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ARS Home » Southeast Area » Byron, Georgia » Fruit and Tree Nut Research » Research » Publications at this Location » Publication #338027
Title: A discussion on disease severity index values: warning on inherent errors and suggestions to maximize accuracy

Author
item CHIANG, KUO-SZU - Chung Hsing University
item LIU, HSUAN - Chung Hsing University
item Bock, Clive

Submitted to: Annals of Applied Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/3/2017
Publication Date: 9/1/2017
Citation: Chiang, K., Liu, H., Bock, C.H. 2017. A discussion on disease severity index values: warning on inherent errors and suggestions to maximize accuracy. Annals of Applied Biology. 171:139-154. https://doi.org/10.1111/aab.12362.
DOI: https://doi.org/10.1111/aab.12362

Interpretive Summary: When estimating severity of a plant disease, a disease interval (or category) scale comprises a number of categories of known numeric values – with plant disease this is generally based on the percent area with symptoms (e.g. the Horsfall-Barratt (H-B) scale). Studies in plant pathology and plant breeding often use quantitative interval scales. The disease severity is estimated by a rater as a value on the interval scale and has been used to determine a disease severity index (DSI) on a percentage basis, where DSI (%) = [sum (class frequency × score of rating class)] / [(total number of plants) × (maximal disease index)] × 100. However, very few studies have investigated the effects of different scales on accuracy of the DSI. Therefore, the objectives of this study were to investigate the process of calculating a DSI on a percentage basis from category scale data, and to use simulation approaches for exploring the effect of using different methods for calculation of the interval and the nature of the category scales used on the DSI estimates (%). We found that the DSI is particularly prone to overestimation when using the above formula for estimating a DSI (%) if the midpoint values of the rating class are not considered. Moreover, the results of these simulation studies show that, if rater estimates are unbiased, compared with other methods tested in this study, the most accurate method for estimation of a DSI is to use the midpoint of the severity range for each class with an amended 10% category scale. As for biased conditions, the accuracy for calculating DSI estimates (%) will depend mainly on the degree and direction of the rater bias relative to the actual mean value. Caution should be used with scale choice if a DSI is the selected method for disease assessment so as to minimize any bias.

Technical Abstract: When estimating severity of a plant disease, a disease interval (or category) scale comprises a number of categories of known numeric values – with plant disease this is generally based on the percent area with symptoms (e.g. the Horsfall-Barratt (H-B) scale). Studies in plant pathology and plant breeding often use quantitative interval scales. The disease severity is estimated by a rater as a value on the interval scale and has been used to determine a disease severity index (DSI) on a percentage basis, where DSI (%) = [sum (class frequency × score of rating class)] / [(total number of plants) × (maximal disease index)] × 100. However, very few studies have investigated the effects of different scales on accuracy of the DSI. Therefore, the objectives of this study were to investigate the process of calculating a DSI on a percentage basis from category scale data, and to use simulation approaches for exploring the effect of using different methods for calculation of the interval and the nature of the category scales used on the DSI estimates (%). We found that the DSI is particularly prone to overestimation when using the above formula for estimating a DSI (%) if the midpoint values of the rating class are not considered. Moreover, the results of these simulation studies show that, if rater estimates are unbiased, compared with other methods tested in this study, the most accurate method for estimation of a DSI is to use the midpoint of the severity range for each class with an amended 10% category scale. As for biased conditions, the accuracy for calculating DSI estimates (%) will depend mainly on the degree and direction of the rater bias relative to the actual mean value.