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At the end of February 1957, the WHO announced that a highly contagious flu epidemic was raging in China, where hundreds of thousands of people had fallen ill. This flu then spreads with furious speed over the rest of the world and becomes known as the Asian flu. The main purpose of the essay is to use a qualitative content analysis to investigate and compare how the Asian flu pandemic was portrayed in the Swedish newspapers Norrskensflamman and Dagens Nyheter at its outbreak in 1957. The reason why these two newspapers have been chosen is due to their differences in quantity, political tendency and their geographical locations. Both dailies have also been available digitally. Based on own investigations in the thesis, as well as research on previous epidemics that have affected Sweden, three themes have been identified; panic, vaccines and societal change. These are a starting point for seeking answers to what the portrayal looks like, what differences there are between the newspapers and whether Norrbotten is affected in a different way compared to the rest of Sweden. The articles on the Asian flu that have been investigated have been located in the two newspapers and compared in relation to each other. The essay shows both similarities and differences in the newspapers' reporting of the Asian flu, where the most prominent are the panic aspects. Together, both newspapers report a much lower death toll from the flu than the actual one, but differ in how much responsibility they take to prevent public panic, for example through word choice. Their early reporting on the work with vaccines also helps to calm and convey that the situation is under control. On the other hand, the content of the articles changes quickly when it is clear that the infection is raging in Sweden. Information in the newspapers regarding societal changes is sparse and there are few indications that they are permanent.There are no clear indications that Norrbotten was hit much harder by the Asian flu than the rest of Sweden, quite the opposite.

Nicotine, in the form of cigarette smoking, chewing tobacco, and most recently as vapor smoking, is one of the most heavily used addictive drugs in the world. Since smoking imposes a significant health-care and economic burden on the population, there have been sustained and significant efforts for the past several decades to control it. However, smoking epidemic is a complex and "policy-resistant" problem that has proven difficult to control. Despite the known importance of social networks in the smoking epidemic, there has been no network-centric intervention available for controlling the smoking epidemic yet. The long-term goal of this work is the development and implementation of an environment needed for developing network-centric interventions for controlling the smoking contagion. In order to develop such an environment we essentially need: an operationalized model of smoking that can be simulated, to determine the role of online social networks on smoking behavior, and actual methods to perform network-centric interventions. The objective of this thesis is to take first steps in all these categories. We perform Twitter-based surveillance of smoking-related tweets, and use mathematical modeling and simulation techniques to achieve our objective. Specifically, we use Twitter data to infer sentiments on smoking and electronic cigarettes, to estimate the proportion of user population that gets exposed to smoking-related messaging that is underage, and to identify statistically anomalous clusters of counties where people discuss about electronic cigarette a lot more than expected. In other work, we employ mathematical modeling and simulation approach to study how different factors such as addictiveness and peer-influence together contribute to smoking behavior diffusion, and also develop two methods to stymie social contagion. This lead to a total of four smoking contagion-related studies. These studies are just a first step towards the development of a network-centric intervention environment for controlling smoking contagion, and also to show that such an environment is realizable. / Ph. D.

