Tuberculosis dynamics using synthetic datasets

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an on-line supplement for

A framework for network-based epidemiological modeling of tuberculosis dynamics using synthetic datasets

[ Denise Kirschner's Homepage | Kirschner Lab Homepage ]

Marissa Renardy, and Denise Kirschner, A framework for network-based epidemiological modeling of tuberculosis dynamics using synthetic datasets, Submitted to the Bulletin of Mathematical Biology, 2020, DOI: (Pending), PMID: (pending), PMCID: (pending)

Graphical representations of the predicted age-based mixing matrix for the US from Prem et al. (2017) (left) and the age-based mixing matrix based on our synthetic population of Washtenaw County (right). This mixing matrix was computed using contact weights of 0.5 for workplace and school contacts and 0.1 for casual contacts.

* LHS_contactweights.m
* advance_in_time.m
* agent.m
* define_params_contactweightLHS.m
* determine_contacts.m
* endogenous_reactivation.m
* initialize_agents.m
* main_nonspatial_SEI.m
* new_exposures.m
* progress_from_exposed.m
* progress_from_secondary_exposure.m
* readdata.m
* recover_from_infected.m
* storedata_network.m
* synthetic-populations/Washtenaw-county-MI/

TB-data.zip