Purpose: Arterial stiffening poses an increased risk of cerebrovascular diseases, cognitive impairments, and even dementia as cardiac pulsations reach further into the brain causing white matter hyperintensities and microbleeds. Therefore it is of interest to obtain methods to estimate and map cardiac related pulsatility in the brain. Improvements of functional magnetic resonance imaging (fMRI) sequences is potentially allowing detection of rapid physiological processes in the echo-planar imaging (EPI) signal in the brainthrough a higher sampling rate. Specifically in this thesis, estimation and localization of cardiac pulsation and respiration is conducted through analysis of resting state data obtained with a multiband EPI sequence that permits whole brain imaging at a shorter repetition time (TR) than conventional EPI. The origin of these physiological signals are likely a mixture of inflow and compartment volume shifts during the cardiac- and respiratory cycles. As the amount of physiologically related signal in the multiband sequence used at the Biomedical Engineering Dept. R&D, Umeå University Hospital is unknown, the aim of this project is to find and map cardiac pulsatility and respiration for future research. Methods: Multiband fMRI data from 8 subjects was used, collected in a 3 Tesla scanner using a 32-channel head coil. The physiological signals were estimated through an algorithm that was developed to down-sample and temporally shift copies of simultaneous recordings of pulse and respiration. These signals were obtained using the scanner’s built-in pulse oximeter and a respiration belt. The shifted copies were voxel-wise, and slice by slice, correlated to the fMRI data using Pearson correlation. The time shift yielding maximum mean correlation within the brain was, for each slice, used to create statistical maps for significant voxels to show the localization and magnitude of correlation for cardiac pulsation andrespiration. Results: Many voxels around and nearby larger vessels and ventricles were highly correlated with the down-sampled, time shifted signals of the cardiac pulsation for all subjects. The cardiac pulsation maps resembled cerebral vasculature and were mostly localized around the Circle of Willis, brainstem, and the ventricles. Respiration signal was also highly correlated, and spatially located at the sides of the brain although mostly concentrated at the parietal- and occipital lobes. Conclusion: The results demonstrated that many voxels in the brain were highly correlated with cardiac pulsation and respiration using multiband EPI, and the statistical maps revealed distinct patterns for both of the physiological signals. This method and results for mapping cardiac related pulsatility, and respiration could be used for future research in order to better understand cerebral diseases and impairments, and alsoto improve fMRI filtering. Keywords: Arterial stiffness, Functional magnetic resonance imaging, Resting state, Multiband, CardiacPulsation, Respiration, Correlation analysis / Syfte: Arteriell förstyvning medför en ökad risk för cerebrovaskulära sjukdomar, kognitiva störningar och till och med demens då hjärtpulsationer når längre in i hjärnan orsakar vit materia hyperintensiteter och mikroblödningar. Av detta skäl är det därför av intresse att ta fram metoder för att estimera och kartlägga hjärtrelaterad pulsationer i hjärnan. Förbättringar av funktionella magnetresonanstomografi (fMRI) sekvenser kan möjliggöra detektering av snabba fysiologiska processer i den eko-planära (EPI) signalen i hjärnan genom en högre samplingsfrekvens. Specifikt i denna uppsats, utförs en skattning och lokalisering av hjärtpulsation och respiration genom analys av ’resting state’ data erhållet av en multiband-EPI sekvens som tillåter bildgivning av hela hjärnan med en kortare repetitionstid (TR) än konventionell EPI. Ursprunget avdessa fysiologiska signaler är sannolikt från en blandning av flöde- och volymsförändringar under hjärt- och respirationscyklerna. Då mängden av fysiologiskt relaterad signaler i multiband sekvensen, som används på Biomedicinska avdelningen, FoU Umeå Universitetssjukhust, är okänd så är målet med projektet att hitta och kartlägga hjärtpulsation och respiration för framtida forskning. Metod: Multiband fMRI data från 8 personer användes, insamlade från en 3 Tesla scanner med en 32-kanals huvudspole. De fysiologiska signalerna uppskattades genom en algoritm som utveckades för att sampla ned och tidsförskjuta kopior av simultant tagna signaler av puls och respiration. Dessa signaler samlades in med skannerns inbyggda pulsoximeter och andningsband. De förskjutna kopiorna var voxelvis, snitt för snitt, korrelerade med fMRI datat med användning av Pearson-korrelation. Det tidsskift