Matching Items (70)
Description
ABSTRACT Water resources in many parts of the world are subject to increasing stress because of (a) the growth in demand caused by population increase and economic development, (b) threats to supply caused by climate and land cover change, and (c) a heightened awareness of the importance of maintaining water supplies to other parts of the ecosystem. An additional factor is the quality of water management. The United States-Mexican border provides an example of poor water management combined with increasing demand for water resources that are both scarce and uncertain. This dissertation focuses on the problem of water management in the border city of Ciudad Juarez, Chihuahua. The city has attracted foreign investment during the last few decades, largely due to relatively low environmental and labor costs, and to a range of tax incentives and concessions. This has led to economic and population growth, but also to higher demand for public services such as water which leads to congestion and scarcity. In particular, as water resources have become scarce, the cost of water supply has increased. The dissertation analyzes the conditions that allow for the efficient use of water resources at sustainable levels of economic activity--i.e., employment and investment. In particular, it analyzes the water management strategies that lead to an efficient and sustainable use of water when the source of water is either an aquifer, or there is conjunctive use of ground and imported water. The first part of the dissertation constructs a model of the interactive effects of water supply, wage rates, inward migration of labor and inward investment of capital. It shows how growing water scarcity affects population growth through the impact it has on real wage rates, and how this erodes the comparative advantage of Ciudad Juarez--low wages--to the point where foreign investment stops. This reveals the very close connection between water management and the level of economic activity in Ciudad Juarez. The second part of the dissertation examines the effect of sustainable and efficient water management strategies on population and economic activity levels under two different settings. In the first Ciudad Juarez relies exclusively on ground water to meet demand--this reflects the current situation of Ciudad Juarez. In the second Ciudad Juarez is able both to import water and to draw on aquifers to meet demand. This situation is motivated by the fact that Ciudad Juarez is considering importing water from elsewhere to maintain its economic growth and mitigate the overdraft of the Bolson del Hueco aquifer. Both models were calibrated on data for Ciudad Juarez, and then used to run experiments with respect to different environmental and economic conditions, and different water management options. It is shown that for a given set of technological, institutional and environmental conditions, the way water is managed in a desert environment determines the long run equilibrium levels of employment, investment and output. It is also shown that the efficiency of water management is consistent with the sustainability of water use and economic activity. Importing water could allow the economy to operate at higher levels of activity than where it relies solely on local aquifers. However, at some scale, water availability will limit the level of economic activity, and the disposable income of the residents of Ciudad Juarez.
ContributorsGarduno Angeles, Gustavo Leopoldo (Author) / Perrings, Charles (Thesis advisor) / Holway, Jim (Thesis advisor) / Aggarwal, Rimjhim (Committee member) / Arizona State University (Publisher)
Created2011
Description
Environmental agencies often want to accomplish additional objectives beyond their central environmental protection objective. This is laudable; however it begets a need for understanding the additional challenges and trade-offs involved in doing so. The goal of this thesis is to examine the trade-offs involved in two such cases that have received considerable attention recently. The two cases I examine are (1) the protection of multiple environmental goods (e.g., bundles of ecosystem services); and (2) the use of payments for ecosystem services as a poverty reduction mechanism. In the first case (chapter 2), I build a model based on the fact that efforts to protect one environmental good often increase or decrease the levels of other environmental goods, what I refer to as "cobenefits" and "disbenefits" respectively. There is often a desire to increase the cobenefits of environmental protection efforts in order to synergize across conservation efforts; and there is also a desire to decrease disbenefits because they are seen as negative externalities of protection efforts. I show that as a result of reciprocal externalities between environmental protection efforts, environmental agencies likely have a disincentive to create cobenefits, but may actually have an incentive to decrease disbenefits. In the second case (chapter 3), I model an environmental agency that wants to increase environmental protection, but would also like to reduce poverty. The model indicates that in theory, the trade-offs between these two goals may depend on relevant parameters of the system, particularly the ratio of the price of monitoring to participant's compliance cost. I show that when the ratio of monitoring costs to compliance cost is higher, trade-offs between environmental protection and poverty reduction are likely to be smaller. And when the ratio of monitoring costs to compliance costs is lower, trade-offs are likely to be larger. This thesis contributes to a deeper understanding of the trade-offs faced by environmental agencies that want to pursue secondary objectives of protecting additional environmental goods or reducing poverty.
