Increasing situational awareness through nowcasting of the reproduction number DOI Creative Commons
Andrea Bizzotto, Giorgio Guzzetta, Valentina Marziano

et al.

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: Aug. 21, 2024

Background The time-varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks; however, delays between infection and reporting of cases hinder its accurate estimation in real-time. A nowcasting methods, leveraging available information on data consolidation delays, have been proposed to mitigate this problem. Methods In work, we retrospectively validate the use algorithm 18 months COVID-19 pandemic Italy by quantitatively assessing performance against standard methods R. Results Nowcasting significantly reduced median lag from 13 8 days, while concurrently enhancing accuracy. Furthermore, it allowed detection periods epidemic growth with lead 6 23 days. Conclusions augments awareness, empowering better informed public health responses.

Language: Английский

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants DOI Creative Commons
Zachary J. Madewell, Yang Yang, Ira M. Longini

et al.

BMC Infectious Diseases, Journal Year: 2023, Volume and Issue: 23(1)

Published: June 26, 2023

The serial interval is the period of time between symptom onset in primary case and secondary case. Understanding important for determining transmission dynamics infectious diseases like COVID-19, including reproduction number attack rates, which could influence control measures. Early meta-analyses COVID-19 reported intervals 5.2 days (95% CI: 4.9-5.5) original wild-type variant 4.87-5.47) Alpha variant. has been shown to decrease over course an epidemic other respiratory diseases, may be due accumulating viral mutations implementation more effective nonpharmaceutical interventions. We therefore aggregated literature estimate Delta Omicron variants.

Language: Английский

Citations

42
Updating Reproduction Number Estimates for Mpox in the Democratic Republic of Congo Using Surveillance Data DOI Creative Commons
Kelly Charniga, Andrea M. McCollum, Christine M. Hughes

et al.

American Journal of Tropical Medicine and Hygiene, Journal Year: 2024, Volume and Issue: 110(3), P. 561 - 568

Published: Feb. 6, 2024

Incidence of human monkeypox (mpox) has been increasing in West and Central Africa, including the Democratic Republic Congo (DRC), where virus (MPXV) is endemic. Most estimates pathogen's transmissibility DRC are based on data from 1980s. Amid global 2022 mpox outbreak, new needed to characterize virus' epidemic potential inform outbreak control strategies. We used R package vimes identify clusters laboratory-confirmed cases Tshuapa Province, DRC. Cases with both temporal spatial were assigned disease's serial interval kernel. size infer effective reproduction number, Rt, rate zoonotic spillover MPXV into population. Out 1,463 confirmed reported Province between 2013 2017, 878 had date symptom onset a location geographic coordinates. Results include an estimated Rt 0.82 (95% CI: 0.79-0.85) 132 122-143) spillovers per year assuming reporting 25%. This estimate larger than most previous estimates. One explanation for this result that could have increased over time owing declining population-level immunity conferred by smallpox vaccination, which was discontinued around 1982. be overestimated if our assumption one event cluster does not hold. Our results consistent Province.

Language: Английский

Citations

16
Innovations in public health surveillance: An overview of novel use of data and analytic methods DOI Creative Commons

Heather Rilkoff,

Shannon Struck,

Chelsea Ziegler

et al.

Canada Communicable Disease Report, Journal Year: 2024, Volume and Issue: 50(3/4), P. 93 - 101

Published: April 30, 2024

Innovative data sources and methods for public health surveillance (PHS) have evolved rapidly over the past 10 years, suggesting need a closer look at scientific maturity, feasibility, utility of use in real-world situations.This article provides an overview recent innovations PHS, including from social media, internet search engines, Internet Things (IoT), wastewater surveillance, participatory artificial intelligence (AI), nowcasting.Examples identified suggest that novel analytic potential to strengthen PHS by improving disease estimates, promoting early warning outbreaks, generating additional and/or more timely information action.For example, has re-emerged as practical tool detection coronavirus 2019 (COVID-19) other pathogens, AI is increasingly used process large amounts digital data.Challenges implementing include lack limited examples implementation settings, privacy security risks, equity implications.Improving governance, developing clear policies technologies, workforce development are important next steps towards advancing innovation PHS.

