Within Host and Multiscale Models of Usutu and SARS-CoV-2 Viral Infections with Animal Hosts

The last five years have shown us the profound impact that SARS-CoV-2 pandemic has had on human kind and made us aware of the dangers that emerging pathogens can present. The goal of this dissertation is to use mathematical models in connection with data to uncover mechanistic interactions governing viral infections. To acquire a holistic understanding of the impact of viral infections, it is necessary to develop mathematical techniques and models that bridge knowledge on multiple biological scales. This dissertation explores the relationship between within-host virus dynamics, the environment and the between-host viral transmission. We will validate the models against data from SARS-CoV-2 infections, and data from infections with an emerging pathogen, the Usutu virus. Our models of SARS-CoV-2 infection looked at the relationship between infectious virus and viral RNA in the body and in the environment. Using golden hamster data and within-host mathematical models, we determined that infectious virus shedding early in infection correlates with transmission events, shedding of infectious virus diminishes late in the infection, and high viral RNA levels late in the infection are a poor indicator of transmission. We further showed that viral infectiousness increases in a density dependent manner with viral RNA and that their relative ratio is time-dependent. Such information is useful for designing interventions. Our models of Usutu virus infection looked at differences between different virus strains during bird infections. Within-host models applied to data showed heterogeneity in viral strain dynamics, and correlated high basic reproductive number with short infected cell lifespan (indicative of immune responses) and correlated low basic reproductive number with low viral peaks and longer lasting viremia (due to lower infection rates and high infected cell lifespan). We expanded the models to investigate multiscale dynamics connecting within-host scale, bird-to-vector transmission scale, and vector-borne epidemiological scale. One important direction of this dissertation is the investigation of uncertainty in parameter estimation and overall model identifiability. We conducted identifiability studies (using several theoretical tools) in the multiscale models of Usutu virus infection and in several within-host influenza models. Model identifiability is critical to the reproducibility of modeling results in any biological systems. In this dissertation, we will show how insights from such analyses inform both modeling practices and experimental design.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Mathematics

Department dc:contributor.department

Mathematics

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Heitzman-Breen, Nora Grace

Chair dc:contributor.committeechair

• Ciupe, Mihaela Stanca

Committee members dc:contributor.committeemember

• Tuncer, Necibe
• Childs, Lauren Maressa
• Saucedo, Omar
• Duggal, Nisha K.

• Mathematical model
• flavivirus
• coronavirus
• data-fitting
• identifiability

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:39919

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/118596