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Forecasting epidemic trajectories: Time Series Growth Curves package tsgc

Author

Listed:
  • Ashby, M.
  • Harvey, A.
  • Kattuman, P.
  • Thamotheram, C.

Abstract

This paper documents the Time Series Growth Curves (tsgc) package for R, which is designed for forecasting epidemics, including the detection of new waves and turning points. The package implements time series growth curve methods founded on a dynamic Gompertz model and can be estimated using techniques based on state space models and the Kalman filter. The model is suitable for predicting future values of any variable which, when cumulated, is subject to some unknown saturation level. In the context of epidemics, the model can adjust to changes in social behavior and policy. It is also relevant for many other domains, such as the diffusion of new products. The tsgc package is demonstrated using data on COVID-19 confirmed cases.

Suggested Citation

  • Ashby, M. & Harvey, A. & Kattuman, P. & Thamotheram, C., 2024. "Forecasting epidemic trajectories: Time Series Growth Curves package tsgc," Cambridge Working Papers in Economics 2407, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:2407
    Note: mwa22, ach34, pak13
    as

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    File URL: https://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe2407.pdf
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    References listed on IDEAS

    as
    1. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
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    More about this item

    Keywords

    Covid-19; Gompertz growth curve; Kalman filter; reproduction number; state space model; stochastic trend; turning points;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • I10 - Health, Education, and Welfare - - Health - - - General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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