Background: Culicoides biting midges transmit viruses resulting in disease in ruminants and equids such as bluetongue, Schmallenberg disease and African horse sickness. In the past decades, these diseases have led to important economic losses for farmers in Europe. Vector abundance is a key factor in determining the risk of vector-borne disease spread and it is, therefore, important to predict the abundance of Culicoides species involved in the transmission of these pathogens. The objectives of this study were to model and map the monthly abundances of Culicoides in Europe. Methods: We obtained entomological data from 904 farms in nine European countries (Spain, France, Germany, Switzerland, Austria, Poland, Denmark, Sweden and Norway) from 2007 to 2013. Using environmental and climatic predictors from satellite imagery and the machine learning technique Random Forests, we predicted the monthly average abundance at a 1 km² resolution. We used independent test sets for validation and to assess model performance. Results: The predictive power of the resulting models varied according to month and the Culicoides species/ensembles predicted. Model performance was lower for winter months. Performance was higher for the Obsoletus ensemble, followed by the Pulicaris ensemble, while the model for Culicoides imicola showed a poor performance. Distribution and abundance patterns corresponded well with the known distributions in Europe. The Random Forests model approach was able to distinguish differences in abundance between countries but was not able to predict vector abundance at individual farm level. Conclusions: The models and maps presented here represent an initial attempt to capture large scale geographical and temporal variations in Culicoides abundance. The models are a first step towards producing abundance inputs for R₀ modelling of Culicoides-borne infections at a continental scale.

Early warning systems rely on statistical prediction models, with environmental risks and remote sensing data serving as essential sources of information for their development. The present work is focused on the use of remote sensing for the estimation of transmission risk and the prediction of malaria cases in northwest Argentina. This study was conducted in the city of San Ramón de la Nueva Orán, where cases of the disease have been reported from 1986 to 2005. The relationship between reported malaria cases and climatic/environmental variables—including the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and land surface temperature (LST)—obtained from Landsat 5 and 7 satellite images was analyzed using multilevel Poisson regression analyses. An increased abundance of reported malaria cases was observed in summer. An ARIMA (autoregressive integrated moving average) temporal series model incorporating environmental variables was developed to forecast malaria cases in the year 2000. The analysis of the relationship between malaria cases and environmental and climatic factors showed that malaria cases were associated with increases in LST and mean temperature and a decrease in the NDVI. Early warning systems that provide information about spatial and temporal predictions of epidemics could help to control and prevent malaria outbreaks. Based on these findings, this study is expected to support the development of future prevention and control measures by health officials.

The early warning systems based on statistical prediction models; indicators of environmental risks and remote sensing constitute an essential source of environmental information for the development of these warning systems. The present work is focused on the use of remote sensing for the estimation of the risk of transmission and the prediction of malaria cases in the northwest of Argentina. The study was carried out in the city of San Ramón de la Nueva Orán, where cases of the disease have been reported from 1986 to 2005. The existent relationship between reported malaria cases and climatic/environmental variables (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Land Surface Temperature (LST)), obtained from Landsat 5 and 7 satellite images, was analyzed through multilevel Poisson regression analyses. A higher abundance of reported cases of malaria in summer was observed. A model of ARIMA (Autoregressive Integrated Moving Average) temporal series, which included the environmental variables, was generated to forecast malaria cases in the year 2000. In turn, the relationship between malaria cases and environmental/climatic factors showed that malaria cases were associated with an increase in LST and mean temperature and a decrease in NDVI. Early warning systems provide information about spatial and temporal predictions of epidemics might help control and prevent malaria outbreaks. Based on the results, this study is expected to be used for the development of future prevention and control actions by the health officials.