Culicoides midges are abundant hematophagous flies that vector arboviruses of veterinary and medical importance. Dramatic changes in the epidemiology of Culicoides-borne arboviruses have occurred since 1998, including the emergence of exotic viruses in northern temperate regions, increases in global disease incidence, and enhanced virus diversity in tropical zones. Drivers may include changes in climate, land use, trade, and animal husbandry. New Culicoides species and new wild reservoir hosts have been implicated in transmission, highlighting the dynamic nature of pathogen-vector-host interactions. Focusing on potential vector species worldwide and key elements of vectorial capacity, we review the sensitivity of Culicoides life cycles to abiotic and biotic factors. We consider implications for designing control measures and understanding impacts of environmental change in different ecological contexts. Critical geographical, biological, and taxonomic knowledge gaps are prioritized. Recent developments in genomics and mathematical modeling may enhance ecological understanding of these complex arbovirus systems.

The risk posed to the United Kingdom by West Nile virus (WNV) has previously been considered low, due to the absence or scarcity of the main Culex sp. bridge vectors. The mosquito Culex modestus is widespread in southern Europe, where it acts as the principle bridge vector of WNV. This species was not previously thought to be present in the United Kingdom.

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global “One Health” initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014–2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014–2018). Consistent with suggestions that KFD is an “ecotonal” disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Models predicted new hotspots of outbreaks in 2019, indicating their value for spatial targeting of intervention. Co-production was vital for: gathering outbreak data that reflected locations of exposure in the landscape; better understanding contextual socio-ecological risk factors; and tailoring the spatial grain and outputs to the scale of forest use, and public health interventions. We argue this inter-disciplinary approach to risk prediction is applicable across zoonotic diseases in tropical settings. Worldwide, impacts of zoonotic diseases, that cycle between animals and people, are concentrated in tropical communities and often linked to the way people use and change ecosystems. Interventions for zoonotic diseases could be targeted better using risk maps based on computer models that integrate social and ecological risk factors across degraded ecosystems. However, such predictive models often perform poorly at local scales, incorporate narrow ranges of risk factors, and are disconnected from policy, managers and interventions. Co-production brings together stakeholders and knowledge, across the human health, animal health and environmental sectors, aligning with the OneHealth Initiative, to develop more informative predictive tools for zoonotic diseases. Through co-production, we develop predictive models for a fatal tick-borne disease, Kyasanur Forest Diseases (KFD) that is spreading across the degraded Western Ghats forest in India. These models incorporating contextual risk factors identified by stakeholders, accurately predicted patterns in human cases of KFD (2014–2018) in Shivamogga district, Karnataka State, and identified new hotspots of infection during the subsequent 2019 outbreak. Landscapes at highest risk encompassed diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high indigenous cattle density, and low coverage of dry deciduous forest. Co-production resulted in outbreak data that reflected where exposure occurred in the landscape and outputs of value for targeting of interventions, matched to the scale of forest use and public health interventions.

Spatial variation in the risk of many mosquito-borne pathogens is strongly influenced by the distribution of communities of suitable vector mosquitoes. The spatial distributions of such communities have been linked to the abiotic habitat requirements of each constituent mosquito species, but the biotic interactions between mosquitoes and other species are less well understood. Determining which fauna restrict the presence and abundance of key mosquito species in vector communities may identify species which could be employed as natural biological control agents. Whilst biotic interactions have been studied in the laboratory, a lack of appropriate statistical methods has prohibited the identification of key interactions which influence mosquito distributions in the field. Joint species distribution models (JSDMs) have recently been developed to identify biotic interactions influencing the distributions of species from empirical data. We apply a JSDM to field data on the spatial distribution of mosquitoes in a UK wetland to identify both abiotic factors and biotic interactions driving the composition of the community. As expected, mosquito larval distributions in this wetland habitat are strongly driven by environmental covariates including water depth, temperature and oxidation-reduction potential. By factoring out these environmental variables, we are able to identify species (ditch shrimp of the genus Palaemonetes and fish) as predators which appear to restrict mosquito distributions. JSDMs offer vector ecologists a way to identify potentially important biotic interactions influencing the distributions of disease vectors from widely available field data. This information is crucial to understand the likely effects of habitat management for vector control and to identify species with the potential for use in biological control programmes. We provide an R package BayesComm to enable the wider application of these models.

Culicoides spp. biting midges transmit bluetongue virus (BTV), the aetiological agent of bluetongue (BT), an economically important disease of ruminants. In southern India, hyperendemic outbreaks of BT exert high cost to subsistence farmers in the region, impacting on sheep production. Effective Culicoides spp. monitoring methods coupled with accurate species identification can accelerate responses for minimising BT outbreaks. Here, we assessed the utility of sampling methods and DNA barcoding for detection and identification of Culicoides spp. in southern India, in order to provide an informed basis for future monitoring of their populations in the region. Culicoides spp. collected from Tamil Nadu and Karnataka were used to construct a framework for future morphological identification in surveillance, based on sequence comparison of the DNA barcode region of the mitochondrial cytochrome c oxidase I (COI) gene and achieving quality standards defined by the Barcode of Life initiative. Pairwise catches of Culicoides spp. were compared in diversity and abundance between green (570 nm) and ultraviolet (UV) (390 nm) light emitting diode (LED) suction traps at a single site in Chennai, Tamil Nadu over 20 nights of sampling in November 2013. DNA barcode sequences of Culicoides spp. were mostly congruent both with existing DNA barcode data from other countries and with morphological identification of major vector species. However, sequence differences symptomatic of cryptic species diversity were present in some groups which require further investigation. While the diversity of species collected by the UV LED Center for Disease Control (CDC) trap did not significantly vary from that collected by the green LED CDC trap, the UV CDC significantly outperformed the green LED CDC trap with regard to the number of Culicoides individuals collected. Morphological identification of the majority of potential vector species of Culicoides spp. samples within southern India appears relatively robust; however, potential cryptic species diversity was present in some groups requiring further investigation. The UV LED CDC trap is recommended for surveillance of Culicoides in southern India.