Neil Alexander

Background: Yellow fever (YF) is an arboviral disease which is endemic to Brazil due to a sylvatic transmission cycle maintained by infected mosquito vectors, non-human primate (NHP) hosts, and humans. Despite the existence of an effective vaccine, recent sporadic YF epidemics have underscored concerns about sylvatic vector surveillance, as very little is known about their spatial distribution. Here, we model and map the environmental suitability of YF’s main vectors in Brazil, Haemagogus spp. and Sabethes spp., and use human population and NHP data to identify locations prone to transmission and spillover risk. Methodology/Principal findings: We compiled a comprehensive set of occurrence records on Hg. janthinomys, Hg. leucocelaenus, and Sabethes spp. from 1991–2019 using primary and secondary data sources. Linking these data with selected environmental and land-cover variables, we adopted a stacked regression ensemble modelling approach (elastic-net regularized GLM, extreme gradient boosted regression trees, and random forest) to predict the environmental suitability of these species across Brazil at a 1 km x 1 km resolution. We show that while suitability for each species varies spatially, high suitability for all species was predicted in the Southeastern region where recent outbreaks have occurred. By integrating data on NHP host reservoirs and human populations, our risk maps further highlight municipalities within the region that are prone to transmission and spillover. Conclusions/Significance: Our maps of sylvatic vector suitability can help elucidate potential locations of sylvatic reservoirs and be used as a tool to help mitigate risk of future YF outbreaks and assist in vector surveillance. Furthermore, at-risk regions identified from our work could help disease control and elucidate gaps in vaccination coverage and NHP host surveillance.

This is the third in a planned series of data papers presenting modelled vector distributions produced during the ECDC and EFSA funded VectorNet project. The data package presented here includes those Culicoides vectors species first modelled in 2015 as part of the VectorNet gap analysis work namely C. imicola, C. obsoletus, C. scoticus, C. dewulfi, C. chiopterus, C. pulicaris, C. lupicaris, C. punctatus, and C. newsteadi. The known distributions of these species within the Project area (Europe, the Mediterranean Basin, North Africa, and Eurasia) are currently incomplete to a greater or lesser degree. The models are designed to fill the gaps with predicted distributions, to provide a) first indication of vector species distributions across the project geographical extent, and b) assistance in targeting surveys to collect distribution data for those areas with no field validated information. The models are based on input data from light trap surveillance of adult Culicoides across continental Europe and surrounding regions (71.8°N –33.5°S, – 11.2°W – 62°E), concentrated in Western countries, supplemented by transect samples in eastern and northern Europe. Data from central EU are relatively sparse. Funding statement: This work was carried out with support from the VectorNet framework contract OC/EFSA/AHAW/2013/02-FWC1 funded by the European Centre for Disease prevention and Control (ECDC) and the European Food Safety Agency (EFSA) and the PALE-Blu H2020 Project ID: 727393.

Ixodes ricinus is a three-host tick, a principal vector of Borrelia burgdorferi (s.l.) and one of the main vectors of tick-borne encephalitis (TBE) virus. Iceland is located in the North Atlantic Ocean with subpolar oceanic climate. During the past 3–4 decades, average temperature has increased, supporting more favourable conditions for ticks. Reports of I. ricinus have increased in recent years. If these ticks were able to establish in a changing climate, Iceland may face new threats posed by tick-borne diseases. Active field surveillance by tick flagging was conducted at 111 sites around Iceland from August 2015 to September 2016. Longworth mammal traps were used to trap Apodemus sylvaticus in southwestern and southern Iceland. Surveillance on tick importation by migratory birds was conducted in southeastern Iceland, using bird nets and a Heligoland trap. Vulpes lagopus carcasses from all regions of the country were inspected for ticks. In addition, existing and new passive surveillance data from two institutes have been merged and are presented. Continental probability of presence models were produced. Boosted Regression Trees spatial modelling methods and its predictions were assessed against reported presence. By field sampling 26 questing I. ricinus ticks (7 males, 3 females and 16 nymphs) were collected from vegetation from three locations in southern and southeastern Iceland. Four ticks were found on migratory birds at their arrival in May 2016. A total of 52 A. sylvaticus were live-trapped but no ticks were found nor on 315 V. lagopus carcasses. Passive surveillance data collected since 1976, reports further 214 I. ricinus ticks from 202 records, with an increase of submissions in recent years. The continental probability of presence model correctly predicts approximately 75% of the recorded presences, but fails to predict a fairly specific category of recorded presence in areas where the records are probably opportunistic and not likely to lead to establishment. To the best of our knowledge, this study represents the first finding of questing I. ricinus ticks in Iceland. The species could possibly be established locally in Iceland in low abundance, although no questing larvae have yet been detected to confirm established populations. Submitted tick records have increased recently, which may reflect an increase in exposure, or in interest in ticks. Furthermore, the amount of records on dogs, cats and humans indicate that ticks were acquired locally, presenting a local biting risk. Tick findings on migratory birds highlight a possible route of importation. Obtaining questing larvae is now a priority to confirm that I. ricinus populations are established in Iceland. Further surveys on wild mammals (e.g. Rangifer tarandus), livestock and migratory birds are recommended to better understand their role as potential hosts for I. ricinus.

