In the United States, the total confirmed and probable cases of Lyme disease have more than doubled in the past decade. The increase in human incidence has been attributed, in part, to the range expansion of the principal vector of the bacterial pathogen, the black-legged tick (Ixodes scapularis). The tick life cycle includes three distinct hematophagous stages, each with different temporal and spatial influence on tick infection and human exposure. Therefore, a model that accurately predicts the dynamics of all life stages would be more accurate in describing the risk of encountering a tick-borne disease (TBD).
To this end, I sought out to develop process-based models grounded in ecological theory and community ecology to make quantitative predictions of questing tick populations. Furthermore, the ultimate goal was to produce iterative, short (< 31 days) to intermediate (6 month - 1 year) forecasts on a daily basis in areas of the United States where Lyme disease is endemic.
In Chapter 1, I built stage-structured population models in a data fusion framework that incorporates environmental variables such as the host population, relative humidity, and temperature, to predict the questing population of each life stage. I found that a four-stage model that includes the ecologically relevant dormant overwintering nymph state outperforms other models. The interplay between weather and host populations was also predictive.
In Chapter 2, I describe a data-assimilation scheme developed to update the tick population model iteratively and evaluate forecast uncertainty and sensitivities. Larval abundances were spatially heterogeneous, likely due to their limited dispersal capacity, and sampling efforts at this stage were less likely to reduce forecast uncertainty than efforts at later stages.
Chapter 3 evaluates the transferability and the structural components of this model for I. scapularis and Amblyomma americanum populations at NEON. A. americanum is arguably the second-most medically important tick species in the US. In general, forecasts were biased and tended to overpredict both species, this trend was on a latitudinal gradient, and forecasts for I. scapularis were more skillful than for A. americanum. Given the model framework, it appears that mouse abundance is less predictive of ticks at NEON than at Cary.
In Chapter 4, I estimated tick density at NEON sites and is used these estimates to constrain the parasitism state of mice through time, which has important implications for TBD management. Knowing when mice are parasitized could lead to management actions for mice removal and is another proxy for disease risk as this information tells us when ticks are active.
Overall, this dissertation focused on building mechanistic ecological forecasts from the ground up. I started with model calibration, then built a data assimilation scheme, and tested it at sites across the US. Therefore, this work represents the first of its kind pipeline from ecological model calibration to forecast implementation.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/47882 |
| Date | 04 December 2023 |
| Creators | Foster, Jr., John R. |
| Contributors | Dietze, Michael, LaDeau, Shannon |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nd/4.0/ |
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