Can You Judge a Disease Host by the Company It Keeps? Predicting Disease Hosts and Their Relative Importance: A Case Study for Leishmaniasis

Phylogenetic and biogeographical traits predict unrecognized hosts of zoonotic leishmaniasis

The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predict the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predicted Leishmania host status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonotic Leishmania hosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles.

What constitutes a community? A co-occurrence exploration of the Costa Rican avifauna

The concept of a "community" as a form of organization for natural biological systems is both widespread and widely accepted within the ecological and biological sciences. Communities have been defined as groups of organisms that interact in ways that denote interdependence between individuals and taxa (e.g. as defined by "food webs") but they have also been defined as groups of co-occurring organisms that are assumed to interact by virtue of their shared spatiotemporal existence. The latter definition has been debated and challenged in the literature, with mounting evidence for co-occurrence being more indicative of coincident ecological niches in space and time rather than being evidence of ecological interaction or dependency. Using a dataset of 460 Costa Rican bird species divided into breeding and non-breeding season datasets, we empirically demonstrate the ways in which co-occurrence can create illusory communities based on similar occupied ecological niches and similar patterns of co-occurrence at different times of year. We discuss the importance of discerning coincidental co-occurrence from true ecological interactions that would manifest a true community, and further address the importance of differentiating communities of co-occurrence from communities of demonstrable ecological interaction. While co-occurrence is a necessary aspect of interspecific interactions, we discuss and demonstrate here that such co-occurrence does not make a community, nor should explicit patterns of co-occurrence be seen as evidence for evolutionarily important ecological interactions.

"Does a Respiratory Virus Have an Ecological Niche, and If So, Can It Be Mapped?" Yes and Yes

Although the utility of Ecological Niche Models (ENM) and Species Distribution Models (SDM) has been demonstrated in many ecological applications, their suitability for modelling epidemics or pandemics, such as SARS-Cov-2, has been questioned. In this paper, contrary to this viewpoint, we show that ENMs and SDMs can be created that can describe the evolution of pandemics, both in space and time. As an illustrative use case, we create models for predicting confirmed cases of COVID-19, viewed as our target "species", in Mexico through 2020 and 2021, showing that the models are predictive in both space and time. In order to achieve this, we extend a recently developed Bayesian framework for niche modelling, to include: (i) dynamic, non-equilibrium "species" distributions; (ii) a wider set of habitat variables, including behavioural, socio-economic and socio-demographic variables, as well as standard climatic variables; (iii) distinct models and associated niches for different species characteristics, showing how the niche, as deduced through presence-absence data, can differ from that deduced from abundance data. We show that the niche associated with those places with the highest abundance of cases has been highly conserved throughout the pandemic, while the inferred niche associated with presence of cases has been changing. Finally, we show how causal chains can be inferred and confounding identified by showing that behavioural and social factors are much more predictive than climate and that, further, the latter is confounded by the former.

Disentangling the contributions of biotic and abiotic predictors in the niche and the species distribution model of Trypanosoma cruzi, etiological agent of Chagas disease

The potential benefits of incorporating biotic, as well as abiotic, predictors in niche and species distribution models (SDMs), as well as how to achieve this, is still debated, with their interpretability and explanatory potential being particularly questioned. It is therefore important to stress test modelling methodologies that include biotic factors against use cases where there is ample knowledge of the potential biotic component of the niche. Relatively well studied and important vector-borne diseases offer just such an opportunity, where knowledge of the agents involved in the transmission cycle -vectors and hosts- can serve to calibrate and test the niche model and corresponding SDM. Here, we study the contributions of biotic -14 vectors, 459 potential hosts- and abiotic -258 climatic categories- predictors to the explanatory and predictive features of the niche and corresponding SDM for the etiological agent of Chagas disease, Trypanosoma cruzi, in Mexico. Using an established spatial data mining technique, we generate biotic, abiotic and biotic+abiotic niche and SDM models. We test our models by comparing predictions of the most important probable hosts of Chagas disease with a previously published list of confirmed hosts. We quantify, compare, and contrast the individual and total contributions of predictors to the niche and distribution of Chagas disease in Mexico. We assess the relative predictive potential of these variables to model performance, showing that models that include relevant biotic niche variables lead to more predictive, more ecologically realistic SDMs. Our research illustrates a useful general procedure for identifying and ranking potential biotic interactions and for assessing the relative importance of biotic and abiotic predictors. We conclude that the inclusion of both abiotic and biotic predictors in SDMs not only provides more predictive and accurate models but also models that are more understandable and explainable from an ecological niche perspective.

