State-level tracking of COVID-19 in the United States

Unwin, H. Juliette T.; Mishra, Swapnil; Bradley, Valerie C.; Gandy, Axel; Mellan, Thomas A.; Coupland, Helen; Ish-Horowicz, Jonathan; Vollmer, Michaela; Whittaker, Charles; Filippi, Sarah; Xi, Xiaoyue; Monod, Mélodie; Ratmann, Oliver; Hutchinson, Michael; Zhu, Fabian Valka Harrison; Hawryluk, Iwona; Milton, Philip; Ainslie, Kylie E. C.; Baguelin, Marc; Boonyasiri, Adhiratha; Brazeau, N; Cattarino, Lorenzo; Cucunubá, Zulma M.; Cuomo-Dannenburg, Gina; Dorigatti, Ilaria; Eales, Oliver; Eaton, Jeffrey W.; Elsland, S Van; FitzJohn, Richard G.; Gaythorpe, Katy A. M.; Green, William; Hinsley, Wes; Jeffrey, Benjamin; Knock, Edward; Laydon, Daniel J.; Lees, John A.; Nedjati-Gilani, Gemma; Nouvellet, Pierre; Okell, Lucy; Parag, Kris V; Siveroni, Igor; Thompson, Hayley; Walker, Patrick; Walters, Caroline E.; Watson, Oliver J; Whittles, Lilith K; Ghani, Azra C; Ferguson, Neil; Riley, Steven; Donnelly, Christl A.; Bhatt, Samir; Flaxman, Seth

doi:10.1101/2020.07.13.20152355

Cited by 64 publications

(129 citation statements)

References 18 publications

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“…The rapid spread of COVID-19 (Zhu et al, 2020;Li et al, 2020) has fundamentally disrupted the modern world. The first confirmed case in the United States was reported on January 21, 2020 (Holshue et al, 2020).…”

Section: The Early Stages Of the Coronavirus Pandemic In The Usmentioning

confidence: 99%

Misinformation During a Pandemic

et al. 2020

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Media outlets often present diverging, even conflicting, perspectives on reality-not only informing, but potentially misinforming audiences. We study the extent to which misinformation broadcast on mass media at the early stages of the coronavirus pandemic influenced health outcomes. We first document large differences in content between the two most popular cable news shows in the US, both on the same network, and in the adoption of preventative behaviors among viewers of these shows. Through both a selection-on-observables strategy and an instrumental variable approach, we find that areas with greater exposure to the show downplaying the threat of COVID-19 experienced a greater number of cases and deaths. We assess magnitudes through an epidemiological model highlighting the role of externalities and provide evidence that contemporaneous information exposure is a key underlying mechanism.

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Section: The Early Stages Of the Coronavirus Pandemic In The Usmentioning

confidence: 99%

Misinformation During a Pandemic

et al. 2020

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“…(13) Recent data from the Imperial College of London suggests that the public health policies by the State of Michigan were efffective in reducing the daily incidence, number of deaths, and the R t in Michigan. (14) There are limited data, however, on actual social distancing behaviors during the COVID-19 pandemic. In data from the United Kingdom between March 17-18, 2020, 45.2% reported they were taking social distancing measures, (15) which is lower than the rates we found in Michigan.…”

Section: Discussionmentioning

confidence: 99%

Social Distancing During the COVID-19 Pandemic: Quantifying the Practice in Michigan – a “Hotspot State” Early in the Pandemic – Using a Volunteer-Based Online Survey

Cassidy‐Bushrow

Baseer

Kippen

et al. 2020

Preprint

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Background: Public Health policies related to social distancing efforts during the COVID-19 pandemic helped slow the infection rate. However, individual-level factors associated with social distancing are largely unknown. We sought to examine social distancing during the COVID-19 pandemic in Michigan, an infection “hotspot” state in the United States early in the pandemic.Methods: Two surveys were distributed to Michigan residents via email lists and social media following COVID-19 related state mandates in March; 45,691 adults responded to the first survey and 8,512 to the second. Staying home ≥ 3 out of 5 previous days defined having more social distancing. Logistic regression models were used to examine potential factors associated with more social distancing.Results: Most respondents were women (86% in Survey 1, 87% in Survey 2). In Survey 1, 63% reported more social distancing, increasing to 78% in Survey 2. Female sex and having someone (or self) sick in the home were consistently associated with higher social distancing, while increasing age was positively associated in Survey 1 but negatively associated in Survey 2. Most respondents felt social distancing policies were important (88% in Survey 1; 91% in Survey 2). Conclusions: Michiganders responding to the surveys were both practicing and supportive of social distancing. State-level executive orders positively impacted behaviors early in the COVID-19 pandemic in Michigan. Additional supports are needed to help vulnerable populations practice social distancing, including older individuals.

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“…For different countries around the world, several researchers [1][2][3][4][5][6][7] have concluded that the number of people actually infected, or affected, by COVID-19 is far greater than the number of cases actually reported, or detected officially. Recent serological surveys for COVID-19 also indicate that the infected people outnumber detected people by about 12 times in Spain 8 and 6 to 24 times in the USA 9 .…”

Section: Introductionmentioning

confidence: 99%

Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model

Tiwari

Vyasarayani

Chatterjee

2020

Preprint

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People in many countries are now infected with COVID-19. By now, it is clear that the number of people infected is much more than the number of reported cases. To estimate the infected but undetected/unreported cases using a mathematical model, we can use a parameter called the probability of quarantining an infected individual. This parameter exists in the time-delayed SEIQR model (Scientific Reports, article number: 3505). Two limiting cases of a network of such models are used to estimate the undetected population. The first limit corresponds to the network collapsing onto a single node and is referred to as the mean-$\beta$ model. In the second case, the number of nodes in the network is infinite and results in a continuum model, treating the infectivity as statistically distributed. We use a shifted Pareto distribution to model the infectivity. This distribution has a long tail and incorporates the presence of super-spreaders that contribute to the disease progression. While both the models capture the {\em detected} numbers equally well, the predictions of {\em affected} numbers from the continuum model are more realistic. Results suggest that affected people outnumber detected people by one to two orders of magnitude in Spain, UK, Italy, and Germany.

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State-level tracking of COVID-19 in the United States

Cited by 64 publications

References 18 publications

Misinformation During a Pandemic

Misinformation During a Pandemic

Social Distancing During the COVID-19 Pandemic: Quantifying the Practice in Michigan – a “Hotspot State” Early in the Pandemic – Using a Volunteer-Based Online Survey

Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model

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