Mario Lamfri

Models for Predicting Aedes aegypti Larval Indices Based on Satellite Images and Climatic Variables

ABSTRACT Forecasting models were developed for predicting Aedes aegypti larval indices in an endemic area for dengue (cities of Tartagal and Orán, northwestern Argentina), based on the Breteau and House indices and environmental variables considered with and without time lags. Descriptive models were first developed for each city and each index by multiple linear regressions, followed by a regional model including both cities together. Finally, two forecasting regional models (FRM) were developed and evaluated. FRM2 for the Breteau index and House index fit the data significantly better than FRM1. An evaluation of these models showed a higher correlation FRM1 than for FRM2 for the Breteau index (R = 0.83 and 0.62 for 3 months; R = 0.86 and 0.67 for 45 days) and the House index (R = 0.85 and 0.79 for 3 months; R = 0.79 and 0.74 for 45 days). Early warning based on these forecasting models can assist health authorities to improve vector control.

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.

Effectiveness of normalized difference water index in modelling Aedes aegypti house index

The application of remotely sensed data to public health has increased in Argentina in the past few years, especially to study vector-borne viral diseases such as dengue. The normalized difference vegetation index (NDVI) has been widely used for remote sensing of vegetation as well as the brightness temperature (BT) for many years. Another environmental variable obtained from satellites is the normalized difference water index (NDWI) for remote sensing of the status of the vegetation liquid water from space. The aim of the present article was to test the effectiveness of NDWI together with other satellite and meteorological data to develop two forecasting models, namely the SATMET (satellite and meteorological variables) model and the SAT (satellite environmental variables) model. The models were developed and validated by dividing the data file into two sets: the data between January 2001 and April 2004 were used to construct the models and the data between May 2004 and May 2005 were used to validate them. The regression analysis for the SATMET and SAT models showed an adjusted R 2 of 0.82 and 0.79, respectively. To validate the models, a correlation between the estimates and the observations was obtained for both the SATMET model (r = 0.57) and the SAT model (r = 0.64). Both models showed the same root mean square error (RMSE) of 0.04 and, therefore, the same forecasting power. For this reason, these models may have applications as decision support tools in assisting public health authorities in the control of Aedes aegypti and risk management planning programmes.

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.

Satellite-derived NDVI, LST, and climatic factors driving the distribution and abundance of Anopheles mosquitoes in a former malarious area in northwest Argentina

ABSTRACT: Distribution and abundance of disease vectors are directly related to climatic conditions and environmental changes. Remote sensing data have been used for monitoring environmental conditions influencing spatial patterns of vector-borne diseases. The aim of this study was to analyze the effect of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic factors (temperature, humidity, wind velocity, and accumulated rainfall) on the distribution and abundance of Anopheles species in northwestern Argentina using Poisson regression analyses. Samples were collected from December, 2001 to December, 2005 at three localities, Aguas Blancas, El Oculto and San Ramón de la Nueva Orán. We collected 11,206 adult Anopheles species, with the major abundance observed at El Oculto (59.11%), followed by Aguas Blancas (22.10%) and San Ramón de la Nueva Orán (18.79%). Anopheles pseudopunctipennis was the most abundant species at El Oculto, Anopheles argyritarsis predominated in Aguas Blancas, and Anopheles strodei in San Ramón de la Nueva Orán. Samples were collected throughout the sampling period, with the highest peaks during the spring seasons. LST and mean temperature appear to be the most important variables determining the distribution patterns and major abundance of An. pseudopunctipennis and An. argyritarsis within malarious areas.

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.

Providing management options to control corn mouse (Calomys musculinus) reservoir populations using a cohort structured model

We evaluate several management options for Calomys musculinus populations through the formulation and validation of a cohort structured model. Initially, a basic model was constructed and validated using field population data. Next, the model was altered to allow us to evaluate different management options. In general, basic model results were in agreement with field data, demonstrating that this model would be useful in describing aspects of corn mouse population dynamics. Restricting control measures to when mouse numbers reach high levels would be inadequate, because population numbers tend to increase in size after some years. In contrast, reducing vegetation cover in spring was more effective in reducing field population abundances. Despite some limitations, the model could be useful for evaluating the relationships between population dynamics and some biotic or physical environmental variables, and thus ensure more efficient use of resources in integrated pest management.