Time series analysis (TSA) of human invasive listeriosis trends, 2008–2015

• Main Authors: EFSA Panel on Biological Hazards (BIOHAZ), Ricci, Antonia, Allende, Ana, Bolton, Declan, Chemaly, Marianne, Davies, Robert, Fernández Escámez, Pablo Salvador, Girones, Rosina, Herman, Lieve, Koutsoumanis, Konstantinos, Nørrung, Birgit, Robertson, Lucy, Ru, Giuseppe, Sanaa, Moez, Simmons, Marion, Skandamis, Panagiotis, Snary, Emma, Speybroeck, Niko, Ter Kuile, Benno, Threlfall, John, Wahlström, Helene, Takkinen, Johanna, Wagner, Martin, Arcella, Davide, Da Silva Felicio, Maria Teresa, Georgiadis, Marios, Messens, Winy, Lindqvist, Roland
• Format: Other/Unknown Material
• Language: English
• Published: Zenodo 2018
• Subjects: Listeria monocytogenes; invasive listeriosis; invasive; risk assessment; time series analysis; aggregated; disaggregated; ready-to-eat foods; gender-age combinations; Iceland
• Online Access: https://doi.org/10.5281/zenodo.1117639

The R codes were developed and applied by the EFSA Working Group on Listeria monocytogenes contamination of ready-to-eat foods during the preparatory work on the Scientific Opinion ‘ Listeria monocytogenes contamination of ready-to-eat foods and the risk for human health in the EU’ (see 10.2903/j.efsa.2018.5134). The codes were used to perform time-series analyses of the number of confirmed invasive listeriosis cases in the EU/EEA for the period 2008–2015. Data from The European Surveillance System – TESSy, provided by Austria, Belgium, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Luxembourg, Malta, Netherlands, Norway, Poland, Romania, Slovakia, Slovenia, Spain, Sweden, United Kingdom, and released by ECDC. Two R codes are provided, one has been used for an analysis using the entire dataset of 14,002 confirmed cases (aggregated analysis) and another one has been used for the analysis using each time a subset of the population (14 subgroups defined by gender and age – disaggregated analysis). The following 14 gender–age group combinations were used: 1–4, 5–14, 15–24, 25–44, 45–64, 65–74, ≥75 years old, for both males and females. The analyses are implemented in R. Two codes are provided, one for the aggregated data analysis and one for the disaggregated data analysis. The working directory needs to be defined in the code (currently there is a placeholder named 'WORKING DIRECTORY'). The data are provided as .csv files ('totals.csv' for the aggregated analysis; 'merged_eu_wide.csv' and 'merged_eu.csv' for the disaggregated analysis) and can be read by the R code if placed in the same working directory. R packages that are required to be installed for the aggregated analysis are 'dlm' and 'strucchange' together with its dependencies, and for the disaggregated analysis the package 'epitools'. Also the script 'pests.R' needs to be downloaded from http://www.utdallas.edu/~pbrandt/code/pests.r and saved in the respective working directory, for ...