This dissertation employs management systems engineering principles, data science, and industrial systems engineering techniques to address pressing challenges in emergency department (ED) efficiency, infectious disease management at mass gatherings, and student self-efficacy. It is structured into three essays, each contributing to a distinct domain of research, and utilizes industrial and systems engineering approaches to provide data-driven insights and recommend solutions. The first essay used data analytics and regression analysis to understand how patient length of stay (LOS) in EDs could be influenced by multi-level variables integrating patient, service, and organizational factors. The findings suggested that specific demographic variables, the complexity of service provided, and staff-related variables significantly impacted LOS, offering guidance for operational improvements and better resource allocation. The second essay utilized system dynamics simulations to develop a modified SEIR model for modeling infectious diseases during mass gatherings and assessing the effectiveness of commonly implemented policies. The results demonstrated the significant collective impact of interventions such as visitor limits, vaccination mandates, and mask wearing, emphasizing their role in preventing health crises. The third essay applied machine learning methods to predict student self-efficacy in Muslim societies, revealing the importance of socio-emotional traits, cognitive abilities, and regulatory competencies. It provided a basis for identifying students with varying levels of self-efficacy and developing tailored strategies to enhance their academic and personal success. Collectively, these essays underscore the value of data-driven and evidence-based decision- making. The dissertation's broader impact lies in its contribution to optimizing healthcare operations, informing public health policy, and shaping educational strategies to be more culturally sensitive and psychologically informed. It provides a roadmap for future research and practical applications across the healthcare, public health, and education sectors, fostering advancements that could significantly benefit society. / Doctor of Philosophy / Divided into three essays, this dissertation uses industrial and systems engineering and data science to help make emergency departments more efficient, manage the spread of diseases at large events, and predict students' belief in their abilities. The first essay investigates factors that influence how long patients stay in emergency departments, including patient demographics, triage level, the complexity of care they receive, and number of emergency department staff when patient arrived. The essay offers suggestions to improve these services and better manage resources. The second essay models the spread of COVID-19 during the Hajj, a religious mass gathering, and evaluates the effectiveness of three safety measures: limiting the number of attendees, vaccinations, and wearing masks. This essay shows how different strategies can work together to prevent outbreaks. The third essay uses artificial intelligence and machine learning to understand what affects Muslim students' confidence in their abilities, focusing on emotional intelligence, thinking skills, and self-discipline. The findings could help to identify students who need extra support and to create more personalized programs that will help them succeed. Overall, this dissertation contributes to advancing industrial and systems engineering and data science knowledge by addressing complex issues in healthcare, public health, and education, leading to more informed decisions and better strategies. Its broader impact includes improving hospital operations, guiding public health decisions, and helping develop educational programs and interventions that consider cultural and psychological factors.

The U.S. healthcare system is facing many public health challenges that affect population health, societal well-being, and quality of healthcare. Infant mortality, opioid overdose death, and hospital readmission after stroke are some of these important public health concerns that can impact the effectiveness and outcomes of the healthcare system. We analyze these problems through the industrial engineering and data analytics lens. The major goal of this dissertation is to enhance understanding of these three challenges and related interventions using different levels of analysis to improve the health outcomes. To attain this objective, I introduced three stand-alone papers to answer the related research questions. In essay 1, we focused on the performance of the state's healthcare systems in reducing unfavorable birth outcomes such as infant mortality, preterm birth, and low birthweight using Data Envelopment Approach. We constructed a unique state-level dataset to answer this main research question: what does make a healthcare system more successful in improving the birth outcomes? Our results indicated that socioeconomic and demographic factors may facilitate or obstruct health systems in improving their outcomes. We realized that states with a lower rate of poverty and African-American women were more successful in effectively reduce unfavorable birth outcomes. In the second essay, we looked into the trends of the opioid overdose mortalities in each state from 2008 to 2017. We investigated the effect of four state laws and programs that have been established to curb the epidemic (i.e., dose and duration limitations on the initial prescription, pain management clinic laws, mandated use of prescription drug monitoring programs, and medical cannabis