Assessment of the potential establishment of Lyme endemic cycles in Mexico

Although Lyme disease is currently classified as exotic in Mexico, recent studies have suggested that it might be endemic there. We assessed the potential risk for the establishment of Borrelia burgdorferi transmission in Mexico. To identify the potential routes of B. burgdorferi spread, Complex Inference Networks were used initially to identify potential vector-host interactions between hard ticks (Ixodes) and migratory birds in the U.S., and a model for predicting the most important potential bird hosts of hard ticks was then obtained. By using network metrics, keystone-vectors were identified as those species with highest connectivity within and between network communities and had the potential to keep the pathogen circulating with many birds and to be dispersed to several regions. The climatic profile where these interactions occur in the U.S. was characterized and a geographic model for each keystone-vector was built. The accuracy of these models to predict areas where hard ticks have been reported positive for B. burgdorferi allows one to identify areas of greater risk of Lyme disease emergence. These hard tick-bird interactions and their climatic profile were mapped into the winter ranges of birds in Mexico. Thus, those regions in Mexico with the highest potential for becoming endemic areas of Lyme disease through the arrival of hard ticks and birds infected by B. burgdorferi were identified. These areas are candidates for future surveillance programs.

Potential distribution of four vectors of American Cutaneous Leishmaniasis: The effect of El Niño and La Niña episodes from the perspective of the ecological niche

The potential distribution of four American Cutaneous Leishmaniasis vectors under average conditions during Neutral (in between episodes), El Niño, and La Niña episodes from 2000 to 2018 were constructed through ecological niche modeling. The potential distribution in El Niño and La Niña were compared with the Neutral distribution. The four vector species (Lutzomyia gomezi, Lutzomyia ovallesi, Lutzomyia panamensis, and Lutzomyia trapidoi) decreased the potential distribution with the occurrence of El Niño and La Niña episodes. During El Niño, the reduction was concentrated over the dry ecosystems, while with the occurrence of La Niña, the potential distribution decreased over most of the Neotropic, leaving areas of climatic suitability concentrated in the Andean and Amazon areas, along with some scattered patches. We found evidence that the occurrence of the climatic anomalies has an effect on the potential distribution of this vector species.

Human-mediated impacts on biodiversity and the consequences for zoonotic disease spillover

Human-mediated changes to natural ecosystems have consequences for both ecosystem and human health. Historically, efforts to preserve or restore 'biodiversity' can seem to be in opposition to human interests. However, the integration of biodiversity conservation and public health has gained significant traction in recent years, and new efforts to identify solutions that benefit both environmental and human health are ongoing. At the forefront of these efforts is an attempt to clarify ways in which biodiversity conservation can help reduce the risk of zoonotic spillover of pathogens from wild animals, sparking epidemics and pandemics in humans and livestock. However, our understanding of the mechanisms by which biodiversity change influences the spillover process is incomplete, limiting the application of integrated strategies aimed at achieving positive outcomes for both conservation and disease management. Here, we review the literature, considering a broad scope of biodiversity dimensions, to identify cases where zoonotic pathogen spillover is mechanistically linked to changes in biodiversity. By reframing the discussion around biodiversity and disease using mechanistic evidence - while encompassing multiple aspects of biodiversity including functional diversity, landscape diversity, phenological diversity, and interaction diversity - we work toward general principles that can guide future research and more effectively integrate the related goals of biodiversity conservation and spillover prevention. We conclude by summarizing how these principles could be used to integrate the goal of spillover prevention into ongoing biodiversity conservation initiatives.

Physiology and ecology combine to determine host and vector importance for Ross River virus

Identifying the key vector and host species that drive the transmission of zoonotic pathogens is notoriously difficult but critical for disease control. We present a nested approach for quantifying the importance of host and vectors that integrates species' physiological competence with their ecological traits. We apply this framework to a medically important arbovirus, Ross River virus (RRV), in Brisbane, Australia. We find that vertebrate hosts with high physiological competence are not the most important for community transmission; interactions between hosts and vectors largely underpin the importance of host species. For vectors, physiological competence is highly important. Our results identify primary and secondary vectors of RRV and suggest two potential transmission cycles in Brisbane: an enzootic cycle involving birds and an urban cycle involving humans. The framework accounts for uncertainty from each fitted statistical model in estimates of species' contributions to transmission and has has direct application to other zoonotic pathogens.

