Modeling social response to the spread of an infectious disease

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 85-88).With the globalization of culture and economic trade, it is increasingly important not only to detect outbreaks of infectious disease early, but also to anticipate the social response to the disease. In this thesis, we use social network analysis and data mining methods to model negative social response (NSR), where a society demonstrates strain associated with a disease. Specifically, we apply real world biosurveillance data on over 11,000 initial events to: 1) describe how negative social response spreads within an outbreak, and 2) analytically predict negative social response to an outbreak. In the first approach, we developed a meta-model that describes the interrelated spread of disease and NSR over a network. This model is based on both a susceptible-infective- recovered (SIR) epidemiology model and a social influence model. It accurately captured the collective behavior of a complex epidemic, providing insights on the volatility of social response. In the second approach, we introduced a multi-step joint methodology to improve the detection and prediction of rare NSR events. The methodology significantly reduced the incidence of false positives over a more conventional supervised learning model. We found that social response to the spread of an infectious disease is predictable, despite the seemingly random occurrence of these events. Together, both approaches offer a framework for expanding a society's critical biosurveillance capability.by Jane A. Evans.S.M

Determination of dengue hemorrhagic fever disease factors using neural network and genetic algorithms / Yuliant Sibaroni, Sri Suryani Prasetiyowati and Iqbal Bahari Sudrajat

The Aedes aegypti mosquito and the Aedes albopictus mosquito are carriers of the virus that causes Dengue Hemorrhagic Fever (DHF). In Indonesia, the spread of DHF disease has taken place for 41 years. Within this period, there was an increase in the number of spreading areas by 97% and an increase in the number of cases by 99%. Based on the data from previous studies, further information is needed related to the factors that have the most influence on the level of DHF infection in a region. Based on the initial study conducted, there are 6 factors that have the potential to influence the number of DHF cases in an area, namely temperature (X1), rainfall (X2), population density (X3), altitude (X4), distribution of males (X5), and distribution of education level (X6). In this study, the problem of determination dengue disease factors was modeled using a neural network. The activation function in this neural network model then estimated using genetic algorithms. Determination of the best factor is carried out in a genetic algorithm by combining several parameters of the crossover probability (Pc) and mutation probability (Pm). This experiment show that the main factors that influence the spread of DHF in Bandung area are temperature, altitude, distribution of gender, and distribution of education levels. The best accuracy system obtained in this study using these 4 factors reached 72%

Enteropathogen antibody dynamics and force of infection among children in low-resource settings.

Little is known about enteropathogen seroepidemiology among children in low-resource settings. We measured serological IgG responses to eight enteropathogens (Giardia intestinalis, Cryptosporidium parvum, Entamoeba histolytica, Salmonella enterica, enterotoxigenic Escherichia coli, Vibrio cholerae, Campylobacter jejuni, norovirus) in cohorts from Haiti, Kenya, and Tanzania. We studied antibody dynamics and force of infection across pathogens and cohorts. Enteropathogens shared common seroepidemiologic features that enabled between-pathogen comparisons of transmission. Overall, exposure was intense: for most pathogens the window of primary infection was <3 years old; for highest transmission pathogens primary infection occurred within the first year. Longitudinal profiles demonstrated significant IgG boosting and waning above seropositivity cutoffs, underscoring the value of longitudinal designs to estimate force of infection. Seroprevalence and force of infection were rank-preserving across pathogens, illustrating the measures provide similar information about transmission heterogeneity. Our findings suggest antibody response can be used to measure population-level transmission of diverse enteropathogens in serologic surveillance

