Using MODIS satellite imagery to predict hantavirus risk

Aims Sin Nombre virus (SNV), a strain of hantavirus, causes hantavirus pulmonary syndrome (HPS) in humans, a deadly disease with high mortality rate (> 50%). The primary virus host is the deer mouse, and greater abundance of deer mice has been shown to increase the human risk of HPS. Our aim is to identify and compare vegetation indices and associated time lags for predicting hantavirus risk using remotely sensed imagery. Location Utah, USA. Methods A 5‐year time‐series of moderate‐resolution imaging spectroradiometer (MODIS) satellite imagery and corresponding field data was utilized to compare various vegetation indices that measure productivity with the goal of indirectly estimating mouse abundance and SNV prevalence. Relationships between the vegetation indices and deer mouse density, SNV prevalence and the number of infected deer mice at various time lags were examined to assess which indices and associated time lags might be valuable in predicting SNV outbreaks. Results The results reveal varying levels of positive correlation between the vegetation indices and deer mouse density as well as the number of infected deer mice. Among the vegetation indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) produced the highest correlations with deer mouse density and the number of infected deer mice using a time lag of 1.0 to 1.3 years for May and June imagery. Main conclusions This study demonstrates the potential for using MODIS time‐series satellite imagery in estimating deer mouse abundance and predicting hantavirus risk. The 1‐year time lag provides a great opportunity to apply satellite imagery to predict upcoming SNV outbreaks, allowing preventive strategies to be adopted. Analysis of different predictive indices and lags could also be valuable in identifying the time windows for data collection for practical uses in monitoring rodent abundance and subsequent disease risk to humans.     

Combined focus of tick-borne encephalitis, tick-borne rickettsiosis and tularemia in the habitat of Haemaphysalis concinna in south central Siberia

For the first time in the Krasnoyarsk region the population Haemaphysalis concinna ticks were found to be infected with the causative agents of three natural focal tick-borne infections--tick-borne encephalitis, tick-borne rickettsiosis and tularemia. The existence of the combined natural focus of these three infections has been confirmed by epidemiological data. Ticks Dermacentor nuttalli also play a similar role in combined foci of tick-borne encephalitis and tick-borne rickettsiosis in these focal territories.     

Tick-borne infections of animals and humans: a common ground

A wide variety of pathogens is transmitted from ticks to vertebrates including viruses, bacteria, protozoa and helminths, of which most have a life cycle that requires passage through the vertebrate host. Tick-borne infections of humans, farm and companion animals are essentially associated with wildlife animal reservoirs. While some flying insect-borne diseases of humans such as malaria, filariasis and Kala Azar caused by Leishmania donovani target people as their main host, major tick-borne infections of humans, although potentially causing disease in large numbers of individuals, are typically an infringement of a circulation between wildlife animal reservoirs and tick vectors. While new tick-borne infectious agents are frequently recognised, emerging agents of human tick-borne infections were probably circulating among wildlife animal and tick populations long before being recognised as clinical causes of human disease as has been shown for Borrelia burgdorferi sensu lato. Co-infection with more than one tick-borne infection is common and can enhance pathogenic processes and augment disease severity as found in B. burgdorferi and Anaplasma phagocytophilum co-infection. The role of wild animal reservoirs in co-infection of human hosts appears to be central, further linking human and animal tick-borne infections. Although transmission of most tick-borne infections is through the tick saliva, additional routes of transmission, shown mostly in animals, include infection by oral uptake of infected ticks, by carnivorism, animal bites and transplacentally. Additionally, artificial infection via blood transfusion is a growing threat in both human and veterinary medicine. Due to the close association between human and animal tick-borne infections, control programs for these diseases require integration of data from veterinary and human reporting systems, surveillance in wildlife and tick populations, and combined teams of experts from several scientific disciplines such as entomology, epidemiology, medicine, public health and veterinary medicine.     