Niche theory-based modeling of assembly processes of viral communities in bats

Understanding the assembly processes of symbiont communities, including viromes and microbiomes, is important for improving predictions on symbionts' biogeography and disease ecology. Here, we use phylogenetic, functional, and geographic filters to predict the similarity between symbiont communities, using as a test case the assembly process in viral communities of Mexican bats. We construct generalized linear models to predict viral community similarity, as measured by the Jaccard index, as a function of differences in host phylogeny, host functionality, and spatial co-occurrence, evaluating the models using the Akaike information criterion. Two model classes are constructed: a "known" model, where virus-host relationships are based only on data reported in Mexico, and a "potential" model, where viral reports of all the Americas are used, but then applied only to bat species that are distributed in Mexico. Although the "known" model shows only weak dependence on any of the filters, the "potential" model highlights the importance of all three filter types-phylogeny, functional traits, and co-occurrence-in the assemblage of viral communities. The differences between the "known" and "potential" models highlight the utility of modeling at different "scales" so as to compare and contrast known information at one scale to another one, where, for example, virus information associated with bats is much scarcer.

Using Data Mining and Network Analysis to Infer Arboviral Dynamics: The Case of Mosquito-Borne Flaviviruses Reported in Mexico

Given the significant impact of mosquito-borne flaviviruses (MBFVs) on both human and animal health, predicting their dynamics and understanding their transmission cycle is of the utmost importance. Usually, predictions about the distribution of priority pathogens, such as Dengue, Yellow fever, West Nile Virus and St. Louis encephalitis, relate abiotic elements to simple biotic components, such as a single causal agent. Furthermore, focusing on single pathogens neglects the possibility of interactions and the existence of common elements in the transmission cycles of multiple pathogens. A necessary, but not sufficient, condition that a mosquito be a vector of a MBFV is that it co-occurs with hosts of the pathogen. We therefore use a recently developed modeling framework, based on co-occurrence data, to infer potential biotic interactions between those mosquito and mammal species which have previously been identified as vectors or confirmed positives of at least one of the considered MBFVs. We thus create models for predicting the relative importance of mosquito species as potential vectors for each pathogen, and also for all pathogens together, using the known vectors to validate the models. We infer that various mosquito species are likely to be significant vectors, even though they have not currently been identified as such, and are likely to harbor multiple pathogens, again validating the predictions with known results. Besides the above "niche-based" viewpoint we also consider an assemblage-based analysis, wherein we use a community-identification algorithm to identify those mosquito and/or mammal species that form assemblages by dint of their significant degree of co-occurrence. The most cohesive assemblage includes important primary vectors, such as A. aegypti, A. albopictus, C. quinquefasciatus, C. pipiens and mammals with abundant populations that are well-adapted to human environments, such as the white-tailed deer (Odocoileus virginianus), peccary (Tayassu pecari), opossum (Didelphis marsupialis) and bats (Artibeus lituratus and Sturnira lilium). Our results suggest that this assemblage has an important role in the transmission dynamics of this viral group viewed as a complex multi-pathogen-vector-host system. By including biotic risk factors our approach also modifies the geographical risk profiles of the spatial distribution of MBFVs in Mexico relative to a consideration of only abiotic niche variables.

Measuring spatial co-occurrences of species potentially involved in Leishmania transmission cycles through a predictive and fieldwork approach

