Modeling potential distribution of Oligoryzomys longicaudatus, the Andes virus (Genus: Hantavirus) reservoir, in Argentina

Mathematical Model of the Spread of Hantavirus Infection

A mathematical epidemiological model incorporating the mobility of rodents and human groups among zones of less or major contact between them is presented. The hantavirus infection dynamics is expressed using a model type SEIR (Susceptible-Exposed-Infectious-Removed), which incorporates the displacement of the rodent and the human, between the urban and rural sector, the latter being subdivided in populated and non-populated. The results show the impact that rodent or human displacement may have on the propagation of hantavirus infection. Human mobility is more significant than rodents in increasing the number of hantavirus infection cases. The results found may be used as a reference by the health authorities to develop more specific campaigns on the territorial dynamics of the rodent, attend to the mobility of humans in these territories, mainly agricultural and forestry workers, and strengthen control-prevention actions in the community, to prevent future outbreaks that are fatal.

Forecasting Outbreaks of Hantaviral Disease: Future Directions in Geospatial Modeling

Hantaviral diseases have been recognized as 'place diseases' from their earliest identification and, epidemiologically, are tied to single host species with transmission occurring from infectious hosts to humans. As such, human populations are most at risk when they are in physical proximity to suitable habitats for reservoir populations, when numbers of infectious hosts are greatest. Because of the lags between improving habitat conditions and increasing infectious host abundance and spillover to humans, it should be possible to anticipate (forecast) where and when outbreaks will most likely occur. Most mammalian hosts are associated with specific habitat requirements, so identifying these habitats and the ecological drivers that impact population growth and the dispersal of viral hosts should be markers of the increased risk for disease outbreaks. These regions could be targeted for public health and medical education. This paper outlines the rationale for forecasting zoonotic outbreaks, and the information that needs to be clarified at various levels of biological organization to make the forecasting of orthohantaviruses successful. Major challenges reflect the transdisciplinary nature of forecasting zoonoses, with needs to better understand the implications of the data collected, how collections are designed, and how chosen methods impact the interpretation of results.

Modeling potential risk areas of Orthohantavirus transmission in Northwestern Argentina using an ecological niche approach

BACKGROUND

Hantavirus Pulmonary Syndrome (HPS) is a rodent-borne zoonosis in the Americas, with up to 50% mortality rates. In Argentina, the Northwestern endemic area presents half of the annually notified HPS cases in the country, transmitted by at least three rodent species recognized as reservoirs of Orthohantavirus. The potential distribution of reservoir species based on ecological niche models (ENM) can be a useful tool to establish risk areas for zoonotic diseases. Our main aim was to generate an Orthohantavirus risk transmission map based on ENM of the reservoir species in northwest Argentina (NWA), to compare this map with the distribution of HPS cases; and to explore the possible effect of climatic and environmental variables on the spatial variation of the infection risk.

METHODS

Using the reservoir geographic occurrence data, climatic/environmental variables, and the maximum entropy method, we created models of potential geographic distribution for each reservoir in NWA. We explored the overlap of the HPS cases with the reservoir-based risk map and a deforestation map. Then, we calculated the human population at risk using a census radius layer and a comparison of the environmental variables' latitudinal variation with the distribution of HPS risk.

RESULTS

We obtained a single best model for each reservoir. The temperature, rainfall, and vegetation cover contributed the most to the models. In total, 945 HPS cases were recorded, of which 97,85% were in the highest risk areas. We estimated that 18% of the NWA population was at risk and 78% of the cases occurred less than 10 km from deforestation. The highest niche overlap was between Calomys fecundus and Oligoryzomys chacoensis.

CONCLUSIONS

This study identifies potential risk areas for HPS transmission based on climatic and environmental factors that determine the distribution of the reservoirs and Orthohantavirus transmission in NWA. This can be used by public health authorities as a tool to generate preventive and control measures for HPS in NWA.

Mechanisms of Hantavirus Transmission in Oligoryzomys longicaudatus

The cricetid rodent Oligoryzomys longicaudatus is the species host of Andes virus (ANDV) which causes hantavirus pulmonary syndrome in southern Argentina and Chile. Population density, behavioral interactions, and spacing patterns are factors that affect viral transmission among wild rodents. We predict that the highest prevalence of hantavirus antibody positive would be found among wounded, reproductive males and that, at high population densities, wounded, reproductive males would be dispersers rather than resident individuals. The study was conducted seasonally from October (spring) 2011 to October (spring) 2013 in a shrubland habitat of Cholila, Argentina. During each trapping session, we classified captured O. longicaudatus as resident or disperser individuals, estimated population density, and recorded wounds as an indicator of aggression among individuals. We obtained blood samples from each individual for serological testing. We used generalized linear models to test the statistical significance of association between antibody prevalence, and sex, resident/dispersal status, wounds and trapping session. The highest proportion of seropositive O. longicaudatus individuals was among wounded reproductive males during periods of the greatest population density, and the characteristics of seroconverted individuals support that transmission is horizontal through male intrasexual competition. A positive association between dispersing individuals and hantavirus antibody was detected at high population density. Our study design allowed us to obtain data on a large number of individuals that are seroconverted, enabling a better understanding of the ecology and epidemiology of the ANDV host system.