High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail

While high resolution satellite remote sensing has been hailed as a very useful source of data for biodiversity assessment and monitoring, applications have been more developed in temperate areas. The biodiverse tropics offer a challenge of an altogether different magnitude for hyperspatial and hyperspectral remote sensing. This paper examines issues related to hyperspatial and hyperspectral remotely sensed imagery, which constitutes one of the most potentially powerful yet underutilized sources of for tropical research on biodiversity. Hyperspatial data with their increased pixel resolution are possibly best suited at facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity, particularly when spatial resolution becomes too fine and pixels are smaller than the size of the object (e.g., tree canopy) being identified. Hyperspectral data on the other hand, with their high spectral resolution, can be used to record information pertaining to a range of critical plant properties related to species identity, and can be very effective used for discriminating tree species in tropical forests, despite the greater complexity of such environments. There remains a glaring gap in the easy availability of hyperspectral and hyperspatial satellite data in the tropics due to reasons of cost, data coverage, and security restrictions. Stimulating discussion on the applications of this powerful, but underutilized tool by ecologists, is the first step in promoting a more extensive use of such data for ecological studies in tropical biodiversity rich areas.     

Drivers of extinction risk in African mammals: the interplay of distribution state,human pressure,conservation response and species biology

Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite-borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.     

Human Genetic Predisposition to Diseases Caused by Viruses from Flaviviridae Family

The identification of human predisposition genes to severe forms of infectious diseases is important for understanding the mechanisms of pathogenesis, as well as for the detection of the risk groups. This will allow one to carry out targeted vaccination and preventive therapy. The most common approaches to the genetic risk estimation include conducting association studies, in which the groups of patients and control individuals are compared using both preliminarily selected candidate genes and using genome-wide analysis. To search for genetic variants predisposed to severe forms of infectious diseases, it is expedient to form a control that consists of patients with clinically proven infections with asymptomatic or mild forms of the disease. The examples of the use of these approaches to identify genetic factors that predispose one to severe forms of infections caused by viruses from the Flaviviridae family are considered in the review. At present, a number of genetic markers associated with predisposition to tick-borne encephalitis, West Nile fever, and Dengue fever have already been detected. These associations must be confirmed in independent samples. Genetic variants, for which the association with spontaneous recovery during infection with hepatitis C virus, patient’s reaction on antiviral drugs, and the development of liver fibrosis was established, were also detected. The gene variants with more pronounced phenotypic effects will probably be found during further studies; they can be used in clinical practice as prognostic markers of the course and outcomes of infection with the Flaviviridae, as well as of the response to treatment.     

Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

1. This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water‐quality standards. 2. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near‐infrared (NIR)‐Green Angle Index (NGAI) and normalized water absorption depth (D_H)_, at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. 3. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral‐derived NDVI. The IKONOS‐based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the D_H. 4. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High‐resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. 5. Interpretation of biophysical parameters derived from high‐resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments.     

Rickettsial disease: classical and modern aspects

Rickettsial diseases have been reassessed in recent years since they represent an important field in today's medicine. New agents have been described: some are non-pathogenic agents and the others are associated with well-defined or peculiar clinical patterns. In addition, different species of rickettsiosis are found in relation to the geographic areas of the world. Some agents may be defined as variants of older diseases whereas most of the newly described forms of rickettsiosis represent distinct entities with unique epidemiologial and clinical features. Probably the main news regards the group of the spotted fevers. An additional new aspect is linked to the medicine of travellers and tourists. However, this aspect may not be significant for the rickettsial diseases in relation to other human illnesses, such as malaria. Therefore, an investigation into the geographical origin of patients has to enter our routine medical work.     