somför varje snitt resulterade i maximal medelkorrelation i hjärnan användes för att skapa statistiska kartor, med endast signifikanta voxlar, för att visa var och hur mycket korrelation av hjärtpulsation och respiration som finns. Resultat: Många voxlar runt och nära större kärl och ventriklar var för alla personer starkt korrelerade medde samtidigt tagna, och tidsförskjutna signalerna av hjärtpulsation. Pulsationskartorna liknade cerebral vaskulatur och var mestadels lokaliserade kring Willis ring, hjärnstammen och ventriklar. Respirationssignalen var även starkt korrelerad och lokaliserad på sidorna av hjärnan, mestadels koncentrerat vid parietal- och occipital loberna. Slutsats: Resultaten visade att många voxlar i hjärnan var starkt korrelerade med hjärtpulsation och respiration vid användning av multiband EPI, och de statistiska kartorna avslöjade distinkta mönster för de båda fysiologiska signalerna. Den framtagna metoden och dess resultat för kartläggning av hjärtrelaterade pulsationer och respiration kan användas i framtida forskning i syfte att bättre förstå cerebrala sjukdomar och nedsättning, även för att förbättre fMRI filtrering. Nyckelord: Arteriell förstyvning, Funktionell magnetresonanstomografi, Resting state, Multiband, Hjärtpulsation, Andning, Korrelationsanalys

In this thesis we examine methods for evaluating a traffic alarm system. Nuisance alarms can quickly increase the volume of alarms experienced by the alarm operator and obstruct their work. We propose two methods for removing a number of these nuisance alarms, so that events of higher priority can be targeted. A parallel correlation analysis demonstrated significant correlation between single and clusters of alarms, presenting a strong cause for causality. While a serial correlation was performed, it could not conclude evidence of consequential alarms. In order to assist Trafikverket with maintenance scheduling, a long short-term model (LSTM) model, to predict univariate time-series of discretely binned alarm sequences. Experiments conclude that the LSTM model provides higher precision for alarm sequences with higher repeatability and recurring patterns. For other, randomly occurring alarms, the model performs unsatisfactory. / Den här examensuppsatsen granskar olika metoder för att utvärdera ett larmsystem med inriktning mot trafiksäkerhet. Störande larm kan skapa stora mängder larm som försvårar arbetet för larmoperatörer. Vi föreslår två metoder för att avlägsna störande larm, så att uppmärksamhet kan riktas mot varningar med högre prioritet. En parallell korrelationsanalys som demonstrerade hög korrelation mellan både enskilda och kluster av larm. Detta presenterar ett starkt orsakssamband. En korskorrelation utfördes även, men denna kunde inte fastställa existens av s.k. följdlarm. För att assistera Trafikverket med schemaläggning av underhåll har en long short-term memory (LSTM) modell implementerats för att förutspå univariata tidsserier av diskretiserade larmsekvenser. Utförda experiment sammanfattar att LSTM modellen presterar bättre för larmsekvenser med återkommande mönster. För mera slumpmässigt genererade larmsekvenser, presterar modellen med lägre precision.

Understanding the background activity of a computer and its operating system when running an arbitrary application can lead to important performance discoveries. This is especially interesting in cases where the same task of an application is run over and over again and there is an expected run time, such as in testing. If a major deviation in the run time occurs, it can be crucial to know the reason to prevent it from happening again. Additionally, finding the relevant measurements to explain the performance in a compact way such as a score can further help both the readability and understanding of the performance. For this project, a tool was developed that, using existing tools, measures various parts of a computer and its operating system and presents their activity during the run time of a selected application over multiple iterations, as well as calculates the relevance of the different measurements with the purpose of finding one that can consistently rate the overall performance. Using the results, no single measurement was found that could rate the overall performance on a consistent level, only for specific scenarios. Possible causes for performance deviations could be found, however. The results show that although there is some activity in the background, most background operating system noise does not have a major effect on performance and that major deviations in the run time are rare. However, inflicting manual noise in either the form of CPU usage or memory usage can cause major performance penalties, sometimes reaching up to the double average run time.