This is the second of a number of planned data papers presenting modelled vector distributions produced originally during the ECDC funded VBORNET project. This work continues under the VectorNet project now jointly funded by ECDC and EFSA. Further data papers will be published after sampling seasons when more field data will become available allowing further species to be modelled or validation and updates to existing models. The data package described here includes those mosquito species first modelled in 2013 & 2014 as part of the VBORNET gap analysis work which aimed to identify areas of potential species distribution in areas lacking records. It comprises three species models together with suitability masks based on land class and environmental limits. The species included as part of this phase are the mosquitoes Aedes vexans, Anopheles plumbeus and Culex modestus.The known distributions of these species within the area covered by the project (Europe, the ­Mediterranean Basin, North Africa, and Eurasia) are currently incomplete to a greater or lesser degree. The models are designed to fill the gaps with predicted distributions, to provide a) assistance in ­targeting surveys to collect distribution data for those areas with no field validated information, and b) a first indication of the species distributions within the project areas.

This is the first of a number of planned data papers presenting modelled vector distributions, the models in this paper were produced during the ECDC funded VBORNET project. This work continues under the VectorNet project now jointly funded by ECDC and EFSA. This data paper contains the sand fly model outputs produced as part of the VBORNET project. Further data papers will be published after sampling seasons when more field data will become available allowing further species to be modelled or validation and updates to existing models.The data package described here includes those sand fly species first modelled in 2013 and 2014 as part of the VBORNET gap analysis work which aimed to identify areas of potential species distribution in areas lacking records. It comprises four species models together with suitability masks based on land class and environmental limits. The species included within this paper are Phlebotomus ariasi, Phlebotomus papatasi, Phlebotomus perniciosus and Phlebotomus tobbi.The known distributions of these species within the project area (Europe, the Mediterranean Basin, North Africa, and Eurasia) are currently incomplete to a greater or lesser degree. The models are designed to fill the gaps with predicted distributions, to provide a) assistance in targeting surveys to collect ­distribution data for those areas with no field validated information, and b) a first indication of project wide distributions.

This paper aims to illustrate the steps needed to produce reliable correlative modelling for arthropod vectors, when process-driven models are unavailable. We use ticks as examples because of the (re)emerging interest in the pathogens they transmit. We argue that many scientific publications on the topic focus on: (i) the use of explanatory variables that do not adequately describe tick habitats; (ii) the automatic removal of variables causing internal (statistical) problems in the models without considering their ecological significance; and (iii) spatial pattern matching rather than niche mapping, therefore losing information that could be used in projections. We focus on extracting information derived from modelling the environmental niche of ticks, as opposed to pattern matching exercises, as a first step in the process of identifying the ecological determinants of tick distributions. We perform models on widely reported species of ticks in Western Palaearctic to derive a set of covariates, describing the climate niche, reconstructing a Fourier transformation of remotely-sensed information. We demonstrate the importance of assembling ecological information that drives the distribution of ticks before undertaking any mapping exercise, from which this kind of information is lost. We also show how customised covariates are more relevant to tick ecology than the widely used set of “Bioclimatic Indicators” (“Biovars”) derived from interpolated datasets, and provide programming scripts to easily calculate them. We demonstrate that standard pre-tailored vegetation categories also fail to describe tick habitats and are best used to describe absence rather than presence of ticks, but could be used in conjunction with the climate based suitability models. We stress the better performance of climatic covariates obtained from remotely sensed information as opposed to interpolated explanatory variables derived from ground measurements which are flawed with internal issues affecting modelling performance. Extracting ecological conclusions from modelling projections is necessary to gain information about the variables driving the distribution of arthropod vectors. Mapping exercises should be a secondary aim in the study of the distribution of health threatening arthropods.

Wild boar is a host of a number of arthropod-vectored diseases and its numbers are on the rise in mainland Europe. The species potentially impacts ecosystems, humans and farming practices and so its distribution is of interest to policy makers in a number of fields beyond that of the primarily epidemiological goal of this study.Three statistical model outputs describing the distribution and abundance of the species Sus scrofa (Wild boar) are included in this data package. The extent of this dataset covers continental Europe. These data were presented as a poster [1] at the conference Genes, Ecosystems and Risk of Infection (GERI 2015).The first of the three models provide a European map presenting the probability of presence of Sus scrofa, which can be used to describe the likely geographical distribution of the species. The second and third models provide indices to help describe the likely abundance across the continent. The two indices include “the proportion of suitable habitat where presence is estimated” and a simple classification of boar abundance across Europe using quantiles of existing abundance data and proxies.

A wide range of infectious diseases may change their geographic range, seasonality and incidence due to climate change, but there is limited research exploring health vulnerabilities to climate change. In order to address this gap, pan-European vulnerability indices were developed for 2035 and 2055, based upon the definition vulnerability = impact/adaptive capacity. Future impacts were projected based upon changes in temperature and precipitation patterns, whilst adaptive capacity was developed from the results of a previous pan-European study. The results were plotted via ArcGIS^TM to EU regional (NUTS2) levels for 2035 and 2055 and ranked according to quintiles. The models demonstrate regional variations with respect to projected climate-related infectious disease challenges that they will face, and with respect to projected vulnerabilities after accounting for regional adaptive capacities. Regions with higher adaptive capacities, such as in Scandinavia and central Europe, will likely be better able to offset any climate change impacts and are thus generally less vulnerable than areas with lower adaptive capacities. The indices developed here provide public health planners with information to guide prioritisation of activities aimed at strengthening regional preparedness for the health impacts of climate change. There are, however, many limitations and uncertainties when modeling health vulnerabilities. To further advance the field, the importance of variables such as coping capacity and governance should be better accounted for, and there is the need to systematically collect and analyse the interlinkages between the numerous and ever-expanding environmental, socioeconomic, demographic and epidemiologic datasets so as to promote the public health capacity to detect, forecast, and prepare for the health threats due to climate change.