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- All Subjects: Influenza
- Creators: Castillo-Chavez, Carlos
- Creators: Favre, Brandon Chetan
- Creators: Black, Andy
Description
Mathematical modeling of infectious diseases can help public health officials to make decisions related to the mitigation of epidemic outbreaks. However, over or under estimations of the morbidity of any infectious disease can be problematic. Therefore, public health officials can always make use of better models to study the potential implication of their decisions and strategies prior to their implementation. Previous work focuses on the mechanisms underlying the different epidemic waves observed in Mexico during the novel swine origin influenza H1N1 pandemic of 2009 and showed extensions of classical models in epidemiology by adding temporal variations in different parameters that are likely to change during the time course of an epidemic, such as, the influence of media, social distancing, school closures, and how vaccination policies may affect different aspects of the dynamics of an epidemic. This current work further examines the influence of different factors considering the randomness of events by adding stochastic processes to meta-population models. I present three different approaches to compare different stochastic methods by considering discrete and continuous time. For the continuous time stochastic modeling approach I consider the continuous-time Markov chain process using forward Kolmogorov equations, for the discrete time stochastic modeling I consider stochastic differential equations using Wiener's increment and Poisson point increments, and also I consider the discrete-time Markov chain process. These first two stochastic modeling approaches will be presented in a one city and two city epidemic models using, as a base, our deterministic model. The last one will be discussed briefly on a one city SIS and SIR-type model.
ContributorsCruz-Aponte, Maytee (Author) / Wirkus, Stephen A. (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Camacho, Erika T. (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
Influenza is a deadly disease for which effective vaccines are sorely lacking. This is largely due to the phenomena of antigenic shift and drift in the influenza virus's surface proteins, hemagglutinin (HA) and neuraminidase (NA). The ectodomain of the matrix 2 protein (M2e) of influenza A, however, has demonstrated high levels of conservation. On its own it is poorly immunogenic and offers little protection against influenza infections, but by combining it with a potent adjuvant, this limitation may be overcome. Recombinant immune complexes, or antigens fused to antibodies that have been engineered to form incredibly immunogenic complexes with one another, were previously shown to be useful, immunogenic platforms for the presentation of various antigens and could provide the boost in immunogenicity that M2e needs to become a powerful universal influenza A vaccine. In this thesis, genetic constructs containing geminiviral replication proteins and coding for a consensus sequence of dimeric M2e fused to antibodies featuring complimentary epitopes and epitope tags were generated and used to transform Agrobacterium tumefaciens. The transformed bacteria was then used to cause Nicotiana benthamiana to transiently express M2e-RICs at very high levels, with enough RICs being gathered to evaluate their potency in future mouse trials. Future directions and areas for further research are discussed.
ContributorsFavre, Brandon Chetan (Author) / Mason, Hugh (Thesis director) / Mor, Tsafrir (Committee member) / Diamos, Andrew (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The 2009-10 influenza and the 2014-15 Ebola pandemics brought once again urgency to an old question: What are the limits on prediction and what can be proposed that is useful in the face of an epidemic outbreak?
This thesis looks first at the impact that limited access to vaccine stockpiles may have on a single influenza outbreak. The purpose is to highlight the challenges faced by populations embedded in inadequate health systems and to identify and assess ways of ameliorating the impact of resource limitations on public health policy.
Age-specific per capita constraint rates play an important role on the dynamics of communicable diseases and, influenza is, of course, no exception. Yet the challenges associated with estimating age-specific contact rates have not been decisively met. And so, this thesis attempts to connect contact theory with age-specific contact data in the context of influenza outbreaks in practical ways. In mathematical epidemiology, proportionate mixing is used as the preferred theoretical mixing structure and so, the frame of discussion of this dissertation follows this specific theoretical framework. The questions that drive this dissertation, in the context of influenza dynamics, proportionate mixing, and control, are:
I. What is the role of age-aggregation on the dynamics of a single outbreak? Or simply speaking, does the number and length of the age-classes used to model a population make a significant difference on quantitative predictions?
II. What would the age-specific optimal influenza vaccination policies be? Or, what are the age-specific vaccination policies needed to control an outbreak in the presence of limited or unlimited vaccine stockpiles?
