spatially-connected-rt

https://github.com/covid-19-routes/spatially-connected-rt

Science Score: 67.0%

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CITATION.cff file
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Links to: zenodo.org
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Low similarity (6.6%) to scientific vocabulary

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Repository

Basic Info

Host: GitHub
Owner: COVID-19-routes
License: mit
Language: MATLAB
Default Branch: main
Homepage:
Size: 9.84 MB

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Watchers: 1
Forks: 2
Open Issues: 0
Releases: 1

Created over 3 years ago · Last pushed over 2 years ago

Metadata Files

Readme License Citation

Spatially explicit reproduction numbers from incidence and mobility data

This repository contains code and data described in

C. Trevisin, E. Bertuzzo, D. Pasetto, L. Mari, S. Miccoli, R. Casagrandi, M. Gatto, A. Rinaldo. Spatially explicit reproduction numbers from incidence and mobility data. Proc Natl Acad Sci USA, in press (2023).

Repository structure

The top level directory contains MATLAB® scripts for running all the analyses described in the above paper and generating the relevant figures:
- run_synthetic.m
- run_SMC.m

Please refer to the comments inside the scripts for more information.

The private directory contains the MATLAB® implementation of the models described in the paper along with auxiliary functions for generating the figures.
The data directory contains input data for the models and routines for data ingestion from the primary sources.
The results is an empty directory in which the driver scripts will store results and intermediate results.

Cross reference

The following table contains a cross reference between the figures in the paper and the figures produced by the driver scripts.

| Paper Figure # | script | parameters | window | |--------|-------------------|-------------------------------------|----------| | Fig. 1 | run_synthetic.m | | Figure 2 | | Fig. 2 | run_SMC.m | purpose = 'make_figure_incidence' | Figure 1 | | Fig. 3 | run_SMC.m | purpose = 'run_veneto' | Figure 1 | | Fig. 4 | run_SMC.m | purpose = 'run_veneto' | Figure 5 | | Fig. 5 | run_SMC.m | purpose = 'run_veneto' | Figure 4 | | Fig. 6 | run_SMC.m | purpose = 'run_veneto' | Figure 2 |

Owner

Name: The routes of COVID-19 in Italy
Login: COVID-19-routes
Kind: organization

Repositories: 1
Profile: https://github.com/COVID-19-routes

Citation (CITATION.cff)

cff-version: 1.2.0
title: >-
  Spatially explicit reproduction numbers from incidence and
  mobility data.
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
version: 0.9.0
date-released: 2023-04-26
doi: 10.5281/zenodo.7869249
authors:
  - given-names: Cristiano
    family-names: Trevisin
    orcid: 'https://orcid.org/0000-0002-1576-6397'
  - given-names: Enrico
    family-names: Bertuzzo
    orcid: 'https://orcid.org/0000-0001-5872-0666'
  - given-names: Damiano
    family-names: Pasetto
    orcid: 'https://orcid.org/0000-0001-6892-9826'
  - given-names: Lorenzo
    family-names: Mari
    orcid: 'https://orcid.org/0000-0003-1326-9992'
  - given-names: Stefano
    family-names: Miccoli
    orcid: 'https://orcid.org/0000-0002-7447-049X'
  - given-names: Renato
    family-names: Casagrandi
    orcid: 'https://orcid.org/0000-0001-5177-803X'
  - given-names: Marino
    family-names: Gatto
    orcid: 'https://orcid.org/0000-0001-8063-9178'
  - given-names: Andrea
    family-names: Rinaldo
    orcid: 'https://orcid.org/0000-0002-2546-9548'
repository-code: 'https://github.com/COVID-19-routes/Spatially-Connected-Rt'
abstract: >-
  Current methods for near real-time estimation of effective
  reproduction numbers from surveillance data overlook
  mobility fluxes of infectors and susceptible individuals
  within a spatially connected network (the
  metapopulation).

  Exchanges of infections among different communities may
  thus be misrepresented unless explicitly measured and
  accounted for in the renewal equations.

  Here, we introduce the spatially-explicit effective
  reproduction numbers, Rk(t), in an arbitrary community k.

  The present code is a tool to estimate, in a Bayesian
  framework involving particle filtering, the values of
  Rk(t) maximizing a suitable likelihood function
  reproducing observed patterns of infections in space and
  time.
keywords:
  - infection spreading mechanisms
  - human mobility
  - disease generation interval
  - particle filtering
  - COVID-19
license: MIT

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