Tree Species Classification by Integrating Satellite Imagery and Topographic Variables Using Maximum Entropy Method in a Mongolian Forest

Forests are an important natural resource that achieve ecological balance by regulating water regimes and promoting soil conservation. Based on forest inventories, the government is able to make decisions to sustainably conserve, improve, and manage forests. Fieldwork for forestry investigation requires intensive physical labor, which is costly and time-consuming, especially for surveys in remote mountainous regions. Remote sensing technology has been recently used for forest investigation on a large scale. An informative forest inventory must include forest attributes, including details of tree species; however, tree species mapping is not always applicable due to the similarity of surface reflectance and texture between tree species. Topographic variables such as elevation, slope, aspect, and curvature are crucial in allocating ecological niches to different species; therefore, this study suggests that integrating topographic information and optical satellite image classification can improve mapping accuracy for tree species. The main purpose of this study is to classify forest tree species in Erdenebulgan County, Huwsgul Province, Mongolia, by integrating Landsat satellite imagery with a Digital Elevation Model (DEM) using a Maximum Entropy algorithm. A forest tree species inventory from the Forest Division of the Mongolian Ministry of Nature and Environment was used as training data and as ground truth to perform the accuracy assessment. In this study, the classification was made using two different experimental approaches. First, classification was done using only Landsat surface reflectance data; and second, topographic variables were integrated with the Landsat surface reflectance data. The integration approach showed a higher overall accuracy and kappa coefficient, indicating that an accurate forest inventory can be achieved by integrating satellite imagery data and other topographic information to enhance the practice of forest management in remote regions.

The ecological dynamics of hantavirus diseases: From environmental variability to disease prevention largely based on data from China

Hantaviruses can cause hantavirus pulmonary syndrome (HPS) in the Americas and hemorrhagic fever with renal syndrome (HFRS) in Eurasia. In recent decades, repeated outbreaks of hantavirus disease have led to public concern and have created a global public health burden. Hantavirus spillover from natural hosts into human populations could be considered an ecological process, in which environmental forces, behavioral determinants of exposure, and dynamics at the human–animal interface affect human susceptibility and the epidemiology of the disease. In this review, we summarize the progress made in understanding hantavirus epidemiology and rodent reservoir population biology. We mainly focus on three species of rodent hosts with longitudinal studies of sufficient scale: the striped field mouse (Apodemus agrarius, the main reservoir host for Hantaan virus [HTNV], which causes HFRS) in Asia, the deer mouse (Peromyscus maniculatus, the main reservoir host for Sin Nombre virus [SNV], which causes HPS) in North America, and the bank vole (Myodes glareolus, the main reservoir host for Puumala virus [PUUV], which causes HFRS) in Europe. Moreover, we discuss the influence of ecological factors on human hantavirus disease outbreaks and provide an overview of research perspectives.

Landscape, Climate and Hantavirus Cardiopulmonary Syndrome Outbreaks

We performed a literature review in order to improve our understanding of how landscape and climate drivers affect HCPS outbreaks. Anthropogenic landscape changes such as forest loss, fragmentation and agricultural land uses are related with a boost in hantavirus reservoir species abundance and hantavirus prevalence in tropical areas, increasing HCPS risk. Additionally, higher precipitation, especially in arid regions, favors an increase in vegetational biomass, which augments the resources for reservoir rodents, also increasing HCPS risk. Although these relationships were observed, few studies described it so far, and the ones that did it are concentrated in few places. To guide future research on this issue, we build a conceptual model relating landscape and climate variables with HCPS outbreaks and identified research opportunities. We point out the need for studies addressing the effects of landscape configuration, temperature and the interaction between climate and landscape variables. Critical landscape thresholds are also highly relevant, once HCPS risk transmission can increase rapidly above a certain degree of landscape degradation. These studies could be relevant to implement preventive measures, creating landscapes that can mitigate disease spread risk.

How much effort is required to accurately describe the complex ecology of a rodent-borne viral disease?

