Host mobility formulations datasets

This dataset contains an archived version of the code and the raw and processed data used in Presigny et al. (2026) . It is made available for replication of the figures of the associated manuscript. The updated version of the code and the manual to treat these data is available here Github link. Please refer to the manuscript and its Supplementary material for details. Note that the mention allfield refers to the force-of-infection model. Data to compute the fixed rate of infected importation is available at the Italian Institute of Health. Repository structure for the data --- estimating_epsilon ├── data/ # raw and processed data to estimate the value of epsilon for italian systems Italy_systems/ ├── raw_data/ # raw data to create and visualize the Italian mobilty networks ├── provinces/ # processed data for the Italian system at the province level (NUTS3) ├── regions/ # processed data for the Italian system at the region level (NUTS2) FigX/ ├── output/ #where the intermediate data are stored --- Description of the data --- estimating_epsilon Raw data comes from the Meta Data 4 Good Program which provides estimates ofthe mobility flow between local administrative areas at the NUTS3 level for Europeancountries (see Iyer S. et al. 2023 for a complete description fo the datasets). However, raw and aggregagted data cannot be publicly redistributed by the authors. Researchers may obtain access directly from Meta. Raw data for the estimation of the mobility flow based on colocation and the population considered are normally located in the directory colocation_map and population_raw, respectively. Data aggregated at the weekly levels are saved in the remaining directories. --- Italy_systems Description of the raw data directory: airport_provinces/regions.xlsx: Number of international arrivals in Italian airports in 2019. Data were manually retrieved from the "Dati-di-Traffico-2019.pdf" and associated with regions/provinces. Data from the ENAC (Italian civil aviation autority), the document is in italian. elenco_italy.xlsx/italy_cities/italy_provincies: Dictionnaries of basic information of admnistrative units in Italy at different level. Data are freely available here (in italian). Cuebiq data: Publicly available in Pepe et al. (2020). Map of italy Geojson: The map of Italy is freely availbale here. It is released by the ISTAT (italian national institute of statistics) under the CC-BY license. Worldcities.csv: Data containing the population at the city level from Simplemaps.com . Raw data cannot be publicly redistributed by the authors. However, subsequent processed data are redistributed for reproducibility purposes. Desription of the variables in the generated Italian networks .dat (in python dictionnary)-: Variables Description distance_matrix distances between each patch in the system (in km) population_distribution human population in each patch position GPS coordinates of each patch (WGS84) name dictionnary that associate the index of patch (keys) to their name mobility_matrix mobility between every patch in the system airport_distribution relative weight of the incoming airline traffic associated with each patch Output In every Fig directory, it contains the numerical experiemnts and intermediate datasets to create the Figures of the manuscript. Deterministic and Monte-Carlo simulations are saved as instances of the models class as coded in src/models/. Except for synthetic data the output directory is systematically divided into a regions and provinces directory: epsilon_p0_...: contains the deterministic simulations with variables epsilon and p0. If airport, airports in the system are considered as infection reservoir with a non zero daily importation rate. attack_epsilon_p0_...: contains the computed attack rates from the simulations. assembled_R0_... : contains the assembled R0 per mobility network. R0_numerical_epsilon_p0...: contains the computed R0 in fucntion of espilon and p0. parameter_MC_simu...: contains the Monte-Carlo simulations to make Figures 6-7 of the manuscript.