COVID-19 vaccine misinformation has been fueling vaccine hesitancy, which has been one of the main factors in slowing down the vaccination rate (Loomba et al., 2021). An increase in vaccine hesitancy, especially among the vulnerable communities, will exacerbate the already overwhelming economic and health burden of COVID-19. The purpose of Health TrueInfo is to use knowledge translation strategies to implement evidence-based health communications via social media in order to tackle COVID-19 vaccine misinformation and hesitancy among vulnerable populations in Bolivia, India, and Canada. The Health TrueInfo initiative is in collaboration with health experts and community members, asking them to create audiovisuals that convey powerful and culturally relevant messages to their communities. Such content combats local misinformation and encourages vaccine uptake. The audiovisual content is then uploaded to our multilingual Android app and social media platforms on Twitter, Facebook, Instagram, and LinkedIn.The concept of using social media to tackle misinformation was informed from systematic reviews, highlighting its potential by health organizations to combat prevalent misinformation as social media is widely used to share and seek health information (Chou et al., 2018; Suarez-Lledo & Alvarez-Galvez, 2021). Community engagement and searching grey literature were important methodologies to understand different local contexts of misinformation. For instance, to better comprehend how misinformation plays a role in increasing vaccine hesitancy among the Indigenous Quechua peoples in Bolivia, we collaborated with a Quechua social media influencer, who helped us create a skit inspired by the current local misinformation. Likewise, we have asked other stakeholders in healthcare like local teenagers, frontline doctors, and health experts to help create content addressing their respective communities. The knowledge translation strategies utilized here were to contextualize information, appeal to potential vaccine-hesitant groups, and use community engagement strategies like involving influencers to help us reach specific demographic groups and overcome linguistic and cultural barriers (Bella et al., 2021). One way to quantitatively estimate the impact of the project is through social media analytics. When contributors or influencers helped create audiovisuals and share with their followers, some of our content have reached over 1000 impressions and 200 views within targeted demographics. This initial success may imply how Health TrueInfo models the idea of health experts, social media influencers, and members of their own communities working together to reduce vaccine misinformation and hesitancy via creating multimodal social media contents, which in turn might help increase health and digital literacy, and battle social isolation. As health misinformation is a relatively new research field and vaccine hesitancy literature for countries like Bolivia and for the Indigenous communities in general are limited, Health TrueInfo can inspire participatory action research in countries and communities that are under-represented in the peer-reviewed literature to better understand different context-specific factors contributing to vaccine misinformation and hesitancy. References Chou, W. S., Oh, A., & Klein, W. M. P. (2018). Addressing Health-Related Misinformation on Social Media. Jama, 320(23), 2417-2418. https://doi.org/10.1001/jama.2018.16865 La Bella, E., Allen, C., & Lirussi, F. (2021). Communication vs evidence: What hinders the outreach of science during an infodemic? A narrative review. Integrative Medicine Research, 10(4), 100731. https://doi.org/10.1016/j.imr.2021.100731 Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021). Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour, 5(3), 337-348. https://doi.org/10.1038/s41562-021-01056-1 Suarez-Lledo, V., & Alvarez-Galvez, J. (2021). Prevalence of Health Misinformation on Social Media: Systematic Review. J Med Internet Res, 23(1), e17187. https://doi.org/10.2196/17187

The manuscript presents a bragging-based ensemble forecasting model for predicting the number of incidences of a disease based on past occurrences. The objectives of this research work are to enhance accuracy, reduce overfitting, and handle overdrift; the proposed model has shown promising results in terms of error metrics. The collated dataset of the diseases is collected from the official government site of Hong Kong from the year 2010 to 2019. The preprocessing is done using log transformation and z score transformation. The proposed ensemble model is applied, and its applicability to a specific disease dataset is presented. The proposed ensemble model is compared against the ensemble models, namely dynamic ensemble for time series, arbitrated dynamic ensemble, and random forest using different error metrics. The proposed model shows the reduced value of MAE (mean average error) by 27.18%, 3.07%, 11.58%, 13.46% for tuberculosis, dengue, food poisoning, and chickenpox, respectively. The comparison drawn between the proposed model and the existing models shows that the proposed ensemble model gives better accuracy in the case of all the four-disease datasets.