Decision Trees for Dynamic Decision Making And System Dynamics Modelling Calibration and Expansion

Many practical problems raise the challenge of making decisions over time in the presence of both dynamic complexity and pronounced uncertainty regarding evolution of important factors that affect the dynamics of the system. In this thesis, we provide an end-to-end implementation of an easy-to-use system to confront such challenges. This system gives policy makers a new approach to take complementary advantage of decision analysis techniques and System Dynamics by allowing easy creation, evaluation, and interactive exploration of hybrid models. As an important application of this methodology, we extended a System Dynamic model within the context of West Nile virus transmission in Saskatchewan

Data Informed Health Simulation Modeling

Combining reliable data with dynamic models can enhance the understanding of health-related phenomena. Smartphone sensor data characterizing discrete states is often suitable for analysis with machine learning classifiers. For dynamic models with continuous states, high-velocity data also serves an important role in model parameterization and calibration. Particle filtering (PF), combined with dynamic models, can support accurate recurrent estimation of continuous system state. This thesis explored these and related ideas with several case studies. The first employed multivariate Hidden Markov models (HMMs) to identify smoking intervals, using time-series of smartphone-based sensor data. Findings demonstrated that multivariate HMMs can achieve notable accuracy in classifying smoking state, with performance being strongly elevated by appropriate data conditioning. Reflecting the advantages of dynamic simulation models, this thesis has contributed two applications of articulated dynamic models: An agent-based model (ABM) of smoking and E-Cigarette use and a hybrid multi-scale model of diabetes in pregnancy (DIP). The ABM of smoking and E-Cigarette use, informed by cross-sectional data, supports investigations of smoking behavior change in light of the influence of social networks and E-Cigarette use. The DIP model was evidenced by both longitudinal and cross-sectional data, and is notable for its use of interwoven ABM, system dynamics (SD), and discrete event simulation elements to explore the interaction of risk factors, coupled dynamics of glycemia regulation, and intervention tradeoffs to address the growing incidence of DIP in the Australia Capital Territory. The final study applied PF with an SD model of mosquito development to estimate the underlying Culex mosquito population using various direct observations, including time series of weather-related factors and mosquito trap counts. The results demonstrate the effectiveness of PF in regrounding the states and evolving model parameters based on incoming observations. Using PF in the context of automated model calibration allows optimization of the values of parameters to markedly reduce model discrepancy. Collectively, the thesis demonstrates how characteristics and availability of data can influence model structure and scope, how dynamic model structure directly affects the ways that data can be used, and how advanced analysis methods for calibration and filtering can enhance model accuracy and versatility

Spread and Control of Rift Valley Fever virus after accidental introduction in the Netherlands: a modelling study.

Rift Valley Fever (RVF) is a zoonotic vector-borne infection and causes a potentially severe disease in both humans and young animals. The Ministry of Economic Affairs, Agriculture and Innovation (EL&I) is interested in the risk of an outbreak of Rift Valley Fever virus (RVFV) for the Netherlands, and more knowledge is needed about the risk of introduction of the virus, the risk of spread (transmission) of the virus in the country once introduced, and the methods for control and surveillance. For this purpose, a mathematical model was developed to study (1) the probability of a RVF outbreak at different days of introduction during the year, (2) the probability of persistence of the infection during the entire year, and (3) outbreak size and duration at different days of introduction during the year

Understanding the survival of Zika virus in a vector interconnected sexual contact network

