Numerous observational studies have attempted to identify risk factors for infection with SARS-CoV-2 and COVID-19 disease outcomes. Studies have used datasets sampled from patients admitted to hospital, people tested for active infection, or people who volunteered to participate. Here, we highlight the challenge of interpreting observational evidence from such non-representative samples. Collider bias can induce associations between two or more variables which affect the likelihood of an individual being sampled, distorting associations between these variables in the sample. Analysing UK Biobank data, compared to the wider cohort the participants tested for COVID-19 were highly selected for a range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the mechanisms inducing these problems, and approaches that could help mitigate them. While collider bias should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage. Many published studies of the current SARS-CoV-2 pandemic have analysed data from non-representative samples from populations. Here, using UK BioBank samples, Gibran Hemani and colleagues discuss the potential for such studies to suffer from collider bias, and provide suggestions for optimising study design to account for this.

/pdf/collider-bias-undermines-our-understanding-of-covid-19-31399ms7i6.pdf

Collider bias undermines our understanding of COVID-19 disease risk and severity

Spatial analysis and GIS in the study of COVID-19. A review.

Observational data on COVID-19 including hypothesised risk factors for infection and progression are accruing rapidly, often from non-random sampling such as hospital admissions, targeted testing or voluntary participation. Here, we highlight the challenge of interpreting observational evidence from such samples of the population, which may be affected by collider bias. We illustrate these issues using data from the UK Biobank in which individuals tested for COVID-19 are highly selected for a wide range of genetic, behavioural, cardiovascular, demographic, and anthropometric traits. We discuss the sampling mechanisms that leave aetiological studies of COVID-19 infection and progression particularly susceptible to collider bias. We also describe several tools and strategies that could help mitigate the effects of collider bias in extant studies of COVID-19 and make available a web app for performing sensitivity analyses. While bias due to non-random sampling should be explored in existing studies, the optimal way to mitigate the problem is to use appropriate sampling strategies at the study design stage.

https://www.medrxiv.org/content/medrxiv/early/2020/05/20/2020.05.04.20090506.full.pdf

Media outlets often present diverging, even con?icting, 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 in?uenced health outcomes We ?rst document large di?erences 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 ?nd 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

/pdf/misinformation-during-a-pandemic-3p59njrlqq.pdf

Misinformation During a Pandemic

Temperature and precipitation associate with Covid-19 new daily cases: A correlation study between weather and Covid-19 pandemic in Oslo, Norway.

https://www.nber.org/system/files/working_papers/w26990/w26990.pdf

The Geographic Spread of COVID-19 Correlates with Structure of Social Networks as Measured by Facebook

We use aggregated data from Facebook to show that COVID-19 is more likely to spread between regions with stronger social network connections. Areas with more social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally had more confirmed COVID-19 cases by the end of March. These relationships hold after controlling for geographic distance to the hotspots as well as the population density and demographics of the regions. As the pandemic progressed in the U.S., a county's social proximity to recent COVID-19 cases and deaths predicts future outbreaks over and above physical proximity and demographics. In part due to its broad coverage, social connectedness data provides additional predictive power to measures based on smartphone location or online search data. These results suggest that data from online social networks can be useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19.

The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

We use de-identified and aggregated data from Facebook to study the structure of social networks across European regions. Social connectedness declines strongly in geographic distance and at country borders. Historical borders and unions—such as the Austro-Hungarian Empire, Czechoslovakia, and East/West Germany—shape present-day social connectedness over and above today’s political boundaries and other controls. All else equal, social connectedness is stronger between regions with residents of similar ages and education levels, as well as between regions that share a language and religion. In contrast, region-pairs with dissimilar incomes tend to be more connected, likely due to increased migration from poorer to richer regions.

/pdf/the-determinants-of-social-connectedness-in-europe-4gkoln65g7.pdf

The Determinants of Social Connectedness in Europe

We show that social network exposure to COVID-19 cases shapes individuals’ beliefs and behaviors concerning the coronavirus. We use de-identified data from Facebook to document that individuals with friends in areas with worse COVID-19 outbreaks reduce their mobility more than otherwise similar individuals with friends in less affected areas. The effects are quantitatively large and long-lasting: a one standard deviation increase in friend-exposure to COVID-19 cases in March 2020 results in a 1.2 percentage point increase in the probability of staying home on a given day through at least the end of May 2020. As the pandemic progresses—and the characteristics of individuals with the highest friend-exposure vary— changes in friend-exposure continue to drive changes in social distancing behavior, ruling out many unobserved effects as drivers of our results. We also show that individuals with higher friend-exposure to COVID-19 are more likely to publicly post in support of social distancing measures and less likely to be members of groups advocating to "reopen"" the economy. These findings suggest that friends can influence individuals’ beliefs about the risks of the disease and thereby induce them to engage in mitigating public health behavior."

/pdf/social-networks-shape-beliefs-and-behavior-evidence-from-ghv9obmk51.pdf

Dominic Russel

Papers

The Geographic Spread of COVID-19 Correlates with Structure of Social Networks as Measured by Facebook

The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook

The Determinants of Social Connectedness in Europe

Social networks shape beliefs and behavior: evidence from social distancing during the COVID-19 pandemic