{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"Research Square"}],"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T01:15:05Z","timestamp":1778634905009,"version":"3.51.4"},"posted":{"date-parts":[[2023,4,21]]},"group-title":"In Review","reference-count":32,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,3,22]]},"abstract":"<title>Abstract<\/title>\n                <p>\n                  <bold>Background<\/bold>\n                  Simple dynamic modeling tools can be useful for generating real-time short-term forecasts with quantified uncertainty of the trajectory of diverse growth processes unfolding in nature and society, including disease outbreaks. An easy-to-use and flexible toolbox for this purpose is lacking.\n                  <bold>Results<\/bold>\n                  In this tutorial-based primer, we introduce and illustrate a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using phenomenological dynamic growth models based on ordinary differential equations. This toolbox is accessible to various audiences, including students training in time-series forecasting, dynamic growth modeling, parameter estimation, parameter uncertainty and identifiability, model comparison, performance metrics, and forecast evaluation, as well as researchers and policymakers who need to conduct short-term forecasts in real-time. The models included in the toolbox capture exponential and sub-exponential growth patterns that typically follow a rising pattern followed by a decline phase, a common feature of contagion processes. Models include the 2-parameter generalized-growth model, which has proved useful to characterize and forecast the ascending phase of epidemic outbreaks, and the Gompertz model as well as the 3-parameter generalized logistic-growth model and the Richards model, which have demonstrated competitive performance in forecasting single peak outbreaks.\nThe toolbox provides a tutorial for forecasting time-series trajectories that include the full uncertainty distribution, derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance across different models, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score.\n                  <bold>Conclusions<\/bold>\n                  We have developed the first comprehensive toolbox to characterize and forecast time-series data using simple phenomenological growth models. As a contagion process takes off, the tools presented in this tutorial can facilitate policymaking to guide the implementation of control strategies and assess the impact of interventions. The toolbox functionality is demonstrated through various examples, including a tutorial video, and is illustrated using weekly data on the monkeypox epidemic in the USA.\n                <\/p>","DOI":"10.21203\/rs.3.rs-2724940\/v2","type":"posted-content","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T15:46:43Z","timestamp":1682092003000},"source":"Crossref","is-referenced-by-count":0,"title":["A MATLAB toolbox to fit and forecast growth trajectories using phenomenological growth models: Application to epidemic outbreaks"],"prefix":"10.21203","author":[{"given":"Gerardo","family":"Chowell","sequence":"first","affiliation":[{"name":"Georgia State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amanda","family":"Bleichrodt","sequence":"additional","affiliation":[{"name":"Georgia State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sushma","family":"Dahal","sequence":"additional","affiliation":[{"name":"Georgia State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amna","family":"Tariq","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kimberlyn","family":"Roosa","sequence":"additional","affiliation":[{"name":"University of Tennessee"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James M.","family":"Hyman","sequence":"additional","affiliation":[{"name":"Tulane University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiyan","family":"Luo","sequence":"additional","affiliation":[{"name":"Georgia State University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"issue":"3","key":"ref1","first-page":"379","article-title":"Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts","volume":"2","author":"Chowell G","year":"2017","unstructured":"Chowell G. Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts. Infect Dis Model. 2017;2(3):379\u201398.","journal-title":"Infect Dis Model"},{"key":"ref2","first-page":"256","article-title":"Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020","volume":"5","author":"Roosa K","year":"2020","unstructured":"Roosa K, Lee Y, Luo R, Kirpich A, Rothenberg R, Hyman JM, et al. Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. 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