Modeling the extrinsic incubation of Dirofilaria immitis in South America based on monthly and continuous climatic data.

The spread and recrudescence of dirofilariosis across several regions, either caused by Dirofilaria immitis or D. repens, responds to many factors. Building upon the temperature model by Slocombe et al. (1989), a number of studies have been performed to generate predictive models for dirofilariosis. These studies have demonstrated the length of transmission periods and the number of Dirofilaria generations supported throughout several regions of the world (either at large or at small-scale). The usual procedure have proved to be extremely time consuming, as it appears impractical when assessing potential transmission at large scale, such as at a country or large-scale level. Due to the increasing need to suggest standardized surveillance protocols and apply adequate preventive measures at national and regional levels, a model for calculation of Dirofilaria HDUs based on monthly data was adapted for large-scale regional use. The models proposed are based on both point data (meteorological stations) and interpolated climate data layers (WorldClim). Three different models (daily and monthly models based on point data, and monthly model based on continuous data) were developed and compared statistically. When compared with the results from the classical daily model, the monthly models proposed accurately predicted the locations were extrinsic incubation was possible. These models proved to be adequate for the regional analysis of the extrinsic incubation of D. immitis and, hence, the relative risk of transmission in South America. Further, these models confirm that favorable temperatures for heartworm transmission in South America are present in most of the countries. D. immitis extrinsic incubation follows a seasonal pattern in Argentina, Chile, Uruguay, eastern Paraguay and southeastern Brazil; while in northern half of South America (less than 25° S) transmission may occur year-round. Moreover, high risk areas suitable for dirofilariasis transmission are not geographically constant throughout the year. The validation procedures indicate that the predicted HDU and HG maps are good predictors of dirofilariosis potential distribution, but estimating dirofilariosis prevalences based on them might not be completely accurate. The resulting distribution and seasonal maps would be useful for heartworm prevention by chemoprophylaxis in different regions known to be endemic for canine dirofilariasis. The information here provided can be an important tool in veterinary public health, as well as a guide for future research.