This dissertation examines the ways international research collaboration affects the ability of Colombian research teams to produce bibliographic outputs, and to contribute to local knowledge. Research hypotheses are tested using Zero Inflated Negative Binomial Regression models to account for the effects of international research collaboration on team output while controlling for team characteristics, partner characteristics, scientific discipline, sector, the characteristics of the teams' home institution, and team location. The study uses control groups and the Propensity Score Matching approach to assess the overall impact of international research collaboration on research team performance while controlling for the effects of endogeneity and selection bias. Results show that international research collaboration is positively associated with both team output and teams' ability to contribute to local knowledge. The study shows that such effects depend on the type of collaboration chosen and the type of partner involved. Particularly, it shows that while co-authoring with colleagues located overseas or receiving foreign funding positively affects team performance, hosting foreign researchers does not seem to affect a team's productivity or its ability to contribute to local knowledge once all other variables are held constant. It also finds that collaborating with partners from the South yields greater productivity counts than collaborating with partners from the North, but that collaboration with partners from northern countries is strongly associated with a team's ability to contribute to local knowledge, while collaboration with partners from southern countries is not. Theoretical and policy implications of these and other counterintuitive findings are discussed.

This dissertation examines the ways international research collaboration affects the ability of Colombian research teams to produce bibliographic outputs, and to contribute to local knowledge. Research hypotheses are tested using Zero Inflated Negative Binomial Regression models to account for the effects of international research collaboration on team output while controlling for team characteristics, partner characteristics, scientific discipline, sector, the characteristics of the teams' home institution, and team location. The study uses control groups and the Propensity Score Matching approach to assess the overall impact of international research collaboration on research team performance while controlling for the effects of endogeneity and selection bias. Results show that international research collaboration is positively associated with both team output and teams' ability to contribute to local knowledge. The study shows that such effects depend on the type of collaboration chosen and the type of partner involved. Particularly, it shows that while co-authoring with colleagues located overseas or receiving foreign funding positively affects team performance, hosting foreign researchers does not seem to affect a team's productivity or its ability to contribute to local knowledge once all other variables are held constant. It also finds that collaborating with partners from the South yields greater productivity counts than collaborating with partners from the North, but that collaboration with partners from northern countries is strongly associated with a team's ability to contribute to local knowledge, while collaboration with partners from southern countries is not. Theoretical and policy implications of these and other counterintuitive findings are discussed.

Dans la première partie de cette thèse, nous avons mis en place un métamodèle de transmission du paludisme basé sur la modélisation compartimentale susceptible-infecté-résistant (SIR) et prenant en compte les flux de mobilité humaine entre différents villages du Centre Sénégal. Les stratégies d’intervention géographiquement ciblées, s’étaient avérées efficaces pour réduire l’incidence du paludisme aussi bien dans les zones d’intervention qu’à l’extérieur de ces zones. Cependant, des actions combinées ciblant à la fois le vecteur et l’hôte, coordonnées à large échelle sont nécessaires dans les régions et pays visant l’élimination du paludisme à court/moyen terme.Dans la deuxième partie nous avons évalué différentes méthodes d’estimation de la mobilité humaine en l’absence de données individuelles. Ces méthodes incluaient la traçabilité spatio-temporelle des téléphones mobiles ainsi que les modèles mathématiques de gravité et de radiation. Le transport de l’agent pathogène dans l’espace géographique, par la mobilité d’un sujet infecté est un déterminant majeur de la vitesse de propagation d’une épidémie. Nous avons introduit le modèle d’impédance qui minimise l’erreur quadratique moyen sur les estimations de mobilité, en particulier dans les contextes où les ensembles de population sont caractérisés par leurs tailles hétérogènes.Nous avons enfin élargi le cadre des hypothèses sous-jacentes à la calibration des modèles de gravité de la mobilité humaine. L’hypothèse d’une distribution avec excès de zéros a fourni un meilleur ajustement et une meilleure prédictibilité, comparée aux hypothèses classiques n’assumant pas un excès de zéros : Poisson, Quasipoisson. / In the first part of this thesis, we have developed a malaria transmission metamodel based on the susceptible-infected-resistant compartmental modeling framework (SIR) and taking into consideration human mobility flows between different villages in the Center of Senegal. Geographically targeted intervention strategies had been shown to be effective in reducing the incidence of malaria both within and outside of intervention areas. However, combined interventions targeting both vector and host, coordinated on a large scale are needed in regions and countries aiming to achieve malaria elimination in the short/medium term.In the second part we have evaluated different methods of estimating human mobility in the absence of real data. These methods included spatio-temporal traceability of mobile phones, mathematical models of gravity and radiation. The transport of the pathogen through the geographical space via the mobility of an infected subject is a major determinant of the spread of an epidemic. We introduced the impedance model that minimized the mean square error on mobility estimates, especially in contexts where population sets are characterized by their heterogeneous sizes.Finally, we have expanded the framework of assumptions underlying the calibration of the gravity models of human mobility. The hypothesis of a zero inflated distribution provided a better fit and a better predictability, compared to the classical approach not assuming an excess of zeros: Poisson, Quasipoisson.

