Date of Award

January 2021

Document Type

Open Access Thesis

Degree Name

Master of Public Health (MPH)

Department

School of Public Health

First Advisor

Robert Heimer

Abstract

Since the emergence of the COVID-19 pandemic in late 2019, several researchers and decision-makers have turned to statistical modeling to inform predictions of the severity of the burden of infection and disease in various settings. While it is understood that no model can perfectly describe the transmission of a novel and highly transmissible pathogen, there still lies the potential for models to be useful tools for local and international decision makers to act proactively to mitigate the burden of disease. In Syria, these challenges are compounded by humanitarian crises resulting from the ongoing civil war in the region. This conflict has led to significant decreases in health system capacity and public health data collection, and has increased the number of internally displaced populations in the country, who are particularly at risk of severe COVID-19-related morbidity and mortality. These factors make ensuring the reliability of transmission dynamic models more challenging, as models often rely on observed data for the estimation of key model parameters (e.g., force of infection, mortality rates) that describe factors contributing to the transmission of SARS-CoV-2. In response, researchers intending to apply modelling techniques to settings hosting active humanitarian crises should tailor models to rely upon a smaller number of parameters for which reasonably plausible estimates can be obtained, and leverage the ability of models to simulate various scenarios (e.g., efficacy of interventions, infectiousness of variants) to inform decision-making in these regions.

Comments

This is an Open Access Thesis.

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