scholarly journals Evaluation of Nowcasting for Real-Time COVID-19 Tracking — New York City, March–May 2020

2020 ◽  
Author(s):  
Sharon K. Greene ◽  
Sarah F. McGough ◽  
Gretchen M. Culp ◽  
Laura E. Graf ◽  
Marc Lipsitch ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Negative Binomial ◽  
Situational Awareness ◽  
Prediction Interval ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Moving Window

AbstractTo account for delays between specimen collection and report, the New York City Department of Health and Mental Hygiene used a time-correlated Bayesian nowcasting approach to support real-time COVID-19 situational awareness. We retrospectively evaluated nowcasting performance for case counts among residents diagnosed during March–May 2020, a period when the median reporting delay was 2 days. Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days the nowcasts were conducted, with Mondays having the lowest mean absolute error, of 183 cases in the context of an average daily weekday case count of 2,914. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported health department leadership in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.

scholarly journals Nowcasting for Real-Time COVID-19 Tracking in New York City: An Evaluation Using Reportable Disease Data From Early in the Pandemic (Preprint)

2020 ◽  
Author(s):  
Sharon K Greene ◽  
Sarah F McGough ◽  
Gretchen M Culp ◽  
Laura E Graf ◽  
Marc Lipsitch ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Negative Binomial ◽  
Prediction Interval ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Moving Window ◽  
Trend Monitoring

BACKGROUND Nowcasting approaches enhance the utility of reportable disease data for trend monitoring by correcting for delays, but implementation details affect accuracy. OBJECTIVE To support real-time COVID-19 situational awareness, the New York City Department of Health and Mental Hygiene used nowcasting to account for testing and reporting delays. We conducted an evaluation to determine which implementation details would yield the most accurate estimated case counts. METHODS A time-correlated Bayesian approach called Nowcasting by Bayesian Smoothing (NobBS) was applied in real time to line lists of reportable disease surveillance data, accounting for the delay from diagnosis to reporting and the shape of the epidemic curve. We retrospectively evaluated nowcasting performance for confirmed case counts among residents diagnosed during the period from March to May 2020, a period when the median reporting delay was 2 days. RESULTS Nowcasts with a 2-week moving window and a negative binomial distribution had lower mean absolute error, lower relative root mean square error, and higher 95% prediction interval coverage than nowcasts conducted with a 3-week moving window or with a Poisson distribution. Nowcasts conducted toward the end of the week outperformed nowcasts performed earlier in the week, given fewer patients diagnosed on weekends and lack of day-of-week adjustments. When estimating case counts for weekdays only, metrics were similar across days when the nowcasts were conducted, with Mondays having the lowest mean absolute error of 183 cases in the context of an average daily weekday case count of 2914. CONCLUSIONS Nowcasting using NobBS can effectively support COVID-19 trend monitoring. Accounting for overdispersion, shortening the moving window, and suppressing diagnoses on weekends—when fewer patients submitted specimens for testing—improved the accuracy of estimated case counts. Nowcasting ensured that recent decreases in observed case counts were not overinterpreted as true declines and supported officials in anticipating the magnitude and timing of hospitalizations and deaths and allocating resources geographically.

A Conversation and Two Epilogues

2016 ◽  
Author(s):  
Judy Malloy
Keyword(s):  
New York ◽  
New York City ◽  
Los Angeles ◽  
Real Time ◽  
York City ◽  
Cultural Centers ◽  
Main Street ◽  
The World ◽  
As If

When Kit Galloway and Sherrie Rabinowitz arrived in Telluride for Tele-Community in the summer of 1993, it seemed as if the whole town joined them on Main Street, as using slow scan video they connected townspeople and visiting digerati with artists, universities, and cultural centers around the world. Their Electronic Café had already presented New York City pedestrians with display windows of people waving and talking real time from Los Angeles (...

