case_images: PMC10045477_01
case_images: Social media (Twitter) reaction in Japan measured by total tweet count (Equation 1) for each COVID-19 wave over period of 12 weeks: 7 weeks before, 1 week during, and 4 weeks after peak of corresponding wave. Values shown are relative to those for 1st wave. Social media reaction in 6th and 7th waves was obviously much less than that in previous waves. Additionally, total tweet count ratio ("COVID-19 related tweets" vs. "all tweets") dropped to 32% in 6th wave and 22% in 7th wave compared with 56-62% in 2nd - 5th waves.
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case_images: PMC10045477_01_fpubh-11-1079315-g0004.jpg
case_images: F4
case_images: Although with differences among the 6 countries appearing in the percentage of emoji usage in COVID-19 related tweets (Germany: 4.08%, India: 8.27%, Indonesia: 8.02%, Japan: 8.66%, South Korea: 2.84%, and Thailand: 4.60%), and the ratio of COVID-19 related social media reaction over general social media reaction (Figure 8 and Table 4), COVID-19 related tweets containing emoji can provide informative features contributing to the improvement of our COVID-19 case prediction system.
case_images: COVID-19 case prediction system performance (MRAE). Error at given date was measured on basis of prediction for evaluation window starting from given date from 2020/11/16 to 2022/08/21. Using additional emoji features yielded better error 69.10-73.91% of the time. Relative error reduction ranged from 0.10 to 94.14% with mean of 28.77% and median of 23.91%. Dynamic ensemble parameters: .
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case_images: PMC10045477_01_fpubh-11-1079315-g0006.jpg
case_images: F6
case_images: As shown in Figure 6, in the evaluation period from 2020/11/16 to 2022/08/21 (+6, +13, +20, or +27 days depending on the evaluation window), using additional emoji features achieved better error 69.10-73.91% of the time.
case_images: Comparison with Google Cloud AI. The "forecast date" indicates the date when each system is executed to produce the predicted number of cases for the next 7, 14, 21, and 28 days. All the periods indicated by the forecast dates are taken from the "COVID-19 Public Forecasts" dataset by Google Cloud AI. Our system achieved better error in 76.92-81.87% of the time. Dynamic ensemble parameters: .
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case_images: PMC10045477_01_fpubh-11-1079315-g0007.jpg
case_images: F7
case_images: AI forecasting system. As shown in Figure 7, in the forecast periods reported by Google Cloud AI forecasting system, our system could achieve better prediction error in 76.92-81.87% of the time.
case_images: COVID-19 related social media (Twitter) reaction depicted by the ratio of weekly total tweet counts of "COVID-19 related tweets" vs. "all tweets" since January 2020. The reported number of infections is plotted in weekly sum.
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case_images: PMC10045477_01_fpubh-11-1079315-g0008.jpg
case_images: F8
case_images: Although with differences among the 6 countries appearing in the percentage of emoji usage in COVID-19 related tweets (Germany: 4.08%, India: 8.27%, Indonesia: 8.02%, Japan: 8.66%, South Korea: 2.84%, and Thailand: 4.60%), and the ratio of COVID-19 related social media reaction over general social media reaction (Figure 8 and Table 4), COVID-19 related tweets containing emoji can provide informative features contributing to the improvement of our COVID-19 case prediction system. In analyzing this situation, we see that, in the mentioned period, India was in an unprecedented epidemic wave when the raising and dropping of the number of cases were dramatic in a relatively shorter period of time comparing to the previous waves (Figure 8).