract
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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