439. Corowa-kun: Impact of a COVID-19 Vaccine Information Chatbot on Vaccine Hesitancy, Japan 2021

Abstract Background Japan has one of the highest vaccine hesitancy rates in the world. According to a previous study, less than 30% of people strongly agreed that vaccines were safe, important, or effective. We created a COVID-19 vaccine information chatbot in a popular messenger app in Japan to answer COVID-19 vaccine frequently asked questions (FAQs) via text messages. We assessed the impact of chatbot text messages on COVID-19 vaccine hesitancy by conducting a cross-sectional survey among chatbot users. Methods LINE is the most popular messenger app in Japan; about 86 million people in Japan (roughly two-thirds of the population) use this messenger app. Corowa-kun, a free chatbot, was created in LINE on February 6, 2021. Corowa-kun provides instant, automated answers to frequently asked COVID-19 vaccine questions. A cross-sectional survey assessing COVID-19 vaccine hesitancy was conducted via Corowa-kun during April 5 to 12, 2021. We included persons ages 16 years old and older who had not received a COVID-19 vaccine. The survey was written in Japanese and consisted of 21 questions. Corowa-kun’s Consultation Room Corowa-kun is the mascot of an online chatbot. This chatbot in LINE is used to answer COVID-19 vaccine frequently asked questions (FAQs) via text messages. As of May 10th, 70 FAQs are available. Results A total of 59,676 persons used Corowa-kun during February to April 2021. The most commonly accessed message categories were: “I have (select comorbidity), can I get a COVID-19 vaccine?” (23%); followed by questions on adverse reactions (22%) and how the vaccine works (20%). 10,192 users (17%) participated in the survey. Median age was 55 years (range 16 to 97), and most were female (74%). Intention to receive a COVID-19 vaccine increased from 59% to 80% after using Corowa-kun (p < 0.01). Overall, 20% remained hesitant: 16% (1,675) were unsure, and 4% (364) did not intend to be vaccinated. Factors associated with vaccine hesitancy were: age 16 to 34 (odds ratio [OR] = 3.7, 95% confidential interval [CI]: 3.0–4.6, compared to age ≥65), female sex (OR = 2.4, Cl: 2.1–2.8), and history of another vaccine side-effect (OR = 2.5, Cl: 2.2–2.9). Being a physician (OR = 0.2, Cl: 0.1-0.4) and having received a flu vaccine the prior season (OR = 0.4, Cl: 0.3-0.4) were protective. COVID-19 vaccine acceptance increased and hesitancy decreased after using Corowa-kun, Japan, 2021 (n=10,192) *There was a statistically significant difference in responses between before and after using Corowa-kun (p < 0.01, Chi-square test). Univariable logistic regression models of factors associated with COVID-19 vaccine hesitancy, Japan, 2021 (n=10,192) Ref: reference NA: Logistic regression was not performed due to too small number (n≤3) Conclusion Corowa-kun reduced vaccine hesitancy by providing COVID-19 vaccine information in a messenger app. Mobile messenger apps could be leveraged to increase COVID-19 vaccine acceptance. Disclosures All Authors: No reported disclosures


Conclusion.
Machine learning clustering methods are promising analytical tools for identifying inflammation marker patterns associated with baseline risk factors and severe illness due to COVID-19. These approaches may offer new insights for COVID19 prognosis, therapy, and prevention.
Disclosures. Simon Pollett, MBBS, Astra Zeneca (Other Financial or Material Support, HJF, in support of USU IDCRP, funded under a CRADA to augment the conduct of an unrelated Phase III COVID-19 vaccine trial sponsored by AstraZeneca as part of USG response (unrelated work)) Background. Japan has one of the highest vaccine hesitancy rates in the world. According to a previous study, less than 30% of people strongly agreed that vaccines were safe, important, or effective. We created a COVID-19 vaccine information chatbot in a popular messenger app in Japan to answer COVID-19 vaccine frequently asked questions (FAQs) via text messages. We assessed the impact of chatbot text messages on COVID-19 vaccine hesitancy by conducting a cross-sectional survey among chatbot users.

Corowa-kun: Impact of a COVID-19 Vaccine Information Chatbot on
Methods. LINE is the most popular messenger app in Japan; about 86 million people in Japan (roughly two-thirds of the population) use this messenger app. Corowa-kun, a free chatbot, was created in LINE on February 6, 2021. Corowa-kun provides instant, automated answers to frequently asked COVID-19 vaccine questions. A cross-sectional survey assessing COVID-19 vaccine hesitancy was conducted via Corowa-kun during April 5 to 12, 2021. We included persons ages 16 years old and older who had not received a COVID-19 vaccine. The survey was written in Japanese and consisted of 21 questions.

Corowa-kun's Consultation Room
Corowa-kun is the mascot of an online chatbot. This chatbot in LINE is used to answer COVID-19 vaccine frequently asked questions (FAQs) via text messages. As of May 10th, 70 FAQs are available.
COVID-19 vaccine acceptance increased and hesitancy decreased after using Corowa-kun, Japan, 2021 (n=10,192) *There was a statistically significant difference in responses between before and after using Corowa-kun (p < 0.01, Chi-square test).
Univariable logistic regression models of factors associated with COVID-19 vaccine hesitancy, Japan, 2021 (n=10,192) Ref: reference NA: Logistic regression was not performed due to too small number (n≤3) Conclusion. Corowa-kun reduced vaccine hesitancy by providing COVID-19 vaccine information in a messenger app. Mobile messenger apps could be leveraged to increase COVID-19 vaccine acceptance.

Background.
A naïve Bayes classifier is a popular tool used in assigning variables an equal and independent contribution to a binary decision. With respect to COVID-19 severity, the naïve Bayes classifier can consider different variables, such as age, gender, race/ethnicity, comorbidities, and initial laboratory values to determine the probability a patient may need to be admitted or transferred to an intensive care unit (ICU). The aim of this study was to develop a screening tool to detect COVID-19 patients that may require escalation to ICU status.
Methods. Patients hospitalized with COVID-19 were gathered from the end of March 2020 to the end of May 2020 from four hospitals in our metropolitan area. We began searching for potential variables to include in the classification model using chi-square analysis or calculating the optimal cutpoint to separate ICU and non-ICU status. After identifying significant variables, we began using standard procedures to construct a classifier. The dataset was split 7:3 to create samples for training and testing. To appraise the model's performance, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and the Matthew's correlation coefficient (MCC) were calculated.