Evaluating Chimpanzee (Pan troglodytes schweinfurthii) Respiratory Illness Susceptibility: A Machine Learning Approach

Respiratory diseases have had devastating impacts on chimpanzee populations. Despite being the most frequently affected species among primate communities, zoonotic disease outbreaks and epidemics in wild chimpanzee populations remain understudied. Artificial Intelligence (AI) and machine learning (ML) have found widespread applications in various fields of biology, but their use in primate research, particularly in understanding social behavior and disease susceptibility, has been limited. This study applies machine learning techniques to a large dataset of wild chimpanzees (Pan troglodytes schweinfurthii) to identify factors influencing the contraction of respiratory illness in primate communities. By leveraging the power of machine learning, this research seeks to predict susceptibility to respiratory illness in chimpanzees based on various factors related to their daily life and environment. Four machine learning algorithms were applied to the data, and the most effective algorithm revealed the number of human observers, party size, and age to be among the most influential variables. This research aims to improve our ability to predict and manage respiratory disease outbreaks in wild primate populations, which has broader implications for primate conservation and public health.

Author

• Bilimoria, Hailee

Language

• en

Keyword

• Primatology
• Chimpanzee
• Respiratory Illness
• Machine Learning

Date created

• 2023

Type of Work

• Master's Thesis

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Hominid Paleobiology

Degree

• M.S.

Advisor

• Wood, Bernard

Committee Member(s)

• Crandall, Keith

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/mc87pr030