Model Accuracy in Forecasting Pathogen Spread Using Climatic Data

Annakate Schatz, a student from Mount Holyoke College, worked with Dr. Andrew Kramer to test the accuracy of models used to predict pathogen spread.

Abstract: Species distribution models (SDMs) are commonly used to predict the total possible area a species can occupy. These models, however, rely on an assumption of equilibrium with the environment. When we discover of a new disease or pathogen, it has not necessarily reached equilibrium, but one of the most urgent questions is where else it might spread. This project sought to answer where a pathogen will spread in the future with a model selection case study focused on time-sensitive predictive ability. Previous research on how well SDMs predict disease spread has worked either with a single model-fitting method or with a simulated disease (Vaclavik and Meentemeyer 2012, Patel unpublished). We extend such investigations by modeling a real disease, Batrachochytrium dendrobatidis, using multiple methods trained on time subsets of the available occurrence data. We split our data into training and testing sets, and evaluated models on each set by calculating the AUC, kappa, True Skill Statistic, and false negative rate. We found that Maximum Entropy performed best on the subsetted data, followed by boosted regression trees and randomForest methods. Predictions tended to improve with more training data, although all AUC scores dropped when the last subset was added in. From this project, we gain a better sense of how quickly we can determine the potential extent of an emerging disease.

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Investigating Accuracy of Climate vs. Yearly Weather for Predicting the Spread of White-Nose Syndrome in the United States

Yaw Kumi-Ansu, a Biology major from Emory University, worked with Dr. Andrew Kramer on an ongoing project to model White-Nose syndrome in bats.

Abstract: White-Nose Syndrome is an epizootic fungal disease caused by Pseudogymnoascus destructans which has caused a significant decline in Vespertilionid bat populations in the United States. It is a psychrophilic fungus and produces infectious conidia during the hibernation season of most cave-dwelling bat species. Studies have found strong ties between the rate and pattern of spread and factors such as the density of caves within an area and temperature. In our foundational paper (Maher et al. 2012), studies based on  models designed to predict the spatial spread of WNS showed that the model based on average length of winter (number of days under 10°C) and density of caves within and between counties (Gravity(caves)+Winter) provided the best fit for projections and observed spread of the disease. In this project, we wanted to know whether yearly variations in temperature (weather) was a better environmental factor than climate (average length of winter) in predicting the spread of White-Nose Syndrome in the contiguous United States. We modified code for the Gravity(caves)+Winter model to run yearly maximum and minimum temperature in place of average length of winter and we also calculated average temperature from 2006 to 2014. Our results showed that Average length of winter remained the best environmental factor in predicting the spatial spread of WNS based on NLL and AIC values which were obtained from the MLE parameter sets. Spatial spread in both the climate and yearly weather models were similar but climate models projected faster spread to counties with caves. In the future, we hope to improve upon our study of spatial spread by using yearly variations in length of winter as well as data on co-occurring species to get a better understanding of how inter-specific differences in hibernation patterns and length of hibernation could contribute to the spread of the disease.


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