Performance of Outbreak Management Plans for Emerging Plant Diseases: The Case of Almond Leaf Scorch Caused by Xylella fastidiosa in Mainland Spain.

Outbreak response to quarantine pathogens and pests in the European Union (EU) is regulated by the EU Plant Health Law, but the performance of outbreak management plans in terms of their effectiveness and efficiency has been quantified only to a limited extent. As a case study, the disease dynamics of almond leaf scorch, caused by Xylella fastidiosa, in the affected area of Alicante, Spain, were approximated using an individual-based spatial epidemiological model. The emergence of this outbreak was dated based on phylogenetic studies, and official surveys were used to delimit the current extent of the disease. Different survey strategies and disease control measures were compared to determine their effectiveness and efficiency for outbreak management in relation to a baseline scenario without interventions. One-step and two-step survey approaches were compared with different confidence levels, buffer zone sizes, and eradication radii, including those set by the EU legislation for X. fastidiosa. The effect of disease control interventions was also considered by decreasing the transmission rate in the buffer zone. All outbreak management plans reduced the number of infected trees (effectiveness), but large differences were observed in the number of susceptible trees not eradicated (efficiency). The two-step survey approach, high confidence level, and the reduction in the transmission rate increased the efficiency. Only the outbreak management plans with the two-step survey approach removed infected trees completely, but they required greater survey efforts. Although control measures reduced disease spread, surveillance was the key factor in the effectiveness and efficiency of the outbreak management plans. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

A mixture of mobility and meteorological data provides a high correlation with COVID-19 growth in an infection-naive population: a study for Spanish provinces.

Introduction: We use Spanish data from August 2020 to March 2021 as a natural experiment to analyze how a standardized measure of COVID-19 growth correlates with asymmetric meteorological and mobility situations in 48 Spanish provinces. The period of time is selected prior to vaccination so that the level of susceptibility was high, and during geographically asymmetric implementation of non-pharmacological interventions. Methods: We develop reliable aggregated mobility data from different public sources and also compute the average meteorological time series of temperature, dew point, and UV radiance in each Spanish province from satellite data. We perform a dimensionality reduction of the data using principal component analysis and investigate univariate and multivariate correlations of mobility and meteorological data with COVID-19 growth. Results: We find significant, but generally weak, univariate correlations for weekday aggregated mobility in some, but not all, provinces. On the other hand, principal component analysis shows that the different mobility time series can be properly reduced to three time series. A multivariate time-lagged canonical correlation analysis of the COVID-19 growth rate with these three time series reveals a highly significant correlation, with a median R-squared of 0.65. The univariate correlation between meteorological data and COVID-19 growth is generally not significant, but adding its two main principal components to the mobility multivariate analysis increases correlations significantly, reaching correlation coefficients between 0.6 and 0.98 in all provinces with a median R-squared of 0.85. This result is robust to different approaches in the reduction of dimensionality of the data series. Discussion: Our results suggest an important effect of mobility on COVID-19 cases growth rate. This effect is generally not observed for meteorological variables, although in some Spanish provinces it can become relevant. The correlation between mobility and growth rate is maximal at a time delay of 2-3 weeks, which agrees well with the expected 5?10 day delays between infection, development of symptoms, and the detection/report of the case.