Crowding and the shape of COVID-19 epidemics

Rader, Benjamin; Scarpino, Samuel V.; Nande, Anjalika; Hill, Alison L.; Adlam, Ben; Reiner, Robert C.; Pigott, David M.; Gutierrez, Bernardo; Zarebski, Alexander E.; Shrestha, Munik; Brownstein, John S.; Castro, Márcia C.; Dye, Christopher; Tian, Huaiyu; Pybus, Oliver G.; Kraemer, Moritz U. G.

doi:10.1038/s41591-020-1104-0

Cited by 240 publications

(221 citation statements)

References 47 publications

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“…For now, there is no one relationship between density and disease that stands out universally. One study of COVID-19 in urban and rural regions of the Netherlands hypothesized that "is evident that the factors explaining different levels of incidence, hospitalisation and mortality are more complex than outright population density" (Boterman, 2020, p. 8); another study of Chinese and Italian cities finds that "spatial context, especially crowding, are important factors for assessing the shape of epidemic curves" (Rader et al, 2020). While the empirical analyses of COVIDdensities are not yet fully available, we do know how they relate to the political and action agendas linked to urbanism.…”

Section: Political Pathologies: Geographies Of Infectious Diseasementioning

confidence: 99%

The density dilemma: there is always too much and too little of it

Keil

2020

Urban Geography

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Section: Political Pathologies: Geographies Of Infectious Diseasementioning

confidence: 99%

The density dilemma: there is always too much and too little of it

Keil

2020

Urban Geography

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“…Restrictions on human mobility, including travel limitations and social isolation, have been found to affect significantly the control of COVID-19 spread [20]; ventilation in indoor public spaces, such as work environments, is positively associated with a decrease in transmission [21,22]; the distance between tables and ventilation have been identified as key measures to prevent infection in public places such as restaurants [23]; and wearing a medical mask and keeping social distance have been widely claimed as the most effective control measures in public open spaces [24,25]. Overall, the role of urban crowding in the spread of the pandemic has been largely discussed [26,27].…”

Section: Introductionmentioning

confidence: 99%

TV News and COVID-19: Media Influence on Healthy Behavior in Public Spaces

Scopelliti

Pacilli

Aquino

2021

IJERPH

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The COVID-19 outbreak has dramatically changed our life. Despite the rapid growth of scientific publications about medical aspects of the pandemic, less has been explored about the effects of media communication regarding COVID-19 on healthy behaviors. Yet, the scientific literature has widely debated on how media can influence people’s health-related evaluations, emotions, and behaviors. To fill this gap, the aim of this study was to investigate the relationships between media exposure, people’s attitudes and emotions toward media contents, and healthy behaviors related to the use of public spaces, such as avoiding crowded places, wearing face masks, and maintaining social distance. A questionnaire referring to these variables was administered to an opportunistic sample of 174 participants in Italy during the off-peak period of the COVID-19 outbreak and before restrictions to mobility were extended to the whole country. Results showed that media exposure, the perception of social initiatives of prevention, and moderate levels of fear increase healthier behaviors in the use of public spaces. Perceiving alarming information did not significantly predict healthy behaviors in the use of public spaces. Results are discussed with reference to the previous literature. Suggestions to media communication to increase preventive behaviors during emergencies are also provided.

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“…The quadratic relationships of youth and GINI inequality with R 0 indicate a more complex underlying tradeoff than is previously appreciated, which was either monotonically positive (36,61) or negative (1,36). A large youth population may confer resilience against the disease (36,37) (34). However, it is unclear why a low level of city dwelling is also associated with a high R 0 , although the rise is relatively slight.…”

Section: Discussionmentioning

confidence: 90%

“…All of these categories have been suggested previously as possible factors for COVID-19 transmission. The most common factors previously studied were temperature (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24), pollution (13,(25)(26)(27)(28)(29)(30)(31), precipitation/humidity (18,32,33), population density (34,35), age structure (1,36,37), and population size (1,11,31). For these and additional covariates either previously studied or only mentioned in the media, we rely on statistics measured at a national level.…”

Section: Introductionmentioning

confidence: 99%

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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Crowding and the shape of COVID-19 epidemics

Cited by 240 publications

References 47 publications

The density dilemma: there is always too much and too little of it

The density dilemma: there is always too much and too little of it

TV News and COVID-19: Media Influence on Healthy Behavior in Public Spaces

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

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