Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060

Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning

BACKGROUND

Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factors contribute to their ability to live in a variety of habitats, which have the potential to change over the years as the climate changes. Therefore, as new habitats emerge, the risk for new introductions of these diseases of interest to occur increases. The aim of this study was to model distributions for two primary vectors for BT and AHS (Culicoides imicola and Culicoides bolitinos) using random forest (RF) machine learning and explore the relative importance of environmental and anthropological factors in a region of South Africa with frequent AHS and BT outbreaks.

METHODS

Culicoides capture data were collected between 1996 and 2022 across 171 different capture locations in the Western Cape. Predictor variables included climate-related variables (temperature, precipitation, humidity), environment-related variables (normalised difference vegetation index-NDVI, soil moisture) and farm-related variables (livestock densities). Random forest (RF) models were developed to explore the spatial distributions of C. imicola, C. bolitinos and a merged species map, where both competent vectors were combined. The maps were then compared to interpolation maps using the same capture data as well as historical locations of BT and AHS outbreaks.

RESULTS

Overall, the RF models performed well with 75.02%, 61.6% and 74.01% variance explained for C. imicola, C. bolitinos and merged species models respectively. Cattle density was the most important predictor for C. imicola and water vapour pressure the most important for C. bolitinos. Compared to interpolation maps, the RF models had higher predictive power throughout most of the year when species were modelled individually; however, when merged, the interpolation maps performed better in all seasons except winter. Finally, midge densities did not show any conclusive correlation with BT or AHS outbreaks.

CONCLUSION

This study yielded novel insight into the spatial abundance and drivers of abundance of competent vectors of BT and AHS. It also provided valuable data to inform mathematical models exploring disease outbreaks so that Culicoides-transmitted diseases in South Africa can be further analysed.

The Potential of Plant-Produced Virus-like Particle Vaccines for African Horse Sickness and Other Equine Orbiviruses

African horse sickness is a devastating viral disease of equids. It is transmitted by biting midges of the genus Culicoides with mortalities reaching over 90% in naïve horses. It is endemic to sub-Saharan Africa and is seasonally endemic in many parts of southern Africa. However, outbreaks in Europe and Asia have occurred that caused significant economic issues. There are attenuated vaccines available for control of the virus but concerns regarding the safety and efficacy means that alternatives are sought. One promising alternative is the use of virus-like particles in vaccine preparations, which have the potential to be safer and more efficacious as vaccines against African horse sickness. These particles are best made in a complex, eukaryotic system, but due to technical challenges, this may cause significant economic strain on the developing countries most affected by the disease. Therefore, this review also summarises the success so far, and potential, of recombinant protein expression in plants to reduce the economic strain of production.

Epidemiological Analyses of the First Incursion of the Epizootic Hemorrhagic Disease Virus Serotype 8 in Tunisia, 2021-2022

Epizootic hemorrhagic disease (EHD) is a non-contagious arthropod-transmitted viral disease and a World Organization for Animal Health (WOAH)-listed disease of domestic and wild ruminants since 2008. EHDV is transmitted among susceptible animals by a few species of midges of genus Culicoides. During the fall of 2021, a large outbreak caused by the epizootic hemorrhagic disease virus (EHDV), identified as serotype 8, was reported in Tunisian dairy and beef farms with Bluetongue virus (BTV)-like clinical signs. The disease was detected later in the south of Italy, in Spain, in Portugal and, more recently, in France, where it caused severe infections in cattle. This was the first evidence of EHDV-8 circulation outside Australia since 1982. In this study, we analyzed the epidemiological situation of the 2021-2022 EHDV outbreaks reported in Tunisia, providing a detailed description of the spatiotemporal evolution of the disease. We attempted to identify the eco-climatic factors associated with infected areas using generalized linear models (GLMs). Our results demonstrated that environmental factors mostly associated with the presence of C. imicola, such as digital elevation model (DEM), slope, normalized difference vegetation index (NDVI), and night-time land surface temperature (NLST)) were by far the most explanatory variables for EHD repartition cases in Tunisia that may have consequences in neighboring countries, both in Africa and Europe through the spread of infected vectors. The risk maps elaborated could be useful for disease control and prevention strategies.

Culicoides-borne Orbivirus epidemiology in a changing climate

Orbiviruses are of significant importance to the health of wildlife and domestic animals worldwide; the major orbiviruses transmitted by multiple biting midge (Culicoides) species include bluetongue virus, epizootic hemorrhagic disease virus, and African horse sickness virus. The viruses, insect vectors, and hosts are anticipated to be impacted by global climate change, altering established Orbivirus epidemiology. Changes in global climate have the potential to alter the vector competence and extrinsic incubation period of certain biting midge species, affect local and long-distance dispersal dynamics, lead to range expansion in the geographic distribution of vector species, and increase transmission period duration (earlier spring onset and later fall transmission). If transmission intensity is associated with weather anomalies such as droughts and wind speeds, there may be changes in the number of outbreaks and periods between outbreaks for some regions. Warmer temperatures and changing climates may impact the viral genome by facilitating reassortment and through the emergence of novel viral mutations. As the climate changes, Orbivirus epidemiology will be inextricably altered as has been seen with recent outbreaks of bluetongue, epizootic hemorrhagic disease, and African horse sickness outside of endemic areas, and requires interdisciplinary teams and approaches to assess and mitigate future outbreak threats.