Citation: Ferdousi, T., Cohnstaedt, L. W., McVey, D. S., & Scoglio, C. M. (2019). Understanding the survival of Zika virus in a vector interconnected sexual contact network. Scientific Reports, 9(1), 7253. https://doi.org/10.1038/s41598-019-43651-3The recent outbreaks of the insect-vectored Zika virus have demonstrated its potential to be sexually transmitted, which complicates modeling and our understanding of disease dynamics. Autochthonous outbreaks in the US mainland may be a consequence of both modes of transmission, which affect the outbreak size, duration, and virus persistence. We propose a novel individual-based interconnected network model that incorporates both insect-vectored and sexual transmission of this pathogen. This model interconnects a homogeneous mosquito vector population with a heterogeneous human host contact network. The model incorporates the seasonal variation of mosquito abundance and characterizes host dynamics based on age group and gender in order to produce realistic projections. We use a sexual contact network which is generated on the basis of real world sexual behavior data. Our findings suggest that for a high relative transmissibility of asymptomatic hosts, Zika virus shows a high probability of sustaining in the human population for up to 3 months without the presence of mosquito vectors. Zika outbreaks are strongly affected by the large proportion of asymptomatic individuals and their relative transmissibility. The outbreak size is also affected by the time of the year when the pathogen is introduced. Although sexual transmission has a relatively low contribution in determining the epidemic size, it plays a role in sustaining the epidemic and creating potential endemic scenarios

Eco-evolutionary dynamics of disease under human-induced selection

More than ten thousand years ago, humans started breeding plants as food supply: they chose those varieties of nutritional interest, grew them, and kept the seeds of the best plants for the next season. These practices were the beginning of agriculture, a long-term evolutionary experiment where humans act as a selective force. Active breeding is not the only way in which humans modify evolutionary trajectories: they also change the environment where species live. For example, global trade creates novel species interactions, and the urbanisation of wild areas alters ecological niches. Another compelling case of human-induced selection – and the topic of interest in this thesis – is the control of pathogens. Pathogens are regarded as a threat for human species survival, either because they are causing diseases in humans or because they constitute a risk to food security. In consequence, humans have developed management practices which intend to reduce or eradicate the population of these pathogens by applying abiotic (e.g. drugs) or biotic (e.g. biocontrol with other species) pressures. These strategies, as they deal with populations of living organisms, involve ecological and evolutionary processes. Thus, to improve pathogen control, we need to apply the current knowledge and techniques of ecology and evolution. This thesis studies how pathogen populations are affected by the alternation of selective pressures to which they are exposed. Mainly, I study the dynamics of pathogen populations when host species are switched along time. The different reproductive rates of the pathogen in each host species can slow down the growth or diminish its population in the long-term. In agriculture, this can be achieved by using crop rotations in a field; in vector-borne diseases, the vector and the host are two different ecological niches for the pathogen, and the administration of drugs to the human host can be disadvantageous for pathogen reproduction in the vector. Using mathematical and computational models, I study host-pathogen interactions in infected crop fields and human populations affected by malaria. I simulate infections under multiple scenarios of selection in alternating host species and observe their progress or regression. The results are used to assess the optimality of human interventions for the control of the disease-causing pathogens. Overall, this thesis confirms that a better knowledge of eco-evolutionary principles in disease management can improve the design of strategies. This is especially true given the need for practices which are both efficient and sustainable across generations

Climate, Environmental and Socio-Economic Change: Weighing Up the Balance in Vector-Borne Disease Transmission

Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10-15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector-pathogen systems

Media coverage of the Zika crisis in Brazil: the construction of a 'war' frame that masked social and gender inequalities

Between 2015 and 2016, Zika became an epidemic of global concern and the focus of intense media coverage. Using a hybrid model of frame and social representations theory, we examine how the Zika outbreak was reported in two major newspapers in Brazil: O Globo and Folha de São Paulo. The analysis of 186 articles published between December 2015 and May 2016 reveals a dominant ‘war’ frame supported by two sub-frames: one focused on eradicating the vector (mosquito) and another on controlling microcephaly, placing the burden of prevention on women. Scientific uncertainties about the virus and its relationship to microcephaly coupled with political uncertainties in Brazil increased the power of the war frame. This frame gave prominence and legitimacy to certain representations of disease management during the crisis, masking social and gender inequalities. We show how the cartography of the disease overlaps with that of poverty and regional inequality in Brazil to argue that addressing socio-economic aspects is essential, but normally neglected, in media communications during disease outbreaks like Zika