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- All Subjects: Epidemiology
- All Subjects: Biology
- Creators: Castillo-Chavez, Carlos
Description
The concept of vaccination dates back further than Edward Jenner's first vaccine using cowpox pustules to confer immunity against smallpox in 1796. Nevertheless, it was Jenner's success that gave vaccines their name and made vaccinia virus (VACV) of particular interest. More than 200 years later there is still the need to understand vaccination from vaccine design to prediction of vaccine efficacy using mathematical models. Post-exposure vaccination with VACV has been suggested to be effective if administered within four days of smallpox exposure although this has not been definitively studied in humans. The first and second chapters analyze post-exposure prophylaxis of VACV in an animal model using v50ΔB13RMγ, a recombinant VACV expressing murine interferon gamma (IFN-γ) also known as type II IFN. While untreated animals infected with wild type VACV die by 10 days post-infection (dpi), animals treated with v50ΔB13RMγ 1 dpi had decreased morbidity and 100% survival. Despite these differences, the viral load was similar in both groups suggesting that v50ΔB13RMγ acts as an immunoregulator rather than as an antiviral. One of the main characteristics of VACV is its resistance to type I IFN, an effect primarily mediated by the E3L protein, which has a Z-DNA binding domain and a double-stranded RNA (dsRNA) binding domain. In the third chapter a VACV that independently expresses both domains of E3L was engineered and compared to wild type in cells in culture. The dual expression virus was unable to replicate in the JC murine cell line where both domains are needed together for replication. Moreover, phosphorylation of the dsRNA dependent protein kinase (PKR) was observed at late times post-infection which indicates that both domains need to be linked together in order to block the IFN response. Because smallpox has already been eradicated, the utility of mathematical modeling as a tool for predicting disease spread and vaccine efficacy was explored in the last chapter using dengue as a disease model. Current modeling approaches were reviewed and the 2000-2001 dengue outbreak in a Peruvian region was analyzed. This last section highlights the importance of interdisciplinary collaboration and how it benefits research on infectious diseases.
ContributorsHolechek, Susan A (Author) / Jacobs, Bertram L (Thesis advisor) / Castillo-Chavez, Carlos (Committee member) / Frasch, Wayne (Committee member) / Hogue, Brenda (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2011
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
A sequence of models is developed to describe urban population growth in the context of the embedded physical, social and economic environments and an urban disease are developed. This set of models is focused on urban growth and the relationship between the desire to move and the utility derived from city life. This utility is measured in terms of the economic opportunities in the city, the level of human constructed amenity, and the level of amenity caused by the natural environment. The set of urban disease models is focused on examining prospects of eliminating a disease for which a vaccine does not exist. It is inspired by an outbreak of the vector-borne disease dengue fever in Peru, during 2000-2001.
ContributorsMurillo, D (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Anderies, John M (Thesis advisor) / Boone, Christopher (Committee member) / Arizona State University (Publisher)
Created2012
Description
Understanding the consequences of changes in social networks is an important an-
thropological research goal. This dissertation looks at the role of data-driven social
networks on infectious disease transmission and evolution. The dissertation has two
projects. The first project is an examination of the effects of the superspreading
phenomenon, wherein a relatively few individuals are responsible for a dispropor-
tionate number of secondary cases, on the patterns of an infectious disease. The
second project examines the timing of the initial introduction of tuberculosis (TB) to
the human population. The results suggest that TB has a long evolutionary history
with hunter-gatherers. Both of these projects demonstrate the consequences of social
networks for infectious disease transmission and evolution.
The introductory chapter provides a review of social network-based studies in an-
thropology and epidemiology. Particular emphasis is paid to the concept and models
of superspreading and why to consider it, as this is central to the discussion in chapter
2. The introductory chapter also reviews relevant epidemic mathematical modeling
studies.
