Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
BACKGROUND Applying nowcasting methods to partially accrued reportable disease data can help policymakers interpret recent epidemic trends and quickly identify and remediate health inequities. During the 2022 mpox outbreak in New York City (NYC), we applied Nowcasting by Bayesian Smoothing (NobBS) to estimate recent cases, citywide and stratified by race or ethnicity. However, in real time, it was unclear if estimates were accurate. OBJECTIVE We evaluated the accuracy of estimated mpox case counts across a range of NobBS implementation options. METHODS We evaluated NobBS performance for NYC residents with confirmed or probable mpox diagnosis or illness onset from July 8 through September 30, 2022, as compared with fully accrued cases. We used the mean absolute error (MAE), relative root mean square error (rRMSE), and 95% prediction interval (PI) coverage to compare moving window lengths, stratifying or not by race or ethnicity, diagnosis and onset time elements, and daily and weekly time units. RESULTS During the study period, 3305 NYC residents were diagnosed with mpox (median 4 days from diagnosis to diagnosis report), and 2278 patients had known illness onset (median 10 days from onset to onset report). No single moving window length performed best. As window lengths increased from 14 to 49 days, generally, MAE improved (decreased), while rRMSE worsened (increased). For the diagnosis time element, for the 14-day moving window used in real time, MAE was 9, rRMSE was 0.23, and 95% PI coverage was 96%; ranges for longer moving windows were MAE: 3–9, rRMSE: 0.25–0.30, and 95% PI coverage: 93%–100%. For the onset time element, for the 21-day moving window used in real time, MAE was 12, rRMSE was 1.07, and 95% PI coverage was 84%; ranges for other moving windows were MAE: 7–11, rRMSE: 0.75–1.42, and 95% PI coverage: 75%–99%. For any given moving window length, rRMSE worsened (increased) for stratified compared with unstratified estimates. For stratified daily diagnosis hindcasts, for the 14-day moving window used in real time, rRMSE was 0.32, and 95% PI coverage was 95%; ranges for other moving window lengths were rRMSE: 0.35–0.50 and 95% PI coverage: 96%–100%. Performance generally worsened when using onset compared with diagnosis time elements and weekly compared with daily time units. Hindcasts underestimated diagnoses in early August after the epidemic peaked, then overestimated diagnoses in late August during epidemic waning. Estimates were most accurate during September, when cases were low and stable. CONCLUSIONS For nowcasting this outbreak using NobBS, accuracy depended on the moving window length and whether cases were stratified. Health departments need additional nowcasting guidance, particularly to promote health equity by ensuring stratified estimates are accurate and to improve robustness, such as by incorporating multiple methods.
BACKGROUND Applying nowcasting methods to partially accrued reportable disease data can help policymakers interpret recent epidemic trends and quickly identify and remediate health inequities. During the 2022 mpox outbreak in New York City (NYC), we applied Nowcasting by Bayesian Smoothing (NobBS) to estimate recent cases, citywide and stratified by race or ethnicity. However, in real time, it was unclear if estimates were accurate. OBJECTIVE We evaluated the accuracy of estimated mpox case counts across a range of NobBS implementation options. METHODS We evaluated NobBS performance for NYC residents with confirmed or probable mpox diagnosis or illness onset from July 8 through September 30, 2022, as compared with fully accrued cases. We used the mean absolute error (MAE), relative root mean square error (rRMSE), and 95% prediction interval (PI) coverage to compare moving window lengths, stratifying or not by race or ethnicity, diagnosis and onset time elements, and daily and weekly time units. RESULTS During the study period, 3305 NYC residents were diagnosed with mpox (median 4 days from diagnosis to diagnosis report), and 2278 patients had known illness onset (median 10 days from onset to onset report). No single moving window length performed best. As window lengths increased from 14 to 49 days, generally, MAE improved (decreased), while rRMSE worsened (increased). For the diagnosis time element, for the 14-day moving window used in real time, MAE was 9, rRMSE was 0.23, and 95% PI coverage was 96%; ranges for longer moving windows were MAE: 3–9, rRMSE: 0.25–0.30, and 95% PI coverage: 93%–100%. For the onset time element, for the 21-day moving window used in real time, MAE was 12, rRMSE was 1.07, and 95% PI coverage was 84%; ranges for other moving windows were MAE: 7–11, rRMSE: 0.75–1.42, and 95% PI coverage: 75%–99%. For any given moving window length, rRMSE worsened (increased) for stratified compared with unstratified estimates. For stratified daily diagnosis hindcasts, for the 14-day moving window used in real time, rRMSE was 0.32, and 95% PI coverage was 95%; ranges for other moving window lengths were rRMSE: 0.35–0.50 and 95% PI coverage: 96%–100%. Performance generally worsened when using onset compared with diagnosis time elements and weekly compared with daily time units. Hindcasts underestimated diagnoses in early August after the epidemic peaked, then overestimated diagnoses in late August during epidemic waning. Estimates were most accurate during September, when cases were low and stable. CONCLUSIONS For nowcasting this outbreak using NobBS, accuracy depended on the moving window length and whether cases were stratified. Health departments need additional nowcasting guidance, particularly to promote health equity by ensuring stratified estimates are accurate and to improve robustness, such as by incorporating multiple methods.
(1) Background: In early May 2022, an increasing number of human monkeypox (mpox) cases were reported in non-endemic disparate regions of the world, which raised concerns. Here, we provide a systematic review and meta-analysis of mpox-confirmed patients presented in peer-reviewed publications over the 10 years before and during the 2022 outbreak from demographic, epidemiological, and clinical perspectives. (2) Methods: A systematic search was performed for relevant studies published in Pubmed/Medline, Embase, Scopus, and Google Scholar from 1 January 2012 up to 15 February 2023. Pooled frequencies with 95% confidence intervals (CIs) were assessed using the random or fixed effect model due to the estimated heterogeneity of the true effect sizes. (3) Results: Out of 10,163 articles, 67 met the inclusion criteria, and 31 cross-sectional studies were included for meta-analysis. Animal-to-human transmission was dominant in pre-2022 cases (61.64%), but almost all post-2022 reported cases had a history of human contact, especially sexual contact. The pooled frequency of MSM individuals was 93.5% (95% CI 91.0–95.4, I2: 86.60%) and was reported only in post-2022 included studies. The male gender was predominant in both pre- and post-2022 outbreaks, and the mean age of confirmed cases was 29.92 years (5.77–41, SD: 9.38). The most common clinical manifestations were rash, fever, lymphadenopathy, and malaise/fatigue. Proctalgia/proctitis (16.6%, 95% CI 10.3–25.6, I2: 97.76) and anal/perianal lesions (39.8%, 95% CI 30.4–49.9, I2: 98.10) were the unprecedented clinical manifestations during the 2022 outbreak, which were not described before. Genitalia involvement was more common in post-2022 mpox patients (55.6%, 95% CI 51.7–59.4, I2: 88.11). (4) Conclusions: There are speculations about the possibility of changes in the pathogenic properties of the virus. It seems that post-2022 mpox cases experience a milder disease with fewer rashes and lower mortality rates. Moreover, the vast majority of post-2022 cases are managed on an outpatient basis. Our study could serve as a basis for ongoing investigations to identify the different aspects of previous mpox outbreaks and compare them with the current ones.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.