Biology subjects

Machado, G.

Publications and source records attributed to Machado, G..

4 recordsLinked to original sources

bioRxiv2019

Global phylogeographical patterns in the spread of foot-and-mouth disease virus

Foot-and-mouth disease virus (FMDV) has proven its potential to propagate across local and international borders on numerous occasions, but yet details about the directionality of the spread along with the role of the different host in transmission remain unexplored. To elucidate FMDV global spread characteristics, we studied the spatiotemporal phylodynamics of serotypes O, A, Asia1, SAT1, SAT2, and SAT3, based on more than 50 years of phylogenetic and epidemiological information. Our results revealed phylogeographic patterns, dispersal rates, and the role of host species in the dispersal and maintenance of virus circulation. Contrary to previous studies, our results showed that three serotypes were monophyletic (O, A, and Asia1), while all SATs serotypes did not evidence a defined common ancestor. Root state posterior probability (RSPP) analysis suggested Belgium as the country of origin for serotype O (RSPP=0.27). India was the ancestral country for serotypes A (RSPP= 0.28), and Asia-1 (RSPP= 0.34), while Uganda appeared as the most likely origin country of all SAT serotypes (RSPP> 0.45). Furthermore, we identified the key centers of dispersal of the virus, being China, India and Uganda the most important ones. Bayes factor analysis revealed cattle as the major source of the virus for most of the serotypes (RSPP> 0.63), where the most important host-species transition route for serotypes O, A, and Asia1 was from cattle Bos taurus to swine Sus scrofa domesticus (BF>500), while, for SAT serotypes was from B. taurus to African buffalo Syncerus caffer. This study provides significant insights into the spatiotemporal dynamics of the global circulation of FMDV serotypes, by characterizing the viral routes of spread at serotype level, especially uncovering the importance of host species for each serotype in the evolution and spread of FMDV which further improve future decisions for more efficient control and eradication.

epidemiology↗

bioRxiv2019

How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure

O_LIPredicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data is available on a subset of individuals and non-linear relationships between variables are common.\nC_LIO_LIHere we take advantage of the latest advances in statistical machine learning to construct pathogen-risk models that automatically incorporate complex non-linear relationships with minimal statistical assumptions from ecological data with missing values. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus.\nC_LIO_LIOur modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualise strong non-linear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years.\nC_LIO_LIWhen combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure which are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns.\nC_LI

ecology↗

bioRxiv2019

Spatial distribution and spread potential of sixteen Leptospira serovars in a subtropical region of Brazil

Leptospirosis is a bacterial disease that represents a major problem in animal and public health due to its high prevalence and widespread distribution. This zoonotic disease is most prevalent in tropical environments where conditions favor pathogen survival. The ecological preferences of Leptospira serovars are poorly understood, limiting our knowledge of where and when outbreaks can occur, which may result in misinformed prevention and control plans. While the disease can occur consistently in time and space in tropical regions, research on the ecology of Leptospirosis remains limited in subtropical regions. This research gap regarding Leptospira ecology brings public and veterinary health problems, impacting local economies. To fill this gap of knowledge, we propose to assess geographic and ecological features among Leptospira serovars in a subtropical area of Brazil where Leptospirosis is endemic to (i) highlight environmental conditions that facilitate or limit Leptospira spread and survival and (ii) reconstruct its geographical distribution. An ecological niche modeling framework was used to characterize and compare Leptospira serovars in both geographical and environmental space. Our results show that, despite the geographic overlap exhibited by the different serovars assessed, we found ecological divergence among their occupied ecological niches. Ecological divergences were expressed as ranges of potential distributions and environmental conditions found suitably by serovar, being Sejroe the most asymmetric. Most important predictors for the potential distribution of most serovars were soil pH (31.7%) and landscape temperature (24.2%). Identification of environmental preferences will allow epidemiologists to better infer the presence of a serovar based on the environmental characteristics of regions rather than inferences based solely on historical epidemiological records. Including geographic and ecological ranges of serovars also may help to forecast transmission potential of Leptospira in public health and the food animal practice.

epidemiology↗

bioRxiv2019

Mapping changes in the spatiotemporal distribution of lumpy skin disease virus

Lumpy skin disease virus (LSDV) is an infectious disease of cattle transmitted by arthropod vectors which results in substantial economic losses due to impact on production efficiency and profitability, and represents an emerging threat to international trade of livestock products and live animals. Since 2015, the disease has spread across many Eastern European countries as well as Russia and Kazakhstan. This rapid expansion highlights the emergent nature of the virus in more temperate regions than those in which LSDV traditionally occurred. The goal of this study was to assess the risk for further LSDV spread in Eurasia through a) analysis of environmental factors conducive for LSDV and b) estimate of the underlying LSDV risk using a fine spatiotemporally explicit Bayesian hierarchical model on LSDV outbreak occurrence information. We used ecological niche modeling to estimate the potential distribution of LSDV outbreaks for 2014-2016. This analysis resulted in a spatial representation of environmental limits where, if introduced, LSDV is expected to efficiently spread. The Bayesian space-time model incorporated both environmental factors and the changing spatiotemporal distribution of the disease to capture the dynamics of disease spread and predict areas in which there is an increased risk of LSDV occurrence. Variables related to the average temperature, precipitation, wind speed, as well as land cover and host densities were found to be important drivers explaining the observed distribution of LSDV in both modeling approaches. Areas of elevated LSDV risks were identified mainly in Russia, Turkey, Serbia, and Bulgaria. Results suggest that prevailing ecological conditions may be compatible with further spread of LSDV in Eurasia, though models should be continually updated to reflect the current epidemiologic conditions. The results presented here advance our understanding of the ecological requirements of LSDV in temperate regions and may help in the design and implementation of prevention and surveillance strategies in the region.

epidemiology↗