The European Green Deal aims to reduce the use of chemical pesticides by half by 2030. Decision support systems are tools to help farmers schedule fungicide spraying based on disease risk and can reduce fungicide application frequency and overall use. However, the potential benefit of decision support systems compared to traditional calendar-based strategies has not yet been rigorously quantified. Here we synthesise 80 experiments and show that globally decision support systems can reduce fungicide treatments by at least 50% without compromising disease control. For a given fixed number of fungicide sprays, decision support systems were as effective as calendar-based programs in reducing disease incidence. When the number of sprays was halved, the increase in disease incidence was lower for decision support system-based strategies than calendar-based strategies. Our findings suggest that decision support systems can reduce fungicide use while limiting the risk to plant health and resistance development.

Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi‐state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R‐packages is also discussed.

At the request of the European Commission, EFSA prepared the general guidelines for surveys of plant pests, describing the legal, international and scientific context in which the surveys are designed, the basic principles implemented for surveillance of quarantine pests and introducing the concepts needed for the design of statistically sound and risk-based surveys. Three types of specific surveys are addressed: detection surveys for substantiation of pest freedom, delimiting surveys for determining the boundaries of an infested zone, and monitoring surveys for prevalence estimation when measuring the progress of eradication measures or for confirming a low pest prevalence area. For each survey, the survey parameters are introduced and their interactions analysed showing the importance of the assumptions that are taken for each one of them: (i) the aims of the survey are defined as the confidence of detecting a given pest prevalence (design prevalence), this reflects the trade-off between the acceptable level of the risk and availability of resources that determine the strength of the evidence to support the conclusion of the survey; (ii) the target population is addressed in terms of its structure and size, including the risk factors; and (iii) the method sensitivity is defined as the combination of the sampling effectiveness and the diagnostic sensitivity. EFSA's RiBESS+ tool is introduced for calculating the sample size using the survey parameters as input values for a statistically sound and risk-based survey design. The mathematical principles behind the tool are in line with the International Standards for Phytosanitary Measures. The survey design is flexible and can be tailored to each pest and specific situation in the Member States. Once the survey is implemented following this approach, the conclusions allow surveys to be compared across time and space, contributing to the harmonisation of surveillance activities across the EU Member States.

Fully Bayesian methods for Cox models specify a model for the baseline hazard function. Parametric approaches generally provide monotone estimations. Semi‐parametric choices allow for more flexible patterns but they can suffer from overfitting and instability. Regularization methods through prior distributions with correlated structures usually give reasonable answers to these types of situations. We discuss Bayesian regularization for Cox survival models defined via flexible baseline hazards specified by a mixture of piecewise constant functions and by a cubic B‐spline function. For those “semi‐parametric” proposals, different prior scenarios ranging from prior independence to particular correlated structures are discussed in a real study with microvirulence data and in an extensive simulation scenario that includes different data sample and time axis partition sizes in order to capture risk variations. The posterior distribution of the parameters was approximated using Markov chain Monte Carlo methods. Model selection was performed in accordance with the deviance information criteria and the log pseudo‐marginal likelihood. The results obtained reveal that, in general, Cox models present great robustness in covariate effects and survival estimates independent of the baseline hazard specification. In relation to the “semi‐parametric” baseline hazard specification, the B‐splines hazard function is less dependent on the regularization process than the piecewise specification because it demands a smaller time axis partition to estimate a similar behavior of the risk.

Cure models in survival analysis deal with populations in which a part of the individuals cannot experience the event of interest. Mixture cure models consider the target population as a mixture of susceptible and non-susceptible individuals. The statistical analysis of these models focuses on examining the probability of cure (incidence model) and inferring on the time-to-event in the susceptible subpopulation (latency model). Bayesian inference on mixture cure models has typically relied upon Markov chain Monte Carlo (MCMC) methods. The integrated nested Laplace approximation (INLA)is a recent and attractive approach for doing Bayesian inference. INLA in its natural definition cannot fit mixture models but recent research has new proposals that combine INLA and MCMC methods to extend its applicability to them 2;8;9 . This paper focuses on the implementation of INLA in mixture cure models. A general mixture cure survival model with covariate information for the latency and the incidence model within a general scenario with censored and non-censored information is discussed. The fact that non-censored individuals undoubtedly belong to the uncured population is a valuable information that was incorporated in the inferential process. Keywords Accelerated failure time mixture cure models, Complete and marginal likelihood function, Gibbs sampling, Proportional hazards mixture cure models, Survival analysis Prepared using sagej.cls [Version: 2013/07/26 v1.00] arXiv:1806.09362v1 [stat.CO]