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Downscaling epidemiological time series data for improving forecasting accuracy: An algorithmic approach
Mahadee Al Mobin1,2, Md Kamrujjaman1
1Department of Mathematics, University of Dhaka, Dhaka, Bangladesh.
Plos One
|December 14, 2023
Summary
Data scarcity in healthcare hinders forecasting. A new Stochastic Bayesian Downscaling (SBD) algorithm generates realistic synthetic data from aggregated datasets, improving epidemiological predictions and reducing forecasting errors significantly.
Area of Science:
- Epidemiology
- Data Science
- Biostatistics
Background:
- Healthcare and epidemiological datasets often suffer from scarcity and discontinuity, complicating decision-making and forecasting.
- Traditional forecasting methods like ARIMA and SARIMA struggle with aggregated data, leading to unsatisfactory results.
- Artificial data synthesis offers a promising solution for overcoming data limitations in time series analysis.
Purpose of the Study:
- To introduce a novel Stochastic Bayesian Downscaling (SBD) algorithm for regenerating downscaled time series from aggregated data.
- To preserve the statistical characteristics and aggregated sums of the original data during the downscaling process.
- To demonstrate the algorithm's utility in epidemiological time series analysis using real-world case studies.
Main Methods:
- Development of the Stochastic Bayesian Downscaling (SBD) algorithm, employing a Bayesian approach.
- Application of the SBD algorithm to generate downscaled time series from aggregated epidemiological data.
- Validation of synthesized data against original data for statistical properties, trend, seasonality, and residuals.
Main Results:
- The SBD algorithm successfully regenerated downscaled time series from aggregated data, maintaining key statistical properties.
- Case studies using Dengue and COVID-19 data from Bangladesh showed strong agreement between synthesized and original data.
- Forecasting Dengue infections improved significantly, with error reduction up to 72.76% using synthetic data compared to aggregated data.
Conclusions:
- The Stochastic Bayesian Downscaling (SBD) algorithm is effective in addressing data scarcity and discontinuity in epidemiological time series.
- Synthesized data generated by SBD accurately reflects the statistical nuances of the original data.
- SBD enhances forecasting accuracy, offering a valuable tool for public health decision-making and scenario planning.
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