The Leishmaniases are a group of neglected tropical diseases caused by different species of the protozoan parasite Leishmania, transmitted to its mammalian hosts by the bites of several species of female Phlebotominae sand flies. Many factors have contributed to shifts in the disease distribution and eco epidemiological outcomes, resulting in the emergence of Cutaneous Leishmaniasis outbreaks and the incrimination of vectors in unreported regions. New research development is vital for establishing the new paradigms of the present transmission cycles, hoping to facilitate new control strategies to reduce parasite transmission. Hereafter, this work aims to model and infer the current transmission cycles of Cutaneous Leishmaniasis in Colombia defined by vector and mammal species distributed and interacting in the different regions and validate them by performing sand fly and mammal collections. Vector-host co-occurrences were computed considering five ecoregions of the Colombian territory defined by the World Wide Fund for Nature (WWF) and downloaded from The Nature Conservancy TNC Maps website. Four validation sites were selected based on Cutaneous Leishmaniasis prevalence reports. Sand flies and mammals captured in the field were processed, and species were defined using conventional taxonomic guidelines. Detection of infection by Leishmania was performed to identify transmission cycles in the selected areas. This study uses predictive models based on available information from international gazetteers and fieldwork to confirm sand fly and mammalian species' sustaining Leishmania transmission cycles. Our results show an uneven distribution of mammal samples in Colombia, possibly due to sampling bias, since only two departments contributed 50% of the available samples. Bats were the vertebrates with the highest score values, suggesting substantial spatial overlap with sand flies than the rest of the vertebrates evaluated. Fieldwork allowed identifying three circulating Leishmania species, isolated from three sand fly species. In the Montane Forest ecosystem, one small marsupial, Gracilinanus marica, was found infected with Leishmania panamensis, constituting the first record of this species infected with Leishmania. In the same locality, an infected sand fly, Pintomyia pia, was found. The overall results could support the understanding of the current transmission cycles of Leishmaniasis in Colombia.

Current Situation and Perspectives on Hantaviruses in Mexico

Hantaviruses are transmitted by rodents producing the hantavirus pulmonary syndrome (HPS) in the Americas. Today, no human cases of HPS have been reported in Mexico in spite of similar environmental conditions with Central America and the USA where several cases have occurred. To understand the current situation of hantaviruses in Mexico and the public health risk, a systematic review of studies was conducted reporting hantaviruses in rodents to known state seroprevalence and hantavirus genotypes. Simultaneously, this study identified the potential hantaviruses based on the phylogenetic diversity (PD) of hantaviruses reported in the Americas in hosts with the distribution in Mexico. A total 3862 rodents belonging to 82 species have been tested since 1999 to 2017. Overall, 392 individuals representing 43 rodent species were seropositive, and the seroprevalence ranged from 0 to 69.22%. Seven hantaviruses genotypes have been described in Mexico and three are zoonotic. Four host species of rodents are widely distributed in Mexico harboring the highest PD of viruses. According to the hosts distribution, 16 genotypes could be circulating in Mexico and some of these represent a potential risk for public health. This study proposed multidisciplinary and interinstitutional collaborations to implement systematic surveillance in rodents.

Inferring distributional shifts of epidemiologically important North and Central American sandflies from Pleistocene to future scenarios

Nine sandfly species (Diptera: Psychodidae) are suspected or proven vectors of Leishmania spp. in the North and Central America region. The ecological niches for these nine species were modelled in three time periods and the overlaps for all time periods of the geographic predictions (G space), and of ecological dimensions using pairwise comparisons of equivalent niches (E space), were calculated. Two Nearctic, six Neotropical and one species in both bioregions occupied a reduced number of distribution areas. The ecological niche projections for most sandfly species other than Lutzomyia shannoni and Lutzomyia ovallesi have not expanded significantly since the Pleistocene. Only three species increase significantly to 2050, whereas all others remain stable. Lutzomyia longipalpis shared a similar ecological niche with more species than any other, although both L. longipalpis and Lutzomyia olmeca olmeca had conserved distributions over time. Climate change, at both regional and local levels, will play a significant role in the temporal and spatial distributions of sandfly species.

[Leishmaniasis, risk of reintroduction]

[No disponible]

SPECIES: A platform for the exploration of ecological data

The modeling of ecological data that include both abiotic and biotic factors is fundamental to our understanding of ecosystems. Repositories of biodiversity data, such as GBIF, iDigBio, Atlas of Living Australia, and SNIB (Mexico's National System of Biodiversity Information), contain a great deal of information that can lead to knowledge discovery about ecosystems. However, there is a lack of tools with which to efficiently extract such knowledge. In this paper, we present SPECIES, an open, web-based platform designed to extract implicit information contained in large scale sets of ecological data. SPECIES is based on a tested methodology, wherein the correlations of variables of arbitrary type and spatial resolution, both biotic and abiotic, discrete and continuous, may be explored from both niche and network perspectives. In distinction to other modeling systems, SPECIES is a full stack exploratory tool that integrates the three basic components: data (which is incrementally growing), a statistical modeling and analysis engine, and an interactive visualization front end. Combined, these components provide a powerful tool that may guide ecologists toward new insights. SPECIES is optimized to support fast hypothesis prototyping and testing, analyzing thousands of biotic and abiotic variables, and presenting descriptive results to the user at different levels of detail. SPECIES is an open-access platform available online (http://species.conabio.gob.mx), that is, powerful, flexible, and easy to use. It allows for the exploration and incorporation of ecological data and its subsequent integration into predictive models for both potential ecological niche and geographic distribution. It also provides an ecosystemic, network-based analysis that may guide the researcher in identifying relations between different biota, such as the relation between disease vectors and potential disease hosts.

