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. 2013 May 9;8(5):e63717.
doi: 10.1371/journal.pone.0063717. Print 2013.

Analysis of effects of meteorological factors on dengue incidence in Sri Lanka using time series data

Kensuke Goto  1 , Balachandran Kumarendran, Sachith Mettananda, Deepa Gunasekara, Yoshito Fujii, Satoshi Kaneko
Affiliations

Analysis of effects of meteorological factors on dengue incidence in Sri Lanka using time series data

Kensuke Goto et al. PLoS One. .

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.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Study Locations.
Figure 2
Figure 2. Meteorological variables and the number of dengue cases.
(A) Ratnapura (B) Colombo (C) Anuradhapura.
Figure 3
Figure 3. The correlogram of difference series data for all variables in Ratnapura.
(A) Logarithm of dengue incidence (B) Logarithm of maximum temperature (C) Logarithm of total rainfall.
Figure 4
Figure 4. The correlogram of difference series data for all variables in Colombo.
(A) Logarithm of dengue incidence (B) Logarithm of maximum temperature (C) Logarithm of total rainfall.
Figure 5
Figure 5. The correlogram of difference series data for all variables in Anuradhapura.
(A) Logarithm of dengue incidence (B) Logarithm of maximum temperature (C) Logarithm of total rainfall.
Figure 6
Figure 6. Impulse response functions.
See this image and copyright information in PMC

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