In the fall of 2006, soils from three small valleys in the Laxemar/Oskarshamn area were sampled. A total of eight composite samples were characterized for a number of soil parameters that are important for geochemical sorption and were later also used in batch sorption experiments. Solid/liquid partition coefficients (Kd values) were then determined for seven radionuclides in each of the eight samples. To contribute to the interpretation of the sorption results together with the soil characterizations, this study aims to describe the sorption behavior of the radionuclides caesium, nickel and uranium and also discern which parameters that could provide a basis for estimating the strength of sorption of radionuclides in general. The methodology included quantitative methodologies such as compilation of chemical equilibrium diagrams by the software Hydra/Medusa and correlation analyses using the statistical software SPSS statistics. Based on the speciation diagrams of each radionuclide and identified important linear and non-linear relationships of the Kd values with a number of soil parameters, the following soil- and soil solution properties were found to have controlled the sorption of Cs, Ni and U, respectively, in the Laxemar soils. Cs: the specific surface area of the soil coupled to the clay content. Ni: the cation exchange capacity, alkaline solution pH, soil organic matter and dissolved organic matter. U: the cation exchange capacity, soil organic matter, dissolved organic matter, dissolved carbonate and alkaline solution pH. The soil that showed the strongest sorption varied between the nuclides, which can be related to the individual sorption behavior of caesium, nickel and uranium, as well as the different physicochemical properties of the soils. The parameters that should be prioritized in characterizations of soil samples are identified to be: solution pH, the cation exchange capacity, the specific surface area of the soil, soil organic matter and soil texture (clay content). / För att kunna fatta beslut relaterade till hypotetisk framtida kontaminering från slutförvar av radioaktivt avfall är det direkt avgörande att förstå mobiliteten av radioaktiva element i miljön. Sorption är en av de viktigaste kemiska mekanismerna som kan minska spridningen av radionuklider i vatten/jord/bergssystem, där nukliderna fördelar sig mellan vätskefasen och ytor på fasta partiklar i dessa system. Fördelningskoefficienter (Kd värden) används generellt som ett kvantitativt mått på sorptionen, där ett högt Kd värde innebär att en större andel av ämnet i fråga är bundet till den fasta fasen. Under hösten 2006 togs jordprover från tre dalgångar i Laxemar/Oskarshamn. Totalt åtta jordprover karakteriserades för ett antal jordparametrar som är viktiga för geokemisk sorption och användes senare i batchförsök tillsammans med ett naturligt grundvatten. Fördelningskoefficienter (Kd värden) bestämdes för sju radionuklider (Cs, Eu, I, Ni, Np, Sr and U) för vart och ett av de åtta jordproverna. För att bidra till tolkningen av sorptionsresultaten tillsammans med jordprovernas egenskaper syftar denna studie till att beskriva sorptionsbeteendet hos radionukliderna caesium, nickel och uran samt urskilja vilka parametrar som kan fungera som grund för att uppskatta sorptionsstyrkan av radionuklider i allmänhet. För att uppnå detta syfte så har studien följande mål. Identifiera de jord- och marklösningsegenskaper som kontrollerar sorptionen av Cs, Ni respektive U i de åtta Laxemar proverna. Bestämma vilket Laxemar-jordprov som starkast sorberar de tre radionukliderna. Identifiera de jordparametrar som bör prioriteras vid jordkarakteriseringar, baserat på deras sorptionsinflytande, för att kunna uppskatta Kd värden endast med begränsad information om ett jordsystem. Metoden innefattade kvantitativa metoder såsom sammanställning av kemiska jämviktsdiagram med programvaran Hydra/Medusa och korrelationsanalyser med hjälp av statistikprogramvaran SPSS statistics. De kemiska jämviktsdiagrammen bidrog till att beskriva specieringen av respektive nuklid som en funktion av pH och korrelationsanalyserna bidrog till att identifiera linjära samband mellan par av variabler, tex mellan Kd och jordparametrar. Baserat på specieringsdiagrammen för varje radionuklid och identifierade viktiga linjära och icke-linjära förhållanden mellan Kd-värdena och ett antal jordparametrar har följande egenskaper hos jordarna och marklösningen visat sig huvudsakligen kontrollera sorptionen av Cs, Ni respektive U i de åtta Laxemar jordarna: För caesium gäller jordens specifika ytarea kopplad till lerinnehållet, medan för nickel är det katjonbytarkapaciteten, organiskt material, alkaliska pH-värden samt löst organiskt material. Sorptionen av uran befanns kontrolleras av katjonbytarkapaciteten, organiskt material, löst organiskt material, alkaliska pH-värden samt lösta karbonater. Den jord som visade starkast sorption varierar mellan de tre nukliderna, vilket kan relateras till nuklidernas individuella sorptionsbeteende i jord samt jordarnas olika fysikaliska och kemiska egenskaper. Parametrarna som bör prioriteras vid karaktärisering av jordprov identifierades vara: pH, katjonbytarkapaciteten, jordens specifika ytarea, mängden organiskt material samt jordtexturen (lerinnehåll).

