Marcelo Scavuzzo

Phlebotominae spatial distribution asssociated with a focus of tegumentary leishmaniasis in Las Lomitas, Formosa, Argentina, 2002

Las Lomitas, Formosa, Argentina, reported 96 cases of tegumentary leishmaniasis during 2002. The urban transmission was suggested although previous outbreaks were related with floods of the Bermejo river (BR) 50 km from the village. Phlebotomine collections were performed during March 2002 to define the spatial distribution of risk, together with satellite imaginery. The phlebotomine/trap obtained was 1679.5 in the southern BR shore, 1.1 in the periruban-rural environment and 2.3 in the northern Pilcomayo river marshes. Lutzomyia neivai was the prevalent species (91.1%) among the 2393 phlebotomine captured, and it was only found in the BR traps. The other species were L. migonei (7.9%), L. cortelezzii (0.9%), and Brumptomyia guimaraesi (0.1%). The satellite images analysis indicates that the fishing spots at the BR were significantly overflowed during the transmission peak, consistent with fishermen recollections. This spatial restricted flood might concentrate vectors, reservoirs, and humans in high places. Therefore, both the spatial distribution of vectors and the sensor remoting data suggests that in Las Lomitas area the higher transmission risk it is still related with the gallery forest of the BR, despite of the urban residence of the cases. The surveillance and control implications of these results are discussed.

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.

On the relationship between the environmental history and the epidemiological situation of Argentine hemorrhagic fever

The aim of this work was to establish the relationship between different Argentine hemorrhagic fever (AHF) epidemiological situations found at different sites and the related large-scale environmental conditions. Large-scale environmental records (vegetation index, temperature, precipitation and elevation) were obtained from a series of monthly NOAA satellite images and global databases considered suitable for modeling climatic and other environmental determinants of large-scale biogeographical regions. The temporal variation in vegetation for cycles of winter-summer showed a greater variation in the nonendemic region than in the other two regions. On the other hand, the average of the temporal variation in precipitation in cycles of spring–autumn was more different in the historic region than in the other two regions, and land surface temperatures in cycles of spring–autumn showed differences between the epidemic region and the other two regions. We found good separation among the epidemic, historic and nonendemic sites, with the greatest difference found between epidemic and nonendemic sites. The classification of sites showed a tendency for grouping according to the epidemiological situation, but there was some variation. It seems possible to establish a close relationship between the state of AHF incidence and the environmental history of sites suggesting the possibility of predicting epidemiological behavior using environmental conditions derived from satellite data.

Spatial pattern of reinfestation by Triatoma infestans in Chancaní, Argentina

ABSTRACT We examined the environmental correlates and the spatial pattern of infestation by Triatoma infestans, a vector of Chagas disease, in a rural area of Argentina five years following an insecticidal campaign. Patterns of infestation were identified in an entomological survey, as mapped with high-resolution satellite imagery and analyzed in a geographic information system. Logistic regression was used to relate infestation to observed household characteristics as well as the location and density of households. Location was the most significant predictor of infestation for domiciles. For peridomestic structures surrounding the domiciles, the combination of location and the presence/absence of goat pens was most significant. In considering any infestation, whether domiciliary or peridomestic, the combination of location, presence/absence of animal pens, and the type of household construction were found to be most significant. Using these statistical relationships to backclassify the field data resulted in accuracies between 85% and 87%. A map of infestation probability for the town of Chancaní was developed from the logistic regression.

Spatial population dynamics and temporal analysis of the distribution of Lutzomyia longipalpis (Lutz & Neiva, 1912) (Diptera: Psychodidae: Phlebotominae) in the city of Clorinda, Formosa, Argentina

BackgroundLutzomyia longipalpis, the vector for the causal agent of visceral leishmaniasis (VL), has extended its distribution in the southern cone in the Americas. The first urban record of Lu. longipalpis in Argentina was from the City of Clorinda in 2004. The aim of this study was to analyse the monthly distribution and abundance of Lu. longipalpis and to evaluate its association with environmental and climatic variables in Clorinda City, Province of Formosa.MethodsPhlebotominae sampling was performed using CDC light mini-traps that were placed in different sites of the city between January 2012 and December 2013. Environmental variables including the normalised difference vegetation index, normalized difference water index, land surface temperature and precipitation were evaluated using a spatiotemporal model.ResultsA total of 4996 phlebotomine sandflies were captured during the study period, and eight species were reported: Lu. longipalpis, Migonemyia migonei, Nyssomyia whitmani, Ny. neivai, Brumptomyia guimaraesi, Evandromyia cortelezzii/sallesi, Psathyromyia bigeniculata and Expapillata firmatoi. This is the first urban record of Ex. firmatoi in Argentina. Lutzomyia longipalpis was the most abundant species between 2012 and 2013, and it appeared in all the sampled sites. Moreover, the model applied showed that ground humidity and temperature were significantly associated with the abundance of Lu. longipalpis.ConclusionsThis longitudinal approach at city scale allows for modelling that explains more than 60% of the temporal variability of the abundance of Lu. longipalpis based exclusively on satellite obtained data. The results support the hypothesis of steady ‘hot spots’ of abundance with time, while other sites could change its abundance due to eventual microenvironment changes. The Lu. longipalpis abundance driving factors are breeding site-related variables, highlighting the importance both for modelling and surveillance to use lag data.

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.

Detection of algal blooms in a eutrophic reservoir based on chlorophyll-a time series data from MODIS

Eutrophication is a phenomenon that affects many water bodies around the world. In severe cases, eutrophication can lead to large algal blooms. This study presents a method to detect algae blooms based on a time series of chlorophyll-a (Chl-a) concentration in the period 2001–2014. This time series is obtained from a semi-empirical algorithm generated with MODIS satellite data and in situ data from the Ministry of Water Resources of Cordoba Province. By detecting algae bloom dates and their statistic characterization, it is possible to define the range of Chl-a values in which the San Roque Dam is going through a bloom event.

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).