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A predictive model for daily cumulative COVID-19 cases in Ghana

Abdul-Karim Iddrisu1, Emmanuel A Amikiya2, Dominic Otoo1

  • 1Mathematics and Statistics, University of Energy and Natural Resources, Sunyani, Ghana.

F1000Research
|April 26, 2022
PubMed

Insights

COVID-19 cases in Ghana are rising due to ineffective measures. A new predictive model using spline and generalized additive models (GAM) accurately forecasts cumulative cases, aiding resource allocation for pandemic management.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • The Coronavirus disease 2019 (COVID-19) pandemic has significantly impacted global daily life, governance, and economies.
  • Ghana is experiencing a surge in COVID-19 cases and deaths, indicating the ineffectiveness of current preventive and management strategies.
  • Inadequate health equipment and facilities contribute to high mortality rates in Ghana, highlighting the need for better resource allocation.

Purpose of the Study:

  • To develop a predictive tool for accurately forecasting cumulative COVID-19 cases in Ghana.
  • To aid policymakers in resource allocation for effective disease management and prevention.

Main Methods:

  • Application of polynomial and spline models to COVID-19 data from Ghana.
  • Development of a generalized additive model (GAM) to capture the growth pattern of cumulative cases.

Main Results:

  • Both spline and GAM models demonstrated accuracy in forecasting cumulative COVID-19 cases.
  • The developed predictive tool provides reliable forecast values.

Conclusions:

  • COVID-19 cumulative cases in Ghana are projected to increase without enhanced preventive measures.
  • Future research should investigate the impact of ongoing vaccination efforts on disease trajectory.

Keywords:
Covid-19forecastsgeneralized additive modelspolynomials and spline models.

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