Weather Variability Associated with Aedes (Stegomyia) aegypti (Dengue Vector) Oviposition Dynamics in Northwestern Argentina

Abstract

This study aims to develop a forecasting model by assessing the weather variability associated with seasonal fluctuation of Aedes aegypti oviposition dynamic at a city level in Orán, in northwestern Argentina. Oviposition dynamics were assessed by weekly monitoring of 90 ovitraps in the urban area during 2005-2007. Correlations were performed between the number of eggs collected weekly and weather variables (rainfall, photoperiod, vapor pressure of water, temperature, and relative humidity) with and without time lags (1 to 6 weeks). A stepwise multiple linear regression analysis was performed with the set of meteorological variables from the first year of study with the variables in the time lags that best correlated with the oviposition. Model validation was conducted using the data from the second year of study (October 2006- 2007). Minimum temperature and rainfall were the most important variables. No eggs were found at temperatures below 10°C. The most significant time lags were 3 weeks for minimum temperature and rains, 3 weeks for water vapor pressure, and 6 weeks for maximum temperature. Aedes aegypti could be expected in Orán three weeks after rains with adequate min temperatures. The best-fit forecasting model for the combined meteorological variables explained 70 % of the variance (adj. R2). The correlation between Ae. aegypti oviposition observed and estimated by the forecasting model resulted in rs = 0.80 (P < 0.05). The forecasting model developed would allow prediction of increases and decreases in the Ae. aegypti oviposition activity based on meteorological data for Orán city and, according to the meteorological variables, vector activity can be predicted three or four weeks in advance.

Temporal models using environmental variables to predict Aedes aegypti oviposition activity in a temperate region of Argentina

“…In temperate zones, the minimum temperature can act as a limiting factor, slowing oviposition activity as happens in Buenos Aires [34], and Córdoba [36] not only during the winter season, resulting of greater importance than the maximum temperature in this study. Both the minimum temperature and precipitations were of the most important variables in this study, in coincidence with other studies [27, 37], however, these two variables were not incorporated into a model because they are highly correlated. In relation to maximum temperature, a positive relationship is observed with the oviposition activity.…”

“…Another possible explanation for this negative relationship may be that when the rains are not abundant, the human population tends to store water which means that there is availability of breeding sites for the vector although rains are scarce [33]. Maybe other explanation is applicable for Córdoba City, but it is remarkable the importance of precipitation peaks that usually occur weeks before finding eggs, adult mosquitoes or dengue cases appear in coincidence with the results of other researchers [27, 34, 35].…”

“…Estallo et al [27] found a positive relationship between Ae. aegypti egg number and the vapor pressure of water showing a decrease in eggs when this variable decreases its value.…”

“…Considering the biological characteristics of Ae. aegypti , time lags were applied to the explanatory variables [7, 27], meaning the length of time that has elapsed since the change in weather conditions that is reflected in the response variable in order to obtain a better fitted model.…”

Spatial pattern evolution of Aedes aegypti breeding sites in an Argentinean city without a dengue vector control programme

“…In addition, different studies propose that Ae. aegypti prevention and control actions should be based on results obtained from spatio-temporal data, both from the field and satellite imagery (Niño, 2011;De Melo et al, 2012;Estallo et al, 2015). The increase in the quality (accuracy) of field information used for the development of predictive maps should allow public health workers to identify areas of low or high risk for adequate disease control (Rotela et al, 2007;Halstead, 2008;Boyer et al, 2014).…”