Predicting ectotherm disease vector spread--benefits from multidisciplinary approaches and directions forward

Optimising predictive modelling of Ross River virus using meteorological variables

BACKGROUND

Statistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia.

METHODOLOGY/PRINCIPAL FINDINGS

We developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance.

CONCLUSIONS/SIGNIFICANCE

We demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.

Scoping review of distribution models for selected Amblyomma ticks and rickettsial group pathogens

The rising prevalence of tick-borne diseases in humans in recent decades has called attention to the need for more information on geographic risk for public health planning. Species distribution models (SDMs) are an increasingly utilized method of constructing potential geographic ranges. There are many knowledge gaps in our understanding of risk of exposure to tick-borne pathogens, particularly for those in the rickettsial group. Here, we conducted a systematic scoping review of the SDM literature for rickettsial pathogens and tick vectors in the genus Amblyomma. Of the 174 reviewed articles, only 24 studies used SDMs to estimate the potential extent of vector and/or pathogen ranges. The majority of studies (79%) estimated only tick distributions using vector presence as a proxy for pathogen exposure. Studies were conducted at different scales and across multiple continents. Few studies undertook original data collection, and SDMs were mostly built with presence-only datasets from public database or surveillance sources. The reliance on existing data sources, using ticks as a proxy for disease risk, may simply reflect a lag in new data acquisition and a thorough understanding of the tick-pathogen ecology involved.

Causal relationship in the interaction between land cover change and underlying surface climate in the grassland ecosystems in China

Land-climate interactions are driven by causal relations that are difficult to ascertain given the complexity and high dimensionality of the systems. Many methods of statistical and mechanistic models exist to identify and quantify the causality in such highly-interacting systems. Recent advances in remote sensing development allowed people to investigate the land-climate interaction with spatially and temporally continuous data. In this study, we present a new approach to measure how climatic factors interact with each other under land cover change. The quantification method is based on the correlation analysis of the different order derivatives, with the canonical mathematical definitions developed from the theories of system dynamics and practices of the macroscopic observations. We examined the causal relationship between the interacting variables on both spatial and temporal dimensions based on macroscopic observations of land cover change and surface climatic factors through a comparative study in the different grassland ecosystems of China. The results suggested that the interaction of land-climate could be used to explain the temporal lag effect in the comparison of the three grassland ecosystems. Significant spatial correlations between the vegetation and the climatic factors confirmed feedback mechanisms described in the theories of eco-climatology, while the uncertain temporal synchronicity reflects the causality among the key indicators. This has been rarely addressed before. Our research show that spatial correlations and the temporal synchronicity among key indicators of the land surface and climatic factors can be explained by a novel method of causality quantification using derivative analysis.

Current and potential future distribution of the American dog tick (Dermacentor variabilis, Say) in North America

The American dog tick (Dermacentor variabilis) is medically and economically important in North America. This species is found across central and eastern North America from the Gulf Coast of Mexico through southern Canada. In parts of this region, D. variabilis is a vector for pathogens that cause diseases in humans and animals. Our aim was to determine whether climate change would affect the distribution of the climatically suitable area for D. variabilis in North America, to aid monitoring for potential future spread of tick-borne pathogens. We developed a species distribution model for D. variabilis to project where climate will likely be suitable for the tick in North America using a maximum entropy method, occurrence records from museum and laboratory archives, and 10 environmental variables relevant to climate requirements for the tick. We used four emissions scenarios from the Intergovernmental Panel on Climate Change's Fifth Assessment Report and 10 climate models from the Coupled Model Intercomparison Project (phase 5) to estimate potential future climate suitability and determine how the tick's distribution could change. Our consensus model projected that the area of suitable climate in North America could increase from present by approximately 50% by 2070. In areas beyond the current northern limit of D. variabilis, climate could become more suitable for the tick than at present, possibly resulting in a northward expansion in Canada, but the potential suitability of the southern range of D. variabilis could decrease, depending on the region and climate model. Due to the ability of D. variabilis to harbor and transmit pathogens, a change in the distribution of this species could also affect the risk of human and animal diseases throughout North America, particularly in the northern range of the tick.

