Spatiotemporal dynamics and forecasting of dengue incidence in Northeastern Thai border provinces, 2014-2023

Abstract

Objective: To identify the underexplored nature of localized, longterm spatiotemporal patterns (2014-2023) and predictive modeling feasibility in these regions. The analysis specifically explored the potential for robust forecasting of dengue incidence trends.

Methods: Dengue incidence trends were analyzed across five northeastern Thai provinces: Sisaket (SSK), Ubon Ratchathani (UBN), Yasothon (YST), Amnat Charoen (ACR), and Mukdahan (MDH) over a ten-year period (2014-2023). Analysis of Kruskal-Wallis H test followed by post hoc Dunn’s tests with Bonferroni adjustment were used for inter-provincial comparisons. Seasonal- Trend Decomposition using Loess (STL) and Seasonal Index calculations were applied to identify seasonal patterns, and Simple Linear Regression to assess the influence of monthly rainfall on dengue seasonality. Additionally, a negative binomial regression analysis was conducted, and spatial autocorrelation was investigated using Moran's I analysis.

Results: The analysis revealed significant spatiotemporal heterogeneity and distinct cyclical patterns in dengue incidence. Pronounced annual peaks consistently occurred during the rainy season (June-September), with major epidemics observed in 2015, 2019, and 2023. Inter-provincial comparisons revealed statistically significant differences in monthly dengue incidence across the study area (H=10.08, P=0.039). Post-hoc Dunn’s tests indicated that UBN and SSK had higher transmission burden compared to YST and MDH. Seasonal Index calculations confirmed July as the predominant overall peak month (indices 1.4-2.5), with minimal dengue activity from January to March. While rainfall significantly influenced seasonality across all provinces. A negative binomial regression analysis indicated that all of the variables included in the model had no statistically significant relationship with dengue incidence. Furthermore, a Moran's I analysis revealed a significant clustering effect only in YST (1=0.314, P=0.037).

Conclusions: The finding underscores the critical requirement for adopting a highly focused approach, necessitating a shift toward region-specific interventions to effectively combat dengue and sustain progress toward good health and well-being for all.

Keywords

Dengue / Dengue forecasting / Epidemic preparedness / Spatiotemporal analysis / Seasonality / Northeastern Thailand / Predictive modeling

Conflict of interest statement

We, the authors, declare that no competing interests exist in relation to the work reported in this manuscript.

Funding

The authors received no extramural funding for the study.

Authors' contributions

KL, WN, SH, DS, KH, CK, and PK performed the data collection, data management, and data cleaning. KL, WN, SH, DS, and PK drafted the initial manuscript. WN was responsible for finalizing the data analysis and results writing. SH performed and finalized the spatiotemporal analysis and associated manuscript sections. PK provided overall supervision and finalized the final version of the manuscript for submission.

Publisher’s note

The Publisher of the Journal remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Edited by Lei Y, Pan Y, Zhang Q

Acknowledgments

The authors gratefully acknowledge the following individuals and organizations for their invaluable contributions to this study: The Office of Disease Prevention and Control Region 10 Ubon Ratchathani for supporting all information about province population, district, subdistrict, as well as dengue case information. Faculty of Public Health, Ubon Ratchathani Rajabhat University and Faculty of Physical Education, Sports and Health, Srinakharinwirot University for their support with study software.

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