Chalita Jainonthee
Veterinary public health researcher working on food safety and animal welfare, from farm to fork. Increasingly, that means machine learning: building models and tools to make surveillance smarter.
Projects
Biofilm-Forming Pathogens on Food-Contact Surfaces
Investigating biofilm-forming Salmonella spp. and Staphylococcus aureus carrying antimicrobial-resistance genes on food-contact surfaces in small- and medium-sized swine slaughterhouses.
Read more →Broiler Condemnation, Cause by Cause
Decomposing the causes behind broiler carcass condemnation, using feed-withdrawal timing and machine learning to trace where losses actually come from.
Read more →MEQ Exam System
A web platform for delivering Modified Essay Question exams to veterinary students, with Chiang Mai University single sign-on integration.
Read more →SEAOHWA / OHPCP
Content development for the One Health Professional Certificate Program (OHPCP) on the SEAOHWA platform, training professionals across Southeast Asia in One Health competencies.
Read more →TLIC Innovative Teaching Grant
An intelligent virtual teaching tool using 360-degree camera capture and AI to build GMP auditing competency in veterinary students.
Read more →About
I'm a lecturer in Veterinary Public Health at the Faculty of Veterinary Medicine, Chiang Mai University. For over a decade before that I worked as a veterinarian at the Veterinary Public Health and Food Safety Centre for Asia Pacific, mostly on Campylobacter, slaughterhouse risk, and food-borne disease.
These days my research leans further into data: Bayesian structural time series, machine learning, and deep learning applied to disease surveillance, poultry welfare, and outbreak forecasting. I like the One Health framing because it forces animal, human, and environmental data to talk to each other.
Outside of papers, I build the tools I wish existed, teaching platforms, small automation, data pipelines, and this site. If it's technical and it makes research or teaching less painful, I'm probably poking at it.
Focus areas
Food Safety
Campylobacter and pathogen risk across the poultry and pork supply chain, from slaughterhouse to consumer.
Animal Welfare
Predicting and reducing dead-on-arrival losses in poultry, using data to improve welfare outcomes across production systems.
One Health
Connecting animal, human, and environmental data for disease outbreak detection and response.
Machine Learning
Bayesian structural time series, deep learning, and explainable ML for forecasting disease and welfare outcomes.
Molecular Biology
LAMP and PCR-based detection methods for faster, field-ready pathogen testing.
Experience & Education
Experience
Lecturer
Faculty of Veterinary Medicine, Chiang Mai University
Veterinarian
Veterinary Public Health and Food Safety Centre for Asia Pacific, Chiang Mai University
Education
PhD, Veterinary Science
Chiang Mai University, Faculty of Veterinary Medicine
MPH
University of Minnesota, School of Public Health & Chiang Mai University, Faculty of Public Health
DVM, First Class Honors
Chiang Mai University, Faculty of Veterinary Medicine
Selected Publications
Modeling and Forecasting Dead-on-Arrival in Broilers Using Time Series Methods: A Case Study from Thailand
doi.org/10.3390/ani15081179 →Predicting and Explaining High Dead-on-Arrival Outcomes in Meat-Type Ducks Using Deep Learning
doi.org/10.1016/j.psj.2025.105439 →Data-Driven Insights into Pre-Slaughter Mortality: Machine Learning for Predicting High Dead on Arrival in Meat-Type Ducks
doi.org/10.1016/j.psj.2024.104648 →Exploring the Influence of Slaughterhouse Type and Slaughtering Steps on Campylobacter jejuni Contamination in Chicken Meat: A Cluster Analysis Approach
doi.org/10.1016/j.heliyon.2024.e32345 →A Cutoff Determination of Real-Time Loop-Mediated Isothermal Amplification (LAMP) for End-Point Detection of Campylobacter jejuni in Chicken Meat
doi.org/10.3390/vetsci9030122 →