Associations between institutional-social-ecological factors and COVID -19 case-fatality: Evidence from 134 countries using multiscale geographically weighted regression (MGWR)

Shi, Xuerui; Ling, Gabriel Hoh Teck; Leng, Pau Chung; Rusli, Noradila; Matusin, Ak Mohd Rafiq Ak

doi:10.1016/j.onehlt.2023.100551

Cited by 3 publications

(2 citation statements)

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“…Social media applications and news media have been critical for communication between officials and the public during the COVID-19 pandemic to share public health information and mitigate the spread of the virus [43]. Our study theorizes that the findings of COVID-19-linked GDELT articles and tweets preceding reports of COVID-19 infections are an indicator of how news media and social media were globally used to encourage the public to take preventative action to mitigate disease spread.…”

Section: Discussionmentioning

confidence: 88%

“…This process cannot be performed using the original time series datasets because the cross-correlation function (CCF) value is affected by the time series structure of the input series and any long-term common patterns between the input and response series, which results in dependencies [43]. These interdependencies can be removed through prewhitening.…”

Section: Cross-correlation Analysismentioning

confidence: 99%

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Temporal Relationship between Daily Reports of COVID-19 Infections and Related GDELT and Tweet Mentions

Owuor,

Hochmair

2023

Geographies

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Social media platforms are valuable data sources in the study of public reactions to events such as natural disasters and epidemics. This research assesses for selected countries around the globe the time lag between daily reports of COVID-19 cases and GDELT (Global Database of Events, Language, and Tone) and Twitter (X) COVID-19 mentions between February 2020 and April 2021 using time series analysis. Results show that GDELT articles and tweets preceded COVID-19 infections in Australia, Brazil, France, Greece, India, Italy, the U.S., Canada, Germany, and the U.K., while for Poland and the Philippines, tweets preceded and GDELT articles lagged behind COVID-19 disease incidences, respectively. This shows that the application of social media and news data for surveillance and management of pandemics needs to be assessed on a case-by-case basis for different countries. It also points towards the applicability of time series data analysis for only a limited number of countries due to strict data requirements (e.g., stationarity). A deviation from generally observed lag patterns in a country, i.e., periods with low COVID-19 infections but unusually high numbers of COVID-19-related GDELT articles or tweets, signals an anomaly. We use the seasonal hybrid extreme Studentized deviate test to detect such anomalies. This is followed by text analysis of news headlines from NewsBank and Google on the date of these anomalies to determine the probable event causing an anomaly, which includes elections, holidays, and protests.

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Section: Discussionmentioning

confidence: 88%

Section: Cross-correlation Analysismentioning

confidence: 99%