We use data collected on 18,1-ha live trapping grids monitored from 1994 through 2005 and on five of those grids through 2013 in the mesic northwestern US to illustrate the complexity of the deer mouse (Peromyscus maniculatus)/Sin Nombre virus (SNV) host-pathogen system. Important factors necessary to understand zoonotic disease ecology include those associated with distribution and population dynamics of reservoir species as well as infection dynamics. Results are based on more than 851,000 trap nights, 16,608 individual deer mice and 10,572 collected blood samples. Deer mice were distributed throughout every habitat we sampled and were present during every sampling period in all habitats except high altitude habitats over1900 m. Abundance varied greatly among locations with peak numbers occurring mostly during fall. However, peak rodent abundance occurred during fall, winter and spring during various years on three grids trapped 12 mo/yr. Prevalence of antibodies to SNV averaged 3.9% to 22.1% but no grids had mice with antibodies during every month. The maximum period without antibody-positive mice ranged from one month to 52 months, or even more at high altitude grids where deer mice were not always present. Months without antibody-positive mice were more prevalent during fall than spring. Population fluctuations were not synchronous over broad geographic areas and antibody prevalences were not well spatially consistent, differing greatly over short distances. We observed an apparently negative, but non-statistically significant relationship between average antibody prevalence and average deer mouse population abundance and a statistically significant positive relationship between the average number of antibody positive mice and average population abundance. We present data from which potential researchers can estimate the effort required to adequately describe the ecology of a rodent-borne viral system. We address different factors affecting population dynamics and hantavirus antibody prevalence and discuss the path to understanding a complex rodent-borne disease system as well as the obstacles in that path.

Estimating hantavirus risk in southern Argentina: a GIS-based approach combining human cases and host distribution

We use a Species Distribution Modeling (SDM) approach along with Geographic Information Systems (GIS) techniques to examine the potential distribution of hantavirus pulmonary syndrome (HPS) caused by Andes virus (ANDV) in southern Argentina and, more precisely, define and estimate the area with the highest infection probability for humans, through the combination with the distribution map for the competent rodent host (Oligoryzomys longicaudatus). Sites with confirmed cases of HPS in the period 1995–2009 were mostly concentrated in a narrow strip (~90 km × 900 km) along the Andes range from northern Neuquén to central Chubut province. This area is characterized by high mean annual precipitation (~1,000 mm on average), but dry summers (less than 100 mm), very low percentages of bare soil (~10% on average) and low temperatures in the coldest month (minimum average temperature −1.5 °C), as compared to the HPS-free areas, features that coincide with sub-Antarctic forests and shrublands (especially those dominated by the invasive plant Rosa rubiginosa), where rodent host abundances and ANDV prevalences are known to be the highest. Through the combination of predictive distribution maps of the reservoir host and disease cases, we found that the area with the highest probability for HPS to occur overlaps only 28% with the most suitable habitat for O. longicaudatus. With this approach, we made a step forward in the understanding of the risk factors that need to be considered in the forecasting and mapping of risk at the regional/national scale. We propose the implementation and use of thematic maps, such as the one built here, as a basic tool allowing public health authorities to focus surveillance efforts and normally scarce resources for prevention and control actions in vast areas like southern Argentina.

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

The occurrence of ectotherm disease vectors outside of their previous distribution area and the emergence of vector-borne diseases can be increasingly observed at a global scale and are accompanied by a growing number of studies which investigate the vast range of determining factors and their causal links. Consequently, a broad span of scientific disciplines is involved in tackling these complex phenomena. First, we evaluate the citation behaviour of relevant scientific literature in order to clarify the question "do scientists consider results of other disciplines to extend their expertise?" We then highlight emerging tools and concepts useful for risk assessment. Correlative models (regression-based, machine-learning and profile techniques), mechanistic models (basic reproduction number R0) and methods of spatial regression, interaction and interpolation are described. We discuss further steps towards multidisciplinary approaches regarding new tools and emerging concepts to combine existing approaches such as Bayesian geostatistical modelling, mechanistic models which avoid the need for parameter fitting, joined correlative and mechanistic models, multi-criteria decision analysis and geographic profiling. We take the quality of both occurrence data for vector, host and disease cases, and data of the predictor variables into consideration as both determine the accuracy of risk area identification. Finally, we underline the importance of multidisciplinary research approaches. Even if the establishment of communication networks between scientific disciplines and the share of specific methods is time consuming, it promises new insights for the surveillance and control of vector-borne diseases worldwide.

Modelling zoonotic diseases in humans: comparison of methods for hantavirus in Sweden

Because their distribution usually depends on the presence of more than one species, modelling zoonotic diseases in humans differs from modelling individual species distribution even though the data are similar in nature. Three approaches can be used to model spatial distributions recorded by points: based on presence/absence, presence/available or presence data. Here, we compared one or two of several existing methods for each of these approaches. Human cases of hantavirus infection reported by place of infection between 1991 and 1998 in Sweden were used as a case study. Puumala virus (PUUV), the most common hantavirus in Europe, circulates among bank voles (Myodes glareolus). In northern Sweden, it causes nephropathia epidemica (NE) in humans, a mild form of hemorrhagic fever with renal syndrome.Logistic binomial regression and boosted regression trees were used to model presence and absence data. Presence and available sites (where the disease may occur) were modelled using cross-validated logistic regression. Finally, the ecological niche model MaxEnt, based on presence-only data, was used.In our study, logistic regression had the best predictive power, followed by boosted regression trees, MaxEnt and cross-validated logistic regression. It is also the most statistically reliable but requires absence data. The cross-validated method partly avoids the issue of absence data but requires fastidious calculations. MaxEnt accounts for non-linear responses but the estimators can be complex. The advantages and disadvantages of each method are reviewed.