Improved prediction of new COVID-19 cases using a simple vector autoregressive model: Evidence from seven New York State counties

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and SARS. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback-they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission.

Rural-Urban Differences in Diagnosed Cervical Artery Dissection in New York State

<b><i>Background:</i></b> Cervical artery dissection (CeAD) is a leading cause of stroke in young adults. Incidence estimates may be limited by under- or overdiagnosis. <b><i>Objective:</i></b> We aimed to investigate if CeAD diagnosis would be higher in urban centers compared to rural regions of New York State (NYS). <b><i>Methods:</i></b> For this ecological study, administrative codes were used to identify CeAD discharges in the NYS Statewide Planning and Research Cooperative System (SPARCS) from 2009 to 2014. Rural Urban Commuting Area (RUCA) codes were taken from the US Department of Agriculture and included the classifications metropolitan, micropolitan, small town, and rural. Negative binomial models were used to calculate effect estimates and 95% confidence limits (e^β; 95% CL) for the association between RUCA classification and the number of dissections per ZIP code. Models were further adjusted by population. <b><i>Results:</i></b> Population information was obtained from the US Census Bureau on 1,797 NYS ZIP codes (70.7% of NYS ZIP codes), 826 of which had at least 1 CeAD-related discharge from 2009 to 2014. Nonrural ZIP codes were more likely to report more CeAD cases relative to rural areas even after adjusting for population (metropolitan effect = e^β 5.00; 95% CI: 3.75–6.66; micropolitan effect 3.02; 95% CI: 2.16–4.23; small town effect 2.34; 95% CI: 1.58–3.47). <b><i>Conclusions:</i></b> CeAD diagnosis correlates with population density as defined by rural-urban status. Our results could be due to underdiagnosis in rural areas or overdiagnosis with increasing urbanicity.

Sex disparities in COVID-19 mortality vary considerably across time: The case of New York State

In order to characterize how sex disparities in COVID-19 mortality evolved over time in New York State (NY), we analyzed sex-disaggregated data from the US Gender/Sex COVID-19 Data Tracker from March 14, 2020 to August 28, 2021. We defined six different time periods and calculated mortality rates by sex and mortality rate ratios, both cumulatively and for each time period separately. As of August 28, 2021, 19 227 (44.2%) women and 24 295 (55.8%) men died from COVID-19 in NY. 72.7% of the cumulative difference in the number of COVID-19 deaths between women and men was accrued between March 14 and May 4, 2020. During this period, the COVID-19 mortality rate ratio for men compared to women was 1.56 (95% CI: 1.52-1.61). In the five subsequent time periods, the corresponding ratio ranged between 1.08 (0.98-1.18) and 1.24 (1.15-1.34). While the cumulative mortality rate ratio of men compared to women was 1.34 (1.31-1.37), the ratio equals 1.19 (1.16-1.22) if deaths during the initial COVID-19 surge are excluded from the analysis. This article shows that in NY the magnitude of sex disparities in COVID-19 mortality was not stable across time. While the initial surge in COVID-19 mortality was characterized by stark sex disparities, these were greatly attenuated after the introduction of public health controls.

Implementation of a statewide Advocacy Week: The New York State experience

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