Wing morphometrics of biting midges (Diptera: Culicoides) of veterinary importance in Madagascar

Biting midges are vectors of arboviruses such as bluetongue virus, bovine ephemeral fever virus, Akabane virus, African horse sickness virus, epizootic haemorrhagic disease virus and Schmallenberg virus. Fast and accurate identification of biting midges is crucial in the study of Culicoides-borne diseases. Morphological identification of biting midges has revealed the presence of cryptic species. A total of 20 species are reported in Madagascar. In this study, we assessed wing morphometric analysis for identification of seven species namely C. dubitatus Kremer, Rebholtz-Hirtzel and Delécolle, C. enderleini Cornet and Brunhes, C. kibatiensis Goetghebuer, C. miombo Meiswinkel, C. moreli Clastrier, C. nevilli Cornet and Brunhes, and C. zuluensis de Meillon. Culicoides enderleini, C. miombo, C. moreli, C. nevilli and C. zuluensis are vectors diseases. A molecular approach, based on the cytochrome oxidase I gene (Cox1), was used for species delimitation. The molecular analysis presented seven different clades grouped two-by-two according to morphological characters. A total of 179 wing images were digitised. We found morphometric variation among seven species based on 11 landmarks and two outlines. Wing shape variation plots showed that species overlapped with species belonging to the same group. The cross-validation revealed a relatively high percentage of correct classification in most species, ranging from 91.3% to 100% for landmarks; 60% to 82.6% for outlines-1 and 77.1% to 91.3% for outlines-2. Our study suggests that wing geometric morphometric analysis is a robust tool for reliable "Moka Fohy" identification in Madagascar. This inexpensive and simple method is a precise supplement to morphological identification, with reaches the accuracy of Cox1 barcoding.

Ecological Niche Modelling Approaches: Challenges and Applications in Vector-Borne Diseases

Vector-borne diseases (VBDs) pose a major threat to human and animal health, with more than 80% of the global population being at risk of acquiring at least one major VBD. Being profoundly affected by the ongoing climate change and anthropogenic disturbances, modelling approaches become an essential tool to assess and compare multiple scenarios (past, present and future), and further the geographic risk of transmission of VBDs. Ecological niche modelling (ENM) is rapidly becoming the gold-standard method for this task. The purpose of this overview is to provide an insight of the use of ENM to assess the geographic risk of transmission of VBDs. We have summarised some fundamental concepts and common approaches to ENM of VBDS, and then focused with a critical view on a number of crucial issues which are often disregarded when modelling the niches of VBDs. Furthermore, we have briefly presented what we consider the most relevant uses of ENM when dealing with VBDs. Niche modelling of VBDs is far from being simple, and there is still a long way to improve. Therefore, this overview is expected to be a useful benchmark for niche modelling of VBDs in future research.

Ecological niche modeling based on ensemble algorithms to predicting current and future potential distribution of African swine fever virus in China

African swine fever (ASF) is a tick-borne infectious disease initially described in Shenyang province China in 2018 but is now currently present nationwide. ASF has high infectivity and mortality rates, which often results in transportation and trade bans, and high expenses to prevent and control the, hence causing huge economic losses and a huge negative impact on the Chinese pig farming industry. Ecological niche modeling has long been adopted in the epidemiology of infectious diseases, in particular vector-borne diseases. This study aimed to establish an ecological niche model combined with data from ASF incidence rates in China from August 2018 to December 2021 in order to predict areas for African swine fever virus (ASFV) distribution in China. The model was developed in R software using the biomod2 package and ensemble modeling techniques. Environmental and topographic variables included were mean diurnal range (°C), isothermality, mean temperature of wettest quarter (°C), precipitation seasonality (cv), mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm), normalized difference vegetation index, wind speed (m/s), solar radiation (kJ /day), and elevation/altitude (m). Contribution rates of the variables normalized difference vegetation index, mean temperature of wettest quarter, mean precipitation of coldest quarter, and mean precipitation of warmest quarter were, respectively, 47.61%, 28.85%, 10.85%, and 7.27% (according to CA), which accounted for over 80% of contribution rates related to variables. According to model prediction, most of areas revealed as suitable for ASF distribution are located in the southeast coast or central region of China, wherein environmental conditions are suitable for soft ticks’ survival. In contrast, areas unsuitable for ASFV distribution in China are associated with arid climate and poor vegetation, which are less conducive to soft ticks’ survival, hence to ASFV transmission. In addition, prediction spatial suitability for future ASFV distribution suggests narrower areas for ASFV spread. Thus, the ensemble model designed herein could be used to conceive more efficient prevention and control measure against ASF according to different geographical locations in China.