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Register a new userCausal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission
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Accurately estimating the effectiveness of stay-at-home orders (SHOs) on reducing social contact and disease spread is crucial for mitigating pandemics. Leveraging individual-level location data for 10 million smartphones, we observe that by April 30th---when nine in ten Americans were under a SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this decline is causally attributable to SHOs, with wide demographic differences in compliance, most notably by political affiliation. Likely Trump voters reduce movement by 9% following a local SHO, compared to a 21% reduction among their Clinton-voting neighbors, who face similar exposure risks and identical government orders. Linking social distancing behavior with an epidemic model, we estimate that reductions in movement have causally reduced SARS-CoV-2 transmission rates by 49%.
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We investigate the treatment effect of the juvenile stay-at-home order (JSAHO) adopted in Saline County, Arkansas, from April 6 to May 7, in mitigating the growth of SARS-CoV-2 infection rates. To estimate the counterfactual control outcome for Saline County, we apply Difference-in-Differences and Synthetic Control design methodologies. Both approaches show that stay-at-home order (SAHO) significantly reduced the growth rate of the infections in Saline County during the period the policy was in effect, contrary to some of the findings in the literature that cast doubt on the general causal impact of SAHO with narrower scopes.
While the SARS-CoV-2 keeps spreading world-wide, comparing its evolution across different nations is a timely challenge of both theoretical and practical importance. The large variety of dissimilar and country-dependent epidemiological factors, in fact, makes extremely difficult to understand their influence on the epidemic trends within a unique and coherent framework. We present a geometric framework to characterize, in an integrated and low-dimensional fashion, the epidemic plume-like trajectories traced by the infection rate, $I$, and the fatality rate, $D$, in the $(I,D)$ plane. Our analysis enables the definition of an epidemiometric system based on three geometric observables rating the SARS-CoV-2 pandemic events via scales analogous to those for the magnitude and the intensity of seismic events. Being exquisitely geometric, our framework can be applied to classify other epidemic data and secondary waves, raising the possibility of designing epidemic alerts or early warning systems to enhance public and governmental responses to a rapidly emerging outbreak.
Background: During 2021 several new variants of the SARS-CoV-2 virus appeared with both increased levels of transmissibility and virulence with respect to the original wild variant. The Delta (B.1.617.2) variation, first seen in India, dominates COVID-19 infections in several large countries including the United States and India. Most recently, the Lambda variant of interest with increased resistance to vaccines has spread through much of South America. Objective: This research explores the degree to which new variants of concern 1) generate spikes and waves of fluctuations in the daily case fatality rates (CFR) across countries in several regions in the face of increasing levels of vaccination of national populations and 2) may increase the vulnerability of persons with certain comorbidities. Methods: This study uses new, openly available, epidemiological statistics reported to the relevant national and international authorities for countries across the Americas, Europe, Africa, Asia and the Middle East. Daily CFRs and correlations of fatal COVID-19 infections with potential cofactors are computed for the first half of 2021 that has been dominated by the wide spread of several variants of concern as denoted by the World Health Organization. Results: The analysis yields a new quantitative measure of the temporal dynamics of mortality due to SARS-CoV-2 infections in the form of variations of a proxy case fatality rate compared on a country to-country basis in the same region. It also finds minimal variation of correlation between the cofactors based on WHO data and on the average apparent case fatality rate.
The quantitative analysis of viral transmission risk in public places such as schools, offices, university lecture halls, hospitals, museums, theaters or shopping malls makes it possible to identify the effective levers for a proactive policy of health security and to evaluate the reduction in transmission thus obtained. The contribution to the epidemic propagation of SARS-CoV-2 in such public spaces can be reduced in the short term to a level compatible with an epidemic decline, i.e. with an overall epidemic reproduction rate below one. Here, we revisit the quantitative assessment of indoor and outdoor transmission risk. We show that the long range aerosol transmission is controlled by the flow rate of fresh air and by the mask filtering quality, and is quantitatively related to the CO2 concentration, regardless the room volume and the number of people. The short range airborne transmission is investigated experimentally using dedicated dispersion experiments performed in two French shopping malls. Exhaled aerosols are dispersed by turbulent draughts in a cone, leading to a concentration inversely proportional to the squared distance and to the flow velocity. We show that the average infection dose, called the viral quantum, can be consistently determined from epidemiological and biological experimental data. Practical implications. The results provide a rational design of sanitary policies to prevent the dominant routes of viral transmission by reinforced ventilation, air purification, mechanical dispersion by fans and incentives for correct wearing of quality masks (surgical mask, possibly covered by a fabric mask, or non-medical FFP2 masks). Combined, such measures significantly reduce the airborne transmission risk of SARS-CoV-2, with a quantitative assessment.
SARS-CoV-2 causing COVID-19 disease has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used and misused to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, is dominated by a small number of individuals, and is driven by super-spreading events (SSEs). The distinct transmission features, such as high stochasticity under low prevalence, and the central role played by SSEs on transmission dynamics, should not be overlooked. Many explosive SSEs have occurred in indoor settings stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, fish factories, cruise ships, family gatherings, parties and night clubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity that outbreak can be effectively contained with targeted interventions to eliminate SSEs. Here, we describe the potential types of SSEs, how they influence transmission, and give recommendations for control of SARS-CoV-2.
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