Mathematical modeling and optimal control of malaria

Malaria continues to infect millions and kill hundreds of thousands of people worldwide each year, despite over a century of research and attempts to control and eliminate this infectious disease. Challenges such as the development and spread of drug resistant malaria parasites, insecticide resistance to mosquitoes, climate change, the presence of individuals with subpatent malaria infections which normally are asymptomatic and behavioral plasticity in the mosquito hinder the prospects of malaria control and elimination. In this thesis, mathematical models of malaria transmission and control that address the role of drug resistance, immunity, iron supplementation and anemia, immigration and visitation, and the presence of asymptomatic carriers in malaria transmission are developed. A within-host mathematical model of severe Plasmodium falciparum malaria is also developed. First, a deterministic mathematical model for transmission of antimalarial drug resistance parasites with superinfection is developed and analyzed. The possibility of increase in the risk of superinfection due to iron supplementation and fortification in malaria endemic areas is discussed. The model results calls upon stakeholders to weigh the pros and cons of iron supplementation to individuals living in malaria endemic regions. Second, a deterministic model of transmission of drug resistant malaria parasites, including the inflow of infective immigrants, is presented and analyzed. The optimal control theory is applied to this model to study the impact of various malaria and vector control strategies, such as screening of immigrants, treatment of drug-sensitive infections, treatment of drug-resistant infections, and the use of insecticide-treated bed nets and indoor spraying of mosquitoes. The results of the model emphasize the importance of using a combination of all four controls tools for effective malaria intervention. Next, a two-age-class mathematical model for malaria transmission with asymptomatic carriers is developed and analyzed. In development of this model, four possible control measures are analyzed: the use of long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic, and screening and treatment of asymptomatic individuals. The numerical results show that a disease-free equilibrium can be attained if all four control measures are used. A common pitfall for most epidemiological models is the absence of real data; model-based conclusions have to be drawn based on uncertain parameter values. In this thesis, an approach to study the robustness of optimal control solutions under such parameter uncertainty is presented. Numerical analysis of the optimal control problem in the presence of parameter uncertainty demonstrate the robustness of the optimal control approach that: when a comprehensive control strategy is used the main conclusions of the optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the design of cost-effective strategies for disease control with multiple interventions, even under considerable uncertainty of model parameters. Finally, a separate work modeling the within-host Plasmodium falciparum infection in humans is presented. The developed model allows re-infection of already-infected red blood cells. The model hypothesizes that in severe malaria due to parasite quest for survival and rapid multiplication, the Plasmodium falciparum can be absorbed in the already-infected red blood cells which accelerates the rupture rate and consequently cause anemia. Analysis of the model and parameter identifiability using Markov chain Monte Carlo methods is presented.

Family ownership – a source of sustainable success in listed companies

Extant research on exchange-listed firms has acknowledged that the concentration of ownership and the identity of owners make a difference. In addition, studies indicate that firms with a dominant owner outperform firms with dispersed ownership. During the last few years, scholars have identified one group of owners, in particular, whose ownership stake in publicly listed firm is positively related to performance: the business family. While acknowledging that family firms represent a unique organizational form, scholars have identified various concepts and theories in order to understand how the family influences organizational processes and firm performance. Despite multitude of research, scholars have not been able to present clear results on how firm performance is actually impacted by the family. In other words, studies comparing the performance of listed family and other types of firms have remained descriptive in nature since they lack empirical data and confirmation from the family business representatives. What seems to be missing is a convincing theory that links the involvement and behavioral consequences. Accordingly, scholars have not yet come to a mutual understanding of what precisely constitutes a family business. The variety of different definitions and theories has made comparability of different results difficult for instance. These two issues have hampered the development of a rigorous theory of family business. The overall objective of this study is to describe and understand how the family as a dominant owner can enhance firm performance, and can act a source of sustainable success in listed companies. In more detail, in order to develop understanding of the unique factors that can act as competitive advantages for listed family firms, this study is based on a qualitative approach and aims at theory development, not theory verification. The data in this study consist of 16 thematic interviews with CEOs, members of the board, supervisory board chairs, and founders of Finnish listed-family firms. The study consists of two parts. The first part introduces the research topic, research paradigm, methods, and publications, and also discusses the overall outcomes and contributions of the publications. The second part consists of four publications that address the research questions from different viewpoints. The analyses of this study indicate that family ownership in listed companies represents a structure that differs from the traditional views of agency and stewardship, as well as from resource-based and stakeholder views. As opposed to these theories and shareholder capitalism which consider humans as individualistic, opportunistic, and self-serving, and assume that the behaviors of an investor are based on the incentives and motivations to maximize private profits, the family owners form a collective social unit that is motivated to act together toward their mutual purpose or benefit. In addition, socio-emotional and psychological elements of ownership define the family members as owners, rather than the legal and financial dimensions of ownership. That is, collective psychological ownership of family over the business (F-CPO) can be seen as a construct that comprehensively captures the fusion between the family and the business. Moreover, it captures the realized, rather than merely potential, family influence on and interaction with the business, and thereby brings more theoretical clarity of the nature of the fusion between the family and the business, and offers a solution to the problem of family business definition. This doctoral dissertation provides academics, policy-makers, family business practitioners, and the society at large with many implications considering family and business relationships.

Visual analytics for behavioral and niche market segmentation

Companies require information in order to gain an improved understanding of their customers. Data concerning customers, their interests and behavior are collected through different loyalty programs. The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how to get to know your customers, Big Data, information visualization, and data warehousing. In this Ph.D. thesis, the Self-Organizing Map and two extensions of it – the Weighted Self-Organizing Map (WSOM) and the Self-Organizing Time Map (SOTM) – are used as data mining methods for extracting information from large amounts of customer data. The thesis focuses on how data mining methods can be used to model and analyze customer data in order to gain an overview of the customer base, as well as, for analyzing niche-markets. The thesis uses real world customer data to create models for customer profiling. Evaluation of the built models is performed by CRM experts from the retailing industry. The experts considered the information gained with help of the models to be valuable and useful for decision making and for making strategic planning for the future.