In this dissertation the potential impact of some social, cultural and economic factors on

Ebola Virus Disease (EVD) dynamics and control are studied. In Chapter two, the inability

to detect and isolate a large fraction of EVD-infected individuals before symptoms onset is

addressed. A mathematical model, calibrated with data from the 2014 West African outbreak,

is used to show the dynamics of EVD control under various quarantine and isolation

effectiveness regimes. It is shown that in order to make a difference it must reach a high

proportion of the infected population. The effect of EVD-dead bodies has been incorporated

in the quarantine effectiveness. In Chapter four, the potential impact of differential

risk is assessed. A two-patch model without explicitly incorporate quarantine is used to

assess the impact of mobility on communities at risk of EVD. It is shown that the

overall EVD burden may lessen when mobility in this artificial high-low risk society is allowed.

The cost that individuals in the low-risk patch must pay, as measured by secondary

cases is highlighted. In Chapter five a model explicitly incorporating patch-specific quarantine

levels is used to show that quarantine a large enough proportion of the population

under effective isolation leads to a measurable reduction of secondary cases in the presence

of mobility. It is shown that sharing limited resources can improve the effectiveness of

EVD effective control in the two-patch high-low risk system. Identifying the conditions

under which the low-risk community would be willing to accept the increases in EVD risk,

needed to reduce the total number of secondary cases in a community composed of two

patches with highly differentiated risks has not been addressed. In summary, this dissertation

looks at EVD dynamics within an idealized highly polarized world where resources

are primarily in the hands of a low-risk community – a community of lower density, higher

levels of education and reasonable health services – that shares a “border” with a high-risk

community that lacks minimal resources to survive an EVD outbreak.

Background
The transmission dynamics of Tuberculosis (TB) involve complex epidemiological and socio-economical interactions between individuals living in highly distinct regional conditions. The level of exogenous reinfection and first time infection rates within high-incidence settings may influence the impact of control programs on TB prevalence. The impact that effective population size and the distribution of individuals’ residence times in different patches have on TB transmission and control are studied using selected scenarios where risk is defined by the estimated or perceive first time infection and/or exogenous re-infection rates.
Methods
This study aims at enhancing the understanding of TB dynamics, within simplified, two patch, risk-defined environments, in the presence of short term mobility and variations in reinfection and infection rates via a mathematical model. The modeling framework captures the role of individuals’ ‘daily’ dynamics within and between places of residency, work or business via the average proportion of time spent in residence and as visitors to TB-risk environments (patches). As a result, the effective population size of Patch i (home of i-residents) at time t must account for visitors and residents of Patch i, at time t.
Results
The study identifies critical social behaviors mechanisms that can facilitate or eliminate TB infection in vulnerable populations. The results suggest that short-term mobility between heterogeneous patches contributes to significant overall increases in TB prevalence when risk is considered only in terms of direct new infection transmission, compared to the effect of exogenous reinfection. Although, the role of exogenous reinfection increases the risk that come from large movement of individuals, due to catastrophes or conflict, to TB-free areas.
Conclusions
The study highlights that allowing infected individuals to move from high to low TB prevalence areas (for example via the sharing of treatment and isolation facilities) may lead to a reduction in the total TB prevalence in the overall population. The higher the population size heterogeneity between distinct risk patches, the larger the benefit (low overall prevalence) under the same “traveling” patterns. Policies need to account for population specific factors (such as risks that are inherent with high levels of migration, local and regional mobility patterns, and first time infection rates) in order to be long lasting, effective and results in low number of drug resistant cases.