Nonconventional opponents: a review of malaria and leishmaniasis among United States Armed Forces

As the United States military engage with different countries and cultures throughout the world, personnel become exposed to new biospheres as well. There are many infectious pathogens that are not endemic to the US, but two of particular importance are Plasmodium and Leishmania, which respectively cause malaria and leishmaniasis. These parasites are both known to cause significant disease burden in their endemic locales, and thus pose a threat to military travelers. This review introduces readers to basic life cycle and disease mechanisms for each. Local and military epidemiology are described, as are the specific actions taken by the US military for prevention and treatment purposes. Complications of such measures with regard to human health are also discussed, including possible chemical toxicities. Additionally, poor recognition of these diseases upon an individual's return leading to complications and treatment delays in the United States are examined. Information about canine leishmaniasis, poorly studied relative to its human manifestation, but of importance due to the utilization of dogs in military endeavors is presented. Future implications for the American healthcare system regarding malaria and leishmaniasis are also presented.

Pathogeography: leveraging the biogeography of human infectious diseases for global health management

Biogeography is an implicit and fundamental component of almost every dimension of modern biology, from natural selection and speciation to invasive species and biodiversity management. However, biogeography has rarely been integrated into human or veterinary medicine nor routinely leveraged for global health management. Here we review the theory and application of biogeography to the research and management of human infectious diseases, an integration we refer to as 'pathogeography'. Pathogeography represents a promising framework for understanding and decomposing the spatial distributions, diversity patterns and emergence risks of human infectious diseases into interpretable components of dynamic socio-ecological systems. Analytical tools from biogeography are already helping to improve our understanding of individual infectious disease distributions and the processes that shape them in space and time. At higher levels of organization, biogeographical studies of diseases are rarer but increasing, improving our ability to describe and explain patterns that emerge at the level of disease communities (e.g. co-occurrence, diversity patterns, biogeographic regionalisation). Even in a highly globalized world most human infectious diseases remain constrained in their geographic distributions by ecological barriers to the dispersal or establishment of their causal pathogens, reservoir hosts and/or vectors. These same processes underpin the spatial arrangement of other taxa, such as mammalian biodiversity, providing a strong empirical 'prior' with which to assess the potential distributions of infectious diseases when data on their occurrence is unavailable or limited. In the absence of quality data, generalized biogeographic patterns could provide the earliest (and in some cases the only) insights into the potential distributions of many poorly known or emerging, or as-yet-unknown, infectious disease risks. Encouraging more community ecologists and biogeographers to collaborate with health professionals (and vice versa) has the potential to improve our understanding of infectious disease systems and identify novel management strategies to improve local, global and planetary health.

Ecology of phlebotomine sandflies and putative reservoir hosts of leishmaniasis in a border area in Northeastern Mexico: implications for the risk of transmission of Leishmania mexicana in Mexico and the USA

Leishmaniases are a group of important diseases transmitted to humans through the bite of sandfly vectors. Several forms of leishmaniases are endemic in Mexico and especially in the Southeast region. In the Northeastern region, however, there have only been isolated reports of cases and scanty records of sandfly vectors. The main objective of this study was to analyze the diversity of sandflies and potential reservoir hosts of Leishmania spp. in the states of Nuevo León and Tamaulipas. Species richness and abundances of sandflies and rodents were recorded. A fraction of the caught sandflies was analyzed by PCR to detect Leishmania spp. Tissues from captured rodents were also screened for infection. Ecological Niche Models (ENMs) were computed for species of rodent and their association with crop-growing areas. We found 13 species of sandflies, several of which are first records for this region. Medically important species such as Lutzomyia anthophora, Lutzomyia diabolica, Lutzomyia cruciata, and Lutzomyia shannoni were documented. Leishmania spp. infection was not detected in sandflies. Nine species of rodents were recorded, and Leishmania (Leishmania) mexicana infection was found in four species of Peromyscus and Sigmodon. ENMs showed that potential distribution of rodent pest species overlaps with allocated crop areas. This shows that Leishmania (L.) mexicana infection is present in the Northeastern region of Mexico, and that previously unrecorded sandfly species occur in the same areas. These findings suggest a potential risk of transmission of Leishmania (L.) mexicana.

