<div>Public health policies increasingly rely on complex models that need to approximate epidemics realistically and be consistent with the available data. Choosing appropriate simplifying assumptions is one of the critical challenges in disease modeling. In this thesis, we focus on some of these assumptions to show how they impact model outcomes. </div><div>In this thesis, an ODE model with a gamma-distributed infectious period is studied and compared with an exponentially distributed infectious period. We show that, for childhood diseases, isolating infected children is a possible mechanism causing oscillatory behavior in incidence. This is shown analytically by identifying a Hopf bifurcation with the isolation period as the bifurcation parameter. The threshold value for isolation to generate sustained oscillations from the model with gamma-distributed isolation period is much more realistic than the exponentially distributed model.</div><div><br></div><div>The consequences of not modeling the spectrum of clinical symptoms of the 2014 Ebola outbreak in Liberia include overestimating the basic reproduction number and effectiveness of control measures. The outcome of this model is compared with those of models with typical symptoms, excluding moderate ones. Our model captures the dynamics of the recent outbreak of Ebola in Liberia better, and the basic reproduction number is more consistent with the WHO response team's estimate. Additionally, the model with only typical symptoms overestimates the basic reproduction number and effectiveness of control measures and exaggerates changes in peak size attributable to interventions' timing.</div><div><br></div>

We used tweets to collect public discussion on organizations' activities during the specified Covid19 period. Through topic modeling, we were able to establish discussed topics in line with the organization's activities. Our research majored on tweets with matching hashtags W.H.O (world health organization) and coronavirus, covid19 or covid. We extracted five latent topics and explored the distribution or evolution of those topics over time. We were able to find people's opinions on hot topics (the period when a topic is mainly discussed); the hot topics reflect activities on the timeline of W.H.O during the specified period of the Pandemic. Our results show that the key topics are identified and characterized by specific events that happened during the specified period in our data. Our result describes the events that happened on the timeline of the W.H.O, showing the public opinion on each period a discussion is hot. It also shows how people's opinions revolve during the period. Our results will be helpful in identifying public sentiment on events, how people's opinion varies, and can also help understand different events of the organization based on the aim and objective of the event.