Understanding COVID-19 vaccine hesitancy in Pakistan: The paradigm of Confidence, Convenience and Complacency; A Cross-sectional study. (preprint)

Background and objectives: Vaccine hesitancy is a big obstacle for vaccination programs, as is anticipated for the COVID-19 vaccination program, resulting in low uptake of vaccines thereby hindering the process of reaching herd immunity. Bearing this in mind the current study was aimed to explore the determinants of vaccine hesitancy amongst the Pakistani population. Methodology: A cross-sectional study was carried out from the 23rd-31st January 2021. The conceptual framework of the study was based on the 3Cs (Confidence, Convenience, Complacency) model. Google-forms-based questionnaire was disseminated amongst the general population. Data collected were entered into SPSS version 26 and analyzed. Results: Of the 421 participants, 68.4% were females. Non-healthcare workers were 55.8% of respondents. Vaccine hesitant individuals, 26.13% reported they were very unlikely to get vaccinated. Vaccine was not safe as it came out too fast was agreed upon by 12.6% individuals, 50.6% were worried about experiencing side-effects, 18% believed vaccine will not offer protection and 5.9% believed vaccine would cause death. Low Practice of SOP in non-Healthcare workers was the strongest contributor to vaccine hesitancy (OR: 5.338, p=0.040, 95% CI: 1.082-26.330) followed by High complacency (p=0.026) and Moderate Complacency (OR: 0.212, p=0.007, 95% CI: 0.069-0.654) towards COVID-19 vaccination. In Healthcare workers the strongest contributor to vaccine hesitancy was having a Moderate Confidence (OR: 0.323, p=0.042, 95% CI: 0.109-0.958) in the vaccine followed by Moderate Convenience (OR: 0.304, p=0.049, 95% CI: 0.093-0.993) for vaccination Conclusion: Campaigning and communication strategies to reaffirm confidence in the COVID-19 vaccine and educating the general population about the vaccine could lead to increased perception of vaccine safety and effectiveness thereby restoring confidence in vaccine and decreasing vaccine hesitancy. Likewise, working to increase vaccine convenience and decreasing complacency towards the COVID-19 vaccine would translate into high vaccine uptake.

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging (preprint)

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of CT scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients data, which is, open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of computed tomography (CT) images. Finally, our results demonstrate a better performance to detect COVID-19 patients.