Predicting Disease Risk,Identifying Stakeholders,and Informing Control Strategies: A Case Study of Anthrax in Montana

Infectious diseases that affect wildlife and livestock are challenging to manage and can lead to large-scale die-offs, economic losses, and threats to human health. The management of infectious diseases in wildlife and livestock is made easier with knowledge of disease risk across space and identifying stakeholders associated with high-risk landscapes. This study focuses on anthrax, caused by the bacterium Bacillus anthracis, risk to wildlife and livestock in Montana. There is a history of anthrax in Montana, but the spatial extent of disease risk and subsequent wildlife species at risk are not known. Our objective was to predict the potential geographic distribution of anthrax risk across Montana, identify wildlife species at risk and their distributions, and define stakeholders. We used an ecological niche model to predict the potential distribution of anthrax risk. We overlaid susceptible wildlife species distributions and land ownership delineations on our risk map. We found that there was an extensive region across Montana predicted as potential anthrax risk. These potentially risky landscapes overlapped the ranges of all 6 ungulate species considered in the analysis and livestock grazing allotments, and this overlap was on public and private land for all species. Our findings suggest that there is the potential for a multi-species anthrax outbreak on multiple landscapes across Montana. Our potential anthrax risk map can be used to prioritize landscapes for surveillance and for implementing livestock vaccination programs.     

Constructing Rigorous and Broad Biosurveillance Networks for Detecting Emerging Zoonotic Outbreaks

Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.     

野生鸟类传染性疾病研究进展

由于具有独特的飞行能力和极强的地理扩散能力,鸟类活动为某些传染性疾病的快速传播和扩散带来了潜在风险.自20世纪以来,以禽霍乱、禽波特淋菌病、西尼罗河热、禽流感等为代表的鸟类疾病频繁暴发,导致为数众多的野生鸟类、家禽甚至人类死亡,给社会造成巨大的经济损失.因此,有关鸟类传染性疾病的研究已引起了国内外学者的广泛关注.从鸟类传染性疾病的生态学特征、疾病对鸟类与人类社会的影响、鸟类对疾病的传播、鸟类疾病的监测、预警和防控等方面对野生鸟类的传染性疾病研究进展进行了综述.不同疾病导致的鸟类死亡量、易感物种数量、暴发频率和地理扩散等特征差异显著.20世纪以来,疾病已成为全球生物多样性的七大威胁因子之一.疾病可能造成鸟类大量死亡,从而对鸟类种群,特别是濒危鸟类种群造成严重影响.其中,人畜共患病还会导致家禽家畜甚至人类的死亡,从而对社会产生严重的影响.野生鸟类作为多种疾病传播的媒介,其移动和迁徙可能会导致疾病的传播与扩散.开展全面的监测活动和建立疾病预警体系,对于疾病的防控具有重要意义.     

Xenosurveillance: A Novel Mosquito-Based Approach for Examining the Human-Pathogen Landscape

BackgroundGlobally, regions at the highest risk for emerging infectious diseases are often the ones with the fewest resources. As a result, implementing sustainable infectious disease surveillance systems in these regions is challenging. The cost of these programs and difficulties associated with collecting, storing and transporting relevant samples have hindered them in the regions where they are most needed. Therefore, we tested the sensitivity and feasibility of a novel surveillance technique called xenosurveillance. This approach utilizes the host feeding preferences and behaviors of Anopheles gambiae, which are highly anthropophilic and rest indoors after feeding, to sample viruses in human beings. We hypothesized that mosquito bloodmeals could be used to detect vertebrate viral pathogens within realistic field collection timeframes and clinically relevant concentrations.Conclusions/SignificanceTogether, these data demonstrate the feasibility of xenosurveillance and in doing so validated a simple and non-invasive surveillance tool that could be used to complement current biosurveillance efforts.     

What is the Value of a Good Map? An Example Using High Spatial Resolution Imagery to Aid Riparian Restoration

Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems.     

