COVID-19 pandemic waves: Identification and interpretation of global data

Heliyon, Год журнала: 2024, Номер 10(3), С. e25090 - e25090

Опубликована: Янв. 27, 2024

The mention of the COVID-19 waves is as prevalent pandemic itself. Identifying beginning and end wave critical to evaluating impact various variants different pharmaceutical non-pharmaceutical (including economic, health social, etc.) interventions. We demonstrate a scientifically robust method identify breaking points at which they begin from January 2020 June 2021. Employing Break Least Square method, we determine significance for global-, regional-, country-level data. results show that works efficiently in detecting points. these health, social other welfare interventions implemented during crisis. our with high frequency data effectively determines start wave(s). country level more relevant than global or regional levels. Our research evidenced takes about 48 days on average subside once it begins, irrespective circumstances.

Язык: Английский

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

BMC Medicine, Год журнала: 2023, Номер 21(1)

Опубликована: Янв. 16, 2023

Beginning May 7, 2022, multiple nations reported an unprecedented surge in monkeypox cases. Unlike past outbreaks, differences affected populations, transmission mode, and clinical characteristics have been noted. With the existing uncertainties of outbreak, real-time short-term forecasting can guide evaluate effectiveness public health measures.

Язык: Английский

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Evaluating the forecasting performance of ensemble sub-epidemic frameworks and other time series models for the 2022–2023 mpox epidemic

Royal Society Open Science, Год журнала: 2024, Номер 11(7)

Опубликована: Июль 1, 2024

During the 2022-2023 unprecedented mpox epidemic, near real-time short-term forecasts of epidemic's trajectory were essential in intervention implementation and guiding policy. However, as case levels have significantly decreased, evaluating model performance is vital to advancing field epidemic forecasting. Using laboratory-confirmed data from Centers for Disease Control Prevention Our World Data teams, we generated retrospective sequential weekly Brazil, Canada, France, Germany, Spain, United Kingdom, States at global scale using an auto-regressive integrated moving average (ARIMA) model, generalized additive simple linear regression, Facebook's Prophet well sub-epidemic wave n-sub-epidemic modelling frameworks. We assessed forecast mean squared error, absolute weighted interval scores, 95% prediction coverage, skill scores Winkler scores. Overall, framework outcompeted other models across most locations forecasting horizons, with unweighted ensemble performing best frequently. The spatial-wave frameworks considerably improved relative ARIMA (greater than 10%) all metrics. Findings further support epidemics emerging re-emerging infectious diseases.

Язык: Английский

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The Future of HIV: Challenges in meeting the 2030 Ending the HIV Epidemic in the U.S. (EHE) reduction goal.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Янв. 6, 2025

To predict the burden of HIV in United States (US) nationally and by region, transmission type, race/ethnicity through 2030. Using publicly available data from CDC NCHHSTP AtlasPlus dashboard, we generated 11-year prospective forecasts incident diagnoses region (South, non-South), (White, Hispanic/Latino, Black/African American), type (Injection-Drug Use, Male-to-Male Sexual Contact (MMSC), Heterosexual (HSC)). We employed weighted (W) unweighted (UW) n -sub-epidemic ensemble models, calibrated using 12 years historical (2008-2019), forecasted trends for 2020-2030. compared results to identify persistent, concerning across models. projected substantial decreases (W: 27.9%, UW: 21.9%), South (W:18.0%, 9.2%) non-South 21.2%, 19.5%) 2019 However, non-decreasing were observed key sub-populations during this period: Hispanic/Latino persons 1.4%, 2.6%), MMSC 9.0%, 9.9%), people who inject drugs (PWID) 25.6%, 9.2%), White PWID 3.5%, 44.9%). The rising among overall consistent regions. Although national-level decrease number is encouraging, US unlikely achieve Ending Epidemic U.S. goal a 90% reduction incidence Additionally, increases specific subpopulations highlight importance targeted equitable approach effectively combat US.