Challenges in dengue research: A computational perspective

This is the final version of the article. Available from Wiley via the DOI in this record.The dengue virus is now the most widespread arbovirus affecting human populations, causing significant economic and social impact in South America and South-East Asia. Increasing urbanization and globalization, coupled with insufficient resources for control, misguided policies or lack of political will, and expansion of its mosquito vectors are some of the reasons why interventions have so far failed to curb this major public health problem. Computational approaches have elucidated on dengue's population dynamics with the aim to provide not only a better understanding of the evolution and epidemiology of the virus but also robust intervention strategies. It is clear, however, that these have been insufficient to address key aspects of dengue's biology, many of which will play a crucial role for the success of future control programmes, including vaccination. Within a multiscale perspective on this biological system, with the aim of linking evolutionary, ecological and epidemiological thinking, as well as to expand on classic modelling assumptions, we here propose, discuss and exemplify a few major computational avenues—real-time computational analysis of genetic data, phylodynamic modelling frameworks, within-host model frameworks and GPU-accelerated computing. We argue that these emerging approaches should offer valuable research opportunities over the coming years, as previously applied and demonstrated in the context of other pathogens.JL, AW and SG received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 268904 - DIVERSITY. MR was supported by a Royal Society University Research Fellowship. NRF by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant number 204311/Z/16/Z). WT has received funding from a doctoral scholarship from the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership

Modeling dengue immune responses mediated by antibodies: A qualitative study

Dengue fever is a viral mosquito-borne infection and a major international public health concern. With 2.5 billion people at risk of acquiring the infection around the world, disease severity is influenced by the immunological status of the individual, seronegative or seropositive, prior to natural infection. Caused by four antigenically related but distinct serotypes, DENV-1 to DENV-4, infection by one serotype confers life-long immunity to that serotype and a period of temporary cross-immunity (TCI) to other serotypes. The clinical response on exposure to a second serotype is complex with the so-called antibody-dependent enhancement (ADE) process, a disease augmentation phenomenon when pre-existing antibodies to previous dengue infection do not neutralize but rather enhance the new infection, used to explain the etiology of severe disease. In this paper, we present a minimalistic mathematical model framework developed to describe qualitatively the dengue immunological response mediated by antibodies. Three models are analyzed and compared: (i) primary dengue infection, (ii) secondary dengue infection with the same (homologous) dengue virus and (iii) secondary dengue infection with a different (heterologous) dengue virus. We explore the features of viral replication, antibody production and infection clearance over time. The model is developed based on body cells and free virus interactions resulting in infected cells activating antibody production. Our mathematical results are qualitatively similar to the ones described in the empiric immunology literature, providing insights into the immunopathogenesis of severe disease. Results presented here are of use for future research directions to evaluate the impact of dengue vaccines.A.S, H.F., and E.S. E.S. has received funded from the Indonesian RistekBrin Grant No. 122M/IT1.C02/TA.00/2021, 2021 (previously RistekDikti 2018-2021). M.A. received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 792494

Trends in Infectious Diseases

This book gives a comprehensive overview of recent trends in infectious diseases, as well as general concepts of infections, immunopathology, diagnosis, treatment, epidemiology and etiology to current clinical recommendations in management of infectious diseases, highlighting the ongoing issues, recent advances, with future directions in diagnostic approaches and therapeutic strategies. The book focuses on various aspects and properties of infectious diseases whose deep understanding is very important for safeguarding human race from more loss of resources and economies due to pathogens