In chapter 2, social networks are connected with superspreading events, followed
by an investigation of how social networks can provide greater understanding of in-
fectious disease transmission through mathematical models. Using the example of
SARS, the research shows how heterogeneity in transmission rate impacts super-
spreading which, in turn, can change epidemiological inference on model parameters
for an epidemic.
Chapter 3 uses a different mathematical model to investigate the evolution of TB
in hunter-gatherers. The underlying question is the timing of the introduction of TB
to the human population. Chapter 3 finds that TB’s long latent period is consistent
with the evolutionary pressure which would be exerted by transmission on a hunter-
igatherer social network. Evidence of a long coevolution with humans indicates an
early introduction of TB to the human population.
Both of the projects in this dissertation are demonstrations of the impact of var-
ious characteristics and types of social networks on infectious disease transmission
dynamics. The projects together force epidemiologists to think about networks and
their context in nontraditional ways.
thropological research goal. This dissertation looks at the role of data-driven social
networks on infectious disease transmission and evolution. The dissertation has two
projects. The first project is an examination of the effects of the superspreading
phenomenon, wherein a relatively few individuals are responsible for a dispropor-
tionate number of secondary cases, on the patterns of an infectious disease. The
second project examines the timing of the initial introduction of tuberculosis (TB) to
the human population. The results suggest that TB has a long evolutionary history
with hunter-gatherers. Both of these projects demonstrate the consequences of social
networks for infectious disease transmission and evolution.
The introductory chapter provides a review of social network-based studies in an-
thropology and epidemiology. Particular emphasis is paid to the concept and models
of superspreading and why to consider it, as this is central to the discussion in chapter
2. The introductory chapter also reviews relevant epidemic mathematical modeling
studies.
In chapter 2, social networks are connected with superspreading events, followed
by an investigation of how social networks can provide greater understanding of in-
fectious disease transmission through mathematical models. Using the example of
SARS, the research shows how heterogeneity in transmission rate impacts super-
spreading which, in turn, can change epidemiological inference on model parameters
for an epidemic.
Chapter 3 uses a different mathematical model to investigate the evolution of TB
in hunter-gatherers. The underlying question is the timing of the introduction of TB
to the human population. Chapter 3 finds that TB’s long latent period is consistent
with the evolutionary pressure which would be exerted by transmission on a hunter-
igatherer social network. Evidence of a long coevolution with humans indicates an
early introduction of TB to the human population.
Both of the projects in this dissertation are demonstrations of the impact of var-
ious characteristics and types of social networks on infectious disease transmission
dynamics. The projects together force epidemiologists to think about networks and
their context in nontraditional ways.
ContributorsNesse, Hans P (Author) / Hurtado, Ana Magdalena (Thesis advisor) / Castillo-Chavez, Carlos (Committee member) / Mubayi, Anuj (Committee member) / Arizona State University (Publisher)
Created2019
Description
The most advanced social insects, the eusocial insects, form often large societies in which there is reproductive division of labor, queens and workers, have overlapping generations, and cooperative brood care where daughter workers remain in the nest with their queen mother and care for their siblings. The eusocial insects are composed of representative species of bees and wasps, and all species of ants and termites. Much is known about their organizational structure, but remains to be discovered.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
ContributorsRodríguez Messan, Marisabel (Author) / Kang, Yun (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Kuang, Yang (Committee member) / Page Jr., Robert E (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2018
Description
The immune system plays a dual role during neoplastic progression. It can suppress tumor growth by eliminating cancer cells, and also promote neoplastic expansion by either selecting for tumor cells that are fitter to survive in an immunocompetent host or by establishing the right conditions within the tumor microenvironment. First, I present a model to study the dynamics of subclonal evolution of cancer. I model selection through time as an epistatic process. That is, the fitness change in a given cell is not simply additive, but depends on previous mutations. Simulation studies indicate that tumors are composed of myriads of small subclones at the time of diagnosis. Because some of these rare subclones harbor pre-existing treatment-resistant mutations, they present a major challenge to precision medicine. Second, I study the question of self and non-self discrimination by the immune system, which is fundamental in the field in cancer immunology. By performing a quantitative analysis of the biochemical properties of thousands of MHC class I peptides, I find that hydrophobicity of T cell receptors contact residues is a hallmark of immunogenic epitopes. Based on these findings, I further develop a computational model to predict immunogenic epitopes which facilitate the development of T cell vaccines against pathogen and tumor antigens. Lastly, I study the effect of early detection in the context of Ebola. I develope a simple mathematical model calibrated to the transmission dynamics of Ebola virus in West Africa. My findings suggest that a strategy that focuses on early diagnosis of high-risk individuals, caregivers, and health-care workers at the pre-symptomatic stage, when combined with public health measures to improve the speed and efficacy of isolation of infectious individuals, can lead to rapid reductions in Ebola transmission.
