Browsing by Author "Peterson, A. Townsend"

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    Distributional ecology of Andes hantavirus: a macroecological approach
    Astorga, Francisca; Escobar, Luis E.; Poo-Muñoz, Daniela A.; Escobar-Dodero, Joaquin; Rojas-Hucks, Sylvia; Alvarado-Rybak, Mario; Duclos, Melanie; Romero-Alvarez, Daniel; Molina-Burgos, Blanca E.; Peñafiel-Ricaurte, Alexandra; Toro, Frederick; Peña-Gómez, Francisco T.; Peterson, A. Townsend (2018-06-22)
    Background: Hantavirus pulmonary syndrome (HPS) is an infection endemic in Chile and Argentina, caused by Andes hantavirus (ANDV). The rodent Oligoryzomys longicaudatus is suggested as the main reservoir, although several other species of Sigmodontinae are known hosts of ANDV. Here, we explore potential ANDV transmission risk to humans in southern South America, based on eco-epidemiological associations among: six rodent host species, seropositive rodents, and human HPS cases. Methods: We used ecological niche modeling and macroecological approaches to determine potential geographic distributions and assess environmental similarity among rodents and human HPS cases. Results: Highest numbers of rodent species (five) were in Chile between 35° and 41°S latitude. Background similarity tests showed niche similarity in 14 of the 56 possible comparisons: similarity between human HPS cases and the background of all species and seropositive rodents was supported (except for Abrothrix sanborni). Of interest among the results is the likely role of O. longicaudatus, Loxodontomys micropus, Abrothrix olivaceus, and Abrothrix longipilis in HPS transmission to humans. Conclusions: Our results support a role of rodent species’ distributions as a risk factor for human HPS at coarse scales, and suggest that the role of the main reservoir (O. longicaudatus) may be supported by the broader rodent host community in some areas.
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    The ecology of chronic wasting disease in wildlife
    Escobar, Luis E.; Pritzkow, Sandra; Winter, Steven N.; Grear, Daniel A.; Kirchgessner, Megan S.; Dominguez-Villegas, Ernesto; Machado, Gustavo; Peterson, A. Townsend; Soto, Claudio (2020-04)
    Prions are misfolded infectious proteins responsible for a group of fatal neurodegenerative diseases termed transmissible spongiform encephalopathy or prion diseases. Chronic Wasting Disease (CWD) is the prion disease with the highest spillover potential, affecting at least seven Cervidae (deer) species. The zoonotic potential of CWD is inconclusive and cannot be ruled out. A risk of infection for other domestic and wildlife species is also plausible. Here, we review the current status of the knowledge with respect to CWD ecology in wildlife. Our current understanding of the geographic distribution of CWD lacks spatial and temporal detail, does not consider the biogeography of infectious diseases, and is largely biased by sampling based on hunters' cooperation and funding available for each region. Limitations of the methods used for data collection suggest that the extent and prevalence of CWD in wildlife is underestimated. If the zoonotic potential of CWD is confirmed in the short term, as suggested by recent results obtained in experimental animal models, there will be limited accurate epidemiological data to inform public health. Research gaps in CWD prion ecology include the need to identify specific biological characteristics of potential CWD reservoir species that better explain susceptibility to spillover, landscape and climate configurations that are suitable for CWD transmission, and the magnitude of sampling bias in our current understanding of CWD distribution and risk. Addressing these research gaps will help anticipate novel areas and species where CWD spillover is expected, which will inform control strategies. From an ecological perspective, control strategies could include assessing restoration of natural predators of CWD reservoirs, ultrasensitive CWD detection in biotic and abiotic reservoirs, and deer density and landscape modification to reduce CWD spread and prevalence.
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    Summary results of the 2014-2015 DARPA Chikungunya challenge
    Del Valle, Sara Y.; McMahon, Benjamin H.; Asher, Jason; Hatchett, Richard; Lega, Joceline C.; Brown, Heidi E.; Leany, Mark E.; Pantazis, Yannis; Roberts, David J.; Moore, Sean; Peterson, A. Townsend; Escobar, Luis E.; Qiao, Huijie; Hengartner, Nicholas W.; Mukundan, Harshini (2018-05-30)
    Background: Emerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting. Methods: To explore the suitability of current approaches to forecasting emerging diseases, the Defense Advanced Research Projects Agency (DARPA) launched the 2014–2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to be evaluated by comparison with incidence data reported to the Pan American Health Organization (PAHO). This manuscript presents an overview of the challenge and a summary of the approaches used by the winners. Results: Participant submissions were evaluated by a team of non-competing government subject matter experts based on numerical accuracy and methodology. Although this manuscript does not include in-depth analyses of the results, cursory analyses suggest that simpler models appear to outperform more complex approaches that included, for example, demographic information and transportation dynamics, due to the reporting biases, which can be implicitly captured in statistical models. Mosquito-dynamics, population specific information, and dengue-specific information correlated best with prediction accuracy. Conclusion: We conclude that with careful consideration and understanding of the relative advantages and disadvantages of particular methods, implementation of an effective prediction system is feasible. However, there is a need to improve the quality of the data in order to more accurately predict the course of epidemics.