Study population and setting
This study estimated the impacts on SARS-CoV-2 transmission of non-pharmaceutical interventions (NPIs) in New York City (NYC) from March 1 to June 6, 2020. COVID-19 cases included all laboratory-confirmed cases reported to the NYC Department of Health and Mental Hygiene, and deaths combined probable and confirmed deaths associated with COVID-19. Mobility data, used as a proxy for contact rates, were obtained from Safegraph and consisted of anonymized, aggregated counts of visitors (measured by mobile phone location) to locations within each ZIP code. The authors used a neighborhood-specific SEIR network model fit to cases and deaths, stratified by age group, to estimate the effects on transmission of 1) all NPIs, 2) contact-reducing NPIs (such as stay-at-home orders, business closures, etc.), and 3) mask use. Mask use was assumed to explain the reduction in estimated transmission rate that was not accounted for by mobility declines during periods when face coverings were mandated in public places. Model projections beyond the end of the study period were compared to observed cases and deaths.
Summary of Main Findings
Observed, diagnosed COVID-19 cases displayed different age-specific patterns compared to model estimates of underlying infection rates: estimated infection rates were highest for those aged 25-44 years and 45-64 years, and rates for all age groups peaked the week of March 22 or one week later. During the first week of the NYC epidemic (beginning March 1), the estimated time-varying reproduction number (Rt) was 2.99, decreased to 1.37 after the stay-at-home order on March 22, and reached a minimum of 0.56 during the week of April 12. Mobility reductions (a proxy for contact rate reductions arising from stay-at-home mandates, school closures, and other contact-reducing interventions) were estimated to result in a 70.7% (95% CI: 65.0% to 76.4%) decline in Rt by the week of April 12. Assuming that effectiveness of mask use would account for the difference between estimates using a) a linear regression with mobility data alone and b) the SEIR model, the authors estimated that mask use reduced the transmission rate and infectious period by 3.4% (95% CI: -1.9% to 8.6%) over eight weeks, with higher effectiveness during the first month. Estimated mask effectiveness was highest in older age groups and remained stable during the study period (for the first month among those 65-74 years old: 20.8%, 95% CI: -0.1 to 41.6%; 75+ years old: 20.8%, 95% CI: 20.8%, 95% CI: -0.9 to 42.5%). Projections from the week of June 7 through the week of July 26 using parameters based on observed mobility data and estimated mask effectiveness underestimated cumulative cases by 27% and underestimated deaths by 2%.
The model was fit to both observed cases and deaths, and projections beyond the study period were compared to observed outcomes.
This is a preprint, and has not yet been subject to peer review. Aggregated zip-code-level mobility data are an imperfect proxy for actual mobility, which is in turn an imperfect proxy for contact rates. The method used to estimate the effectiveness of mask use relies on strong assumptions and oversimplifications (e.g., all residual reduction in predicted transmission rate after accounting for mobility decline is attributable to mask use; mask use affects both transmission risk per contact and infectious period; dates of mask mandates are a perfect proxy for actual mask use; etc.). Projections did not fit observed data well, which may be an indication that the effect of interventions was overestimated.
This study provides a useful picture of age-specific patterns of SARS-CoV-2 infection during the spring of 2020 in New York City.