Improving prediction of conditions that modulate dengue fever risks in Yucatán, México.

Abstract:

Accurately predicting vector-borne diseases is essential for communities everywhere around the world. Yet this is a difficult task, even in areas where annual epidemics occur. The primary vector for dengue virus disease (DENV) is Aedes aegypti. This is a tropical-subtropical mosquito that proliferates in urban areas. Precipitation and increased temperatures are known to promote growth, reproduction and transmission of DENV. This study assesses potential health risks on coastal communities in the northwest Yucatan Peninsula, Mexico. We studied the relation between DENV incidences and environmental data. We hypothesized that environmental parameters such as rainfall, sea surface temperature (SST), air temperature, humidity, and past DENV cases are the primary drivers of DENV incidences. We collected DENV data from the National Health Information System and demographic data from the National Institute of Statistics and Geography. Precipitation and air temperature were obtained from the National Water Commission. SST was derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite sensor. In addition, incidence of DENV cases per year was calculated. Multiple regression analyses show that previous DENV cases, minimum air temperature, humidity, and precipitation are positively related to DENV cases and explain 82% of the variation, with 77% explained by previous DENV cases (cases that took place 2-weeks before the target). A second regression model without the previous DENV cases showed 30% of the variation explained by humidity and precipitation (p<0.05). Satellite-derived SST was also included to test whether the percent variation of DENV explained increased. These results imply that if these environmental variables continue to increase with time, the trend of DENV cases will also increase. This study suggests that it is possible to significantly improve DENV prevention and prediction of potential outcomes in Yucatan using remote sensing data.