Exploratory wavelet analysis of dengue seasonal patterns in Colombia.
BACKGROUND
Dengue has a seasonal behavior associated with climatic changes, vector cycles, circulating serotypes, and population dynamics. The wavelet analysis makes it possible to separate a very long time series into calendar time and periods. This is the first time this technique is used in an exploratory manner to model the behavior of dengue in Colombia.


OBJECTIVE
To explore the annual seasonal dengue patterns in Colombia and in its five most endemic municipalities for the period 2007 to 2012, and for roughly annual cycles between 1978 and 2013 at the national level.


METHODS
We made an exploratory wavelet analysis using data from all incident cases of dengue per epidemiological week for the period 2007 to 2012, and per year for 1978 to 2013. We used a first-order autoregressive model as the null hypothesis.


RESULTS
The effect of the 2010 epidemic was evident in both the national time series and the series for the five municipalities. Differences in interannual seasonal patterns were observed among municipalities. In addition, we identified roughly annual cycles of 2 to 5 years since 2004 at a national level.


CONCLUSIONS
Wavelet analysis is useful to study a long time series containing changing seasonal patterns, as is the case of dengue in Colombia, and to identify differences among regions. These patterns need to be explored at smaller aggregate levels, and their relationships with different predictive variables need to be investigated.
Centro de Información para Decisiones en Salud Pública (CENIDSP), Instituto Nacional de Salud Pública, Cuernavaca, Morelos, México. julian.fernandez@insp.mx.
Julián Alfredo
JA
Fernández-Niño
Luz Mery
LM
Cárdenas-Cárdenas
Juan Eugenio
JE
Hernández-Ávila
Lina Sofía
LS
Palacio-Mejía
Carlos Andrés
CA
Castañeda-Orjuela
D003105:Colombia; D003715:Dengue / Q000453:epidemiology*; D004196:Disease Outbreaks; D006801:Humans; D012621:Seasons; D058067:Wavelet Analysis
Colombia; Dengue/epidemiology; epidemiological surveillance; seasons; time series studies; wavelet analysis