Real-Time Measurements of SO2, H2CO, and CH4Emissions from In-Use Curbside Passenger Buses in New York City Using a Chase Vehicle

2005 ◽  
Vol 39 (20) ◽  
pp. 7984-7990 ◽  
Author(s):  
Scott C. Herndon ◽  
Joanne H. Shorter ◽  
Mark S. Zahniser ◽  
Joda Wormhoudt ◽  
David D. Nelson ◽  
...  
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City

Monitoring Emergency Department Visits From Puerto Rico in the Aftermath of Hurricane Maria Using Syndromic Surveillance – New York City, 2017

2019 ◽  
Vol 14 (1) ◽  
pp. 44-48
Author(s):  
Priscilla W. Wong ◽  
Hilary B. Parton
Keyword(s):  
Emergency Department ◽  
New York ◽  
New York City ◽  
Puerto Rico ◽  
York City ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
Syndromic Surveillance System ◽  
Ed Visits

ABSTRACTObjective:Syndromic surveillance has been useful for routine surveillance on a variety of health outcomes and for informing situational awareness during public health emergencies. Following the landfall of Hurricane Maria in 2017, the New York City (NYC) Department of Health and Mental Hygiene (DOHMH) implemented an enhanced syndromic surveillance system to characterize related emergency department (ED) visits.Methods:ED visits with any mention of specific key words (“Puerto,” “Rico,” “hurricane,” “Maria”) in the ED chief complaint or Puerto Rico patient home Zip Code were identified from the DOHMH syndromic surveillance system in the 8-week window leading up to and following landfall. Visit volume comparisons pre- and post-Hurricane Maria were performed using Fisher’s exact test.Results:Analyses identified an overall increase in NYC ED utilization relating to Puerto Rico following Hurricane Maria landfall. In particular, there was a small but significant increase in visits involving a medication refill or essential medical equipment. Visits for other outcomes, such as mental illness, also increased, but the differences were not statistically significant.Conclusions:Gaining this situational awareness of medical service use was informative following Hurricane Maria, and, following any natural disaster, the same surveillance methods could be easily established to aid an effective emergency response.

Excess Deaths in Mexico City and New York City During the COVID-19 Pandemic, March to August 2020

2021 ◽  
pp. e1-e4
Author(s):  
Martín Lajous ◽  
Rodrigo Huerta-Gutiérrez ◽  
Joseph Kennedy ◽  
Donald R. Olson ◽  
Daniel M. Weinberger
Keyword(s):  
Public Health ◽  
New York ◽  
New York City ◽  
Mexico City ◽  
York City ◽  
Excess Mortality ◽  
Negative Binomial ◽  
Age Groups ◽  
Mitigation Strategies ◽  
Excess Deaths

Objectives. To estimate all-cause excess deaths in Mexico City (MXC) and New York City (NYC) during the COVID-19 pandemic. Methods. We estimated expected deaths among residents of both cities between March 1 and August 29, 2020, using log-linked negative binomial regression and compared these deaths with observed deaths during the same period. We calculated total and age-specific excess deaths and 95% prediction intervals (PIs). Results. There were 259 excess deaths per 100 000 (95% PI = 249, 269) in MXC and 311 (95% PI = 305, 318) in NYC during the study period. The number of excess deaths among individuals 25 to 44 years old was much higher in MXC (77 per 100 000; 95% PI = 69, 80) than in NYC (34 per 100 000; 95% PI = 30, 38). Corresponding estimates among adults 65 years or older were 1263 (95% PI = 1199, 1317) per 100 000 in MXC and 1581 (95% PI = 1549, 1621) per 100 000 in NYC. Conclusions. Overall, excess mortality was higher in NYC than in MXC; however, the excess mortality rate among young adults was higher in MXC. Public Health Implications. Excess all-cause mortality comparisons across populations and age groups may represent a more complete measure of pandemic effects and provide information on mitigation strategies and susceptibility factors. (Am J Public Health. Published online ahead of print September 9, 2021: e1–e4. https://doi.org/10.2105/AJPH.2021.306430 )

