Mathematical Modeling and Machine Learning Prediction for Prevalence Dynamics of Clinical Mastitis in Dairy Herds

Aug 4, 2026

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Mastitis remains a major dairy herd disease driving substantial economic losses across the milk production chain. The study applies mathematical modeling and machine learning to predict mastitis transmission in dairy cows using data from a one-year cross-sectional longitudinal survey across three herds. Simple SIR and SIRS models without vital dynamics and Weka-based decision tables with 10-fold cross-validation are used for prevalence and incidence prediction. Results report annual clinical mastitis prevalence of 34.13% (cow level) and 30.07% (lactation level), with lactation incident risk of 45.86% and most cows showing one or two clinical cases.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Disease impact on dairy herds

Prevalence, transmission, and risk factors

Modeling and prediction approach

DOI 10.5937/FeMeSPRumNS24022NUDC 519.87 Article Originalni rad MATHEMATICAL MODELING AND MACHINE LEARNING PREDICTION FOR PREVALENCE DYNAMICS OF CLINICAL MASTITIS IN DAIRY HERDS MATEMATIČKO MODELIRANJE I PREDVIĐANJE MAŠINSKOG UČENJA ZA DINAMIKU PREVALENCIJE KLINIČKOG MASTITISA U MLEČNIM STADAMA Dimitar Nakova*, Biljana Zlatanovskab, Mirjana Kocaleva Vitanovab, Marija Mitevab, Slavča Hristovc, Branislav Stankovićc aFaculty of Agriculture, Goce Delcev University, Stip, North Macedonia, bFaculty of Computer Science, Goce Delcev University, Stip, North Macedonia cFaculty of Agriculture, University of Belgrade, Belgrade-Zemun, Serbia *Corresponding author: [dimitar.nakov@ugd.edu.mk](dimitar.nakov@ugd.edu.mk) ABSTRACT Mastitis remains one of the major diseases in dairy herds, causing profound economic losses to the entire milk production chain. The main aim of the study was an application of mathematical models and machine learning algorithms for the prediction of mastitis transmission in the dairy cow population. Data used for mathematical models and machine learning algorithms were obtained in a cross-sectional longitudinal survey lasting for one year by analyzing data for clinical mastitis occurrence in three dairy herds. For data prediction, simple SIR and SIRS mathematical models without vital dynamics and Weka software were applied. The annual prevalence rate of clinical mastitis for the entire population of cows was 34.13% on the cow level, 30.07% on the lactation level, while lactation incident risk was 45. 86% . Most of the cows manifested one (68.24%) or two (18.63%) cases of clinical mastitis during lactation. The SIR model revealed that after a short time, the epidemic will disappear. From the explanation and the graphical presentations, it can be concluded that the stable point DFE attracts the trajectories of the system. The mastitis on the farms is calming down, and with these parameters of the model, an epidemic cannot occur. With the use of the decision table as one of the most used classification rules and cross-validation folds 10 we can best predict mastitis occurrence in dairy farms. Implementation of a good mastitis prevention program in dairy herds by increasing the rates of control parameters will reduce the mastitis pathogens transmission rates leading to a reduction of mastitis incidence. Key words: mastitis, dairy cows, SIR, SIRS, machine learning SAŽETAK astitis je jedna od najznačajnijih bolesti u mlečnim stadama, koji izaziva velike ekonomske gubitke ucelom lancu proizvodnje mleka. Osnovni cilj rada je bila primena matematičkih modela i algoritamamašinskog učenja za predviđanje prenošenja uzročnika mastitisa u populaciji muznih krava. odaci, korišćeni za matematičke modele i algoritme mašinskog učenja, su dobijeni u longitudinalnom istraživanju poprečnog preseka u trajanju od godinu dana analizom podataka o kliničkoj pojavi mastitisa u tri mlečnastada. Za predviđanje podataka primenjeni su jednostavni SIR i SIRS matematički modeli bez vitalnedinamike i Veka softver. Godišnja stopa prevalencije kliničkog mastitisa za celokupnu populaciju krava iznosila je 34,13% na nivou krava, 30,07% na nivou laktacije, dok je rizik pojave u laktaciji iznosio 45,86% . Većina krava je u toku laktacije ispoljila jedan (68,24%) ili dva (18,63%) slučaja kliničkog mastitisa. SIR model je otkrio da će nakon kratkog vremena epidemija mastitisa nestati. Iz objašnjenja i grafičkih prikaza može se zaključiti da stabilna tačka DF privlači putanje sistema. astitis na farmama se smiruje, a sa ovim parametrima modela ne može doći do epidemije. rimenom tabele odluka kao jednog od najčešće korišćenihklasifikacionih pravila i preklopa za unakrsnu validaciju 10 može se najbolje predvideti pojava mastitisa na farmama mlečnih krava. Sprovođenje dobrog programa prevencije mastitisa u mlečnim stadima povećanjem stope kontrolnih parametara će smanjiti stope prenošenja patogenih mikroorganizama uzročnika mastitisa,što će

FAQs

What is the main objective of this study?

To apply mathematical models and machine learning algorithms for predicting mastitis transmission dynamics in dairy cow populations.

What data and period were used for modeling?

Data came from a cross-sectional longitudinal survey lasting one year, analyzing clinical mastitis occurrence in three dairy herds.

Which mathematical models and software were used to make predictions?

The study used simple SIR and SIRS models without vital dynamics and applied Weka for predictive classification using decision tables with 10-fold cross-validation.

Aug 4, 2026

Upload by Violet