ContributorsGilliland, Ted (Author) / Perrings, Charles (Thesis advisor) / Abbott, Josh K (Committee member) / Kinzig, Ann P (Committee member) / Arizona State University (Publisher)
Created2012
Description
This work examines one dimension of the effect that complex human transport systems have on the spread of Chikungunya Virus (CHIKV) in the Caribbean from 2013 to 2015. CHIKV is transmitted by mosquitos and its novel spread through the Caribbean islands provided a chance to examine disease transmission through complex human transportation systems. Previous work by Cauchemez et al. had shown a simple distance-based model successfully predict CHIKV spread in the Caribbean using Markov chain Monte Carlo (MCMC) statistical methods. A MCMC simulation is used to evaluate different transportation methods (air travel, cruise ships, and local maritime traffic) for the primary transmission patterns through linear regression. Other metrics including population density to account for island size variation and dengue fever incidence rates as a proxy for vector control and health spending were included. Air travel and cruise travel were gathered from monthly passenger arrivals by island. Local maritime traffic is approximated with a gravity model proxy incorporating GDP-per-capita and distance and historic dengue rates were used for determine existing vector control measures for the islands. The Caribbean represents the largest cruise passenger market in the world, cruise ship arrivals were expected to show the strongest signal; however, the gravity model representing local traffic was the best predictor of infection routes. The early infected islands (<30 days) showed a heavy trend towards an alternate primary transmission but our consensus model able to predict the time until initial infection reporting with 94.5% accuracy for islands 30 days post initial reporting. This result can assist public health entities in enacting measures to mitigate future epidemics and provide a modelling basis for determining transmission modes in future CHIKV outbreaks.
ContributorsFries, Brendan F (Author) / Perrings, Charles (Thesis director) / Wilson Sayres, Melissa (Committee member) / Morin, Ben (Committee member) / School of Life Sciences (Contributor) / Department of Military Science (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
This project examines a complex issue in urban ecology: the impact of biodiversity on ecosystem services, and considers how this varies across cities. Data were gathered on multiple economic and ecological parameters for a selection of seven cities around the world and analyzed via multiple linear regression in order to assess any relationships that may be at play. Significance values were then calculated to further define the relationships between the data. Analysis found that both biophysical and socioeconomic factors affected ecosystem services, although not all hypotheses regarding these relationships were met. Conclusions indicate that this model was fairly effective in describing physical drivers of ecosystem services, but were not as clear regarding social drivers. Further study regarding social parameters' effect on ecosystem services is recommended.
ContributorsMcDannald, Lindsay JoAnne (Author) / Perrings, Charles (Thesis director) / Kinzig, Ann (Committee member) / Grimm, Nancy (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / School of Sustainability (Contributor)
Created2014-05
Description
General ecological thought pertaining to plant biology, conservation, and urban areas has rested on two potentially contradictory underlying assumptions. The first is that non-native plants can spread easily from human developments to “pristine” areas. The second is that native plants cannot disperse through developed areas. Both assume anthropogenic changes to ecosystems create conditions that favor non-native plants and hinder native species. However, it is just as likely that anthropogenic alterations of habitats will favor certain groups of plant species with similar functional traits, whether native or not. Migration of plants can be divided into the following stages: dispersal, germination, establishment, reproduction and spread. Functional traits of species determine which are most successful at each of the stages of invasion or range enlargement. I studied the traits that allow both native and non-native plant species to disperse into freeway corridors, germinate, establish, reproduce, and then disperse along those corridors in Phoenix, Arizona. Field methods included seed bank sample collection and germination, vegetation surveys, and seed trapping. I also evaluated concentrations of plant-available nitrate as a result of localized nitrogen deposition. While many plant species found on the roadsides are either landscape varieties or typical weedy species, some uncommon native species and unexpected non-native species were also encountered. Maintenance regimes greatly influence the amount of vegetative cover and species composition along roadsides. Understanding which traits permit success at various stages of the invasion process indicates whether it is native, non-native, or species with particular traits that are likely to move through the city and establish in the desert. In a related case study conducted in Victoria, Australia, transportation professionals and ecologists were surveyed regarding preferences for roadside landscape design. Roadside design and maintenance projects are typically influenced by different groups of transportation professionals at various stages in a linear project cycle. Landscape architects and design professionals have distinct preferences and priorities compared to other transportation professionals and trained ecologists. The case study reveals the need for collaboration throughout the stages of design, construction and maintenance in order to efficiently manage roadsides for multiple priorities.
