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Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July–October 2022

BACKGROUND: Beginning May 7, 2022, multiple nations reported an unprecedented surge in monkeypox cases. Unlike past outbreaks, differences in affected populations, transmission mode, and clinical characteristics have been noted. With the existing uncertainties of the outbreak, real-time short-term f...

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Autores principales: Bleichrodt, Amanda, Dahal, Sushma, Maloney, Kevin, Casanova, Lisa, Luo, Ruiyan, Chowell, Gerardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841951/
https://www.ncbi.nlm.nih.gov/pubmed/36647108
http://dx.doi.org/10.1186/s12916-022-02725-2
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author Bleichrodt, Amanda
Dahal, Sushma
Maloney, Kevin
Casanova, Lisa
Luo, Ruiyan
Chowell, Gerardo
author_facet Bleichrodt, Amanda
Dahal, Sushma
Maloney, Kevin
Casanova, Lisa
Luo, Ruiyan
Chowell, Gerardo
author_sort Bleichrodt, Amanda
collection PubMed
description BACKGROUND: Beginning May 7, 2022, multiple nations reported an unprecedented surge in monkeypox cases. Unlike past outbreaks, differences in affected populations, transmission mode, and clinical characteristics have been noted. With the existing uncertainties of the outbreak, real-time short-term forecasting can guide and evaluate the effectiveness of public health measures. METHODS: We obtained publicly available data on confirmed weekly cases of monkeypox at the global level and for seven countries (with the highest burden of disease at the time this study was initiated) from the Our World in Data (OWID) GitHub repository and CDC website. We generated short-term forecasts of new cases of monkeypox across the study areas using an ensemble n-sub-epidemic modeling framework based on weekly cases using 10-week calibration periods. We report and assess the weekly forecasts with quantified uncertainty from the top-ranked, second-ranked, and ensemble sub-epidemic models. Overall, we conducted 324 weekly sequential 4-week ahead forecasts across the models from the week of July 28th, 2022, to the week of October 13th, 2022. RESULTS: The last 10 of 12 forecasting periods (starting the week of August 11th, 2022) show either a plateauing or declining trend of monkeypox cases for all models and areas of study. According to our latest 4-week ahead forecast from the top-ranked model, a total of 6232 (95% PI 487.8, 12,468.0) cases could be added globally from the week of 10/20/2022 to the week of 11/10/2022. At the country level, the top-ranked model predicts that the USA will report the highest cumulative number of new cases for the 4-week forecasts (median based on OWID data: 1806 (95% PI 0.0, 5544.5)). The top-ranked and weighted ensemble models outperformed all other models in short-term forecasts. CONCLUSIONS: Our top-ranked model consistently predicted a decreasing trend in monkeypox cases on the global and country-specific scale during the last ten sequential forecasting periods. Our findings reflect the potential impact of increased immunity, and behavioral modification among high-risk populations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02725-2.
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spelling pubmed-98419512023-01-17 Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July–October 2022 Bleichrodt, Amanda Dahal, Sushma Maloney, Kevin Casanova, Lisa Luo, Ruiyan Chowell, Gerardo BMC Med Research Article BACKGROUND: Beginning May 7, 2022, multiple nations reported an unprecedented surge in monkeypox cases. Unlike past outbreaks, differences in affected populations, transmission mode, and clinical characteristics have been noted. With the existing uncertainties of the outbreak, real-time short-term forecasting can guide and evaluate the effectiveness of public health measures. METHODS: We obtained publicly available data on confirmed weekly cases of monkeypox at the global level and for seven countries (with the highest burden of disease at the time this study was initiated) from the Our World in Data (OWID) GitHub repository and CDC website. We generated short-term forecasts of new cases of monkeypox across the study areas using an ensemble n-sub-epidemic modeling framework based on weekly cases using 10-week calibration periods. We report and assess the weekly forecasts with quantified uncertainty from the top-ranked, second-ranked, and ensemble sub-epidemic models. Overall, we conducted 324 weekly sequential 4-week ahead forecasts across the models from the week of July 28th, 2022, to the week of October 13th, 2022. RESULTS: The last 10 of 12 forecasting periods (starting the week of August 11th, 2022) show either a plateauing or declining trend of monkeypox cases for all models and areas of study. According to our latest 4-week ahead forecast from the top-ranked model, a total of 6232 (95% PI 487.8, 12,468.0) cases could be added globally from the week of 10/20/2022 to the week of 11/10/2022. At the country level, the top-ranked model predicts that the USA will report the highest cumulative number of new cases for the 4-week forecasts (median based on OWID data: 1806 (95% PI 0.0, 5544.5)). The top-ranked and weighted ensemble models outperformed all other models in short-term forecasts. CONCLUSIONS: Our top-ranked model consistently predicted a decreasing trend in monkeypox cases on the global and country-specific scale during the last ten sequential forecasting periods. Our findings reflect the potential impact of increased immunity, and behavioral modification among high-risk populations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02725-2. BioMed Central 2023-01-16 /pmc/articles/PMC9841951/ /pubmed/36647108 http://dx.doi.org/10.1186/s12916-022-02725-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Bleichrodt, Amanda
Dahal, Sushma
Maloney, Kevin
Casanova, Lisa
Luo, Ruiyan
Chowell, Gerardo
Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July–October 2022
title Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July–October 2022
title_full Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July–October 2022
title_fullStr Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July–October 2022
title_full_unstemmed Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July–October 2022
title_short Real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, July–October 2022
title_sort real-time forecasting the trajectory of monkeypox outbreaks at the national and global levels, july–october 2022
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841951/
https://www.ncbi.nlm.nih.gov/pubmed/36647108
http://dx.doi.org/10.1186/s12916-022-02725-2
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