Spatio-temporal analysis of infectious diseases

Los sistemas de vigilancia de salud pública colectan y analizan datos que soportan los programas de control y prevención de enfermedades en todo el mundo. En Colombia, el sistema de vigilancia en salud pública (SIVIGILA) esta encargado del flujo de datos e información de la vigilancia de las enfermedades de notificación obligatoria que afectan la salud de los Colombianos. Las enfermedades transmitidas por mosquitos tales como el dengue, la malaria, la fiebre amarilla, la enfermedad del virus del Chikungunya, la enfermedad del virus del Zika (EVZ) entre otras afectan seriamente la salud de las poblaciones a través de todo el país. Dentro de estas enfermedades se destacan la enfermedad del dengue y la EVZ. El dengue es responsable de una gran cantidad de personas enfermas con algunos casos de mortalidad, desde la decada de los ochenta en el siglo veinte, mientras que la EVZ se ha reportado en el país desde el segundo semestre del a˜no 2015 asociada a severos síndromes neurológicos en neonatos y adultos. En esta tesis por compendio de publicaciones se exploran métodos estadísticos jerárquicos Bayesianos para la evaluación del riesgo espacial y temporal del dengue y la EVZ en varios niveles de agregación temporal y espacial de los datos post-procesados del sistema de vigilancia en Colombia, especialmente motivados por explorar los problemas y desafíos de la implementación de estos modelos. La estructura de la tesis consiste de un capítulo introductorio, y ocho capítulos que corresponden a un número igual de artículos de investigación. El capítulo uno es un resumen general de la disertación que presenta los objetivos, la metodología, resultados y conclusiones del trabajo de investigación. El segundo capítulo analiza datos temporalmente agregados de casos de dengue y covariables meteorológicas asociadas a la enfermedad utilizando modelos con parámetros que varian en el tiempo. El capítulo tres estudia modelos espaciales de riesgo de dengue con parámetros que varian en el espacio y covariables derivadas de datos de sat´elite a nivel de ciudad. El capítulo cuatro explora modelos espacio-temporales de riesgo de dengue incluyendo covariables derivadas de datos de satélite con parámetros que varian en el tiempo a nivel de ciudad. El capítulo cinco desarrolla modelos espacio-temporales de varios niveles geográficos de agregación para la estimación de riesgo de dengue a nivel de ciudad. El sexto capítulo desarrolla la estimación del riesgo en paralelo de dengue y la EVZ a nivel de ciudad y de departamento. El capítulo siete desarrolla la estimación de riesgo conjunto de dengue y EVZ utilizando modelos multivariados jerárquicos Bayesianos a nivel de ciudad y de departamento. La estimación de los parámetros de los modelos en los capítulos dos, tres, cuatro y siete se desarrolla usando métodos de Monte Carlo de Cadenas de Markov, mientras que lo capítulos cinco y seis utilizan “integrated nested Laplace approximation” (INLA). Los capítulos ocho y nueve presentan modelos no-lineales para los datos acumulados de los casos de EVZ en diferentes ciudades de Colombia. El capítulo ocho realiza la estimación de los parámetros por medio del método de mínimos cuadrados no-lineales, mientras que el capítulo nueve utiliza Monte Carlo Hamiltoniano para el mismo objetivo.Public health surveillance systems collect and analyze data supporting programs of controlling and preventing diseases all around the world. In Colombia, the public health surveillance system (SIVIGILA) is in charge of the data and information flow for the surveillance of obligatory notification diseases affecting the Colombian population health. Diseases transmitted by mosquitoes such as dengue, malaria, yellow fever, Chikungunya fever, Zika virus disease (ZVD) among other seriously affect health populations along the country. Within these diseases, dengue and ZVD are highlighted. Dengue is responsible of a great burden of sick people with some cases of mortality since the eighties in the twenty century, while ZVD has been reported in the country since the second semester of year 2015 associated to severe neurological syndrome in newborns and adults. In this thesis by compendium of publications are explored hierarchical Bayesian statistical methods for the assessment of temporal and spatial dengue and ZVD risk at some temporal and spatial aggregation level using post-processed data obtained from the surveillance system in Colombia, specially motivated by exploring model implementation problems and challenges. The dissertation structure consist in one introductory chapter, and eight chapters corresponding to an equal number of research papers. Chapter one is an overall summary of the dissertation presenting the objectives, methodology, results and conclusions of the research work. The second chapter analyzes temporally aggregated data of dengue and meteorological covariates associated with the disease using dynamic models with time-varying parameters. The chapter three studies spatial models of dengue risk with space-varying parameters and covariates derived from satellite data at city-level. The chapter four explores spatio-temporal models of dengue risk including covariates derived from satellite data with time-varying parameters. The chapter five develops spatio-temporal models of dengue risk at two geographic levels of aggregation at city-level. The chapter six develops the parallel estimation of dengue and ZVD risk at departmental and city level. The chapter seven develops the joint estimation of dengue and ZVD risk using hierarchical Bayesian multivariate models at departmental and city level. Parameter estimation in chapters two, three, four, and seven are developed using Monte Carlo Markov Chain methods, while chapters five and six used ”integrated nested Laplace approximation” (INLA). The chapters eight and nine present nonlinear models for the cumulative data of the ZVD cases in several Colombian cities. The chapter eight makes parameter estimation by means of the nonlinear least squares, while chapter nine presents Hamiltonian Monte Carlo for the same objective