<p><strong>Background:</strong> A better application of evidence-based available therapies and optimal patient care are suggested to have a positive association with patient outcomes for cardiovascular disease (CVD) patients. Electronic integration of care tested in the Computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness (COMPETE) Π study showed that a shared electronic decision-support system to support the primary care of diabetes improved the process of care and some clinical markers of the quality of diabetes care. On the basis of COMPETE Π trial, COMPETE Ш study showed that older adults at increased risk of cardiovascular events, if connected with their family physicians and other providers via an electronic network sharing an intensive, individualized cardiovascular tracking, advice and support program, enhanced their process of care – using a process composite score to lower their cardiovascular risk more than those in conventional care. However, results of the effect of intervention on composite process and clinical outcomes were not similar – there was no significant effect on clinical outcomes.</p> <p><strong>Objectives:</strong> Our objectives were to investigate the robustness of the results based the commonly used statistical models using COMPETE III dataset and explore the validity of the surrogate process composite score using a correlation analysis between the clinical outcomes and process composite score.</p> <p><strong>Methods:</strong> Generalized estimating equations (GEE) were used as a primary statistical model in this study. Three patient-level statistical methods (simple linear regression, fixed-effects regression, and mixed-effects regression) and two center-level statistical approaches (center-level fixed-effects model and center-level random-effects model) were compared to reference GEE model in terms of the robustness of the results – magnitude, direction and statistical significance of the estimated effects on the change of process composite score / on-target clinical composite score. GEE was also used to investigate thecorrelation between the clinical outcomes and surrogate process composite scores.</p> <p><strong>Results:</strong> All six statistical models used in this study produced robust estimates of intervention effect. No significant association between cardiovascular events and on-target clinical composite score and individual component of on-target clinical composite score were found between the intervention group and control group. However, blood pressure, LDL cholesterol, and psychosocial index are significant predictors of cardiovascular events. Process composite score can both predict the cardiovascular events and clinical improvement, but the results were not statistically significant- possibly due to the small number of events. However, the process composite score was significantly associated with the on-target clinical composite score.</p> <p><strong>Conclusions:</strong> We concluded that all five analytic models yielded similar robust estimation of intervention effect comparing to the reference GEE model. The relatively smaller estimate effects in the center-level fixed-effects model suggest that the within-center variation should be considered in the analysis of multicenter RCTs. Process composite score may serve as a good predictor for CVD outcomes.</p> / Master of Science (MSc)

Malaria persists in 13 of Bangladesh's 64 districts, notably in the Chittagong Hill Tract (CHT) districts consisting of Bandarban, Rangamati, and Khagrachhari. While prior studies have explored malaria in Bangladesh, none have delved into the behavioral and climatic factors that simultaneously contribute to its persistence in the CHT districts. This study aims to fill this gap by investigating behaviors influencing malaria persistence in Bangladesh's endemic region, focusing on the Lama, Alikodom, and Naikhongchhari subdistricts of Bandarban district. Data were collected through surveys and key informant interviews (KIIs) in Lama and Alikodom, revealing tribal villages as having the highest concentration of cases, with inhabitants lacking essential knowledge about malaria and prevention methods. Socio-economic dynamics between tribal and Bengali communities emerged as a barrier to accepting information provided by NGOs. Additionally, occupation (employment) was found to be closely linked to malarial sickness. These findings can inform policies to eradicate malaria and protect tribal minorities. Meanwhile, in Naikhongchhari, this study analyzes the relationship between malaria incidence and climatic variables such as rainfall and temperature from 2013 to 2022. Utilizing NGO malaria registry data and meteorological data, significant correlations between rainfall, temperature, and malaria incidence were identified, with temperature and rainfall spikes preceding increases in cases. Despite limitations such as retrospective data collection inaccuracies and omitted determinants, these findings underscore the importance of considering climatic factors in malaria control efforts, necessitating further research for a comprehensive understanding of malaria dynamics. Combined, the overall findings suggest the need for greater education measures using improved communication devoted to preventative efforts among the ethnic minority residents of the CHTs, particularly during the time periods immediately after high rainfall and temperature. Such efforts could contribute greatly to Bangladesh's attempt to eliminate malaria within its borders. / Master of Science / Malaria remains a persistent issue in 13 out of Bangladesh's 64 districts, particularly prevalent in the Chittagong Hill Tract (CHT) districts like Khagrachhari, Bandarban, and Rangamati. Previous studies have overlooked the behavioral and climatic factors contributing to malaria's endurance in the CHT districts. This study fills this gap by investigating behavioral influences on malaria persistence, focusing on subdistricts like Lama, Alikodom, and Naikhongchhari in Bandarban district. Surveys and key informant interviews in Lama and Alikodom revealed tribal villages as hotspots for malaria, with inhabitants lacking crucial knowledge about prevention methods. Socio-economic disparities between tribal and Bengali communities hinder the acceptance of information provided by NGOs. Occupation was identified as closely linked to malarial sickness. In Naikhongchhari, the study explores the correlation between climatic variables and malaria incidence from 2013 to 2022, finding significant relationships between rainfall, temperature, and malaria cases. Temperature and rainfall spikes preceded increases in malaria cases. Despite limitations like retrospective data collection issues, the findings stress the importance of considering climatic factors in malaria control strategies. Enhanced education and communication efforts, particularly targeting ethnic minority residents of the CHTs, could significantly aid Bangladesh's malaria elimination efforts.