Little pigeons can carry great messages: potential distribution and ecology of Uranotaenia (Pseudoficalbia) unguiculata Edwards, 1913 (Diptera: Culicidae), a lesser-known mosquito species from the Western Palaearctic

BACKGROUND

Uranotaenia unguiculata is a Palaearctic mosquito species with poorly known distribution and ecology. This study is aimed at filling the gap in our understanding of the species potential distribution and its environmental requirements through a species distribution modelling (SDM) exercise. Furthermore, aspects of the mosquito ecology that may be relevant to the epidemiology of certain zoonotic vector-borne diseases in Europe are discussed.

RESULTS

A maximum entropy (Maxent) modelling approach has been applied to predict the potential distribution of Ur. unguiculata in the Western Palaearctic. Along with the high accuracy and predictive power, the model reflects well the known species distribution and predicts as highly suitable some areas where the occurrence of the species is hitherto unknown.

CONCLUSIONS

To our knowledge, the potential distribution of a mosquito species from the genus Uranotaenia is modelled for the first time. Provided that Ur. unguiculata is a widely-distributed species, and some pathogens of zoonotic concern have been detected in this mosquito on several occasions, the question regarding its host associations and possible epidemiological role warrants further investigation.

Measuring the robustness of link prediction algorithms under noisy environment

Link prediction in complex networks is to estimate the likelihood of two nodes to interact with each other in the future. As this problem has applications in a large number of real systems, many link prediction methods have been proposed. However, the validation of these methods is so far mainly conducted in the assumed noise-free networks. Therefore, we still miss a clear understanding of how the prediction results would be affected if the observed network data is no longer accurate. In this paper, we comprehensively study the robustness of the existing link prediction algorithms in the real networks where some links are missing, fake or swapped with other links. We find that missing links are more destructive than fake and swapped links for prediction accuracy. An index is proposed to quantify the robustness of the link prediction methods. Among the twenty-two studied link prediction methods, we find that though some methods have low prediction accuracy, they tend to perform reliably in the "noisy" environment.

[Mosquitoes as vectors for exotic pathogens in Germany]

As a result of intensified globalization of international trade and of substantial travel activities, mosquito-borne exotic pathogens are becoming an increasing threat for Europe. In Germany some 50 different mosquito species are known, several of which have vector competence for pathogens. During the last few years a number of zoonotic arboviruses that are pathogenic for humans have been isolated from mosquitoes in Germany including Usutu, Sindbis and Batai viruses. In addition, filarial worms, such as Dirofilaria repens have been repeatedly detected in mosquitoes from the federal state of Brandenburg. Other pathogens, in particular West Nile virus, are expected to emerge sooner or later in Germany as the virus is already circulating in neighboring countries, e.g. France, Austria and the Czech Republic. In upcoming years the risk for arbovirus transmission might increase in Germany due to increased occurrence of new so-called "invasive" mosquito species, such as the Asian bush mosquito Ochlerotatus japonicus or the Asian tiger mosquito Aedes albopictus. These invasive species are characterized by high vector competence for a broad range of pathogens and a preference for human blood meals. For risk assessment, a number of mosquito and pathogen surveillance projects have been initiated in Germany during the last few years; however, mosquito control strategies and plans of action have to be developed and put into place to allow early and efficient action against possible vector-borne epidemics.

Implementing cargo movement into climate based risk assessment of vector-borne diseases

During the last decades the disease vector Aedes albopictus (Asian tiger mosquito) has rapidly spread around the globe. Global shipment of goods contributes to its permanent introduction. Invaded regions are facing novel and serious public health concerns, especially regarding the transmission of formerly non-endemic arboviruses such as dengue and chikungunya. The further development and potential spread to other regions depends largely on their climatic suitability. Here, we have developed a tool for identifying and prioritizing European areas at risk for the establishment of Aedes albopictus by taking into account, for the first time, the freight imports from this mosquito's endemic countries and the climate suitability at harbors and their surrounding regions. In a second step we consider the further transport of containers by train and inland waterways because these types of transport can be well controlled. We identify European regions at risk, where a huge amount of transported goods meet climatically suitable conditions for the disease vector. The current and future suitability of the climate for Aedes albopictus was modeled by a correlative niche model approach and the Regional Climate Model COSMO-CLM. This risk assessment combines impacts of globalization and global warming to improve effective and proactive interventions in disease vector surveillance and control actions.