Intertwined with the above questions are issues of resilience and uncertainty including, whether or not data collected on mixing (by social scientists) can be used effectively to address both questions in the context of influenza and proportionate mixing. The objective is to provide answers to these questions by assessing the role of aggregation (number and length of age classes) and model robustness (does the aggregation scheme selected makes a difference on influenza dynamics and control) via comparisons between purely data-driven model and proportionate mixing models.
This thesis looks first at the impact that limited access to vaccine stockpiles may have on a single influenza outbreak. The purpose is to highlight the challenges faced by populations embedded in inadequate health systems and to identify and assess ways of ameliorating the impact of resource limitations on public health policy.
Age-specific per capita constraint rates play an important role on the dynamics of communicable diseases and, influenza is, of course, no exception. Yet the challenges associated with estimating age-specific contact rates have not been decisively met. And so, this thesis attempts to connect contact theory with age-specific contact data in the context of influenza outbreaks in practical ways. In mathematical epidemiology, proportionate mixing is used as the preferred theoretical mixing structure and so, the frame of discussion of this dissertation follows this specific theoretical framework. The questions that drive this dissertation, in the context of influenza dynamics, proportionate mixing, and control, are:
I. What is the role of age-aggregation on the dynamics of a single outbreak? Or simply speaking, does the number and length of the age-classes used to model a population make a significant difference on quantitative predictions?
II. What would the age-specific optimal influenza vaccination policies be? Or, what are the age-specific vaccination policies needed to control an outbreak in the presence of limited or unlimited vaccine stockpiles?
Intertwined with the above questions are issues of resilience and uncertainty including, whether or not data collected on mixing (by social scientists) can be used effectively to address both questions in the context of influenza and proportionate mixing. The objective is to provide answers to these questions by assessing the role of aggregation (number and length of age classes) and model robustness (does the aggregation scheme selected makes a difference on influenza dynamics and control) via comparisons between purely data-driven model and proportionate mixing models.
ContributorsMorales, Romarie (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Mubayi, Anuj (Thesis advisor) / Towers, Sherry (Committee member) / Arizona State University (Publisher)
Created2016
Description
Extraordinary medical advances have led to significant reductions in the burden of infectious diseases in humans. However, infectious diseases still account for more than 13 million annual deaths. This large burden is partly due to some pathogens having found suitable conditions to emerge and spread in denser and more connected host populations, and others having evolved to escape the pressures imposed by the rampant use of antimicrobials. It is then critical to improve our understanding of how diseases spread in these modern landscapes, characterized by new host population structures and socio-economic environments, as well as containment measures such as the deployment of drugs. Thus, the motivation of this dissertation is two-fold. First, we study, using both data-driven and modeling approaches, the the spread of infectious diseases in urban areas. As a case study, we use confirmed-cases data on sexually transmitted diseases (STDs) in the United States to assess the conduciveness of population size of urban areas and their socio-economic characteristics as predictors of STD incidence. We find that the scaling of STD incidence in cities is superlinear, and that the percent of African-Americans residing in cities largely determines these statistical patterns. Since disparities in access to health care are often exacerbated in urban areas, within this project we also develop two modeling frameworks to study the effect of health care disparities on epidemic outcomes. Discrepant results between the two approaches indicate that knowledge of the shape of the recovery period distribution, not just its mean and variance, is key for assessing the epidemiological impact of inequalities. The second project proposes to study, from a modeling perspective, the spread of drug resistance in human populations featuring vital dynamics, stochasticity and contact structure. We derive effective treatment regimes that minimize both the overall disease burden and the spread of resistance. Additionally, targeted treatment in structured host populations may lead to higher levels of drug resistance, and if drug-resistant strains are compensated, they can spread widely even when the wild-type strain is below its epidemic threshold.
