Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence
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 ev...
| Autores principales: | , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2020
|
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.11939/6376 https://www.mdpi.com/2073-4395/10/4/560 |
| _version_ | 1855492072430108672 |
|---|---|
| author | Lázaro, Elena Makowski, David Martínez-Minaya, Joaquín Vicent, Antonio |
| author_browse | Lázaro, Elena Makowski, David Martínez-Minaya, Joaquín Vicent, Antonio |
| author_facet | Lázaro, Elena Makowski, David Martínez-Minaya, Joaquín Vicent, Antonio |
| author_sort | Lázaro, Elena |
| collection | ReDivia |
| description | 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. |
| format | Artículo |
| id | ReDivia6376 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | ReDivia63762025-04-25T14:46:55Z Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence Lázaro, Elena Makowski, David Martínez-Minaya, Joaquín Vicent, Antonio Bayesian models Confidence/credibility intervals Disease management Epidemiological models Generalized linear mixed models Incidence ratio JAGS software Predictive distribution Odds ratio H20 Plant diseases 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. 2020-04-16T10:01:16Z 2020-04-16T10:01:16Z 2020 article publishedVersion Lázaro, E.; Makowski, D.; Martínez-Minaya, J.; Vicent, A. (2020). Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence. Agronomy, 10(4), 560. 2073-4395 http://hdl.handle.net/20.500.11939/6376 10.3390/agronomy10040560 https://www.mdpi.com/2073-4395/10/4/560 en Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ MDPI electronico |
| spellingShingle | Bayesian models Confidence/credibility intervals Disease management Epidemiological models Generalized linear mixed models Incidence ratio JAGS software Predictive distribution Odds ratio H20 Plant diseases Lázaro, Elena Makowski, David Martínez-Minaya, Joaquín Vicent, Antonio Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence |
| title | Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence |
| title_full | Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence |
| title_fullStr | Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence |
| title_full_unstemmed | Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence |
| title_short | Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence |
| title_sort | comparison of frequentist and bayesian meta analysis models for assessing the efficacy of decision support systems in reducing fungal disease incidence |
| topic | Bayesian models Confidence/credibility intervals Disease management Epidemiological models Generalized linear mixed models Incidence ratio JAGS software Predictive distribution Odds ratio H20 Plant diseases |
| url | http://hdl.handle.net/20.500.11939/6376 https://www.mdpi.com/2073-4395/10/4/560 |
| work_keys_str_mv | AT lazaroelena comparisonoffrequentistandbayesianmetaanalysismodelsforassessingtheefficacyofdecisionsupportsystemsinreducingfungaldiseaseincidence AT makowskidavid comparisonoffrequentistandbayesianmetaanalysismodelsforassessingtheefficacyofdecisionsupportsystemsinreducingfungaldiseaseincidence AT martinezminayajoaquin comparisonoffrequentistandbayesianmetaanalysismodelsforassessingtheefficacyofdecisionsupportsystemsinreducingfungaldiseaseincidence AT vicentantonio comparisonoffrequentistandbayesianmetaanalysismodelsforassessingtheefficacyofdecisionsupportsystemsinreducingfungaldiseaseincidence |