Язык: Английский

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SubEpiPredict: A tutorial-based primer and toolbox for fitting and forecasting growth trajectories using the ensemble n-sub-epidemic modeling framework

Infectious Disease Modelling, Год журнала: 2024, Номер 9(2), С. 411 - 436

Опубликована: Фев. 9, 2024

An ensemble

Язык: Английский

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Using neural ordinary differential equations to predict complex ecological dynamics from population density data

Journal of The Royal Society Interface, Год журнала: 2024, Номер 21(214)

Опубликована: Май 1, 2024

Simple models have been used to describe ecological processes for over a century. However, the complexity of systems makes simple subject modelling bias due simplifying assumptions or unaccounted factors, limiting their predictive power. Neural ordinary differential equations (NODEs) surged as machine-learning algorithm that preserves dynamic nature data (Chen et al. 2018 Adv. Inf. Process. Syst. ). Although preserving dynamics in is an advantage, question how NODEs perform forecasting tool communities unanswered. Here, we explore this using simulated time series competing species time-varying environment. We find provide more precise forecasts than autoregressive integrated moving average (ARIMA) models. also untuned similar accuracy long-short term memory neural networks and both are outperformed precision by empirical dynamical . generally outperform all other methods when evaluating with interval score, which evaluates terms prediction intervals rather pointwise accuracy. discuss ways improve performance NODEs. The power such it can insights into population should thus broaden approaches studying communities.

Язык: Английский

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StatModPredict: A User-Friendly R-Shiny Interface for Fitting and Forecasting with Statistical Models

Опубликована: Янв. 1, 2024

Many fields, such as public health, employ statistical time series models for real-time and retrospective forecasting efforts. However, their successful implementation often requires extensive programming knowledge. This paper presents StatModPredict, a user-friendly R-Shiny interface fitting, forecasting, evaluating, comparing the results from ARIMA, GLM, GAM, Facebook's Prophet models. Utilizing any data, users can customize model parameters to obtain fits, forecasts, evaluation statistics compare "outside" Therefore, StatModPredict facilitates by removing all requirements, facilitating timely efficient decisions obtained through

Язык: Английский

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SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework

BMC Medical Research Methodology, Год журнала: 2024, Номер 24(1)

Опубликована: Июнь 7, 2024

Abstract Background Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types can help gain insights into the mechanisms driving process and may outcompete simpler phenomenological growth models. Here we introduce friendly toolbox, SpatialWavePredict , characterize forecast spatial wave sub-epidemic model, which captures diverse dynamics aggregating multiple asynchronous processes has outperformed in short-term various infectious diseases outbreaks including SARS, Ebola, early waves COVID-19 pandemic US. Results This tutorial-based primer introduces illustrates user-friendly MATLAB toolbox for fitting forecasting time-series trajectories using an ensemble model ordinary equations. Scientists, policymakers, students use conduct real-time forecasts. The five-parameter epidemic aggregates linked overlapping sub-epidemics rich spectrum dynamics, oscillatory behavior plateaus. An strategy aims improve performance combining resulting top-ranked provides tutorial trajectories, full uncertainty distribution derived through parametric bootstrapping, is needed construct prediction intervals evaluate their accuracy. Functions available assess performance, estimation methods, error structures data, horizons. also includes functions quantify metrics that point distributional forecasts, weighted interval score. Conclusions We have developed first comprehensive data model. As situation or contagion occurs, tools presented this facilitate policymakers guide implementation containment strategies impact control interventions. demonstrate functionality with examples, video, illustrated daily USA.

Язык: Английский

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Forecasting the changes between endemic and epidemic phases of a contagious disease, with the example of COVID-19

Mathematical Medicine and Biology A Journal of the IMA, Год журнала: 2024, Номер unknown

Опубликована: Авг. 20, 2024

Abstract Background: Predicting the endemic/epidemic transition during temporal evolution of a contagious disease. Methods: Indicators for detecting endemic/epidemic, with four scalars to be compared, are calculated from daily reported news cases: coefficient variation, skewness, kurtosis and entropy. The indicators selected related shape empirical distribution new cases observed over 14 days. This duration has been chosen smooth out effect weekends when fewer registered. For finding forecasting variable, we have used principal component analysis (PCA), whose first (a linear combination indicators) explains large part variance can then as predictor phenomenon studied (here occurrence an epidemic wave). Results: A score built proposed using PCA, which allows acceptable level performance by giving realistic retro-predicted date rupture stationary endemic model corresponding entrance in exponential growth phase. is applied retro-prediction limits different phases COVID-19 outbreak successive transitions three countries, France, India Japan. Conclusion: We provided method predicting wave occurring after phase

Язык: Английский

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A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization

Chaos Solitons & Fractals, Год журнала: 2024, Номер 181, С. 114695 - 114695

Опубликована: Март 14, 2024

Язык: Английский

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