Browsing by Author "Qiao, Huijie"

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    Climate change linked to vampire bat expansion and rabies virus spillover
    Van de Vuurst, Paige; Qiao, Huijie; Soler-Tovar, Diego; Escobar, Luis E. (Wiley, 2023-10)
    Bat-borne pathogens are a threat to global health and in recent history have had major impacts on human morbidity and mortality. Examples include diseases such as rabies, Nipah virus encephalitis, and severe acute respiratory syndrome (SARS). Climate change may exacerbate the emergence of bat-borne pathogens by affecting the ecology of bats in tropical ecosystems. Here, we report the impacts of climate change on the distributional ecology of the common vampire bat Desmodus rotundus across the last century. Our retrospective analysis revealed a positive relationship between changes in climate and the northern expansion of the distribution of D. rotundus in North America. Furthermore, we also found a reduction in the standard deviation of temperatures at D. rotundus capture locations during the last century, expressed as more consistent, less-seasonal climate in recent years. These results elucidate an association between D. rotundus range expansion and a continental-level rise in rabies virus spillover transmission from D. rotundus to cattle in the last 50 years of the 120-year study period. This correlative study, based on field observations, offers empirical evidence supporting previous statistical and mathematical simulation-based studies reporting a likely increase of bat-borne diseases in response to climate change. We conclude that the D. rotundus rabies system exemplifies the consequences of climate change augmentation at the wildlife–livestock–human interface, demonstrating how global change acts upon these complex and interconnected systems to drive increased disease emergence.
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    Ecological niche modeling re-examined: A case study with the Darwin’s fox
    Escobar, Luis E.; Qiao, Huijie; Cabello, Javier (Wiley, 2018)
    Many previous studies have attempted to assess ecological niche modeling performance using receiver operating characteristic (ROC) approaches, even though diverse problems with this metric have been pointed out in the literature. We explored different evaluation metrics based on independent testing data using the Darwin’s Fox (Lycalopex fulvipes) as a detailed case in point. Six ecological niche models (ENMs; generalized linear models, boosted regression trees, Maxent, GARP, multivariable kernel density estimation, and NicheA) were explored and tested using six evaluation metrics (partial ROC, Akaike information criterion, omission rate, cumulative binomial probability), including two novel metrics to quantify model extrapolation versus interpolation (E-space index I) and extent of extrapolation versus Jaccard similarity (E-space index II). Different ENMs showed diverse and mixed performance, depending on the evaluation metric used. Because ENMs performed differently according to the evaluation metric employed, model selection should be based on the data available, assumptions necessary, and the particular research question. The typical ROC AUC evaluation approach should be discontinued when only presence data are available, and evaluations in environmental dimensions should be adopted as part of the toolkit of ENM researchers. Our results suggest that selecting Maxent ENM based solely on previous reports of its performance is a questionable practice. Instead, model comparisons, including diverse algorithms and parameterizations, should be the sine qua non for every study using ecological niche modeling. ENM evaluations should be developed using metrics that assess desired model characteristics instead of single measurement of fit between model and data. The metrics proposed herein that assess model performance in environmental space (i.e., E-space indices I and II) may complement current methods for ENM evaluation.
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    Network connectivity of Minnesota waterbodies and implications for aquatic invasive species prevention
    Kao, Szu-Yu Zoe; Enns, Eva A.; Tomamichel, Megan; Doll, Adam; Escobar, Luis E.; Qiao, Huijie; Craft, Meggan E.; Phelps, Nicholas B. D. (2021-05-23)
    Connectivity between waterbodies influences the risk of aquatic invasive species (AIS) invasion. Understanding and characterizing the connectivity between waterbodies through high-risk pathways, such as recreational boats, is essential to develop economical and effective prevention intervention to control the spread of AIS. Fortunately, state and local watercraft inspection programs are collecting significant data that can be used to quantify boater connectivity. We created a series of predictive models to capture the patterns of boater movements across all lakes in Minnesota, USA. Informed by more than 1.3 million watercraft inspection surveys from 2014-2017, we simulated boater movements connecting 9182 lakes with a high degree of accuracy. Our predictive model accurately predicted 97.36% of the lake pairs known to be connected and predicted 91.01% of the lake pairs known not to be connected. Lakes with high degree and betweenness centrality were more likely to be infested with an AIS than lakes with low degree (p < 0.001) and centrality (p < 0.001). On average, infested lakes were connected to 1200 more lakes than uninfested lakes. In addition, boaters that visited infested lakes were more likely to visit other lakes, increasing the risk of AIS spread to uninfested lakes. The use of the simulated boater networks can be helpful for determining the risk of AIS invasion for each lake and for developing management tools to assist decision makers to develop intervention strategies.
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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.