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. 2019 Aug 21;3(8):202-217.
doi: 10.1029/2019GH000186. eCollection 2019 Aug.

Spatial-Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in São Paulo, Brazil

I Ogashawara  1 , L Li  1 , M J Moreno-Madriñán  2
Affiliations

Spatial-Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in São Paulo, Brazil

I Ogashawara et al. Geohealth. .

Abstract

Dengue fever, a disease caused by a vector-borne flavivirus, is endemic to tropical countries, but its occurrence has been reported worldwide. This study aimed to understand important factors contributing to the spatial and temporal patterns of dengue occurrence in São Paulo, the largest municipality of Brazil. The temporal assessment of dengue occurrence covered the 2011-2016 time period and was based on climatological data, such as the El Niño indices and time series statistical tools such as the continuous wavelet transformation. The spatial assessment used Landsat 8 data for years 2014-2016 to estimate land surface temperature and normalized indices for vegetation, urban areas, and leaf water. Results from a cross correlation for the temporal analysis found a relationship between the sea surface temperature anomalies index and the number of reported dengue cases in São Paulo (r = 0.5) with a lag of +29 (weeks) between the climatic event and the response on the dengue incidence. This relationship, initially nonlinear, became linear after correcting for the lag period. For the spatial assessment, the linear stepwise regression model detected a low relationship between dengue incidence and minimum surface temperature (r = 0.357) and no relationship with other environmental parameters. The poor relationship might be due to confounding effects of socioeconomic factors as these seem to influence the spatial dynamics of dengue incidence. More testing is needed to validate these methods in other locations. Nevertheless, we presented possible tools to be used for the improvement of dengue control programs.

Keywords: El Niño; Landsat 8; continuous wavelet transformation; dengue; land surface temperature; remote sensing indices.

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Conflict of interest statement

The authors declare no actual or perceived conflicts of interest.

Figures

Figure 1
Figure 1
Location of São Paulo Municipality within Brazil and its 96 districts.
Figure 2
Figure 2
Daily time series (2011–2016) of precipitation (mm) and temperature (°C) from the weather station located at “Mirante do Santana,” São Paulo, Brazil.
Figure 3
Figure 3
Distribution of confirmed dengue cases (in log scale) per epidemiological months in the municipality of São Paulo between the years of 2011 and 2017.
Figure 4
Figure 4
Time series of precipitation, temperature, and confirmed dengue cases (from 2011 to 2016).
Figure 5
Figure 5
(a) Monthly SSTA index and dengue cases plot, and (b) wavelet transformation of the monthly SSTA index and dengue cases plot. SSTA = sea surface temperature anomalies.
Figure 6
Figure 6
Absolute Pearson's correlation coefficient and absolute Spearman's rank correlation coefficient for the relationship between denge cases and both SSTA index and continuous wavelet transformation scales. SSTA = sea surface temperature anomalies.
Figure 7
Figure 7
Land surface temperature for São Paulo city (a) for 2014, (b) for 2015, and (c) for 2016.
Figure 8
Figure 8
Reported dengue cases per 10,000 habitants per district of São Paulo city (a) for 2014, (b) for 2015, and (c) for 2016.
See this image and copyright information in PMC

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