Disease management with ARIMA model in time series

Area of Science:

• Epidemiology
• Biostatistics
• Health Services Research

Background:

• Effective management of infectious and noninfectious diseases requires robust evaluation methods.
• Traditional evaluation techniques may not fully capture dynamic disease trends or intervention impacts.
• Time series analysis offers a powerful framework for analyzing health data over time.

Purpose of the Study:

• To demonstrate the utility of time series analysis for evaluating disease management strategies.
• To highlight the application of the Autoregressive Integrated Moving Average (ARIMA) model in public health research.
• To assess the impact of interventions on disease patterns within specific populations.

Main Methods:

• Utilizing time series analysis techniques.
• Applying the Autoregressive Integrated Moving Average (ARIMA) model for data analysis.
• Evaluating the effectiveness of healthcare interventions through statistical modeling.

Main Results:

• Time series analysis provides a quantitative approach to measure intervention effects.
• The ARIMA model is applicable to diverse clinical and public health datasets.
• This analytical tool can identify trends and predict disease progression.

Conclusions:

• Time series analysis, particularly ARIMA, is a valuable tool for researchers and healthcare managers.
• It enables the evaluation of healthcare interventions in specific populations.
• This approach contributes to improved disease management and public health outcomes.