Author ORCID Identifier

Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)


Public Health

First Advisor

Gerardo Chowell-Puente

Second Advisor

Alexander Kirpich

Third Advisor

Martin Meltzer


A growing number of public health officials rely on mathematical modeling to aid in making decisions, especially during outbreak responses. Math models simulate health phenomenon with equations and are useful for forecasting a disease’s progression in a population and evaluating the potential effects of interventions. We generated three models to aid practicing health officials with addressing real-world issues.

The first model estimates the impact of immunization strategies on RSV-associated lower respiratory tract infections (LRTIs) among infants <12 months. Users input RSV burden and seasonality and examine the influence of altering product efficacy and uptake assumptions. We used the model to evaluate anticipated immunization products among a US birth cohort. We estimated without immunization, 339,650 – 475,980 LRTIs are attended annually in outpatient clinics, 126,070 – 168,510 in emergency departments (EDs), and 24,760 – 42,900 in hospitals. A passive antibody candidate given to all infants prevented the most LRTIs: 48% of outpatient visits without immunization, 51% of ED visits, and 55% of hospitalizations.

Our second model creates projections of healthcare demand during the early phase of the COVID19 pandemic and evaluates the impacts of social-distancing interventions. Users input case counts, healthcare resources, and select intervention strategies. Using data from Chile, we illustrated the tool as the pandemic unfolded there in April 2020. Our scenarios indicated COVID19 patients could overwhelm hospitals by June 2020, peaking in July or August at more than 6 times the current supply of beds and ventilators. A lockdown strategy or combination of case isolation, home-quarantine, social distancing individuals >70 years, and telework interventions could keep treatment demand below capacity.

Our third model estimated the impact of COVID19 case investigation and contact tracing programs (CICT) in the US. By inputting CICT program data from 23 jurisdictions into our model we estimated CICT averted between 1.1 to 1.4 million cases over 60 days during the pandemic’s first winter peak. Our upper estimate assumes all interviewed cases and monitored contacts complied with isolation and quarantine guidelines, while the lower estimate assumes fractions of interviewed cases and contacts did so. These results suggest CICT programs played a critical role in curtailing the pandemic.


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