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Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19

Through multiple iterations of estimations using a total of p RSV time series, as shown in Tables 2 and 3, we can finally obtain an MSV time series for the search term from t − (n − 1) to t + (p − 1): Continuing from our previous example, we can eventually obtain the MSV time series for “Coronavirus (virus)” as follows: In this study, we opted to use RSV time series data with a length of 30 days (n=30), and the RSV time series for January 30, 2020, was used as the baseline against which other subsequent RSV

Amanda MY Chu, Andy C Y Chong, Nick H T Lai, Agnes Tiwari, Mike K P So

JMIR Public Health Surveill 2023;9:e42446

Improving Self-Care in Patients With Coexisting Type 2 Diabetes and Hypertension by Technological Surrogate Nursing: Randomized Controlled Trial

Improving Self-Care in Patients With Coexisting Type 2 Diabetes and Hypertension by Technological Surrogate Nursing: Randomized Controlled Trial

As shown in Table 1, there were no statistically significant differences between the groups with regard to baseline characteristics, except for experience in the use of computer-based self-monitoring systems (P=.02). Trial flowchart. CG: control group; IG: intervention group. Baseline characteristics by study group. Outcomes with direct reference to TSN effectiveness are displayed in Table 2.

Calvin Kalun Or, Kaifeng Liu, Mike K P So, Bernard Cheung, Loretta Y C Yam, Agnes Tiwari, Yuen Fun Emmy Lau, Tracy Lau, Pui Sze Grace Hui, Hop Chun Cheng, Joseph Tan, Michael Tow Cheung

J Med Internet Res 2020;22(3):e16769