A Quantitative Prioritisation of Human and Domestic Animal Pathogens in Europe

Disease or pathogen risk prioritisations aid understanding of infectious agent impact within surveillance or mitigation and biosecurity work, but take significant development. Previous work has shown the H-(Hirsch-)index as an alternative proxy. We present a weighted risk analysis describing infectious pathogen impact for human health (human pathogens) and well-being (domestic animal pathogens) using an objective, evidence-based, repeatable approach; the H-index. This study established the highest H-index European pathogens. Commonalities amongst pathogens not included in previous surveillance or risk analyses were examined. Differences between host types (humans/animals/zoonotic) in pathogen H-indices were explored as a One Health impact indicator. Finally, the acceptability of the H-index proxy for animal pathogen impact was examined by comparison with other measures. 57 pathogens appeared solely in the top 100 highest H-indices (1) human or (2) animal pathogens list, and 43 occurred in both. Of human pathogens, 66 were zoonotic and 67 were emerging, compared to 67 and 57 for animals. There were statistically significant differences between H-indices for host types (humans, animal, zoonotic), and there was limited evidence that H-indices are a reasonable proxy for animal pathogen impact. This work addresses measures outlined by the European Commission to strengthen climate change resilience and biosecurity for infectious diseases. The results include a quantitative evaluation of infectious pathogen impact, and suggest greater impacts of human-only compared to zoonotic pathogens or scientific under-representation of zoonoses. The outputs separate high and low impact pathogens, and should be combined with other risk assessment methods relying on expert opinion or qualitative data for priority setting, or could be used to prioritise diseases for which formal risk assessments are not possible because of data gaps.     

Regional epidemiologic characteristics of combined foci of tick-borne encephalitis, endemic rickettsioses and leptospirosis in Western Siberia

The authors carried out complex study of combined foci of infections with natural foci in Western Siberia and their reflection in human pathology. The results of serological examination of 5917 persons and of 1743 of farm animals in respect to tick-borne encephalitis, Asian tick-borne rickettsiosis, Q-rickettsiosis, and leptospiroses are analysed. Affection of the population with all the four infections in all the landscape zones under study was shown; the intensity of this affection with different infections differed. Combined natural foci of the mentioned infections were found to be widespread; epidemiological significance of such combination was unequal in different ladscapes, this depending on the ladscape characteristics of the natural foci of infections under study and of different ways of transmission of their causative agents.     

Differentiating geological fertility derived vegetation zones in Kruger National Park,South Africa,using Landsat and MODIS imagery

Spatial technologies present possibilities for producing frequently updated and accurate habitat maps, which are important in biodiversity conservation. Assemblages of vegetation are equivalent to habitats. This study examined the use of satellite imagery in vegetation differentiation in South Africa's Kruger National Park (KNP). A vegetation classification scheme based on dominant tree species but also related to the park's geology was tested, the geology generally consisting of high and low fertility lithology. Currently available multispectral satellite imagery is broadly either of high spatial but low temporal resolution or low spatial but high temporal resolution. Landsat TM/ETM+ and MODIS images were used to represent these broad categories. Rain season dates were selected as the period when discrimination between key habitats in KNP is most likely to be successful. Principal Component Analysis enhanced vegetated areas on the Landsat images, while NDVI vegetation enhancement was employed on the MODIS image. The images were classified into six field sampling derived classes depicting a vegetation density and phenology gradient, with high (about 89%) indicative classification accuracy. The results indicate that, using image processing procedures that enhance vegetation density, image classification can be used to map the park's vegetation at the high versus low geological fertility zone level, to accuracies above 80% on high spatial resolution imagery and slightly lower accuracy on lower spatial resolution imagery. Rainfall just prior to the image date influences herbaceous vegetation and, therefore, success at image scene vegetation mapping, while cloud cover limits image availability. Small scale habitat differentiation using multispectral satellite imagery for large protected savanna areas appears feasible, indicating the potential for use of remote sensing in savanna habitat monitoring. However, factors affecting successful habitat mapping need to be considered. Therefore, adoption of remote sensing in vegetation mapping and monitoring for large protected savanna areas merits consideration by conservation agencies.     