ContributorsChowell-Puente, Diego (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Anderson, Karen S (Thesis advisor) / Maley, Carlo C (Committee member) / Wilson Sayres, Melissa A (Committee member) / Blattman, Joseph N (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
In the field of infectious disease epidemiology, the assessment of model robustness outcomes plays a significant role in the identification, reformulation, and evaluation of preparedness strategies aimed at limiting the impact of catastrophic events (pandemics or the deliberate release of biological agents) or used in the management of disease prevention strategies, or employed in the identification and evaluation of control or mitigation measures. The research work in this dissertation focuses on: The comparison and assessment of the role of exponentially distributed waiting times versus the use of generalized non-exponential parametric distributed waiting times of infectious periods on the quantitative and qualitative outcomes generated by Susceptible-Infectious-Removed (SIR) models. Specifically, Gamma distributed infectious periods are considered in the three research projects developed following the applications found in (Bailey 1964, Anderson 1980, Wearing 2005, Feng 2007, Feng 2007, Yan 2008, lloyd 2009, Vergu 2010). i) The first project focuses on the influence of input model parameters, such as the transmission rate, mean and variance of Gamma distributed infectious periods, on disease prevalence, the peak epidemic size and its timing, final epidemic size, epidemic duration and basic reproduction number. Global uncertainty and sensitivity analyses are carried out using a deterministic Susceptible-Infectious-Recovered (SIR) model. The quantitative effect and qualitative relation between input model parameters and outcome variables are established using Latin Hypercube Sampling (LHS) and Partial rank correlation coefficient (PRCC) and Spearman rank correlation coefficient (RCC) sensitivity indices. We learnt that: For relatively low (R0 close to one) to high (mean of R0 equals 15) transmissibility, the variance of the Gamma distribution for the infectious period, input parameter of the deterministic age-of-infection SIR model, is key (statistically significant) on the predictability of the epidemiological variables such as the epidemic duration and the peak size and timing of the prevalence of infectious individuals and therefore, for the predictability these variables, it is preferable to utilize a nonlinear system of Volterra integral equations, rather than a nonlinear system of ordinary differential equations. The predictability of epidemiological variables such as the final epidemic size and the basic reproduction number are unaffected by (or independent of) the variance of the Gamma distribution for the infectious period and therefore for the choice on which type of nonlinear system for the description of the SIR model (VIE's or ODE's) is irrelevant. Although, for practical proposes, with the aim of lowering the complexity and number operations in the numerical methods, a nonlinear system of ordinary differential equations is preferred. The main contribution lies in the development of a model based decision-tool that helps determine when SIR models given in terms of Volterra integral equations are equivalent or better suited than SIR models that only consider exponentially distributed infectious periods. ii) The second project addresses the question of whether or not there is sufficient evidence to conclude that two empirical distributions for a single epidemiological outcome, one generated using a stochastic SIR model under exponentially distributed infectious periods and the other under the non-exponentially distributed infectious period, are statistically dissimilar. The stochastic formulations are modeled via a continuous time Markov chain model. The statistical hypothesis test is conducted using the non-parametric Kolmogorov-Smirnov test. We found evidence that shows that for low to moderate transmissibility, all empirical distribution pairs (generated from exponential and non-exponential distributions) for each of the epidemiological quantities considered are statistically dissimilar. The research in this project helps determine whether the weakening exponential distribution assumption must be considered in the estimation of probability of events defined from the empirical distribution of specific random variables. iii) The third project involves the assessment of the effect of exponentially distributed infectious periods on estimates of input parameter and the associated outcome variable predictions. Quantities unaffected by the use of exponentially distributed infectious period within low transmissibility scenarios include, the prevalence peak time, final epidemic size, epidemic duration and basic reproduction number and for high transmissibility scenarios only the prevalence peak time and final epidemic size. An application designed to determine from incidence data whether there is sufficient statistical evidence to conclude that the infectious period distribution should not be modeled by an exponential distribution is developed. A method for estimating explicitly specified non-exponential parametric probability density functions for the infectious period from epidemiological data is developed. The methodologies presented in this dissertation may be applicable to models where waiting times are used to model transitions between stages, a process that is common in the study of life-history dynamics of many ecological systems.
