Development and Comparison of Dengue Vulnerability Indices Using GIS-Based Multi-Criteria Decision Analysis in Lao PDR and Thailand

Abstract

Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon’s Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson’s correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVIWADI was significantly correlated on average in 19% (4–40%) of districts in Laos (mean r = 0.5) and 27% (15–53%) of subdistricts in Thailand (mean r = 0.85). The DVISE was validated in 22% (12–40%) of districts in Laos and in 13% (3–38%) of subdistricts in Thailand. The DVIBWM was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0–28%) of Lao districts. The DVIWADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVIWADI values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVIWADI was the most suitable vulnerability index for the study area. The DVIWADI can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions.

Attitude towards dengue control efforts with the potential of digital technology during COVID-19: partial least squares-structural equation modeling

“…[27][28][29][30][31] Developing a system based on technology and the environment using spatial mapping makes it possible to predict the potential for outbreaks in an area. 32,33…”

Epidemiological profile of dengue in Champasak and Savannakhet provinces, Lao People’s Democratic Republic, 2003–2020

“…( 11 ) Previous work has also shown a correlation between high density of built-up areas and high levels of development and dengue vulnerability within Champasak and Savannakhet provinces during 2003–2019. ( 6 )…”

“…This signals a change in dengue case distributions that may be linked to climatic and land cover changes, specifically an increase in mean temperature and in the number of new settlements in previously remote, less developed areas. ( 6 )…”

“…The LTM was used as a threshold to determine the number of months when the monthly number of cases exceeded or remained below the LTM. When the monthly number of cases exceeded the LTM for 3 or more consecutive months, this period was considered to be a “high transmission season.” ( 6 ) LTMs and the number of months that exceeded them were calculated and mapped for each district within the two provinces.…”

“…When the monthly number of cases exceeded the LTM for 3 or more consecutive months, this period was considered to be a "high transmission season". 6 LTMs and the number of months that exceeded them were calculated and mapped for each district within the two provinces. inform risk assessment of dengue transmission as well as prevention and control strategies.…”