The standard methodology when building statistical models has been to use one of several algorithms to systematically search the model space for a good model. If the number of variables is small then all possible models or best subset procedures may be used, but for data sets with a large number of variables, a stepwise procedure is usually implemented. The stepwise procedure of model selection was designed for its computational efficiency and is not guaranteed to find the best model with respect to any optimality criteria. While the model selected may not be the best possible of those in the model space, commonly it is almost as good as the best model. Many times there will be several models that exist that may be competitors of the best model in terms of the selection criterion, but classical model building dictates that a single model be chosen to the exclusion of all others. An alternative to this is Bayesian model averaging (BMA), which uses the information from all models based on how well each is supported by the data. Using BMA allows a variance component due to the uncertainty of the model selection process to be estimated. The variance of any statistic of interest is conditional on the model selected so if there is model uncertainty then variance estimates should reflect this. BMA methodology can also be used for variable assessment since the probability that a given variable is active is readily obtained from the individual model posterior probabilities. The multivariate methods considered in this research are principal components analysis (PCA), canonical variate analysis (CVA), and canonical correlation analysis (CCA). Each method is viewed as a particular multivariate extension of univariate multiple regression. The marginal likelihood of a univariate multiple regression model has been approximated using the Bayes information criteria (BIC), hence the marginal likelihood for these multivariate extensions also makes use of this approximation. One of the main criticisms of multivariate techniques in general is that they are difficult to interpret. To aid interpretation, BMA methodology is used to assess the contribution of each variable to the methods investigated. A second issue that is addressed is displaying of results of an analysis graphically. The goal here is to effectively convey the germane elements of an analysis when BMA is used in order to obtain a clearer picture of what conclusions should be drawn. Finally, the model uncertainty variance component can be estimated using BMA. The variance due to model uncertainty is ignored when the standard model building tenets are used giving overly optimistic variance estimates. Even though the model attained via standard techniques may be adequate, in general, it would be difficult to argue that the chosen model is in fact the correct model. It seems more appropriate to incorporate the information from all plausible models that are well supported by the data to make decisions and to use variance estimates that account for the uncertainty in the model estimation as well as model selection. / Ph. D.

Recent research suggests that 60 % of quantified plate waste in school canteens was caused by only 20 % of students. This finding indicates a large potential for minimising plate waste through better-tailored interventions targeting individuals with a high-waste profile. This study uses the triangulation of three methods that combine both qualitative and quantitative approaches to answer the following two research questions: (1) “Which characteristics and behavioural patterns correlate with high plate waste generation?” and (2) “What derivations can be drawn from these findings to inform better-tailored conceptualisations of interventions to decrease plate waste?”. To investigate the relationship between plate waste generation and the parameters “age”, “sex”, “foreign background”, “educational background of parents”, “extroversion” and “group dynamics”, a correlation analysis has been conducted with food waste and school data on the national level and complemented with a correlation analysis on the local level based on observation data collected at five schools in the municipality of Uppsala. These findings were put in context with additional insights won through interviews with canteen staff at the same five schools. Each parameter was evaluated by combining the results of the applied methods as far as applicable. The strongest correlation was found for the parameter “age”, indicating that older students cause more plate waste. The findings for students’ sex on the local level are especially interesting because, in contrast to the national trend and results from previous research, male students were found to waste more than females. The parameter “foreign background” could be assigned with tendencies for a positive and “educational background of parents” with tendencies for a negative correlation with plate waste generation but both warrant further investigation due to limited assessability in the scope of this study. Students’ degrees of extroversion and the presence of group dynamics appear to be correlated and tend to result in higher amounts of plate waste. It was found that strong individuals with a “leader”-character, who tend to be located on the more extroverted side of the scale, can exert a strong influence on other students’ eating decisions. This peer pressure appears to be especially strong among female students and increase with age. Based on these findings, future interventions are recommended to focus on: students from 6th grade onwards (potentially with a special focus on upper secondary schools), female students (not because of the results for the parameter “sex” but due to peer pressure having the strongest effect among girls), facilitating a better understanding of ingredients (especially if students are not familiar with Swedish cuisine or come from a household with a smaller diversity in what is typically eaten), increasing the involvement of parents in shaping sustainable eating habits and pedagogical work to raise students’ awareness of how their words can impact others and to strengthen them to stand up against peer pressures.

Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are widely-used mathematical models for decomposing multivariate data. They capture spatial relationships between variables, but ignore any temporal relationships that might exist between observations. Probabilistic PCA (PPCA) and Probabilistic CCA (ProbCCA) are versions of these two models that explain the statistical properties of the observed variables as linear mixtures of an alternative, hypothetical set of hidden, or latent, variables and explicitly model noise. Both the noise and the latent variables are assumed to be Gaussian distributed. This thesis introduces two new models, named PPCA-AR and ProbCCA-AR, that augment PPCA and ProbCCA respectively with autoregressive processes over the latent variables to additionally capture temporal relationships between the observations. To make PPCA-AR and ProbCCA-AR robust to outliers and able to model leptokurtic data, the Gaussian assumptions are replaced with infinite scale mixtures of Gaussians, using the Student-t distribution. Bayesian inference calculates posterior probability distributions for each of the parameter variables, from which we obtain a measure of confidence in the inference. It avoids the pitfalls associated with the maximum likelihood method: integrating over all possible values of the parameter variables guards against overfitting. For these new models the integrals required for exact Bayesian inference are intractable; instead a method of approximation, the variational Bayesian approach, is used. This enables the use of automatic relevance determination to estimate the model orders. PPCA-AR and ProbCCA-AR can be viewed as linear dynamical systems, so the forward-backward algorithm, also known as the Baum-Welch algorithm, is used as an efficient method for inferring the posterior distributions of the latent variables. The exact algorithm is tractable because Gaussian assumptions are made regarding the distribution of the latent variables. This thesis introduces a variational Bayesian forward-backward algorithm based on Student-t assumptions. The new models are demonstrated on synthetic datasets and on real remote sensing and EEG data.

Central America is a region regularly affected by natural disasters, with most of them having a hydro-meteorological origin. Therefore, the understanding of annual changes of precipitation upon the region is relevant for planning and mitigation of natural disasters. This thesis focuses on studying the precipitation variability at annual scales in Central America within the framework of the Swedish Centre for Natural Disaster Science. The aims of this thesis are: i) to establish the main climate variability sources during the boreal winter, spring and summer by using different statistical techniques, and ii) to study the connection of sea surface temperature anomalies of the neighbouring oceans with extreme precipitation events in the region. Composites analysis is used to establish the variability sources during winter. Canonical correlation analysis is employed to explore the connection between the SST anomalies and extreme rainfall events during May-June and August-October. In addition, a global circulation model is used to replicate the results found with canonical correlation analysis, but also to study the relationship between the Caribbean Sea surface temperature and the Caribbean low-level jet. The results show that during winter both El Niño Southern Oscillation and the Pacific Decadal Oscillation, are associated with changes of the sea level pressure near the North Atlantic Subtropical High and the Aleutian low. In addition, the El Niño Southern Oscillation signal is intensified (destroyed) when El Niño and the Pacific Decadal Oscillation have the same (opposite) sign. Sea surface temperature anomalies have been related to changes in both the amount and temporal distribution of rainfall. Precipitation anomalies during May-June are associated with sea surface temperature anomalies over the Tropical North Atlantic region. Whereas, precipitation anomalies during August-September-October are associated with the sea surface temperature anomalies contrast between the Pacific Ocean and the Tropical North Atlantic region. Model outputs show no association between sea surface temperature gradients and the Caribbean low-level jet intensification. Canonical correlation analysis shows potential for prediction of extreme precipitation events, however, forecast validation shows that socio-economic variables must be included for more comprehensive natural disaster assessments.