Mario Lanfri

Spatio-temporal dynamics of dengue 2009 outbreak in Córdoba City, Argentina.

During 2009 the biggest dengue epidemic to date occurred in Argentina, affecting almost half the country. We studied the spatio-temporal dynamics of the outbreak in the second most populated city of the country, Córdoba city. Confirmed cases and the results of an Aedes aegypti monitoring during the outbreak were geolocated. The imported cases began in January, and the autochthonous in March. Thirty-three percent of the 130 confirmed cases were imported, and occurred mainly at the center of the city. The autochthonous cases were more frequent in the outskirts, specially in the NE and SE. Aedes aegypti infestation showed no difference between neighborhoods with or without autochthonous cases, neither between neighborhoods with autochthonous vs. imported cases. The neighborhoods with imported cases presented higher population densities. The majority of autochthonous cases occurred at ages between 25 and 44 years old. Cases formed a spatio-temporal cluster of up to 20 days and 12km. According to a mathematical model that estimates the required number of days needed for transmission according to daily temperature, the number of cases begun to fall when more than 15.5 days were needed. This may be a coarse estimation of mean mosquito survival in the area, provided that the study area is close to the global distribution limit of the vector, and that cases prevalence was very low.

Estimating Hantavirus Risk in Southern Argentina: A GIS-Based Approach Combining Human Cases and Host Distribution

We use a Species Distribution Modeling (SDM) approach along with Geographic Information Systems (GIS) techniques to examine the potential distribution of hantavirus pulmonary syndrome (HPS) caused by Andes virus (ANDV) in southern Argentina and, more precisely, define and estimate the area with the highest infection probability for humans, through the combination with the distribution map for the competent rodent host (Oligoryzomys longicaudatus). Sites with confirmed cases of HPS in the period 1995–2009 were mostly concentrated in a narrow strip (~90 km × 900 km) along the Andes range from northern Neuquén to central Chubut province. This area is characterized by high mean annual precipitation (~1,000 mm on average), but dry summers (less than 100 mm), very low percentages of bare soil (~10% on average) and low temperatures in the coldest month (minimum average temperature −1.5 °C), as compared to the HPS-free areas, features that coincide with sub-Antarctic forests and shrublands (especially those dominated by the invasive plant Rosa rubiginosa), where rodent host abundances and ANDV prevalences are known to be the highest. Through the combination of predictive distribution maps of the reservoir host and disease cases, we found that the area with the highest probability for HPS to occur overlaps only 28% with the most suitable habitat for O. longicaudatus. With this approach, we made a step forward in the understanding of the risk factors that need to be considered in the forecasting and mapping of risk at the regional/national scale. We propose the implementation and use of thematic maps, such as the one built here, as a basic tool allowing public health authorities to focus surveillance efforts and normally scarce resources for prevention and control actions in vast areas like southern Argentina.

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

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. Moderate resolution imaging spectroradiometer (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 normalized difference vegetation index and diurnal land surface temperature 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.

Spatial Patterns of High Aedes aegypti Oviposition Activity in Northwestern Argentina

Background In Argentina, dengue has affected mainly the Northern provinces, including Salta. The objective of this study was to analyze the spatial patterns of high Aedes aegypti oviposition activity in San Ramón de la Nueva Orán, northwestern Argentina. The location of clusters as hot spot areas should help control programs to identify priority areas and allocate their resources more effectively. Methodology Oviposition activity was detected in Orán City (Salta province) using ovitraps, weekly replaced (October 2005–2007). Spatial autocorrelation was measured with Moran’s Index and depicted through cluster maps to identify hot spots. Total egg numbers were spatially interpolated and a classified map with Ae. aegypti high oviposition activity areas was performed. Potential breeding and resting (PBR) sites were geo-referenced. A logistic regression analysis of interpolated egg numbers and PBR location was performed to generate a predictive mapping of mosquito oviposition activity. Principal Findings Both cluster maps and predictive map were consistent, identifying in central and southern areas of the city high Ae. aegypti oviposition activity. A logistic regression model was successfully developed to predict Ae. aegypti oviposition activity based on distance to PBR sites, with tire dumps having the strongest association with mosquito oviposition activity. A predictive map reflecting probability of oviposition activity was produced. The predictive map delimitated an area of maximum probability of Ae. aegypti oviposition activity in the south of Orán city where tire dumps predominate. The overall fit of the model was acceptable (ROC = 0.77), obtaining 99% of sensitivity and 75.29% of specificity. Conclusions Distance to tire dumps is inversely associated with high mosquito activity, allowing us to identify hot spots. These methodologies are useful for prevention, surveillance, and control of tropical vector borne diseases and might assist National Health Ministry to focus resources more effectively.