ContributorsGade, Kristin Joan (Author) / Kinzig, Ann P (Thesis advisor) / Grimm, Nancy (Committee member) / Perrings, Charles (Committee member) / Robbins, Paul (Committee member) / Stromberg, Juliet C. (Committee member) / Arizona State University (Publisher)
Created2010
Description
Mathematical epidemiology, one of the oldest and richest areas in mathematical biology, has significantly enhanced our understanding of how pathogens emerge, evolve, and spread. Classical epidemiological models, the standard for predicting and managing the spread of infectious disease, assume that contacts between susceptible and infectious individuals depend on their relative frequency in the population. The behavioral factors that underpin contact rates are not generally addressed. There is, however, an emerging a class of models that addresses the feedbacks between infectious disease dynamics and the behavioral decisions driving host contact. Referred to as “economic epidemiology” or “epidemiological economics,” the approach explores the determinants of decisions about the number and type of contacts made by individuals, using insights and methods from economics. We show how the approach has the potential both to improve predictions of the course of infectious disease, and to support development of novel approaches to infectious disease management.
ContributorsPerrings, Charles (Author) / Castillo-Chavez, Carlos (Author) / Chowell-Puente, Gerardo (Author) / Daszak, Peter (Author) / Fenichel, Eli P. (Author) / Finnoff, David (Author) / Horan, Richard D. (Author) / Kilpatrick, A. Marm (Author) / Kinzig, Ann (Author) / Kuminoff, Nicolai (Author) / Levin, Simon (Author) / Morin, Benjamin (Author) / Smith, Katherine F. (Author) / Springborn, Michael (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / W.P. Carey School of Business (Contributor) / Economics (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor)
Created2015-12-01
Description
Preserving a system’s viability in the presence of diversity erosion is critical if the goal is to sustainably support biodiversity. Reduction in population heterogeneity, whether inter- or intraspecies, may increase population fragility, either decreasing its ability to adapt effectively to environmental changes or facilitating the survival and success of ordinarily rare phenotypes. The latter may result in over-representation of individuals who may participate in resource utilization patterns that can lead to over-exploitation, exhaustion, and, ultimately, collapse of both the resource and the population that depends on it. Here, we aim to identify regimes that can signal whether a consumer–resource system is capable of supporting viable degrees of heterogeneity. The framework used here is an expansion of a previously introduced consumer–resource type system of a population of individuals classified by their resource consumption. Application of the Reduction Theorem to the system enables us to evaluate the health of the system through tracking both the mean value of the parameter of resource (over)consumption, and the population variance, as both change over time. The article concludes with a discussion that highlights applicability of the proposed system to investigation of systems that are affected by particularly devastating overly adapted populations, namely cancerous cells. Potential intervention approaches for system management are discussed in the context of cancer therapies.
ContributorsKareva, Irina (Author) / Morin, Benjamin (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor)
Created2015-02-01
Description
Background
In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.
Methodology
We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.
Conclusions
We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.
In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.
Methodology
We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.
Conclusions
We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.
