Computing and Information Systems

Sarcoptes scabiei is a skin pathogen that causes substantial health burdens in humans. An estimated 455 million people are affected by scabies annually, resulting in an estimate of 3.8 million disability-adjusted life years annually. Scratching from scabies can result in further bacterial skin infections including Group A Streptococcus (GAS) infections which increases the burden of scabies. GAS infections can lead to severe health conditions such as acute rheumatic fever and rheumatic heart disease. Each year, around 18 million people worldwide suffer from severe GAS-related diseases, resulting in 500,000 deaths. Sarcoptes scabiei and Group A Streptococcus are endemic in many underprivileged populations such as Indigenous communities in Australia. A number of factors are likely to play a role in the high burden of skin pathogens in these settings including heterogeneities in the pathogen population (pathogens having multiple strains with varying characteristics) and host population (populations with varying disease prevalence and transmission rate). While these factors make it difficult to manage disease burden, computational models can help us to understand transmission mechanisms as well as control health burden. In this thesis, I focus on Sarcoptes scabiei and GAS and aim to understand the underlying transmission mechanisms of these skin pathogens and to provide insights into the efficacy of community-specific control strategies to reduce the disease burden using computational modelling. I focus on three key research questions in which I investigate the impact of pathogen and host heterogeneities on disease transmission and identify effective control strategies using computational models. Controlling the spread of pathogens with multiple strains can be challenging due to the strain interactions. It is uncertain how strain interactions play a role in the persistence of high strain diversity in endemic settings and what that implied for future interventions. As my first research question, I focused on “What role do within-host dynamics play in maintaining high diversity of pathogen strains?”. I developed an individual-based model with a synthetic population representing the characteristics of Indigenous communities in Australia. I discovered that within-host competition among strains can impact epidemiological dynamics. My findings revealed that within-host and between-host competition reduces strain diversity when operating independently. However, when they function together, they could significantly increase the diversity of strains. My model suggested that an intervention that reduces the transmission of all the strains had the potential to later increase the level of pathogen diversity, complicating the efficacy of further interventions. In addition, I discussed how this modelling framework can be adapted to investigate the impact of GAS strain interactions on population-level dynamics. To apply mass drug administrations in the areas that need them the most, it is essential to estimate the prevalence of scabies at the community level. Currently, there is no standardisation of approaches to estimate scabies prevalence. Given that prevalence and transmission mechanisms differ among communities, there is a need to thoroughly understand how sampling procedures aiming to asses scabies prevalence interplay with epidemiology. As my second research question, I focused on “Which sampling strategies - individual, household, or school-based - are most effective for estimating the prevalence of scabies in a population?”. I developed another computational model and explored the effectiveness of sampling methods to estimate prevalence of scabies in remote Indigenous communities in Australia. I found that when there is an underlying household-specific heterogeneity in scabies prevalence, the household sampling approach introduces more variance than simple random sampling. I concluded that while the simple random sampling approach seems to be more effective than other sampling methods in estimating scabies prevalence, the efficacy of surveillance strategies depends on how prevalence is distributed within the community. In addition, I built a table for use of future surveillance studies to estimate the sampling percentage based on population size, an accuracy threshold and a priori knowledge of scabies prevalence. To reduce scabies burden in communities with high endemic levels of scabies, three to five annual mass drug administration rounds are recommended by the experts convened by the World Health Organization. Because current guidelines are only based on expert opinions, WHO recommends quantitative evaluations to assess the likely efficacy of MDA recommendations. As my third research question, I focused on “Which mass drug administration strategy is most effective for controlling scabies?”. I developed an individual-based model to evaluate the efficacy of mass drug administration strategies in decreasing burden of scabies in Liberia. I found that while MDAs can be effective in the short and medium term, prevalence will rise over longer time periods until it reaches pre-MDA levels. The modelling results also indicated that low level of scabies prevalence can be sustained long-term when MDAs are combined with behavioral and systemic changes, such as improvements in education and access to the health care system, that shorten the time involved in effective scabies treatment. In this thesis, I conclude that understanding the complex dynamics of skin pathogens remains a challenging problem because of the heterogeneities in host and pathogen populations. While this thesis provides practical results for controlling skin pathogens, it also highlights the need for developing pathogen-specific and community-specific models to reduce the burden of skin pathogens.

Digital service platforms are increasingly important in the economy, given that almost any economic activity can occur online. However, the role and impact of service orchestration, that is, the coordination and facilitation of effective and efficient value cocreating interactions between interdependent economic actors, on digital service platforms is poorly understood. This is problematic because it reveals a limited understanding of effective orchestration mechanisms for facilitating value cocreation; thus, hindering the future development of digital service platforms. This thesis aims to address this gap in knowledge through a multi-study research design. Studies 1 and 2 reconceptualize digital service platforms as complex adaptive systems; therefore, introducing and explaining their currently uninvestigated systemic elements and properties (Chapters 2 and 3). Study 3 introduces agent-based modelling (ABM) as a research method to study digital service platforms as complex adaptive systems (Chapter 4). In particular, this study delineates guidelines for crafting ABM simulations of digital service platforms and demonstrates their application for the peer-to-peer (P2P) lending context. Lastly, Study 4 empirically examines the role and impact of service orchestration on digital service platforms. It defines a typology of orchestration mechanisms displayed by P2P lending platforms and provides insights on the impact of these orchestration mechanisms on actors and on platform performance (Chapter 5). Overall, this thesis offers a broadened theoretical understanding of digital service platforms by providing clearer meaning and scope to a concept that has been theoretically fragmented; therefore, enabling a more comprehensive exploration of their systemic properties and dynamics. This thesis also contributes methodologically by introducing ABMs and demonstrating their applicability for the study of value cocreation in digital service platforms. Lastly, this thesis offers conceptual and empirical insights on the role and impact of service orchestration in P2P lending platforms, which has implications relevant for digital service platforms more broadly.

Computing and Information Systems - Theses

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