Influence of climate variables on dengue fever occurrence in the southern region of Thailand

Abstract

The 3-5year epidemic cycle of dengue fever in Thailand makes it a major re-emerging public health problem resulting in being a burden in endemic areas. Although the Thai Ministry of Public Health adopted the WHO dengue control strategy, all dengue virus serotypes continue to circulate. Health officers and village health volunteers implement some intervention options but there is a need to ascertain most appropriate (or a combination of) interventions regarding the environment and contextual factors that may undermine the effectiveness of such interventions. This study aims to understand the dengue-climate relationship patterns at the district level in the southern region of Thailand from 2002 to 2018 by examining the statistical association between dengue incidence rate and eight environmental patterns, testing the hypothesis of equal incidence of these. Data on environmental variables and dengue reported cases in Nakhon Si Thammarat province situated in the south of Thailand from 2002 to 2018 were analysed to (1) detect the environmental factors that affect the risk of dengue infectious disease; to (2) determine if disease risk is increasing or decreasing over time; and to (3) identify the high-risk district areas for dengue cases that need to be targeted for interventions. To identify the predictors that have a high and significant impact on reported dengue infection, three steps of analysis were used. First, we used Partial Least Squares (PLS) Regression and Poisson Regression, a variant of the Generalized Linear Model (GLM). Negative co-efficient in correspondence with the PLS components suggests that sea-level pressure, wind speed, and pan evaporation are associated with dengue occurrence rate, while other variables were positively associated. Using the Akaike information criterion in the stepwise GLM, the filtered predictors were temperature, precipitation, cloudiness, and sea level pressure with the standardized coefficients showing that the most influential variable is cloud cover (three times more than temperature and precipitation). Also, dengue occurrence showed a constant negative response to the average increase in sea-level pressure values. In southern Thailand, the predictors that have been locally determined to drive dengue occurrence are temperature, rainfall, cloud cover, and sea-level pressure. These explanatory variables should have important future implications for epidemiological studies of mosquito-borne diseases, particularly at the district level. Predictive indicators guide effective and dynamic risk assessments, targeting pre-emptive interventions.

Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability

“…It is important to understand how the interactions between climatic factors over past decades have defined infection trends and risks, unique to every region. Results of previous studies have shown that mean annual temperature, sea level pressure, number of days of rainfall and wind speed are among the variables having a significant influence on the rate of occurrence of dengue fever in the study area ( Abdulsalam et al, 2022 ), which was confirmed in this study ( Table 1 ). In coastal areas, temperature and precipitation tend to be higher and the study region is located in a coastal region (the Gulf of Thailand).…”

“…In coastal areas, temperature and precipitation tend to be higher and the study region is located in a coastal region (the Gulf of Thailand). While there may not be a substantial change in geographical patterns over time, there is a visible increase in precipitation in the region for the past decade ( Abdulsalam et al, 2022 ) and from April to October, increasing temperature and precipitation levels also increase mosquito density ultimately driving up the number of reported dengue cases. The temporal trend in dengue incidence risk observed in Fig.…”

“…With a current population of about 1,550,720 (765,369 males and 785,351 females), it has a total area of approximately 9,943 km 2 and a population density of 156 persons/km 2 ( Provincial Office, 2021 ; Thailand Interior Ministry, 2021 ). Primarily rural, the province is divided into 23 districts and 165 sub-districts; however, the capital city called Mueang is the most densely populated area ( Abdulsalam et al, 2022 ). The region has an average annual temperature and precipitation of 26.7 °C, and 1,978 mm respectively ( ClimateData.org, 2015 ).…”

“…They are (i) average monthly temperature (° C), (ii) average monthly relative humidity (%), (iii) average monthly rainfall (mm), (iv) average monthly number of rainy days (days), (v) average monthly wind speed (knot, where 1 knot = 1.334 km/h), (vi) average monthly evaporation (mm), (vii) average monthly cloud cover (okta), and (viii) average monthly sea-level pressure (hPa). These variables were chosen because they have been among the most relevant environmental variables in previous studies in the area mainly temperature, rainfall, cloud cover, and sea-level pressure ( Abdulsalam et al, 2022 ).…”

Determinant factor affected the presence of Aedes sp. in a customary village

“…Indonesia is an archipelagic country, until 2021 there were 1,331 tribes and 488 Customary Villages [19], this makes the variables that affect the breeding and reproduction of dengue vectors very large and complex [20] because each tribe and custom have different characteristics. Risk factors for potential vector breeding places and physical environment can provide important information to fill the gap in our knowledge about dengue vector dynamics, especially data related to indigenous peoples in Indonesia, which so far has not been available.…”