laws) in short and long-term, while we controlled for several protentional risk factors. The results of fixed-effect regression and significant tests indicated that state policies and laws were unlikely to result in an immediate reduction in overdose mortalities and comprehensive interventions were needed to restrain the epidemic. The third essay investigated the risk factors of 30-day readmission in patients with ischemic stroke at an individual level. We aimed to identify the main risk factors of stroke readmissions and prioritized them using machine learning techniques and logistic regression. We also introduced the most effective predictive model based on different performance metrics. We used the electronic health records of stroke patients extracted from two stroke centers within the Geisinger Health System from 2015 to 2018. This data set included a comprehensive list of clinical features, patients' comorbidities, demographical characteristics, discharge status, and type of health insurance. One of the major findings of this study was that stroke severity, insert an indwelling urinary catheter, and hypercoagulable state were more important than generally known diagnoses such as diabetes and hypertension in the prediction of stroke 30-day readmission. Furthermore, machine learning-based models can be designed to provide a better predictive model. Overall, this dissertation provided new insights to better understand the three major challenges of the U.S. healthcare system and improve its outcomes. / Doctor of Philosophy / The major goal of a healthcare system can be summarized in three main objectives: preventing preterm birth and premature mortality, advancing the quality of life, and preparing for a good death. Despite all the national efforts to achieve these goals, the U.S. healthcare system still faces many obstacles and crises and suffers from inefficiencies. The U.S. infant mortality rate is still higher than any other comparable advanced country. The opioid overdose death rate has been steadily increasing since 1999 and has risen exponentially in recent years. Hospital readmissions especially in stroke patients impose a substantial cost burden on the healthcare system in the U.S. Also, readmitted stroke patients are at higher risk of mortality compared to the first admission. I believe that industrial engineering and data analytics approaches can help in advancing the understanding of these health challenges, their important risk factors, and effective interventions. In this dissertation, the main focus was on the performance, trends, variations, and processes of the healthcare systems. We applied innovative methods to provide answers to the following questions in three essays: What does make a healthcare system more successful in improving the birth outcomes? What factors do explain mortality from opioid painkillers? What are the determinants of state variations in mortalities from an opioid overdose? What is the impact of states' laws and programs and opioid prescription rates and overdose mortality rates? What are the most important contributors to stroke readmissions? The results of the first essay showed that not all the state's healthcare systems perform the same in terms of reducing unfavorable birth outcomes. States with lower people in poverty and lower African American women were more successful in improving their birth outcomes. The second study revealed that states with a higher share of uninsured people and binge drinkers were suffering from higher opioid overdose deaths. Also, our results implied that in addition to upstream prevention policies, states need to implement downstream programs to curb the epidemic. Finally, the third study showed that the top predictors of stroke readmissions within 30 days consist of the severity of the stroke, insert an indwelling urinary catheter, being overweight, and malnourished. The results of this dissertation can help to educate policymakers and practitioners at state and organizational level in a way to better serve the society and ultimately enhance the population health, quality of healthcare, and societal well-being.

This dissertation explores U.S. military surgeons’ purposeful efforts to alter how medical and line officers in the U.S. Army and Navy conceived of disease, appreciated surgeons’ roles, and organized medical war preparations through education, training, exposure, and medico-military professionalization between 1884 and 1918. It traces surgeons’ postwar efforts to change American military cultures in response to the revelations of the germ theory of disease and deadly typhoid fever epidemics in the American training camps of the Spanish-American War. Medical and line officers required academic education and practical lessons to contextualize disease, surgeons, and medical care, understand and appreciate germs’ role in medicine, and train to apply these lessons to benefit their soldiers and sailors. Surgeons also reinforced their scientific education and grew military medicine through postgraduate education and tactical training designed to enhance the line’s perception of surgeons and medical science.This dissertation rests on the contention that surgeons contributed to military preparation for the next war by effecting cultural change to prevent the epidemics of previous wars. This culture of medical preparation shaped how military medical departments recruited, organized, and trained medical officers, procured supplies, and managed civil-military relationships. Entwined cultural change and war preparation were expressed in the multiple mobilization activities through which surgeons validated the success or failure of their efforts. Troops participated in organized camps of instruction, maneuver camps, and major mobilizations to the U.S.