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Open AccessArticle

Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence

1
Centre de Protecció Vegetal i Biotecnologia, Institut Valencià d’Investigacions Agràries (IVIA), 46113 Moncada, Valencia, Spain
2
INRA, UMR 211 INRA AgroParisTech Université Paris-Saclay, 78850 Thiverval-Grignon, France
3
CIRED, CIRAD, 94130 Nogent-sur-Marne, France
4
BCAM—Basque Center for Applied Mathematics, Mazarredo, 14 E48009 Bilbao, Basque Country, Spain
*
Author to whom correspondence should be addressed.
Agronomy 2020, 10(4), 560; https://doi.org/10.3390/agronomy10040560
Received: 20 March 2020 / Accepted: 3 April 2020 / Published: 13 April 2020
Diseases of fruit and foliage caused by fungi and oomycetes are generally controlled by the application of fungicides. The use of decision support systems (DSSs) may assist to optimize fungicide programs to enhance application on the basis of risk associated with disease outbreak. Case-by-case evaluations demonstrated the performance of DSSs for disease control, but an overall assessment of the efficacy of DSSs is lacking. A literature review was conducted to synthesize the results of 67 experiments assessing DSSs. Disease incidence data were obtained from published peer-reviewed field trials comparing untreated controls, calendar-based and DSS-based fungicide programs. Two meta-analysis generic models, a “fixed-effects” vs. a “random-effects” model within the framework of generalized linear models were evaluated to assess the efficacy of DSSs in reducing incidence. All models were fit using both frequentist and Bayesian estimation procedures and the results compared. Model including random effects showed better performance in terms of AIC or DIC and goodness of fit. In general, the frequentist and Bayesian approaches produced similar results. Odds ratio and incidence ratio values showed that calendar-based and DSS-based fungicide programs considerably reduced disease incidence compared to the untreated control. Moreover, calendar-based and DSS-based programs provided similar reductions in disease incidence, further supporting the efficacy of DSSs. View Full-Text
Keywords: Bayesian models; confidence/credibility intervals; disease management; epidemiological models; generalized linear mixed models; incidence ratio; JAGS software; predictive distribution; odds ratio Bayesian models; confidence/credibility intervals; disease management; epidemiological models; generalized linear mixed models; incidence ratio; JAGS software; predictive distribution; odds ratio
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Lázaro, E.; Makowski, D.; Martínez-Minaya, J.; Vicent, A. Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence. Agronomy 2020, 10, 560.

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