How public perceptions of social distancing evolved over a critical time period: communication lessons learnt from the American state of Wisconsin

Chen, Kaiping; Bao, Luye; Shao, Anqi; Ho, Pauline; Yang, Shujuan; Wirz, Christopher D.; Brossard, Dominique; Bräuer, Markus; Brown, Lou

doi:10.22323/2.19050211

Cited by 14 publications

(12 citation statements)

References 26 publications

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“…More importantly, these disparities among age groups and race and ethnicity groups in the COVID-19 outcome and in the self-reported social distancing behavior flags an urgent policy need. Policy makers and health communicators must investigate the underlying reasons for demographically specific barriers to adoption of social distancing and other mitigation measures (71). The Walktrap algorithm uses short random walks to detect communities in a large graph.…”

Section: Intracounty Modeling Resultsmentioning

confidence: 99%

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

Hou

Gao

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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102

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The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible–exposed–infectious–removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What’s more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

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Section: Intracounty Modeling Resultsmentioning

confidence: 99%

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

Hou

Gao

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

102

View full text Add to dashboard Cite

show abstract

“…More importantly, this disparities among age groups and race & ethnicity groups in the COVID-19 outcome and in the self-reported social distancing behavior flags an urgent policy need. Policymakers and health communicators must investigate the underlying reasons for demographically specific barriers to adoption of social distancing and other mitigation measures 54 .…”

Section: Resultsmentioning

confidence: 99%

Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race

Hou¹,

Gao

Li³

et al. 2020

Preprint

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The novel coronavirus disease (COVID-19) pandemic is a global threat presenting health, economic and social challenges that continue to escalate. Meta-population epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health and shaping policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What's more, variation of intra-county environments creates spatial heterogeneity of transmission in different sub-regions. To address this issue, we develop a new human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behavior. This new modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and human mobility, business foot-traffic, race & ethnicity, and age-group are then investigated. The results reveal that in a college town (Dane County) the most important heterogeneity is spatial, while in a large city area (Milwaukee County) racial-ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate on various reopening policies, which suggests that policymakers may need to take these heterogeneities into account very carefully when designing policies for mitigating the spread of COVID-19 and reopening.

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“…Finally, our findings further suggest that users should be more alert to manipulation in messages containing strong, negative emotions. Awareness and skepticism of anger-or fear-based messages may help individuals become more resistant to misinformation (Chen et al, 2020b;Pennycook et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Platform Effects on Alternative Influencer Content: Understanding How Audiences and Channels Shape Misinformation Online

2021

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People are increasingly exposed to science and political information from social media. One consequence is that these sites play host to “alternative influencers,” who spread misinformation. However, content posted by alternative influencers on different social media platforms is unlikely to be homogenous. Our study uses computational methods to investigate how dimensions we refer to as audience and channel of social media platforms influence emotion and topics in content posted by “alternative influencers” on different platforms. Using COVID-19 as an example, we find that alternative influencers’ content contained more anger and fear words on Facebook and Twitter compared to YouTube. We also found that these actors discussed substantively different topics in their COVID-19 content on YouTube compared to Twitter and Facebook. With these findings, we discuss how the audience and channel of different social media platforms affect alternative influencers’ ability to spread misinformation online.

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How public perceptions of social distancing evolved over a critical time period: communication lessons learnt from the American state of Wisconsin

Cited by 14 publications

References 26 publications

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race

Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race

Platform Effects on Alternative Influencer Content: Understanding How Audiences and Channels Shape Misinformation Online

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