Analysis of Effects of Meteorological Factors on Dengue Incidence in Sri Lanka Using Time Series Data

Abstract

In tropical and subtropical regions of eastern and South-eastern Asia, dengue fever (DF) and dengue hemorrhagic fever (DHF) outbreaks occur frequently. Previous studies indicate an association between meteorological variables and dengue incidence using time series analyses. The impacts of meteorological changes can affect dengue outbreak. However, difficulties in collecting detailed time series data in developing countries have led to common use of monthly data in most previous studies. In addition, time series analyses are often limited to one area because of the difficulty in collecting meteorological and dengue incidence data in multiple areas. To gain better understanding, we examined the effects of meteorological factors on dengue incidence in three geographically distinct areas (Ratnapura, Colombo, and Anuradhapura) of Sri Lanka by time series analysis of weekly data. The weekly average maximum temperature and total rainfall and the total number of dengue cases from 2005 to 2011 (7 years) were used as time series data in this study. Subsequently, time series analyses were performed on the basis of ordinary least squares regression analysis followed by the vector autoregressive model (VAR). In conclusion, weekly average maximum temperatures and the weekly total rainfall did not significantly affect dengue incidence in three geographically different areas of Sri Lanka. However, the weekly total rainfall slightly influenced dengue incidence in the cities of Colombo and Anuradhapura.

Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission

“…Statistical models help to determine the relative contribution of environmental drivers to temporal variations in disease manifestation, support a wide variety of data types, and may offer a platform for developing early-warning systems based on disease and host surveillance [127][128][129]. Models may differ in their choice of health outcome measure, definitions of exposure or methods of quantifying associations and how different combinations of these components lead to different uncertainties in model estimates.…”

“…The classic time-series models and regression models have been widely used to analyse surveillance data [130]. Similar models are also applied to study the health effects of environmental exposures and meteorological conditions [127,131,132]. While illustrating the shape and magnitude of relationships between meteorological parameters and health outcomes, reported results of time-series models are often presented in a non-uniform way, which, in general, complicates comparisons between different studies and inhibits wider generalization.…”

A Model for Forecasting the Number of Cases and Distribution Pattern of Dengue Hemorrhagic Fever in Indonesia

“…The study using the VAR method to predict the number of DHF cases has been done by [22]. The main advantage of VAR is that multivariate variables are both explained and explanatory variables.…”

Global Diffusion Pattern and Hot Spot Analysis of Vaccine-Preventable Diseases

“…However, implementing GIS to analyze disease diffusion arising from spatially non-stationary processes, such as ordinary least squares (OLS) and geographically weighted regression (GWR) is limited (Goto, 2013;Hu, 2012).…”

“…The objectives of this project are to: 1) Identify spatial characteristics of vaccine-preventable diseases by measuring central tendency, dispersion, and directional trend (Hinman, Corresponding author 2006;Samphutthanon, 2013); 2) Highlight one of the vaccinepreventable diseases to determine statistically significant hot spots (Hinman, 2006;Jeefo, 2010;Samphutthanon, 2013); and 3) Explore variables that explain the spread of the highlighted disease (Goto, 2013).…”