Guide to Essential Biostatistics XXIV: Area under the Disease Progress Curve (AUDPC)

In the other articles in this series, we explore Designing and Implementing Experiments (the?Scientific Method,?Proposing Hypotheses?and?Type-I and Type-II?errors, Significance, Power, Effect,?Variance,?Replication,?Experimental Degrees of Freedom and Randomization), Descriptive statistics (critically evaluating experimental data: Q-test;?SD, SE, and 95%CI and Chi-square test), Inferential statistics (accepting or rejecting hypotheses by the parametric and non-parametric methods:?F-test, One- and Two-Sample Means Comparisons (the t-test), ANOVA and post-ANOVA tests and dose-response evaluation (linear and non-linear regression) as well as determining synergy from efficacy data.

Most plant pathogens are polycyclic, with recurring production of inoculum and reinfection leading to increases in the disease during the growing season. By evaluating disease levels over time, it is possible to combine multiple observations from disease progress experiments into a single cumulative value (a measure of duration and intensity) by integrating the Area under the Disease Progress Curve (AUDPC). AUDPC is used in crop protection efficacy studies to compare levels of efficacy between treatments over time.

To obtain the AUDPC, disease evaluations commence the symptoms appear, typically when the untreated control plots reach 10% disease severity and are repeated at 3-10 day intervals (days after treatment, DAT) until disease severity approaches 100%, to ensure data is available for low, medium and high disease severity.

Ideally, the AUDPC would be calculated through integration, although the midpoint rule method provides a simple estimate of the area under the infection curve. The midpoint method approximates the area between the infection curve and the x-axis by summing the areas of rectangles with midpoints that are points on the curve between evaluations times.:

The midpoint method is expressed as:

where SEV = disease severity and T = evaluation times (DAT) for two evaluations typically made at 3-10 day intervals.

Example: calculating AUDPC using the midpoint rule

For a treatment A, the following disease severity evaluations were made at four timepoints following application of treatment A:

1. add the first and second score (20 and 30)
2. multiply by the time between them (50 x 2 days=100) and divide by two (=50).
3. repeat for second and third score: 30 + 80 = 110. 110 x 4 = 440.
4. divide by 2 = 220.

Repeat for all consecutive pairs of scores and add the values together to determine the AUDPC:?

Example: comparing treatments over time using AUDPC

In this example, the efficacy of three fungicides (ABC) is compared at multiple timepoints over the duration of the infection by evaluating disease severity at 1, 3, 7 and 14 days after treatment:

The data can be graphically presented as:

For trials with multiple treatments and evaluations, it can be beneficial to create an overview and rank the treatments by?combining multiple observations from disease progress experiments into a single, cumulative AUDPC value:

In this example, treatment A is the most effective (lowest AUDPC) while treatment C is the least effective (greatest AUDPC).

While the advantage of AUDPC is to combine multiple observations of disease progress into a single value, this method tends to underestimate the effect of the first and last observations (upper and lower asymptotes). This is a characteristic of the sigmoid curve and was identified in the previous chapter for EC50 calculation and will be encountered again in the following chapter on synergy. This shortcoming may be alleviated by restricting the AUDPC calculation to the range between 20% and 80% disease severity i.e., to the linear phase of the curve.

Thanks for reading - please feel free to?read and share my other articles?in this series!

GUIDE TO ESSENTIAL BIOSTATISTICS is now published and available in eBook and Print formats!

Are you a student, researcher or science leader looking for an overview of the essential principles of Biostatistics?

Guide To Essential Biostatistics is an easily accessible primer for scientists and research workers not trained in mathematical theory, but who have previously followed a course in Biological Statistics.

This book provides a readily accessible overview on how to plan, implement and analyse experiments without access to a dedicated staff of statisticians.

Guide To Essential Biostatistics contains few calculations (the “how” of Biostatistics) but instead provides a plain-English overview of the “why” - what is it the numbers are telling us, and how can we use this to plan trials, understand our data and make decisions.

Designed to fit in a lab coat pocket for easy access, Guide To Essential Biostatistics compiles some of the most-used biostatistical techniques, approximations and rules-of-thumb used in the design and analysis of biological experiments.

Buy this book to obtain an overview of essential aspects of Biostatistics! By purchasing the print edition of this book on AMAZON, you are eligible for a FREE download of the eBook version, providing access to high-resolution, zoomable color images.

A little about myself

I am a Plant Scientist with a background in Molecular Plant Biology and Crop Protection.

20 years ago, I worked at Copenhagen University and the University of Adelaide on plant responses to biotic and abiotic stress in crops.

At that time, biology-based crop protection strategies had not taken off commercially, so I transitioned to conventional (chemical) crop protection R&D at Cheminova, later FMC.

During this period, public opinion, as well as increasing regulatory requirements, gradually closed the door of opportunity for conventional crop protection strategies, while the biological crop protection technology I had contributed to earlier began to reach commercial viability.

I am available to provide independent Strategic R&D Management as well as Scientific Development and Regulatory support to AgChem & BioScience organizations developing science-based products.

For more information, visit?BIOSCIENCE SOLUTIONS?- Strategic R&D Management Consultancy.