Evidence for the Convergence Model: The Emergence of Highly Pathogenic Avian Influenza (H5N1) in Viet Nam

Building on a series of ground breaking reviews that first defined and drew attention to emerging infectious diseases (EID), the ‘convergence model’ was proposed to explain the multifactorial causality of disease emergence. The model broadly hypothesizes disease emergence is driven by the co-incidence of genetic, physical environmental, ecological, and social factors. We developed and tested a model of the emergence of highly pathogenic avian influenza (HPAI) H5N1 based on suspected convergence factors that are mainly associated with land-use change. Building on previous geospatial statistical studies that identified natural and human risk factors associated with urbanization, we added new factors to test whether causal mechanisms and pathogenic landscapes could be more specifically identified. Our findings suggest that urbanization spatially combines risk factors to produce particular types of peri-urban landscapes with significantly higher HPAI H5N1 emergence risk. The work highlights that peri-urban areas of Viet Nam have higher levels of chicken densities, duck and geese flock size diversities, and fraction of land under rice or aquaculture than rural and urban areas. We also found that land-use diversity, a surrogate measure for potential mixing of host populations and other factors that likely influence viral transmission, significantly improves the model’s predictability. Similarly, landscapes where intensive and extensive forms of poultry production overlap were found at greater risk. These results support the convergence hypothesis in general and demonstrate the potential to improve EID prevention and control by combing geospatial monitoring of these factors along with pathogen surveillance programs.     

Removing politics from innovations that improve food security

Genetically modified (GM) organisms and crops have been a feature of food production for over 30 years. Despite extensive science-based risk assessment, the public and many politicians remain concerned with the genetic manipulation of crops, particularly food crops. Many governments have addressed public concern through biosafety legislation and regulatory frameworks that identify and regulate risks to ensure human health and environmental safety. These domestic regulatory frameworks align to international scientific risk assessment methodologies on a case-by-case basis. Regulatory agencies in 70 countries around the world have conducted in excess of 4400 risk assessments, all reaching the same conclusion: GM crops and foods that have been assessed provide no greater risk to human health or the environment than non-GM crops and foods. Yet, while the science regarding the safety of GM crops and food appears conclusive and societal benefits have been globally demonstrated, the use of innovative products have only contributed minimal improvements to global food security. Regrettably, politically-motivated regulatory barriers are currently being implemented with the next genomic innovation, genome editing, the implications of which are also discussed in this article. A decade of reduced global food insecurity was witnessed from 2005 to 2015, but regrettably, the figure has subsequently risen. Why is this the case? Reasons have been attributed to climate variability, biotic and abiotic stresses, lack of access to innovative technologies and political interference in decision making processes. This commentary highlights how political interference in the regulatory approval process of GM crops is adversely affecting the adoption of innovative, yield enhancing crop varieties, thereby limiting food security opportunities in food insecure economies.

     

Predictability of reef fish diversity and abundance using remote sensing data in Diego Garcia (Chagos Archipelago)

The diversity, abundance and distribution of reef fish are related to heterogeneity and physical complexity of benthic habitat. However, the field effort required to evaluate these aspects of the benthos in situ, at the scale of entire reefscapes, is greatly constrained by logistical and resource limitations. With moderate ground truthing, both substratum type and seabed topography are amenable to monitoring using satellite data. Here, remote sensing imagery was used to resolve the bathymetry and benthic character of a reef system in Diego Garcia (British Indian Ocean Territory). Replicate fish counts were made at seven measurement stations across the study area using visual census. Monte Carlo simulation revealed that species richness and abundance of several guilds and size groupings of reef fish appraised in situ were correlated with the satellite-derived seabed parameters over areas of seafloor as large as 5,030 m². The study suggests that satellite remote sensing is capable of predicting habitat complexity at a scale relevant to fish. Furthermore, as larger size classes of fish were better predicted with the satellite habitat complexity data, this technique could be used to predict fish stocks and identify potential sites for marine protected areas where intensive field surveys are not practical.