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The serial interval of an infectious disease is the time between illness onset in an infector-infectee pair. This repository contains routines to estimate features of the serial interval based on transmission pair data. The approach is entirely data-driven and relies on a resampling technique (bootstrap).

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Nonparametric serial interval estimation

Oswaldo Gressani

Reproducibility instructions

This repository contains all the routines required to reproduce the results in the preprint entitled “Nonparametric serial interval estimation” by Oswaldo Gressani and Niel Hens available on MedRxiv https://doi.org/10.1101/2024.10.16.24315600.

This repository is organized in two main folders (01-Simulations and 02-RealData).

  • 01-Simulations can be used to recover the results from the simulation study.

  • 02-RealData can be used to recover results related to real data applications.

Readers and users are encouraged to reproduce the results contained in this folder. EpiLPS (v1.4.0) will be needed and can be installed from the GitHub repository https://github.com/oswaldogressani/EpiLPS.

Website

https://epilps.com/

Repository version

This is version 0.0.1 (2024-10-14) - “SILPS-V1”.

License

Copyright © Oswaldo Gressani. All rights reserved.

Funding statement

This project was supported by the VERDI project (101045989) and the ESCAPE project (101095619), funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Health and Digital Executive Agency (HADEA). Neither the European Union nor the granting authority can be held responsible for them. This research project is also supported by BE-PIN (contract nr. TD/231/BE-PIN) funded by BELSPO (Belgian Science Policy Office) as part of the POST-COVID programme.

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The serial interval of an infectious disease is the time between illness onset in an infector-infectee pair. This repository contains routines to estimate features of the serial interval based on transmission pair data. The approach is entirely data-driven and relies on a resampling technique (bootstrap).

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