ContributorsMorales Butler, Emmanuel J (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Aparicio, Juan P (Thesis advisor) / Camacho, Erika T (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Created2014
Description
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.
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.
ContributorsEspinoza Cortes, Baltazar (Author) / Castillo-Chavez, Carlos (Thesis advisor) / Kang, Yun (Committee member) / Safan, Muntaser (Committee member) / Arizona State University (Publisher)
Created2018
Description
A key factor in the success of social animals is their organization of work. Mathematical models have been instrumental in unraveling how simple, individual-based rules can generate collective patterns via self-organization. However, existing models offer limited insights into how these patterns are shaped by behavioral differences within groups, in part because they focus on analyzing specific rules rather than general mechanisms that can explain behavior at the individual-level. My work argues for a more principled approach that focuses on the question of how individuals make decisions in costly environments.
In Chapters 2 and 3, I demonstrate how this approach provides novel insights into factors that shape the flexibility and robustness of task organization in harvester ant colonies (Pogonomyrmex barbatus). My results show that the degree to which colonies can respond to work in fluctuating environments depends on how individuals weigh the costs of activity and update their behavior in response to social information. In Chapter 4, I introduce a mathematical framework to study the emergence of collective organization in heterogenous groups. My approach, which is based on the theory of multi-agent systems, focuses on myopic agents whose behavior emerges out of an independent valuation of alternative choices in a given work environment. The product of this dynamic is an equilibrium organization in which agents perform different tasks (or abstain from work) with an analytically defined set of threshold probabilities. The framework is minimally developed, but can be extended to include other factors known to affect task decisions including individual experience and social facilitation. This research contributes a novel approach to developing (and analyzing) models of task organization that can be applied in a broader range of contexts where animals cooperate.
In Chapters 2 and 3, I demonstrate how this approach provides novel insights into factors that shape the flexibility and robustness of task organization in harvester ant colonies (Pogonomyrmex barbatus). My results show that the degree to which colonies can respond to work in fluctuating environments depends on how individuals weigh the costs of activity and update their behavior in response to social information. In Chapter 4, I introduce a mathematical framework to study the emergence of collective organization in heterogenous groups. My approach, which is based on the theory of multi-agent systems, focuses on myopic agents whose behavior emerges out of an independent valuation of alternative choices in a given work environment. The product of this dynamic is an equilibrium organization in which agents perform different tasks (or abstain from work) with an analytically defined set of threshold probabilities. The framework is minimally developed, but can be extended to include other factors known to affect task decisions including individual experience and social facilitation. This research contributes a novel approach to developing (and analyzing) models of task organization that can be applied in a broader range of contexts where animals cooperate.
ContributorsUdiani, Oyita (Author) / Kang, Yun (Thesis advisor) / Fewell, Jennifer H (Thesis advisor) / Janssen, Marcus A (Committee member) / Castillo-Chavez, Carlos (Committee member) / Arizona State University (Publisher)
Created2016