A Common Target: Anti-Jewish Hate Crime in New York City Communities, 1995-2010

2020 ◽  
Vol 57 (6) ◽  
pp. 643-692
Author(s):  
Colleen E. Mills
Keyword(s):  
New York ◽  
New York City ◽  
Social Disorganization ◽  
York City ◽  
Hate Crime ◽  
Negative Binomial ◽  
Minority Group ◽  
Anti Jewish ◽  
Defended Neighborhoods ◽  
The Impact

Objectives: There is a growing body of macro-level studies examining hate crime. These studies however largely focus on ethnoracial hate crime, leading to a relative dearth of research investigating the etiology of anti-Jewish hate crime. The current study seeks to fill this gap by conducting a community-level analysis of anti-Jewish hate crime in New York City. Methods: Using data from the New York Police Department’s Hate Crimes Task Force, the current study employs a series of negative binomial regressions to investigate the impact of defended neighborhoods, social disorganization, and strain variables on anti-Jewish hate crime. Results: The results show that defended neighborhoods consistently predict higher levels of anti-Jewish hate crime in White, Black, and non-White neighborhoods even when accounting for social disorganization and strain variables. Results also demonstrate that anti-Jewish crime occurs in communities that are more socially organized and with better economic conditions. Conclusions: This study’s findings reveal Jewish victims to be a catchall target when a minority group increasingly moves into a majority area. These defended neighborhoods, and other findings have intriguing implications for both criminology’s social disorganization theory and the police and others charged with combatting bias crimes.

Automated Train Identification and Train Position Monitoring at New York City Transit

2020 ◽  
Vol 2674 (9) ◽  
pp. 843-854
Author(s):  
Shay Lehmann ◽  
Alla Reddy ◽  
Chan Samsundar ◽  
Tuan Huynh
Keyword(s):  
New York ◽  
New York City ◽  
Real Time ◽  
York City ◽  
Service Management ◽  
Short Term ◽  
Subway System ◽  
Competing Priorities ◽  
Position Monitoring

Like any legacy subway system that first opened in the early 1900s, the New York City subway system operates using technology that dates from many different eras. Although some of this technology may be outdated, efforts to modernize are often hindered by budgetary limits, competing priorities, and managing the tradeoff between short-term service disruptions and long-term service improvements. At New York City Transit (NYCT), the locations of all trains on all lines are not visible to any one person in any one place and, for much of the system, train locations can only be seen at field towers for the handful of interlockings in its operational jurisdiction as result of the legacy signal system, which may come as a surprise to many daily commuters or personnel at newer metros. In 2019, developers at NYCT gained full access to the legacy signal system’s underlying track circuit occupancy data and developed an algorithm to automatically track trains and match these data with schedules and manual dispatchers’ logs in real time. This data-driven solution enables real-time train identification and tracking long before a full system modernization could be completed. This information is being provided to select personnel as part of a pilot program via several different tools with the aim of improving service management and reporting.

scholarly journals Weather Effects on Social Movements: Evidence from Washington, D.C., and New York City, 1960–95

2016 ◽  
Vol 8 (3) ◽  
pp. 299-311 ◽  
Author(s):  
Tony Huiquan Zhang
Keyword(s):  
New York ◽  
New York City ◽  
Social Movements ◽  
Social Movement ◽  
York City ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Future Research ◽  
The Impact

Abstract Scholars have been taking the impact of weather on social movements for granted for some time, despite a lack of supporting empirical evidence. This paper takes the topic more seriously, analyzing more than 7000 social movement events and 36 years of weather records in Washington, D.C., and New York City (1960–95). Here, “good weather” is defined as midrange temperature and little to no precipitation. This paper uses negative binomial regression models to predict the number of social movements per day and finds social movements are more likely to happen on good days than bad, with seasonal patterns controlled for. Results from logistic regression models indicate violence occurs more frequently at social movement events when it is warmer. Most interestingly, the effect of weather is more salient when there are more political opportunities and resources available. This paper discusses the implications and suggests future research on weather and social movement studies.

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