Trending on Social Media: Integrating Social Media into Infectious Disease Dynamics

Sooknanan, Joanna; Comissiong, Donna M. G.

doi:10.1007/s11538-020-00757-4

Cited by 25 publications

(21 citation statements)

References 39 publications

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“…However, social media may help slow a contagion’s spread once scientific information becomes available. Our result highlights the need to consider the dynamic role that social media plays in epidemics [ 69 ].…”

Section: Discussionmentioning

confidence: 91%

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

2021

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Objective To assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus. Methods We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0. Findings We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors—population between 20–34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality—as having strong relationships with R0, across countries. An intermediate level of youth and GINI inequality are associated with high R0, (n-shape relationships), while high city population and high social media use are associated with high R0. Pollution, temperature, and humidity did not have strong relationships with R0 but were positive. Conclusion Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.

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Section: Discussionmentioning

confidence: 91%

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

2021

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“…However, social media may help slow a contagion's spread once scientific information becomes available. Our result highlights the need to consider the dynamic role that social media plays in epidemics (60).…”

Section: Discussionmentioning

confidence: 78%

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

Kong

Tekwa

Gignoux‐Wolfsohn

2021

Preprint

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ObjectiveTo assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors characterize initial vulnerability to the virus.MethodsWe fit logistic growth curves to reported daily case numbers, up to the first epidemic peak. This fitting estimates R0. We then use a generalized additive model to discern the effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0.FindingsWe found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R0. An intermediate level of youth and GINI inequality are associated with high R0, while high city population and high social media use are associated with high R0. Environmental and climate factors were not found to have strong relationships with R0.ConclusionStudies that aim to measure the effectiveness of interventions should account for the intrinsic differences between populations.

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“…Despite some limitations and concerns, a better understanding of the behavioural change induced by social media can strengthen mathematical modelling efforts and assist in the development of public policy so as to make the best use of this increasingly ubiquitous resource in controlling the spread of disease [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region

et al. 2021

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The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people “overcrowded” social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis.

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Trending on Social Media: Integrating Social Media into Infectious Disease Dynamics

Cited by 25 publications

References 39 publications

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

Social, economic, and environmental factors influencing the basic reproduction number of COVID-19 across countries

CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region

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