Differences in beliefs about COVID-19 by gun ownership: a cross-sectional survey of Texas adults

OBJECTIVES: We investigated the association between gun ownership and perceptions about COVID-19 among Texas adults as the pandemic emerged. We considered perceived likelihood that the pandemic would lead to civil unrest, perceived importance of taking precautions to prevent transmission and perceptions that the threat of COVID-19 has been exaggerated. METHODS: Data were collected from 5 to 12 April 2020, shortly after Texas’ stay-at-home declaration. We generated a sample using random digit dial methods for a telephone survey (n=77, response rate=8%) and by randomly selecting adults from an ongoing panel to complete the survey online (n=1120, non-probability sample). We conducted a logistic regression to estimate differences in perceptions by gun ownership. To account for bias associated with use of a non-probability sample, we used Bayesian data integration and ran linear regression models to produce more accurate measures of association. RESULTS: Among the 60% of Texas adults who reported gun ownership, estimates of past 7-day gun purchases, ammunition purchases and gun carrying were 15% (n=78), 20% (n=100) and 24% (n=130), respectively. We found no evidence of an association between gun ownership with perceived importance of taking precautions to prevent transmission or with perceived likelihood of civil unrest. Results from the logistic regression (OR 1.27, 95% CI 0.99 to 1.63) and the linear regression (β=0.18, 95% CI 0.07 to 0.29) suggest that gun owners may be more likely to believe the threat of COVID-19 was exaggerated. CONCLUSIONS: Compared with those without guns, gun owners may have been inclined to downplay the threat of COVID-19 early in the pandemic

Assessing time series models for forecasting international migration : lessons from the United Kingdom

Funding: This work was funded by the Migration Advisory Committee (MAC), UK Home Office, under the Home Office Science contract HOS/14/040, and also supported by the ESRC Centre for Population Change grant ES/K007394/1.Migration is one of the most unpredictable demographic processes. The aim of this article is to provide a blueprint for assessing various possible forecasting approaches in order to help safeguard producers and users of official migration statistics against misguided forecasts. To achieve that, we first evaluate the various existing approaches to modelling and forecasting of international migration flows. Subsequently, we present an empirical comparison of ex post performance of various forecasting methods, applied to international migration to and from the United Kingdom. The overarching goal is to assess the uncertainty of forecasts produced by using different forecasting methods, both in terms of their errors (biases) and calibration of uncertainty. The empirical assessment, comparing the results of various forecasting models against past migration estimates, confirms the intuition about weak predictability of migration, but also highlights varying levels of forecast errors for different migration streams. There is no single forecasting approach that would be well suited for different flows. We therefore recommend adopting a tailored approach to forecasts, and applying a risk management framework to their results, taking into account the levels of uncertainty of the individual flows, as well as the differences in their potential societal impact.Publisher PDFPeer reviewe

Inferring transient dynamics of human populations from matrix non-normality

This is the final version of the article. Available from Springer Verlag via the DOI in this record.In our increasingly unstable and unpredictable world, population dynamics rarely settle uniformly to long-term behaviour. However, projecting period-by-period through the preceding fluctuations is more data-intensive and analytically involved than evaluating at equilibrium. To efficiently model populations and best inform policy, we require pragmatic suggestions as to when it is necessary to incorporate short-term transient dynamics and their effect on eventual projected population size. To estimate this need for matrix population modelling, we adopt a linear algebraic quantity known as non-normality. Matrix non-normality is distinct from normality in the Gaussian sense, and indicates the amplificatory potential of the population projection matrix given a particular population vector. In this paper, we compare and contrast three well-regarded metrics of non-normality, which were calculated for over 1000 age-structured human population projection matrices from 42 European countries in the period 1960 to 2014. Non-normality increased over time, mirroring the indices of transient dynamics that peaked around the millennium. By standardising the matrices to focus on transient dynamics and not changes in the asymptotic growth rate, we show that the damping ratio is an uninformative predictor of whether a population is prone to transient booms or busts in its size. These analyses suggest that population ecology approaches to inferring transient dynamics have too often relied on suboptimal analytical tools focussed on an initial population vector rather than the capacity of the life cycle to amplify or dampen transient fluctuations. Finally, we introduce the engineering technique of pseudospectra analysis to population ecology, which, like matrix non-normality, provides a more complete description of the transient fluctuations than the damping ratio. Pseudospectra analysis could further support non-normality assessment to enable a greater understanding of when we might expect transient phases to impact eventual population dynamics.This work was funded by Wellcome Trust New Investigator 103780 to TE, who is also funded by NERC Fellowship NE/J018163/1. JB gratefully acknowledges the ESRC Centre for Population Change ES/K007394/1