A  Comparative Study of the GM(1,1) Model and Curve Fitting Method for Forecasting Viral Hepatitis Incidence in Afghanistan up to 2030

Raz Mohammad Mohammadi; Mohammad Farooq Hakimi; Abdul Raqib Muslimyar; Wali Mohammad Aziz

doi:10.62810/jnsr.v3i4.324

Authors

Raz Mohammad Mohammadi Department of Mathematics, Education Faculty, Kandahar University, Kandahar, Afghanistan
Mohammad Farooq Hakimi Department of Mathematics, Natural Science Faculty, Kabul Education University, Kabul, Afghanistan
Abdul Raqib Muslimyar Department of Mathematics, Natural Science Faculty, Kabul Education University, Kabul, Afghanistan
Wali Mohammad Aziz Department of Mathematics, Education Faculty, Kandahar University, Kandahar, Afghanistan

DOI:

https://doi.org/10.62810/jnsr.v3i4.324

Keywords:

Afghanistan, Curve Fitting Method, GM (1,1) model, Incidence, Prediction, Viral Hepatitis

Abstract

This study aimed to forecast the incidence of viral hepatitis in Afghanistan using the GM(1,1) model and curve fitting methods, and to compare the predictive performance of both approaches using mean absolute error (MAE) and mean absolute percentage error (MAPE). Annual incidence data were obtained from the Hospital of Infectious Diseases through a formal request. Linear and nonlinear regression techniques, along with the first-order univariate grey prediction model (GM(1,1)), were applied to model historical trends. The model with superior predictive accuracy was used to project viral hepatitis incidence for 2015–2030. Both GM(1,1) and curve-fitting models accurately captured the incidence trends; however, GM(1,1) demonstrated superior performance (MAE = 557.95; MAPE = 6.76%) compared with exponential curve regression (MAE = 558.30; MAPE = 7.07%). Forecasts indicated 15,328.6 cases in 2025 and 39058 cases in 2030, with both models projecting a consistent upward trend, reflecting a growing public health burden. The projections highlight a growing public health burden of viral hepatitis in Afghanistan, emphasizing the urgency of effective prevention and vaccination programs. The findings can guide policymakers in resource allocation and healthcare planning, while also informing strategies to strengthen surveillance and early detection. Moreover, the study demonstrates that the GM(1,1) model is a reliable forecasting tool in contexts with limited or incomplete data, providing valuable support for evidence-based decision-making in public health. This study is the first to compare GM(1,1) and curve fitting for forecasting viral hepatitis in Afghanistan, using viral hepatitis data from Afghanistan. It provides context-specific projections through 2030 and demonstrates that GM(1,1) is a reliable tool in data-limited settings.

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