MODIS Environmental Data to Assess Chikungunya, Dengue, and Zika Diseases Through Aedes (Stegomia) aegypti Oviposition Activity Estimation

ESTALLO, ELIZABET LILIA; BENITEZ, ELISABET M.; LANFRI, MARIO ALBERTO; SCAVUZZO, CARLOS MARCELO; ALMIRON, WALTER R.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Lugar: New York; Año: 2016 vol. 9 p. 5461 - 5466

1939-1404

Abstract? Aedes aegypti is the main vector for Chikungunya, Dengue and Zika viruses in Latin America and it represents a main threat for our region. Taking into account this situation, several efforts have been done to use remote sensing to support public health decision making. MODIS sensor provides moderate-resolution remote sensing products; therefore we explore the application of MODIS products to vector-borne disease problems in Argentina. We develop temporal forecasting models of Ae. aegypti oviposition, and we include its validation and its application to the 2016 Dengue outbreak. Temporal series (10/2005 to 09/2007) from MODIS products of NDVI and diurnal LST were built. Two linear regression models were developed: model 1 which uses environmental variables with time lag and model 2 uses environmental variables without time lags. Model 2 was the best model (AIC = 112) with high correlation (r = 0.88, p< 0.05) between observed and predicted data. We can suggest that MODIS products could be a good tool for estimating both Ae. aegypti oviposition activity and risks for Ae. aegypti-borne diseases. That statement is also supported by model results for 2016 when a dengue outbreak that started unusually earlier this season. If such activity could be forecast by a model based on remote sensing data, then a potential outbreak could be predicted.