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Vector Competence Analyses on Aedes aegypti Mosquitoes using Zika Virus

Vector Competence Analyses on Aedes aegypti Mosquitoes using Zika Virus

Published on: May 31, 2020

A probabilistic spatial dengue fever risk assessment by a threshold-based-quantile regression method.

Chuan-Hung Chiu¹, Tzai-Hung Wen², Lung-Chang Chien³

¹Department of Bioenvironmental systems engineering, National Taiwan University, Taipei, Taiwan.

|October 11, 2014

Summary

Environmental and socioeconomic factors significantly influence dengue fever (DF) spatial distribution. Water-related infrastructure and residential density increase DF risk, while higher income can mitigate it, informing targeted public health interventions.

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Area of Science:

Environmental epidemiology
Spatial analysis
Public health

Background:

Dengue fever (DF) poses a significant public health challenge, necessitating a deep understanding of its spatial transmission patterns.
Effective disease control strategies rely on identifying key environmental and socioeconomic drivers of DF incidence.

Purpose of the Study:

To investigate the associations between environmental and socioeconomic factors and the geographic distribution of dengue fever.
To propose and apply a probabilistic risk assessment approach for DF transmission.
To identify significant risk factors and estimate spatial DF risk distributions.

Main Methods:

Utilized threshold-based quantile regression to analyze DF risk factors.
Incorporated return period analysis to characterize DF occurrence frequency.
Focused study on old Kaohsiung City and Fongshan District, Taiwan, areas with historical DF outbreaks.

Main Results:

Water-related facilities (canals, ditches) and residential types were significant risk factors for DF.
Increased per capita income and its interactions with residential areas showed a mitigating effect on DF risk.
Nonlinear associations were observed, with water-related factors defining spatial patterns and high-density housing indicating potential for clustered infections.

Conclusions:

Probability-based spatial risk maps, including expected incidence, return periods, and distinct incidence rates, were generated for Kaohsiung.
These maps highlight distinct DF risks linked to environmental factors, varying in magnitude and probability.
The findings provide a valuable reference for local governmental agencies in developing targeted dengue control strategies.