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No. of bitstreams: 1 TeseEAMms.pdf: 1490434 bytes, checksum: e7a807666b453630ffb423774d2539b9 (MD5) Previous issue date: 2016-03-23 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / From the generalized normal distribution and concepts of the generalized autoregressive moving averages models we introduce the generalized normal-ARMA model as an alternative way to model time series exhibiting symmetry and tails that may be lighter or heavier when compared the normal distribution. We present application for proposed model using three time series in the hydrology, economy and publics policy areas. The proposed model is presented as good alternative when compared to ARMA model with normal distribution. We extended this model the case of the asymmetric time series. In this case we used the Box-Cox transformation, denoted by Box-Cox generalized normal ARMA. The particular case, when we use the logarithmic transformation is called generalized log-normal ARMA. We adjusted the models with transformation to the series on monthly average affluent streamflow of the Furnas and Sobradinho hydroelectric plants. We obtain the prediction values for the model with transformation, that are better when compared with the model without transformation. To treat time series that exhibit periodic in the correlation function we defined three extensions for periodic autoregressive model, called generalized normal periodic autoregressive model, generalized log-normal periodic autoregressive model and Box-Cox generalized normal periodic autoregressive model. We can observed that the series on monthly average affluent streamflow of the Furnas and Sobradinho hydroelectric plants have periodic correlation. We present two applications of periodic models from these series. In the models, we note that is not necessary the use of generalized normal distribution in every months, just in some the generalized normal distribution presented better results than the normal distribution. Finally, we define the generalized normal zero inflated distribution and the generalized normal zero inflated ARMA model for time series. Adopting the model for series that have zero inflation and the maximum likelihood method for estimation of parameters, we analyze the serie of the amount of rainfall in the city of São Carlos. / A partir da distribuição normal generalizada e dos conceitos do modelo autorregressivo e de médias móveis generalizado, introduzimos o modelo normal generalizada- ARMA, como alternativa para modelar séries temporais, que exibem simetria e caudas mais leves ou mais pesadas quando comparadas com a distribuição normal. Apresentamos aplicações do modelo proposto, usando três séries temporais, das áreas de hidrologia, políticas públicas e economia. O modelo proposto se apresentou como uma boa alternativa ao modelo ARMA com distribuição normal. Estendemos o modelo para o caso de séries que apresentam assimetria. Neste caso, utilizamos a transformação de Box-Cox, denotado por Box-Cox normal generalizada-ARMA. O caso particular quando utilizamos a transformação logarítmica é chamado de log-normal generalizada-ARMA. Ajustamos os modelos com transformação à séries de vazões das usinas hidrelétricas de Furnas e Sobradinho. Calculamos predições, que para o modelo com transformação, foram melhores, quando comparado ao modelo sem transformação. Com o objetivo de tratar séries que apresentam periodicidade na função de correlação, definimos três extensões do modelo autorregressivo periódico, chamando-os de modelo normal generalizada autorregressivo periódico, modelo log-normal generalizada autorregressivo periódico e modelo Box-Cox normal generalizada autorregressivo periódico. Constatamos que as séries de vazões das usinas hidrelétricas de Furnas e Sobradinho apresentam correlação periódica. Apresentamos duas aplicações dos modelos periódicos propostos usando estas séries. Nos ajustes dos modelos, notamos que não há necessidade da utilização da distribuição normal generalizada em todos os meses, mas em alguns a distribuição normal generalizada se sobressaiu em relação a distribuição normal. Por último, definimos a distribuição normal generalizada zero inflacionada e o modelo para séries temporais normal generalizada zero inflacionada-ARMA. Adotando o método de máxima verossimilhança e o modelo para séries que apresentam inflação de zeros, analisamos a série da quantidade de precipitação pluviométrica da cidade de São Carlos.