ContributorsPatterson-Lomba, Oscar (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Towers, Sherry (Thesis advisor) / Chowell-Puente, Gerardo (Committee member) / Arizona State University (Publisher)
Created2014
Description
This study tested the preliminary effectiveness of a health belief and text messaging intervention for parents of five- to eight-year-old children to determine whether health beliefs and influenza vaccine receipt differ when compared to a text messaging control group. Children are almost four times more likely to be infected with influenza than adults (Belshe Piedra, & Block, 2009), shed the greatest quantities of influenza virus, and have been recognized as vectors for spread of disease (Neuzil, Mellen, Wright, Mitchel, Jr., & Griffin, 2002b). The influenza immunization rate for school-age children is less than 56% (Centers for Disease Control and Prevention [CDC], 2014). Reasons for the low vaccination rate include parents’ misperceptions of influenza disease and vaccinations (Bhat-Schelbert et al., 2012; Taylor et al., 2002). There are few theory-based interventions for increasing influenza vaccination rates of school-age children; however, promising results have been found when using the constructs of the health belief model (HBM) (Chen et al., 2011; Coe, Gatewood, Moczygemba, Goode, & Beckner, 2012). Mobile technology using Short Message Service (SMS) text messaging may increase vaccination rates to a greater extent than traditional vaccine reminders (Daley et al., 2002; Grajalva, 2006). Prior to starting this study, only one randomized controlled trial testing text messaging to increase children’s influenza vaccination rates was found (Stockwell et al., 2012). In this study, text messaging was effective in promoting behavioral changes leading to a 4% increase in influenza vaccination (27.1% vs. 22.8%, RR = 1.19, p < .001). This study was a randomized controlled trial using a two-group pre- and posttest experimental design. This study found that a theory-based intervention (SayNo2Flu) guided by the HBM and combined with the use of mobile technology (SMS text messaging) did change parents’ influenza vaccination perceptions. It had an overall increase of 38.1% in Influenza vaccination rates in the intervention group (OR: 4.46, 95% CL, 1.705-11.706, p < .001). These results offer some insight into the use of theory-based preventative interventions for parents of young school-age children.
ContributorsWiseman, Patricia (Author) / Reifsnider, Elizabeth G. (Thesis advisor) / Cesarotti, Evelyn (Committee member) / Black, Andy (Committee member) / Kim, Sunny (Committee member) / Arizona State University (Publisher)
Created2015
Description
Influenza is a deadly disease that poses a major threat to global health. The surface proteins of influenza A, the type most often associated with epidemics and pandemics, mutate at a very high frequency from season to season, reducing the efficacy of seasonal influenza vaccines. However, certain regions of these proteins are conserved between strains of influenza A, making them attractive targets for the development of a ‘universal’ influenza vaccine. One of these highly conserved regions is the ectodomain of the influenza matrix 2 protein (M2e). Studies have shown that M2e is poorly immunogenic on its own, but when properly adjuvanted it can be used to induce protective immune responses against many strains of influenza A. In this thesis, M2e was fused to a pair experimental ‘vaccine platforms’: an antibody fusion protein designed to assemble into a recombinant immune complex (RIC) and the hepatitis B core antigen (HBc) that can assemble into virus-like particles (VLP). The two antigens were produced in Nicotiana benthamiana plants through the use of geminiviral vectors and were subsequently evaluated in mouse trials. Mice were administered three doses of either the VLP alone or a 1:1 combination of the VLP and the RIC, and recipients of both the VLP and RIC exhibited endpoint anti-M2e antibody titers that were 2 to 3 times higher than mice that received the VLP alone. While IgG2a:IgG1 ratios, which can suggest the type of immune response (TH1 vs TH2) an antigen will elicit, were higher in mice vaccinated solely with the VLP, the higher overall titers are encouraging and demonstrate a degree of interaction between the RIC and VLP vaccines. Further research is necessary to determine the optimal balance of VLP and RIC to maximize IgG2a:IGg1 ratios as well as whether such interaction would be observed through the use of a variety of diverse antigens, though the results of other studies conducted in this lab suggests that this is indeed the case. The results of this study demonstrate not only the successful development of a promising new universal influenza A vaccine, but also that co-delivering different types of recombinant vaccines could reduce the total number of vaccine doses needed to achieve a protective immune response.
ContributorsFavre, Brandon Chetan (Author) / Mason, Hugh S (Thesis advisor) / Mor, Tsafrir (Committee member) / Chen, Qiang (Committee member) / Arizona State University (Publisher)
Created2019