The short course “Making Sense of Microbiology Data – From data to predictions; from quantitative assessment to decisions” will be offered in the Department of Veterinary, UFV, between October 14 and 18. This course will be taught by prof. József Baranyi, from University of Debrecen, Hungary, organized by InsPOA and supported by the Graduate Program in Veterinary Medicine (UFV).

The full programme for the course is detailed below; considering the academic activities, only 15 students will be able to register. Registrations will be accepted up to October 19th (31 3612 5607 and mev@ufv.br). The course will be in English and the academic activities will be registered as a 2 credits course in UFV ("Tópicos Especiais").

 

Making Sense of Microbiology Data

From data to predictions; from quantitative assessment to decisions

Course for quantitative food microbiology

 

Introduction

This course is intended primarily for food scientists to demonstrate the proper use of mathematical modelling, computational and statistical techniques to analyze their data, to generate predictions and to make decisions based on the data and the predictions.

Have you ever contemplated if it is always beneficial to get prepared for the most probable outcome of a future event?  Or what, say, the expression “significant term (p < 0.05)” really means? Sooner or later you inevitably write such expressions in your reports, papers and thesis, but are you confident that you interpret them correctly?

This course is an opportunity to boost your confidence, from a mathematician who has been working with microbiologists for 30 years.

 

Pre-requisites

Participants will understand and practice the presented concepts and methods using Microsoft Excel. For mathematical tools, the built-in functions / procedures and the Data Analysis and Solver Add-ins of Excel will be used.

 

PROGRAMME

 

Day 1. Predictive microbiology

1.1. Basic concepts

– Variables and parameters

– Scaling and reparameterizaton.

– Linearization and approximation in practical applications.

 

1.2. Primary models

– Malthusian model of exponential growth

– Logistic model and its variations.

– Deterministic models for cell population, stochastic models for single cell kinetics.

 

1.3. Secondary models

– Temperature-dependence of kinetic parameters

– Is death modelling a mirror-image to that of growth?

– Multivariate models

 

Day 2. Regression analysis

2.1. Basics of probability

– Random variables. Expected value, deviation and variance

– Random number generation for simulation

– Distributions of transformed random variables.

 

2.2. Fitting models to data

– The Least Squares Method

– Linear regression. Fitting by polynomials.

– Estimates and their standard errors.

– Confidence intervals.

 

Day 3. Combining models and data

3.1. Decisions, decisions

– Analysing food microbiology data using ComBase.

– Sampling

– Cost functions based on dissimilarities

– Expectation and real outcome; the concept of risk

 

3.2. Risk minimization

– Decision making

– Cost-benefit analysis.

– Objective functions. Optimization.

 

Day 4. Study and doubts

Common mistakes and misconcepts. Consultation

 

Day 5. Wrapping up

Test and review. Common mistakes and misconcepts. Consultation