Network screening and countermeasure selection are two crucial steps in the highway improvement process. In network screening, potential improvement locations are ranked and prioritized based on a specific method with a set of criteria. The most common practice by transportation agencies has been to use a simple scoring method, which, in general, weighs and scores each criterion and then ranks the locations based on their relative overall scoring. The method does not deal well with criteria that are qualitative in nature, nor does it account for the impacts of correlation among the criteria. The introduction of Analytic Hierarchy Process (AHP) provides agencies with a method to include both quantitative and qualitative criteria. However, it does not address the issue on correlation. This dissertation explores the use of both Analytic Network Process (ANP) and Fuzzy Analytic Network Process (FANP) for their potential capabilities to address both issues. Using urban four-lane divided highways in Florida for bicycle safety improvements, both ANP and FANP were shown to provide more reasonable rankings than AHP, with FANP providing the best results among the methods. After the locations are ranked and prioritized for improvements, the next step is to evaluate the potential countermeasures for improvements at the selected top-ranked locations. In this step, the standard practice has been to use Crash Modification Factors (CMFs) to quantify the potential impacts from implementing specific countermeasures. In this research, CMFs for bicycle crashes on urban facilities in Florida were developed using the Generalized Linear Model approach with a Zero-Inflated Negative Binomial (ZINB) distribution. The CMFs were tested for their spatial and temporal transferability and the results show only limited transferability both spatially and temporally. The CMFs show that, in general, wider lanes, lower speed limits, and presence of vegetation in the median reduce bicycle crashes, while presence of sidewalk and sidewalk barrier increase bicycle crashes. The research further considered bicycle exposure using the bicycle activity data from the Strava smartphone application. It was found that increased bicycle activity reduces bicycle crash probabilities on segments but increases bicycle crash probabilities at signalized intersections. Also, presence of bus stops and use of permissive signal phasing at intersections were found to increase bicycle crash probabilities.

Background: Shifting the healthcare payment system from a volume-based to a value-based model has been a significant effort to improve the quality of care and reduce healthcare costs in the US. In 2018, Massachusetts Medicaid launched Accountable Care Organizations (ACOs) as part of the effort. Constructing, assessing, and risk-adjusting quality measures are integral parts of the reform process. Methods: Using data from the MassHealth Data Warehouse (2016-2019), we assessed the loss of community tenure (CTloss) as a potential quality measure for patients with bipolar, schizophrenia, or other psychotic disorders (BSP). We evaluated various statistical models for predicting CTloss using deviance, Akaike information criterion, Vuong test, squared correlation and observed vs. expected (O/E) ratios. We also used logistic regression to investigate risk factors that impacted medication nonadherence, another quality measure for patients with bipolar disorders (BD). Results: Mean CTloss was 12.1 (±31.0 SD) days in the study population; it varied greatly across ACOs. For risk adjustment modeling, we recommended the zero-inflated Poisson or doubly augmented beta model. The O/E ratio ranged from 0.4 to 1.2, suggesting variation in quality, after adjusting for differences in patient characteristics for which ACOs served as reflected in E. Almost half (47.7%) of BD patients were nonadherent to second-generation antipsychotics. Patient demographics, medical and mental comorbidities, receiving institutional services like those from the Department of Mental Health, homelessness, and neighborhood socioeconomic stress impacted medication nonadherence. Conclusions: Valid quality measures are essential to value-based payment. Heterogeneity implies the need for risk adjustment. The search for a model type is driven by the non-standard distribution of CTloss.