Hierarchical Forecasting of Dengue Incidence in Sri Lanka

TL;DR

This paper develops a hierarchical time series forecasting method to predict dengue incidence in Sri Lanka at multiple administrative levels, aiming to improve resource planning and control strategies.

Contribution

It introduces a hierarchical forecasting framework combining various models and evaluates their accuracy for dengue prediction across different regions in Sri Lanka.

Findings

Hierarchical forecasts outperform non-hierarchical benchmarks in accuracy.

Different models perform best at different levels and regions.

Visualization explains variations in model performance.

Abstract

The recurrent thread of dengue incidence in Sri Lanka is still abundant and it creates a huge burden to the country. Hence, the National Dengue Control Unit of Sri Lanka propose a national action plan to prevent and control the dengue incidence. To implement the necessary actions for short and long terms the proposed plan operates under three levels:country-level, province-level, and district levels. In order to optimize resource allocation, the health officers require the forecasts for country, province and district levels, which preserves the aggregate consistency associated with the district, province, and country levels as well as time correlations. Hence, the objective of this study is to forecast the dengue incidence in Sri Lanka using a hierarchical time series forecasting approach based on spatial and temporal hierarchical structures. Hierarchical forecasting involves two steps such as generating base forecasts and reconciliation of these base forecasts. In this study, Exponential smoothing(ETS) and Autoregressive Integrated Moving Average (ARIMA), NAIVE, Seasonal NAIVEapproaches and average method are used to generate base forecasts. Accuracy of forecasts is evaluated using Mean Absolute Scale Error (MASE). We compare the accuracy of hierarchical time series forecasts with other benchmark approaches. The forecast accuracy reveals that the best forecasting approaches for country, provinces and districts are not limited to a single approach. Hence, we investigate reasons for the variations of performance in different forecasting approaches based on a time series feature-based visualization approach.