Trypanosoma cruzi reservoir-triatomine vector co-occurrence networks reveal meta-community effects by synanthropic mammals on geographic dispersal

Contemporary patterns of land use and global climate change are modifying regional pools of parasite host species. The impact of host community changes on human disease risk, however, is difficult to assess due to a lack of information about zoonotic parasite host assemblages. We have used a recently developed method to infer parasite-host interactions for Chagas Disease (CD) from vector-host co-occurrence networks. Vector-host networks were constructed to analyze topological characteristics of the network and ecological traits of species’ nodes, which could provide information regarding parasite regional dispersal in Mexico. Twenty-eight triatomine species (vectors) and 396 mammal species (potential hosts) were included using a data-mining approach to develop models to infer most-likely interactions. The final network contained 1,576 links which were analyzed to calculate centrality, connectivity, and modularity. The model predicted links of independently registered Trypanosoma cruzi hosts, which correlated with the degree of parasite-vector co-occurrence. Wiring patterns differed according to node location, while edge density was greater in Neotropical as compared to Nearctic regions. Vectors with greatest public health importance (i.e., Triatoma dimidiata, T. barberi, T. pallidipennis, T. longipennis, etc), did not have stronger links with particular host species, although they had a greater frequency of significant links. In contrast, hosts classified as important based on network properties were synanthropic mammals. The latter were the most common parasite hosts and are likely bridge species between these communities, thereby integrating meta-community scenarios beneficial for long-range parasite dispersal. This was particularly true for rodents, >50% of species are synanthropic and more than 20% have been identified as T. cruzi hosts. In addition to predicting potential host species using the co-occurrence networks, they reveal regions with greater expected parasite mobility. The Neotropical region, which includes the Mexican south and southeast, and the Transvolcanic belt, had greatest potential active T. cruzi dispersal, as well as greatest edge density. This information could be directly applied for stratification of transmission risk and to design and analyze human-infected vector contact intervention efficacy.

Predicting the Potential Role of Non-human Hosts in Zika Virus Maintenance

Arboviruses are often maintained in complex cycles involving vertebrates such as mammals or birds and blood-feeding mosquitoes. However, the role of wildlife hosts in their emergence or re-emergence in human populations has received little attention. The recent emergence of Zika virus in America, and previous occurrences of chikungunya and dengue, forces us to confront a potential new disease-emergence phenomenon. Using a spatial data mining framework to identify potential biotic interactions, based on the degree of co-occurrence between different species, we identified those mammal species with the highest potential for establishing mammal-vector interactions, considering as principal vector Aedes aegypti. Seven of the top ten identified mammal species with highest potential were bats, with two of them having previously been confirmed as positive hosts for dengue in Mexico. We hope that this will raise interest of Mexican public health authorities and academic institutions to assess the role of wild hosts in the maintenance and spread of arboviruses.

Understanding transmissibility patterns of Chagas disease through complex vector–host networks

Chagas disease is one of the most important vector-borne zoonotic diseases in Latin America. Control strategies could be improved if transmissibility patterns of its aetiologic agent, Trypanosoma cruzi, were better understood. To understand transmissibility patterns of Chagas disease in Mexico, we inferred potential vectors and hosts of T. cruzi from geographic distributions of nine species of Triatominae and 396 wild mammal species, respectively. The most probable vectors and hosts of T. cruzi were represented in a Complex Inference Network, from which we formulated a predictive model and several associated hypotheses about the ecological epidemiology of Chagas disease. We compiled a list of confirmed mammal hosts to test our hypotheses. Our tests allowed us to predict the most important potential hosts of T. cruzi and to validate the model showing that the confirmed hosts were those predicted to be the most important hosts. We were also able to predict differences in the transmissibility of T. cruzi among triatomine species from spatial data. We hope our findings help drive efforts for future experimental studies.