Analytical report of the 2016 dengue outbreak in Córdoba city, Argentina

After elimination of the Aedes aegypti vector in South America in the 1960s, dengue outbreaks started to reoccur during the 1990s; strongly in Argentina since 1998. In 2016, Córdoba City had the largest dengue outbreak in its history. In this article we report this outbreak including spatio-temporal analysis of cases and vectors in the city. A total of 653 dengue cases were recorded by the laboratory-based dengue surveillance system and georeferenced by their residential addresses. Case maps were generated from the epidemiological week 1 (beginning of January) to week 19 (mid-May). Dengue outbreak temporal evolution was analysed globally and three specific, high-incidence zones were detected using Knox analysis to characterising its spatio-temporal attributes. Field and remotely sensed data were collected and analysed in real time and a vector presence map based on the MaxEnt approach was generated to define hotspots, towards which the pesticide- based strategy was then targeted. The recorded pattern of cases evolution within the community suggests that dengue control measures should be improved.

First Implementation of the WRF-CHIMERE-EDGAR Modeling System Over Argentina

Air quality monitoring and research have been gaining importance in Argentina and Latin America, mainly in megacities where pollution reaches critical levels as in other places of the world. This work is a first attempt at simulating pollution levels at the country scale, in order to support air quality management and forecasting activities. We implemented the global scale inventory of anthropogenic emissions EDGAR v4.2 into the CHIMERE chemistry-transport model, driven by WRF meteorological fields, at a resolution of about 50 km, a performance evaluation of the modeling system is presented by the use of ground-based and satellite data. The lack of monitoring stations in the country constrained the evaluation to the March-May 2009 time period in three cities. We obtain a generally large underestimation of nitrogen oxides and particulate matter, but a good simulation of the daily cycles. The magnitude of pollution levels is underestimated probably because of the misrepresentation of the monitoring stations (sites heavily affected by local traffic) and of the coarse resolution of the model. Nitrogen dioxide tropospheric column obtained by the OMI sensor (onboard Aura/NASA) was used to evaluate spatial correspondence with the simulation outputs, revealing that spatial features are broadly captured by the model. Further work would imply an emission inventory refinement and the use of other satellite data available considering other periods of time; however, a more dense and representative air quality monitoring network throughout the country is very much needed.

Information content in COSMO-SkyMed data

We analyze the information content in COSMO-SkyMed data with different acquisition modes and polarizations. A set of discrimination problems ranging from difficult to simple using samples from different land cover types is presented. Several separability measures, i.e. stochastic distances and their derived hypothesis tests, are applied to pairs of samples, and their ability to discriminate is assessed. From the studied modes, class separability of water, pasture, forest and urban is enhanced if the lowest resolution mode is used. Both, ascending and left looking acquisition geometry yield better classification results. Distance measurement tests between samples of the same class give better results for HH polarization than for VV polarization suggesting that the analyzed cover properties are better described by that microwave configuration.

Sistemas de alerta temprana a emergencias ambientales basados en modelos numéricos de predicción meteorológica

In this work we present an operative implementation of three numerical weather prediction models with the goal of being used in early warning systems for environmental emergencies. Models are executed in different temporal ranges (from days to months) in which emergencies, such as ground frost, floods, fires, droughts and epidemics, are developed. Products generated are thought and adapted to be used in Geographical Information Systems (GIS) and are freely distributed through the CONAE website. Models and softwares used are open-source softwares (OSS).