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ARS Home » Pacific West Area » Pullman, Washington » Animal Disease Research » Research » Research Project #438499

Research Project: Develop Predictive Models of Potential Babesia Disease Spread in the U.S. to Assist in Mitigating Potential Future Outbreaks

Location: Animal Disease Research

Project Number: 2090-32000-040-017-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2020
End Date: Aug 31, 2025

In the United States, cattle fever ticks have been eradicated and cattle are at constant risk of infection with emerging tick-borne pathogens such as Babesia bovis. Infection of naïve cattle with this parasite results in severe disease and high mortality. Consequently, an outbreak of bovine babesiosis would have a devastating effect on U.S. agriculture and could compromise food security in the U.S. Asymptomatic, persistently infected cattle may have been introduced into the US from endemic regions to the south and could serve as reservoirs for tick acquisition and transmission. If cattle fever ticks become re-established in the US, there is a serious risk of transmission from these asymptomatic, persistently infected cattle to native naïve cattle resulting in a severe disease outbreak. There are no vaccines for bovine babesiosis or the tick vector, Rhipicephalus microplus, as preventive measures in the U.S. The knowledge gap regarding cattle fever ticks and bovine babesiosis is associated with the potential of tick invasion into the US and the resulting tick transmission of B. bovis. Resolving this gap would allow the creation of a model to predict bovine babesiosis outbreaks. This information is critical for protecting the US cattle industry. Specific Objectives: 1) Determine if ticks found in the U.S. carry the parasites that cause bovine babesiosis. 2) Determine the efficacy of R. microplus collected from deer in transmitting Babesia. 3) Understand the influence of humidity and temperature on the R. microplus lifecycle. 4) Assess the relative importance of climatic and landscape factors driving the establishment and population dynamic of the insect vector responsible for the spread of babesiosis.

Arthropod-borne Apicomplexan pathogens remain a great concern and challenge for disease control of livestock. Due to inefficient and unsafe strategies to prevent Babesia infection in the U.S., new frontiers need to be explored to predict and prevent outbreaks. In a research collaboration with the Crowder lab in the Department of Entomology at Washington State University, we will provide more accurate information regarding the risk of cattle fever ticks and bovine babesiosis spreading in the U.S. In Objective 1, we will collect ticks on deer from several locations in Texas and determine if the ticks are infected with B. bovis or B. bigemina. We will gather information regarding tick infection and the association of deer as carriers of pathogens. This information is important to determine the current risk of babesiosis outbreaks in the US. In Objective 2, we will focus on the behavior of the ticks collected from deer, the efficacy of deer-collected ticks in acquiring parasites from acutely infected animals and subsequent transmission of parasites to naïve cattle. Our collaborators will determine behavioral changes of ticks fed on deer. These behaviors include how long the tick takes to complete the lifecycle, the weight of the females, efficacy in oviposition, hatchability and larvae viability. The results obtained from the study will illuminate the survival rate of ticks carried by deer and associated risks to the U.S. In Objective 3, we will study how humidity and temperature variation affect the lifecycle of R. microplus. In the laboratory, we will mimic field conditions and assess the impact of the two environmental parameters on tick fitness. The lab results will allow us to apply this information as part of a modeling study to predict potential outbreaks in the U.S. Finally, Objective 4 will be largely focused on predictive ecological modelling of the ticks responsible of the spread of B. babesisosis. We will develop machine learning algorithms based on environmental factors, such as climatic conditions and landscape structure. These models will evaluate both the risk of invasion of the ticks into new regions, and the potential establishment in areas where they have already been sporadically detected, but do not represent a consistent population. We will use a multi-scale approach for the parameterization of the models, with national and regional scale models that will facilitate the assessment of the impact in the most problematic locations.