Climatic suitability of Aedes albopictus in Europe referring to climate change projections: comparison of mechanistic and correlative niche modelling approaches

The Asian tiger mosquito, Aedes albopictus, is capable of transmitting a broad range of viruses to humans. Since its introduction at the end of the 20th century, it has become well established in large parts of southern Europe. As future expansion as a result of climate change can be expected, determining the current and projected future climatic suitability of this invasive mosquito in Europe is of interest. Several studies have tried to detect the potential habitats for this species, but differing data sources and modelling approaches must be considered when interpreting the findings. Here, various modelling methodologies are compared with special emphasis on model set-up and study design. Basic approaches and model algorithms for the projection of spatio-temporal trends within the 21st century differ substantially. Applied methods range from mechanistic models (e.g. overlay of climatic constraints based on geographic information systems or rather process-based approaches) to correlative niche models. We conclude that spatial characteristics such as introduction gateways and dispersal pathways need to be considered. Laboratory experiments addressing the climatic constraints of the mosquito are required for improved modelling results. However, the main source of uncertainty remains the insufficient knowledge about the species' ability to adapt to novel environments.

Climate change effects on Chikungunya transmission in Europe: geospatial analysis of vector's climatic suitability and virus' temperature requirements

BACKGROUND

Chikungunya was, from the European perspective, considered to be a travel-related tropical mosquito-borne disease prior to the first European outbreak in Northern Italy in 2007. This was followed by cases of autochthonous transmission reported in South-eastern France in 2010. Both events occurred after the introduction, establishment and expansion of the Chikungunya-competent and highly invasive disease vector Aedes albopictus (Asian tiger mosquito) in Europe. In order to assess whether these outbreaks are indicative of the beginning of a trend or one-off events, there is a need to further examine the factors driving the potential transmission of Chikungunya in Europe. The climatic suitability, both now and in the future, is an essential starting point for such an analysis.

METHODS

The climatic suitability for Chikungunya outbreaks was determined by using bioclimatic factors that influence, both vector and, pathogen. Climatic suitability for the European distribution of the vector Aedes albopictus was based upon previous correlative environmental niche models. Climatic risk classes were derived by combining climatic suitability for the vector with known temperature requirements for pathogen transmission, obtained from outbreak regions. In addition, the longest potential intra-annual season for Chikungunya transmission was estimated for regions with expected vector occurrences.In order to analyse spatio-temporal trends for risk exposure and season of transmission in Europe, climate change impacts are projected for three time-frames (2011-2040, 2041-2070 and 2071-2100) and two climate scenarios (A1B and B1) from the Intergovernmental Panel on Climate Change (IPCC). These climatic projections are based on regional climate model COSMO-CLM, which builds on the global model ECHAM5.

RESULTS

European areas with current and future climatic suitability of Chikungunya transmission are identified. An increase in risk is projected for Western Europe (e.g. France and Benelux-States) in the first half of the 21st century and from mid-century onwards for central parts of Europe (e.g. Germany). Interestingly, the southernmost parts of Europe do not generally provide suitable conditions in these projections. Nevertheless, many Mediterranean regions will persist to be climatically suitable for transmission. Overall, the highest risk of transmission by the end of the 21st century was projected for France, Northern Italy and the Pannonian Basin (East-Central Europe). This general tendency is depicted in both, the A1B and B1 climate change scenarios.

CONCLUSION

In order to guide preparedness for further outbreaks, it is crucial to anticipate risk as to identify areas where specific public health measures, such as surveillance and vector control, can be implemented. However, public health practitioners need to be aware that climate is only one factor driving the transmission of vector-borne disease.