INIKA - WP4: Data Analysis

Preface

The cover picture of this book was generated by Gemini, Large language model. Google 2024.

WP4 of INIKA Project

Work package leader for WP4: - Dr. Eve Fiskebeck (NVI) - Dr. Elpidius Rukambile (TVLA)

Project leader: - Prof Madelaine Norström

In the present document, we only will present the part for data analysis of WP4.

Objectives (Pasted from the Project description)

• T4.1. Merge and assess the quality of data collected through WP1 to WP3. D.4.1 A clean data “set” for further analyses in T.4.2-T.4.4.

• T4.2.Determine possible associations between drivers such as AMU/attitudes and the phenotypic resistance of bacteria within and between sectors using logistic regression models. Possible differences in AMR and AMU according to geographical area and season will also be assessed. D.4.2 Peer review scientific publications and/or proceedings.

• T4.3 Assess the patterns of AMR determinants (i.e. genes, transferable plasmids) in the three sectors using statistical modelling. The number of isolates obtained from each source (human, animal and environment) and the number of isolates where the WGS analyses have been performed determines what will be included in the statistical analyses. D.4.3 Peer review scientific publications and/or proceedings.

• T4.4 Comparative genomics of bacterial isolates from different sources and sectors, and use a “OH” approach to elucidate associations between reservoirs. D.4.2 Per review scientific publications and/or proceedings.

Tasks - Working plan (& Status)

T4.1

• File: data_description/data_information.qmd. Short presentation of the datasets sources. Including notes of collections that are important to consider for analyses

• File: data_description/data_wrangling.qmd.

  • Dataset quality check (individually) (might want to consider splitting in subparts one per dataset)
  • Dataset merging & quality check (globally)

• File: data_description/data_description.qmd.

  • Basic statistics merged dataset (getting an overview of what we have currently to be able to discuss)

• Discussion together :

  • further check?
  • correction of datasets ?
  • do we know what we can obtain from the dataset ?

T4.2

• Eve Refreshing stats : Logistic regression (in parallel maybe course material)

Determine possible associations between drivers such as AMU/attitudes and the phenotypic resistance of bacteria within and between sectors using logistic regression models. Possible differences in AMR and AMU according to geographical area and season will also be assessed. D.4.2 Peer review scientific publications and/or proceedings.

T4.3

T4.4