ContributorsTowers, Sherry (Author) / Afzal, Shehzad (Author) / Bernal, Gilbert (Author) / Bliss, Nadya (Author) / Brown, Shala (Author) / Espinoza, Baltazar (Author) / Jackson, Jasmine (Author) / Judson-Garcia, Julia (Author) / Khan, Maryam (Author) / Lin, Michael (Author) / Mamada, Robert (Author) / Moreno, Victor (Author) / Nazari, Fereshteh (Author) / Okuneye, Kamaldeen (Author) / Ross, Mary (Author) / Rodriguez, Claudia (Author) / Medlock, Jan (Author) / Ebert, David (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor) / Mary Lou Fulton Teachers College (Contributor) / Educational Leadership and Innovation (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Global Security Initiative (Contributor)
Created2015-06-11
Description
Background
Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.
Methodology/Principal Findings
Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.
Conclusions
Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.
Seroepidemiological studies before and after the epidemic wave of H1N1-2009 are useful for estimating population attack rates with a potential to validate early estimates of the reproduction number, R, in modeling studies.
Methodology/Principal Findings
Since the final epidemic size, the proportion of individuals in a population who become infected during an epidemic, is not the result of a binomial sampling process because infection events are not independent of each other, we propose the use of an asymptotic distribution of the final size to compute approximate 95% confidence intervals of the observed final size. This allows the comparison of the observed final sizes against predictions based on the modeling study (R = 1.15, 1.40 and 1.90), which also yields simple formulae for determining sample sizes for future seroepidemiological studies. We examine a total of eleven published seroepidemiological studies of H1N1-2009 that took place after observing the peak incidence in a number of countries. Observed seropositive proportions in six studies appear to be smaller than that predicted from R = 1.40; four of the six studies sampled serum less than one month after the reported peak incidence. The comparison of the observed final sizes against R = 1.15 and 1.90 reveals that all eleven studies appear not to be significantly deviating from the prediction with R = 1.15, but final sizes in nine studies indicate overestimation if the value R = 1.90 is used.
Conclusions
Sample sizes of published seroepidemiological studies were too small to assess the validity of model predictions except when R = 1.90 was used. We recommend the use of the proposed approach in determining the sample size of post-epidemic seroepidemiological studies, calculating the 95% confidence interval of observed final size, and conducting relevant hypothesis testing instead of the use of methods that rely on a binomial proportion.
ContributorsNishiura, Hiroshi (Author) / Chowell-Puente, Gerardo (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2011-03-24
Description
Background
Several past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts.
Methods
Here we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event.
Conclusions
We find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings.
Several past studies have found that media reports of suicides and homicides appear to subsequently increase the incidence of similar events in the community, apparently due to the coverage planting the seeds of ideation in at-risk individuals to commit similar acts.
Methods
Here we explore whether or not contagion is evident in more high-profile incidents, such as school shootings and mass killings (incidents with four or more people killed). We fit a contagion model to recent data sets related to such incidents in the US, with terms that take into account the fact that a school shooting or mass murder may temporarily increase the probability of a similar event in the immediate future, by assuming an exponential decay in contagiousness after an event.
Conclusions
We find significant evidence that mass killings involving firearms are incented by similar events in the immediate past. On average, this temporary increase in probability lasts 13 days, and each incident incites at least 0.30 new incidents (p = 0.0015). We also find significant evidence of contagion in school shootings, for which an incident is contagious for an average of 13 days, and incites an average of at least 0.22 new incidents (p = 0.0001). All p-values are assessed based on a likelihood ratio test comparing the likelihood of a contagion model to that of a null model with no contagion. On average, mass killings involving firearms occur approximately every two weeks in the US, while school shootings occur on average monthly. We find that state prevalence of firearm ownership is significantly associated with the state incidence of mass killings with firearms, school shootings, and mass shootings.
ContributorsTowers, Sherry (Author) / Gomez-Lievano, Andres (Author) / Khan, Maryam (Author) / Mubayi, Anuj (Author) / Castillo-Chavez, Carlos (Author) / Simon M. Levin Mathematical, Computational and Modeling Sciences Center (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Human Evolution and Social Change (Contributor)
Created2015-07-02