-Mexico border, allowing surgeons to use the physical encampments, hospitals, and other surgeons to test assumptions, exercise and refine theory, validate operational principles, and improve from previous iterations. As the United States entered the Great War in 1917, epidemics of measles, influenza, and meningitis attacked Army and Navy recruit training camps. Rather than demonstrate failure, this dissertation positions the 1917 and 1918 epidemics to demonstrate medical officers’ successful military cultural change. A comparative approach between 1898 and 1918 also highlights cultural and medico-military evolution through the lenses of preparation and mobilization. Official military reports and archival sources illuminate cultural divisions between line and medical officers and track the curricular development of military hygiene and sanitation courses in undergraduate and professional military schools and specialized fields at military medical schools. This dissertation intervenes in military and medical historiographies by pushing the conversation beyond disease’s impact on war to center disease and changing perceptions of disease, culturally and medically, as features of military preparation. It also recasts military surgeons as central agents in the U.S. military’s turn-of-the-century professionalization and modernization efforts. As the world addresses the outcomes and aftermath of the COVID-19 pandemic, this dissertation demonstrates that physicians and societies met previous epidemics and pandemics on medical science’s past frontiers where the germ theory of disease had barely won acceptance. It also illustrates the power of individuals in subordinate classes to affect institutional cultures for the betterment of all. Lastly, as military operations during future pandemics are all but guaranteed, this dissertation proves that dedication and preparation are just as vital to epidemic defense as good science. / History

Resumen: En esta tesis doctoral se presentan tres modelos matemáticos que describen el comportamiento de dos enfermedades parasitarias con vector de transmisión; de los cuales dos modelos están dedicados a la Toxoplasmosis donde se explora la dinámica de la enfermedad a nivel de la población humana y de gatos domésticos. Los gatos juegan un papel de agentes infecciosos del Toxoplasma gondii. La dinámica cualitativa del modelo es determinada por el umbral básico de reproducción, R0. Si el parámetro R0 < 1, entonces la solución converge al punto de equilibrio libre de la enfermedad. Por otro lado, si R0 > 1, la convergencia es al punto de equilibrio endémico. Las simulaciones numéricas ilustran diferentes dinámicas en función del parámetro umbral R0 y muestra la importancia de este parámetro en el sector salud. Finalmente la Babesiosis bovina se modela a partir de cinco ecuaciones diferenciales ordinarias, que permiten explicar la influencia de los parámetros epidemiológicos en la evolución de la enfermedad. Los estados estacionarios del sistema y el número básico de reproducción R0 son determinados. La existencia del punto endémico y libre de enfermedad se expone, puntos que dependen del R0, ratificando la importancia del parámetro umbral en la salud publica. Objetivo: Construir modelos matemáticos epidemiológicos aplicados a enfermedades parasitarias (Toxoplasmosis y Babesiosis) con vector de transmisión. Metodología: Para la construcción de los modelos matemáticos epidemiológicos es necesario representar la enfermedad a partir de modelos de flujo, permitiendo ver la dinámica de la población entre los diferentes estadíos de la enfermedad, dichos movimientos son analizados a partir de sistemas dinámicos, análisis matemático y métodos numéricos; con estas herramientas es posible hacer un estudio detallado del modelo, permitiendo calcular parámetros umbrales que dominan la dinámica de la enfermedad y a su vez simular escenarios reales e hipotéticos. / Aranda Lozano, DF. (2011). Modeling of parasitic diseases with vector of transmission: toxoplasmosis and babesiosis bovine [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11539

Data transmission has become a need in various fields, like in social networks with the diverse interaction applications, or in the scientific and engineering areas where for example the use of sensors to capture data is growing, or in emergency situations where there is the imperative need to have a communication system to coordinate rescue operations. Wireless networks have been able to solve these issues to a great extent, but what can we do when a fixed supporting infrastructure is not available or becomes inoperative because of saturation? Opportunistic wireless networks are an alternative to consider in these situations, since their operation does not depend on the existence of a telecommunications infrastructure but they provide connectivity through the organized cooperation of users. This research thesis focuses on these types of networks and is aimed at improving the dissemination of information in opportunistic networks analyzing the main causes that influence the performance of data transmission. Opportunistic networks do not depend on a fixed topology but depend on the number and mobility of users, the type and quantity of information generated and sent, as well as the physical characteristics of the mobile devices that users have to transmit the data. The combination of these elements impacts on the duration of the contact time between mobile users, directly affecting the information delivery probability. This thesis starts by