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A Literature Review of Methods for Dengue Outbreak Prediction

Authors:
Duc Nghia Pham
Syahrul Nellis
Arun Anand Sadanand
Juraina binti Abd. Jamil
Jing Jing Khoo
Tarique Aziz
Dickson Lukose
Sazaly bin Abu Bakar
Abdul Sattar

Keywords: Dengue Outbreak Prediction, Statistical Analysis, Spatial Analysis, Machine Learning

Abstract:
This review provides the current dengue surveillance situation including (i) the factors that contribute to dengue transmission and (ii) the method to combat the disease. Dengue fever now is the most common mosquito-borne disease that infected around 100 billion population, mostly from Asia Pacific. This alboviral disease not only worsens people's health, but also has a great social and economic impact in areas where these endemics arise. Currently, the transmission of this disease is influenced not only by the climatic factors (e.g., rainfall, temperature, wind speed and humidity) but also by non-climatic factors like socio-environmental factors (e.g., population density, land use activity, vector control and transportation). Previously, prevention methods such as vector control, were used by public health agencies in combating the transmission of dengue outbreak. Recently, with the improvement of knowledge and technology, new methods and models are developed, not only for detection but also for prediction of dengue trends and outbreaks. An effective prediction model would be particularly helpful to detect unusual occurrences of disease and to allow for targeted surveillance and control efforts of the disease. In this paper, we review and summarize the development of dengue outbreak tools by researchers in the Asia Pacific region.

Pages: 7 to 13

Copyright: Copyright (c) IARIA, 2016

Publication date: April 24, 2016

Published in: conference

ISSN: 2308-4375

ISBN: 978-1-61208-472-5

Location: Venice, Italy

Dates: from April 24, 2016 to April 28, 2016