Past, present and future distribution of the yellow fever mosquito Aedes aegypti: The European paradox

The global distribution of the yellow fever mosquito Aedes aegypti is the subject of considerable attention because of its pivotal role as a biological vector of several high profile disease pathogens including dengue, chikungunya, yellow fever, and Zika viruses. There is also a lot of interest in the projected future species' distribution. However, less effort has been focused on its historical distribution, which has changed substantially over the past 100 years, especially in southern Europe where it was once widespread, but largely disappeared by the middle of the 20th century. The present work utilises all available historical records of the distribution of Ae. aegypti in southern Europe, the Near East within the Mediterranean Basin and North Africa from the late 19th century until the 1960's to construct a spatial distribution model using matching historical climatic and demographic data. The resulting model was then implemented using current climate and demographic data to assess the potential distribution of the vector in the present. The models were rerun with several different assumptions about the thresholds that determine habitat suitability for Ae. aegypti. The historical model matches the historical distributions well. When it is run with current climate values, the predicted present day distribution is somewhat broader than it used to be particularly in north-west France, North Africa and Turkey. Though it is beginning to reappear in the eastern Caucasus, this 'potential' distribution clearly does not match the actual distribution of the species, which suggests some other factors are responsible for its absence. Future distributions based on the historical model also do not match future distributions derived from models based only on present day vector distributions, which predict little or no presence in the Mediterranean Region. At the same time, the vector is widespread in the USA which is predicted to consolidate its range there in future. This contradiction and the implication for possible re-invasion of Europe are discussed

Modelling the West Nile virus force of infection in the European human population

West Nile virus (WNV) is among the most recent emerging mosquito-borne pathogens in Europe where each year hundreds of human cases are recorded. We developed a relatively simple technique to model the WNV force of infection (FOI) in the human population to assess its dependence on environmental and human demographic factors. To this aim, we collated WNV human case-based data reported to the European Surveillance System from 15 European Countries during the period 2010–2021. We modelled the regional WNV FOI for each year through normal distributions and calibrated the constituent parameters, namely average (peak timing), variance and overall intensity, to observed cases. Finally, we investigated through regression models how these parameters are associated to a set of climatic, environmental and human demographic covariates. Our modelling approach shows good agreement between expected and observed epidemiological curves. We found that FOI magnitude is positively associated with spring temperature and larger in more anthropogenic semi-natural areas, while FOI peak timing is negatively related to summer temperature. Unsurprisingly, FOI is estimated to be greater in regions with a larger fraction of elderly people, who are more likely to contract severe infections. Our results confirm that temperature plays a key role in shaping WNV transmission in Europe and provide some interesting hints on how human presence and demography might affect WNV burden. This simple yet reliable approach could be easily adopted for early warning and to address epidemiological investigations of other vector-borne diseases, especially where eco-epidemiological data are scarc

High habitat richness reduces the risk of tick-borne encephalitis in Europe: a multi-scale study

Background The natural transmission cycle of tick-borne encephalitis (TBE) virus is enhanced by complex interactions between ticks and key hosts strongly connected to habitat characteristics. The diversity of wildlife host species and their relative abundance is known to affect transmission of tick-borne diseases. Therefore, in the current context of global biodiversity loss, we explored the relationship between habitat richness and the pattern of human TBE cases in Europe to assess biodiversity's role in disease risk mitigation. Methods We assessed human TBE case distribution across 879 European regions using official epidemiological data reported to The European Surveillance System (TESSy) between 2017 and 2021 from 15 countries. We explored the relationship between TBE presence and the habitat richness index (HRI1) by means of binomial regression. We validated our findings at local scale using data collected between 2017 and 2021 in 227 municipalities located in Trento and Belluno provinces, two known TBE foci in northern Italy. Findings Our results showed a significant parabolic effect of HRI on the probability of presence of human TBE cases in the European regions included in our dataset, and a significant, negative effect of HRI on the local presence of TBE in northern Italy. At both spatial scales, TBE risk decreases in areas with higher values of HRI. Interpretation To our knowledge, no efforts have yet been made to explore the relationship between biodiversity and TBE risk, probably due to the scarcity of high-resolution, large-scale data about the abundance or density of critical host species. Hence, in this study we considered habitat richness as proxy for vertebrate host diversity. The results suggest that in highly diverse habitats TBE risk decreases. Hence, biodiversity loss could enhance TBE risk for both humans and wildlife. This association is relevant to support the hypothesis that the maintenance of highly diverse ecosystems mitigates disease ris

Using geographically weighted regression to explore the spatially heterogeneous spread of bovine tuberculosis in England and Wales

An understanding of the factors that affect the spread of endemic bovine tuberculosis (bTB) is critical for the development of measures to stop and reverse this spread. Analyses of spatial data need to account for the inherent spatial heterogeneity within the data, or else spatial autocorrelation can lead to an overestimate of the significance of variables. This study used three methods of analysis—least-squares linear regression with a spatial autocorrelation term, geographically weighted regression (GWR) and boosted regression tree (BRT) analysis—to identify the factors that influence the spread of endemic bTB at a local level in England and Wales. The linear regression and GWR methods demonstrated the importance of accounting for spatial differences in risk factors for bTB, and showed some consistency in the identification of certain factors related to flooding, disease history and the presence of multiple genotypes of bTB. This is the first attempt to explore the factors associated with the spread of endemic bTB in England and Wales using GWR. This technique improves on least-squares linear regression approaches by identifying regional differences in the factors associated with bTB spread. However, interpretation of these complex regional differences is difficult and the approach does not lend itself to predictive models which are likely to be of more value to policy makers. Methods such as BRT may be more suited to such a task. Here we have demonstrated that GWR and BRT can produce comparable outputs

Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data

Background. Remotely-sensed environmental data from earth-orbiting satellites are increasingly used to model the distribution and abundance of both plant and animal species, especially those of economic or conservation importance. Time series of data from the MODerate-resolution Imaging Spectroradiometer (MODIS) sensors on-board NASA's Terra and Aqua satellites offer the potential to capture environmental thermal and vegetation seasonality, through temporal Fourier analysis, more accurately than was previously possible using the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor data. MODIS data are composited over 8- or 16-day time intervals that pose unique problems for temporal Fourier analysis. Applying standard techniques to MODIS data can introduce errors of up to 30% in the estimation of the amplitudes and phases of the Fourier harmonics. Methodology/Principal Findings. We present a novel spline-based algorithm that overcomes the processing problems of composited MODIS data. The algorithm is tested on artificial data generated using randomly selected values of both amplitudes and phases, and provides an accurate estimate of the input variables under all conditions. The algorithm was then applied to produce layers that capture the seasonality in MODIS data for the period from 2001 to 2005. Conclusions/Significance. Global temporal Fourier processed images of 1 km MODIS data for Middle Infrared Reflectance, day- and night-time Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) are presented for ecological and epidemiological applications. The finer spatial and temporal resolution, combined with the greater geolocational and spectral accuracy of the MODIS instruments, compared with previous multi-temporal data sets, mean that these data may be used with greater confidence in species' distribution modelling

Using imperfect data in predictive mapping of vector : a regional example of Ixodes ricinus distribution

Background: Knowledge of Ixodes ricinus tick distribution is critical for surveillance and risk management of transmissible tick-borne diseases such as Lyme borreliosis. However, as the ecology of I. ricinus is complex, and robust long-term geographically extensive distribution tick data are limited, mapping often relies on datasets collected for other purposes. We compared the modelled distributions derived from three datasets with information on I. ricinus distribution (quantitative I. ricinus count data from scientific surveys; I. ricinus presence-only data from public submissions; and a combined I. ricinus dataset from multiple sources) to assess which could be reliably used to inform Public Health strategy. The outputs also illustrate the strengths and limitations of these three types of data, which are commonly used in mapping tick distributions. Methods: Using the Integrated Nested Laplace algorithm we predicted I. ricinus abundance and presence–absence in Scotland and tested the robustness of the predictions, accounting for errors and uncertainty. Results: All models fitted the data well and the covariate predictors for I. ricinus distribution, i.e. deer presence, temperature, habitat, index of vegetation, were as expected. Differences in the spatial trend of I. ricinus distribution were evident between the three predictive maps. Uncertainties in the spatial models resulted from inherent characteristics of the datasets, particularly the number of data points, and coverage over the covariate range used in making the predictions. Conclusions: Quantitative I. ricinus data from scientific surveys are usually considered to be gold standard data and we recommend their use where high data coverage can be achieved. However in this study their value was limited by poor data coverage. Combined datasets with I. ricinus distribution data from multiple sources are valuable in addressing issues of low coverage and this dataset produced the most appropriate map for national scale decision-making in Scotland. When mapping vector distributions for public-health decision making, model uncertainties and limitations of extrapolation need to be considered; these are often not included in published vector distribution maps. Further development of tools to better assess uncertainties in the models and predictions are necessary to allow more informed interpretation of distribution maps

The global distribution and burden of dengue

Dengue is a systemic viral infection transmitted between humans by Aedes mosquitoes1. For some patients dengue is a life-threatening illness2. There are currently no licensed vaccines or specific therapeutics, and substantial vector control efforts have not stopped its rapid emergence and global spread3. The contemporary worldwide distribution of the risk of dengue virus infection4 and its public health burden are poorly known2,5. Here we undertake an exhaustive assembly of known records of dengue occurrence worldwide, and use a formal modelling framework to map the global distribution of dengue risk. We then pair the resulting risk map with detailed longitudinal information from dengue cohort studies and population surfaces to infer the public health burden of dengue in 2010. We predict dengue to be ubiquitous throughout the tropics, with local spatial variations in risk influenced strongly by rainfall, temperature and the degree of urbanisation. Using cartographic approaches, we estimate there to be 390 million (95 percent credible interval 284-528) dengue infections per year, of which 96 million (67-136) manifest apparently (any level of clinical or sub-clinical severity). This infection total is more than three times the dengue burden estimate of the World Health Organization2. Stratification of our estimates by country allows comparison with national dengue reporting, after taking into account the probability of an apparent infection being formally reported. The most notable differences are discussed. These new risk maps and infection estimates provide novel insights into the global, regional and national public health burden imposed by dengue. We anticipate that they will provide a starting point for a wider discussion about the global impact of this disease and will help guide improvements in disease control strategies using vaccine, drug and vector control methods and in their economic evaluation. [285