presenting a thorough "state of the art" study where we present the most important contributions related to this area and the solutions offered for the evaluation of the opportunistic networks, such as simulation models, routing protocols, simulation tools, among others. After offering this broad background, we evaluate the consumption of the resources of the mobile devices that affect the performance of the the applications of opportunistic networks, both from the energetic and the memory point of view. Next, we analyze the performance of opportunistic networks considering either pedestrian and vehicular environments. The studied approaches include the use of additional fixed nodes and different data transmission technologies, to improve the duration of the contact between mobile devices. Finally, we propose a diffusion scheme to improve the performance of data transmission based on extending the duration of the contact time and the likelihood that users will collaborate in this process. This approach is complemented by the efficient management of the resources of the mobile devices. / La transmisión de datos se ha convertido en una necesidad en diversos ámbitos, como en las redes sociales con sus diversas aplicaciones, o en las áreas científicas y de ingeniería donde, por ejemplo, el uso de sensores para capturar datos está creciendo, o en situaciones de emergencia donde impera la necesidad de tener un sistema de comunicación para coordinar las operaciones de rescate. Las redes inalámbricas actuales han sido capaces de resolver estos problemas en gran medida, pero ¿qué podemos hacer cuando una infraestructura de soporte fija no está disponible o estas se vuelven inoperantes debido a la saturación de peticiones de red? Las redes inalámbricas oportunísticas son una alternativa a considerar en estas situaciones, ya que su funcionamiento no depende de la existencia de una infraestructura de telecomunicaciones sino que la conectividad es a través de la cooperación organizada de los usuarios. Esta tesis de investigación se centra en estos tipos de redes oportunísticas y tiene como objetivo mejorar la difusión de información analizando las principales causas que influyen en el rendimiento de la transmisión de datos. Las redes oportunísticas no dependen de una topología fija, sino que dependen del número y la movilidad de los usuarios, del tipo y cantidad de información generada y enviada, así como de las características físicas de los dispositivos móviles que los usuarios tienen para transmitir los datos. La combinación de estos elementos influye en la duración del tiempo de contacto entre usuarios móviles, afectando directamente a la probabilidad de entrega de información. Esta tesis comienza presentando un exhaustivo estudio del ``estado del arte", donde presentamos las contribuciones más importantes relacionadas con esta área y las soluciones existentes para la evaluación de las redes oportunísticas, tales como modelos de simulación, protocolos de enrutamiento, herramientas de simulación, entre otros. Tras ofrecer esta amplia compilación de investigaciones, se evalúa el consumo de recursos de los dispositivos móviles que afectan al rendimiento de las aplicaciones de redes oportunísticas, desde el punto de vista energético así como de la memoria. A continuación, analizamos el rendimiento de las redes oportunísticas considerando tanto los entornos peatonales como vehiculares. Los enfoques estudiados incluyen el uso de nodos fijos adicionales y diferentes tecnologías de transmisión de datos, para mejorar la duración del contacto entre dispositivos móviles. Finalmente, proponemos un esquema de difusión para mejorar el rendimiento de la transmisión de datos basado en la extensión de la duración del tiempo de contacto, y de la probabilidad de que los usuarios colaboren en este proceso. Este enfoque se complementa con la gestión eficiente de los recursos de los dispositivos móviles. / La transmissió de dades s'ha convertit en una necessitat en diversos àmbits, com ara en les xarxes socials amb les diverses aplicacions d'interacció, o en les àrees científiques i d'enginyeria, en les quals, per exemple, l'ús de sensors per a capturar dades creix en l'actualitat, o en situacions d'emergència en què impera la necessitat de tenir un sistema de comunicació per a coordinar les operacions de rescat. Les xarxes sense fil han sigut capaces de resoldre aquests problemes en gran manera, però què podem fer quan una infraestructura de suport fixa no està disponible, o bé aquestes es tornen inoperants a causa de la saturació de peticions de xarxa? Les xarxes sense fil oportunistes són una alternativa que cal considerar en aquestes situacions, ja que el funcionament d'aquestes xarxes no depèn de l'existència d'una infraestructura de telecomunicacions, sinó que la connectivitat s'hi aconsegueix a través de la cooperació organitzada dels usuaris. Aquesta tesi de recerca se centra en aquest tipus de xarxes, i té com a objectiu millorar la difusió d'informació en xarxes oportunistes tot analitzant les principals causes que influeixen en el rendiment de la transmissió de dades. Les xarxes oportunistes no depenen d'una topologia fixa, sinó del nombre i la mobilitat dels usuaris, del tipus i la quantitat d'informació generada i enviada, i de les característiques físiques dels dispositius mòbils que els usuaris tenen per a transmetre les dades. La combinació d'aquests elements influeix en la durada del temps de contacte entre usuaris mòbils, i afecta directament la probabilitat de lliurament d'informació. Aquesta tesi comença amb un estudi exhaustiu de l'estat de la qüestió, en què presentem les contribucions més importants relacionades