What does this research mean for the field?

Dengue incidence rates in Northern Thailand exhibit clear seasonality with annual peaks during the rainy season, and predictive models indicate a regional peak in July 2025. Novelty: ClaimNovelty.CONFIRMATORY. Consensus alignment: ConsensusAlignment.SUPPORTS_CONSENSUS.

What question did this study set out to answer?

The study aims to examine the spatial and temporal dynamics of dengue incidence rates in Northern Thailand, enhancing predictive capabilities for outbreaks.

February 19, 2026Discover Public Health2 citationsOpen Access

Spatial and temporal analysis of dengue incidence in Northern Thailand: a 13-year retrospective study (2012–2024)

PCPongpat ChaidilokMinistry of Public Health SSSayambhu SaitaThammasat University

Key Points

The study aims to examine the spatial and temporal dynamics of dengue incidence rates in Northern Thailand, enhancing predictive capabilities for outbreaks.
Collected monthly dengue case data from 2012 to 2024 at the provincial level.
Performed spatial analyses using SEB smoothing and assessed spatial clustering with Moran’s I and LISA.
Employed SARIMA modeling for temporal forecasts while excluding COVID-19 data.
Evaluated model performance through MAE, RMSE, and MAPE.
Dengue incidence showed consistent seasonality with peaks during June to August.
Chiang Mai and Mae Hong Son exhibited high incidence rates and significant spatial clustering.
The best SARIMA model captured regional dynamics, showing MAE of 2.93, RMSE of 3.30, and MAPE of 31.86%.
Forecasts for 2025 predict a peak in July, with significant provincial rates in Lamphun and Chiang Mai.

Abstract

Abstract Background Dengue fever remains a major public health concern in the northern region of Thailand, where periodic outbreaks impose substantial health and economic burdens. Understanding spatial and temporal patterns of dengue incidence rate is essential for improving surveillance, forecasting, and targeted control strategies. Objectives This study examined the spatial and temporal dynamics of dengue incidence rate across eight provinces in the northern region of Thailand and developed predictive models to support early warning and prevention efforts. Methods Monthly dengue case data from 2012 to 2024 were obtained from the national surveillance system and aggregated at the provincial level. Spatial analyses included the calculation of incidence rates, spatial empirical Bayesian (SEB) smoothing, and assessment of spatial clustering using Moran’s I and local indicators of spatial association (LISA). Temporal analyses employed seasonal-trend decomposition and seasonal autoregressive integrated moving average (SARIMA) modeling to forecast dengue incidence rate at regional and provincial levels. To ensure normal transmission patterns, incidence rates during the COVID-19 period were excluded. Model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). Results Dengue incidence rate showed clear seasonality, with annual peaks during the rainy season (June–August). Spatial heterogeneity was evident, with Chiang Mai and Mae Hong Son consistently exhibiting high incidence rate and significant spatial clustering. The SARIMA (2,0,0)(2,1,0) 12 model accurately captured regional dengue dynamics, yielding MAE, RMSE, and MAPE of 2.93, 3.30, and 31.86%, respectively. Forecasts for 2025 indicated a regional peak in July (88.07 per 100,000), with the highest provincial peaks expected in Lamphun (74.76, August) and Chiang Mai (60.25, July). Conclusions Integrating spatial clustering with temporal forecasting enhances understanding of dengue transmission dynamics and supports locally tailored, data-driven interventions. These findings provide actionable insights for strengthening surveillance, optimizing resource allocation, and improving vector control strategies in endemic regions.

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