Language: Английский

Citations

3
Nowcasting to Monitor Real-Time Mpox Trends During the 2022 Outbreak in New York City: An Evaluation Using Reportable Disease Data Stratified by Race or Ethnicity (Preprint) DOI
Rebecca Rohrer, Allegra Wilson, Jennifer Baumgartner

et al.

Published: Jan. 31, 2024

BACKGROUND Applying nowcasting methods to partially accrued reportable disease data can help policymakers interpret recent epidemic trends and quickly identify remediate health inequities. During the 2022 mpox outbreak in New York City (NYC), we applied Nowcasting by Bayesian Smoothing (NobBS) estimate cases, citywide stratified race or ethnicity. However, real time, it was unclear if estimates were accurate. OBJECTIVE We evaluated accuracy of estimated case counts across a range NobBS implementation options. METHODS performance for NYC residents with confirmed probable diagnosis illness onset from July 8 through September 30, 2022, as compared fully cases. used mean absolute error (MAE), relative root square (rRMSE), 95% prediction interval (PI) coverage compare moving window lengths, stratifying not ethnicity, time elements, daily weekly units. RESULTS study period, 3305 diagnosed (median 4 days report), 2278 patients had known 10 report). No single length performed best. As lengths increased 14 49 days, generally, MAE improved (decreased), while rRMSE worsened (increased). For element, 14-day 9, 0.23, PI 96%; ranges longer windows MAE: 3–9, rRMSE: 0.25–0.30, coverage: 93%–100%. 21-day 12, 1.07, 84%; other 7–11, 0.75–1.42, 75%–99%. any given length, (increased) unstratified estimates. hindcasts, 0.32, 95%; 0.35–0.50 96%–100%. Performance generally when using elements Hindcasts underestimated diagnoses early August after peaked, then overestimated late during waning. Estimates most accurate September, cases low stable. CONCLUSIONS this NobBS, depended on whether stratified. Health departments need additional guidance, particularly promote equity ensuring are improve robustness, such incorporating multiple methods.

Language: Английский

Citations

0
Innovations dans la surveillance de la santé publique : un aperçu de l'utilisation novatrice des données et des méthodes d'analyse DOI Creative Commons

Heather Rilkoff,

Shannon Struck,

Chelsea Ziegler

et al.

Relevé des maladies transmissibles au Canada, Journal Year: 2024, Volume and Issue: 50(3/4), P. 104 - 114

Published: April 30, 2024

Recevez le RMTC dans votre boîte courriel ABONNEZ-VOUS AUJOURD'HUI Recherche web : RMTC+abonnez-vous Connaître les tendances Recevoir directives en matière de dépistage Être à l'affût des nouveaux vaccins Apprendre sur infections émergentes la table matières directement

Citations

0
Increasing situational awareness through nowcasting of the reproduction number DOI Creative Commons
Andrea Bizzotto, Giorgio Guzzetta, Valentina Marziano

et al.

Frontiers in Public Health, Journal Year: 2024, Volume and Issue: 12

Published: Aug. 21, 2024

Background The time-varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks; however, delays between infection and reporting of cases hinder its accurate estimation in real-time. A nowcasting methods, leveraging available information on data consolidation delays, have been proposed to mitigate this problem. Methods In work, we retrospectively validate the use algorithm 18 months COVID-19 pandemic Italy by quantitatively assessing performance against standard methods R. Results Nowcasting significantly reduced median lag from 13 8 days, while concurrently enhancing accuracy. Furthermore, it allowed detection periods epidemic growth with lead 6 23 days. Conclusions augments awareness, empowering better informed public health responses.

Language: Английский

Citations

0