Author ORCID Identifier

Date of Award

Spring 5-13-2022

Degree Type


Degree Name

Doctor of Philosophy (PhD)


Public Health

First Advisor

Dr. Gerardo Chowell

Second Advisor

Dr. Ruiyan Luo

Third Advisor

Dr. Richard Rothenberg

Fourth Advisor

Dr. Isaac Chun-Hai Fung

Fifth Advisor

Dr. Alexander Kirpich


Emerging and re-emerging infectious diseases present one of the most important health and security risks to humanity. Mathematical models and statistical and simulation approaches can be useful tools to assess the disease transmission dynamics and forecast the epidemic trends in near real time to inform the public health policies.

We employ mathematical and statistical methods to assess transmission dynamics and forecast the trajectory of epidemics in context of the COVID-19 pandemic in four Latin American countries. Our first investigation utilizes time-series case incidence data from Peru modeled by the generalized growth model to short-term forecast the pandemic trajectory and estimate the reproduction number. The second study employs a generalized logistic growth model along with the generalized growth model to assess the transmission dynamics and effectiveness of control interventions in Chile. The third and fourth study employs two additional phenomenological growth models; the Richards growth model, and the sub-epidemic wave model to reveal the unfolding of the COVID-19 pandemic in Mexico and Colombia. These models are utilized to short term forecast the epidemic trajectory, compare the forecasting performance across models as well as estimate the reproduction number using a renewal equation method. Simultaneously, genomic analysis and Cori et. al method are also employed to estimate the fluctuations in reproduction number throughout the pandemic.

Across the four studies, the results show that the sub-epidemic model outperforms the GLM and Richards growth model for short-term forecasting the epidemic trajectory capturing complex epidemic shapes. Moreover, the estimates of transmission potential indicate continued virus transmission during the early growth phase of the pandemic, exhibiting sub-exponential growth dynamics, and fluctuations in the reproduction number around 1.0 for the later part of the pandemic indicating the effectiveness of control interventions. Our findings indicate that phenomenological models are useful tools for short-term epidemic forecasting albeit predictions need to be interpreted with caution as the policy makers rely on the results inferred from these mathematical models for making key decisions about prevention and mitigation plans. The methodology presented in this dissertation provides a thorough guide for conducting model-based inferences and presenting the uncertainty associated with parameter estimation results.


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