Risk assessment of dengue fever in Zhongshan, China: a time-series regression tree analysis

Abstract

Dengue fever (DF) is the most prevalent and rapidly spreading mosquito-borne disease globally. Control of DF is limited by barriers to vector control and integrated management approaches. This study aimed to explore the potential risk factors for autochthonous DF transmission and to estimate the threshold effects of high-order interactions among risk factors. A time-series regression tree model was applied to estimate the hierarchical relationship between reported autochthonous DF cases and the potential risk factors including the timeliness of DF surveillance systems (median time interval between symptom onset date and diagnosis date, MTIOD), mosquito density, imported cases and meteorological factors in Zhongshan, China from 2001 to 2013. We found that MTIOD was the most influential factor in autochthonous DF transmission. Monthly autochthonous DF incidence rate increased by 36·02-fold [relative risk (RR) 36·02, 95% confidence interval (CI) 25·26-46·78, compared to the average DF incidence rate during the study period] when the 2-month lagged moving average of MTIOD was >4·15 days and the 3-month lagged moving average of the mean Breteau Index (BI) was ⩾16·57. If the 2-month lagged moving average MTIOD was between 1·11 and 4·15 days and the monthly maximum diurnal temperature range at a lag of 1 month was <9·6 °C, the monthly mean autochthonous DF incidence rate increased by 14·67-fold (RR 14·67, 95% CI 8·84-20·51, compared to the average DF incidence rate during the study period). This study demonstrates that the timeliness of DF surveillance systems, mosquito density and diurnal temperature range play critical roles in the autochthonous DF transmission in Zhongshan. Better assessment and prediction of the risk of DF transmission is beneficial for establishing scientific strategies for DF early warning surveillance and control.

Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability

“…Consistent with available reports on the impact of temperature variation on dengue incidence (Liu et al 2017;Sharmin et al 2015), we observed both positive and negative statistically significant associations with temperature variation. Positive statistically significant associations with temperature variation were evident in seven cities of Guangdong province (Figure 4B); these cities are almost the same with cities having positive associations with temperature and rainfall (Figure 4A, 4C, 4E), probably mirroring the effects of these two weather variables.…”

From importation to autochthonous transmission: Drivers of chikungunya and dengue emergence in a temperate area

“…Reporting-based failures constitute a major factor in the occurrence of autochthonous transmission. A delay in case identification has already been indicated as a contributing factor in contexts of extended viral circulation [58], and remains the most important factor for the occurrence of foci of limited transmission. Our current findings are consistent with our Table 5.…”

Temporal Modeling of Dengue Fever: A Comprehensive Literature Review

“…Four studies used time series regression tree models [23][24][25][26] to identify the possible threshold for predicting dengue fever outbreak. Time-series classification and regression tree (CART) models allow a flexible nonparametric approach for predicting the association between dependent and independent variables [25]. Liu et al [25] found that mosquito density, diurnal temperature fluctuation, and timeliness of diagnosis play critical roles in dengue fever Green Materials and Technology outbreaks.…”

“…Time-series classification and regression tree (CART) models allow a flexible nonparametric approach for predicting the association between dependent and independent variables [25]. Liu et al [25] found that mosquito density, diurnal temperature fluctuation, and timeliness of diagnosis play critical roles in dengue fever Green Materials and Technology outbreaks. Gu et al [26] investigated the delayed effect of climatic factor and the risk of dengue fever by BRT (boosted regression tree).…”