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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
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Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
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A dynamic hybrid model for efficient tuberculosis incidence rate prediction.

Jamilu Yahaya Maipan-Uku1,2,3, Nadire Cavus2,3

  • 1Department of Computer Science, Ibrahim Badamasi Babangida University, Lapai, Nigeria.

International Journal of Environmental Health Research
|June 4, 2024
PubMed
Summary

This study introduces an innovative ARIMA-NARX model for predicting tuberculosis (TB) incidence rates, outperforming individual ARIMA and NARX models. This advancement aids global health organizations in planning effective TB control strategies.

Keywords:
ARIMA and NARX algorithmsEuropeTuberculosis incidence rate

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Tuberculosis (TB) poses a significant global health threat, necessitating accurate incidence prediction for effective public health interventions.
  • Current prediction methods may lack the precision required for optimal resource allocation and strategic planning in TB control.

Purpose of the Study:

  • To develop and evaluate an advanced hybrid model for predicting tuberculosis incidence rates.
  • To compare the predictive accuracy of the proposed hybrid model against traditional time-series models.

Main Methods:

  • A hybrid AutoRegressive Integrated Moving Average (ARIMA) and Nonlinear AutoRegressive with exogenous input (NARX) model was developed for TB incidence prediction.
  • Model performance was rigorously assessed using standard statistical metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

Main Results:

  • The combined ARIMA-NARX model demonstrated superior predictive accuracy, achieving the lowest error metrics (MSE: 0.0622, RMSE: 0.0851, MAE: 0.07520, MAPE: 0.05535).
  • Individual NARX and ARIMA models showed significantly higher error rates, indicating the enhanced efficacy of the hybrid approach.

Conclusions:

  • The developed ARIMA-NARX model offers a more accurate and reliable tool for forecasting TB incidence rates.
  • This predictive capability can significantly support policymakers and health organizations, like the WHO, in implementing proactive TB control and intervention strategies worldwide.