Logistic regression model for predicting microbial growth and antibiotic resistance occurrence in swiftlet (Aerodramus fuciphagus) faeces

• Main Authors: Sui Sien, Leong, Lihan, Samuel, Ling, Teck Yee, Hwa, Chuan Chia
• Format: Article
• Language: English
• Published: UMT 2021
• Subjects: QR Microbiology
• Online Access: http://ir.unimas.my/id/eprint/36169/1/regression1.pdf; http://ir.unimas.my/id/eprint/36169/; https://jssm.umt.edu.my/?page_id=544; https://doi. org/10.46754/jssm.2021.06.010
• Institution: Universiti Malaysia Sarawak

This study proposes a logistic model of the environmental factors which may affect bacterial growth and antibiotic resistance in the swiftlet industry. The highest total mean faecal bacterial (FB) colonies counts (11.86±3.11 log10 cfu/ g) were collected from Kota Samarahan in Sarawak, Malaysia, and the lowest (6.71±1.09 log10 cfu/g) from Sibu in both rainy and dry season from March 2016 till September 2017. FB isolates were highly resistant against penicillin G (42.20±18.35%). Enterobacter and Enterococcal bacteria were resistant to streptomycin (40.00±51.64%) and vancomycin (77.50±41.58%). The model indicated that the bacteria could grow well under conditions of higher faecal acidity (pH 8.27), dry season, higher mean daily temperature (33.83°C) and faecal moisture content (41.24%) of swiftlet houses built in an urban area with significant regression (P<0.0005, N=100). The probability of the development of antibiotic resistance (%) increased 0.50 times if the faecal acidity increased by one unit with significant contribution to the prediction (P = 0.012). Understanding how these microbial species react to environmental parameters according to this model, allowed us to estimate their interaction outcomes and growth, especially in an urban environment, which may pose a health hazard to people.