amb aquesta àrea i les solucions oferides per a l'avaluació de les xarxes oportunistes, com ara models de simulació, protocols d'encaminament o eines de simulació, entre d'altres. Després de mostrar aquest ampli panorama, s'avalua el consum dels recursos dels dispositius mòbils que afecten l'acompliment de les aplicacions de xarxes oportunistes, tant des del punt de vista energètic com de la memòria. A continuació, analitzem l'acompliment de xarxes oportunistes considerant tant els entorns de vianants com els vehiculars. Els enfocaments estudiats inclouen l'ús de nodes fixos addicionals i diferents tecnologies de transmissió de dades per a millorar la durada del contacte entre dispositius mòbils. Finalment, proposem un esquema de difusió per a millorar el rendiment de la transmissió de dades basat en l'extensió de la durada del temps de contacte, i de la probabilitat que els usuaris col·laboren en aquest procés. Aquest enfocament es complementa amb la gestió eficient dels recursos dels dispositius mòbils. / Herrera Tapia, J. (2017). Improving Message Dissemination in Opportunistic Networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86129

Heterogeneity, influenced by diverse factors such as age, gender, immunity, behavior, and spatial distribution, plays a critical role in the dynamics of infectious disease transmission. Discrete mathematical structures, including matrices and graphs, can offer effective tools for modeling the interactions among these diverse factors, resulting heterogeneous epidemiological models. This dissertation explores analytical approaches, specifically utilizing eigenvalues and eigenvectors of discrete structures, to characterize heterogeneity within mathematical models of infectious diseases. Theoretical results, along with numerical simulations, enhance our understanding of heterogeneous epidemiological processes and their significant implications for disease control strategies. In this dissertation, we introduce a unified approach to establish the final size formula in heterogeneous epidemic models, based on a new concept of “total infectious contacts” as an eigenvector-based aggregation of disease compartments. This approach allows us to identify the peak of total infectious contacts, offering a novel method to pinpoint the turning point of a disease outbreak. Furthermore, we examine spatial heterogeneity through two distinct mathematical frameworks: the Lagrangian and Eulerian models. The Lagrangian model assesses the epidemiological consequences of spatio-temporal residence time matrices, while the Eulerian model investigates “Turing instability” as a new underlying mechanism for spatial heterogeneity observed in disease prevalence data.

Porcine Epidemic Diarrhea Virus (PEDV) was first detected in Europe in the 1970s, but did not emerge in the United States until 2013. When it arrived, it ran rampant due to the lack of previous exposure, causing the death of 7-8 million neonatal piglets and $900 million to $1.8 billion in losses to the U.S. pork industry in 2013 and 2014. This virus causes diarrhea and vomiting which leads to dehydration and in extreme cases, death. Neonatal piglets rely heavily on passive lactogenic immunity from their mother's milk, thus making them especially vulnerable to this disease. Within 2-3 days of infection during the initial outbreak, there was a 90-95% mortality rate among these weaning piglets. Additionally, this virus is highly contagious, with high rates of fecal shedding during infection. To control the outbreak, the USDA had approved two emergency-relief vaccines, but both have proved to be ineffective at preventing disease or reducing fecal shedding. These vaccines are still available today. As such, it is necessary to develop a vaccine that will be effective at preventing illness and viral shedding. PEDV is a single-stranded RNA virus made of four major subunits: a structural spike (S), membrane (M), envelope (E), and nucleocapsid (N) proteins. The one most studied and of particular interest is the S protein as it facilitates the virus' attachment and entry into the host cell. The S protein is made of two domains, the S1 domain which allows for protein interactions between the virus and the host cell, and the S2 domain which allows for membrane fusion. Because of the S1's role in protein interaction, it is often the target of potential vaccines. Within the S1 domain, it's C-terminal domain encodes for the receptor binding domain (RBD), which is why the S1 CTD is the target of this study. In this study we focused on the expression, purification, and immunogenicity testing of the CTD protein using T7 Express E. coli as the expression host. We used PCR, gel electrophoresis, Sanger Sequencing, western blots, and mass spectrometry to ensure that the protein was being expressed properly. The future goal is to use this protein as the antigen in a future nanoparticle-based PEDV vaccine. / Master of Science / In 2013, Porcine Epidemic Diarrhea Virus (PEDV) emerged in the United States, causing an estimated $900 million to $1.8 billion in damages to the pork industry and the death of 7 to 8 million newborn piglets in just one year. This virus causes diarrhea and vomiting which causes dehydration and death, and newborn piglets are particularly vulnerable. During the initial outbreak, two emergency-relief vaccines were approved but have not been proven effective against the disease. Thus, it is of great importance to develop a vaccine that is both effective and safe. Therefore, our task was to express, purify, and test the immunogenicity of a segment of the PEDV spike protein to be used as the antigen of a future nanoparticle-based vaccine.