Statistical Analysis of an SEIR Epidemic Model

This thesis was focussed on statistical analysis methods and proposes the use of Bayesian inference to extract information contained in experimental data by estimating Ebola model parameters. The model is a system of differential equations expressing the behavior and dynamics of Ebola. Two sets of data (onset and death data) were both used to estimate parameters, which has not been done by previous researchers in (Chowell, 2004). To be able to use both data, a new version of the model has been built. Model parameters have been estimated and then used to calculate the basic reproduction number and to study the disease-free equilibrium. Estimates of the parameters were useful to determine how well the model fits the data and how good estimates were, in terms of the information they provided about the possible relationship between variables. The solution showed that Ebola model fits the observed onset data at 98.95% and the observed death data at 93.6%. Since Bayesian inference can not be performed analytically, the Markov chain Monte Carlo approach has been used to generate samples from the posterior distribution over parameters. Samples have been used to check the accuracy of the model and other characteristics of the target posteriors.

MCMC Analysis for Optimization of Stochastic Models

In any decision making under uncertainties, the goal is mostly to minimize the expected cost. The minimization of cost under uncertainties is usually done by optimization. For simple models, the optimization can easily be done using deterministic methods.However, many models practically contain some complex and varying parameters that can not easily be taken into account using usual deterministic methods of optimization. Thus, it is very important to look for other methods that can be used to get insight into such models. MCMC method is one of the practical methods that can be used for optimization of stochastic models under uncertainty. This method is based on simulation that provides a general methodology which can be applied in nonlinear and non-Gaussian state models. MCMC method is very important for practical applications because it is a uni ed estimation procedure which simultaneously estimates both parameters and state variables. MCMC computes the distribution of the state variables and parameters of the given data measurements. MCMC method is faster in terms of computing time when compared to other optimization methods. This thesis discusses the use of Markov chain Monte Carlo (MCMC) methods for optimization of Stochastic models under uncertainties .The thesis begins with a short discussion about Bayesian Inference, MCMC and Stochastic optimization methods. Then an example is given of how MCMC can be applied for maximizing production at a minimum cost in a chemical reaction process. It is observed that this method performs better in optimizing the given cost function with a very high certainty.

The estimation of tuberculosis transmission parameters using ABC and MCMC methods

The aim of this work is to apply approximate Bayesian computation in combination with Marcov chain Monte Carlo methods in order to estimate the parameters of tuberculosis transmission. The methods are applied to San Francisco data and the results are compared with the outcomes of previous works. Moreover, a methodological idea with the aim to reduce computational time is also described. Despite the fact that this approach is proved to work in an appropriate way, further analysis is needed to understand and test its behaviour in different cases. Some related suggestions to its further enhancement are described in the corresponding chapter.

Minimum error contrast enhancement

Contrast enhancement is an image processing technique where the objective is to preprocess the image so that relevant information can be either seen or further processed more reliably. These techniques are typically applied when the image itself or the device used for image reproduction provides poor visibility and distinguishability of different regions of interest inthe image. In most studies, the emphasis is on the visualization of image data,but this human observer biased goal often results to images which are not optimal for automated processing. The main contribution of this study is to express the contrast enhancement as a mapping from N-channel image data to 1-channel gray-level image, and to devise a projection method which results to an image with minimal error to the correct contrast image. The projection, the minimum-error contrast image, possess the optimal contrast between the regions of interest in the image. The method is based on estimation of the probability density distributions of the region values, and it employs Bayesian inference to establish the minimum error projection.

Design and evaluation of web interface signs to improve web usability : a semiotic framework

Technological innovations, the development of the internet, and globalization have increased the number and complexity of web applications. As a result, keeping web user interfaces understandable and usable (in terms of ease-of-use, effectiveness, and satisfaction) is a challenge. As part of this, designing userintuitive interface signs (i.e., the small elements of web user interface, e.g., navigational link, command buttons, icons, small images, thumbnails, etc.) is an issue for designers. Interface signs are key elements of web user interfaces because ‘interface signs’ act as a communication artefact to convey web content and system functionality, and because users interact with systems by means of interface signs. In the light of the above, applying semiotic (i.e., the study of signs) concepts on web interface signs will contribute to discover new and important perspectives on web user interface design and evaluation. The thesis mainly focuses on web interface signs and uses the theory of semiotic as a background theory. The underlying aim of this thesis is to provide valuable insights to design and evaluate web user interfaces from a semiotic perspective in order to improve overall web usability. The fundamental research question is formulated as What do practitioners and researchers need to be aware of from a semiotic perspective when designing or evaluating web user interfaces to improve web usability? From a methodological perspective, the thesis follows a design science research (DSR) approach. A systematic literature review and six empirical studies are carried out in this thesis. The empirical studies are carried out with a total of 74 participants in Finland. The steps of a design science research process are followed while the studies were designed and conducted; that includes (a) problem identification and motivation, (b) definition of objectives of a solution, (c) design and development, (d) demonstration, (e) evaluation, and (f) communication. The data is collected using observations in a usability testing lab, by analytical (expert) inspection, with questionnaires, and in structured and semi-structured interviews. User behaviour analysis, qualitative analysis and statistics are used to analyze the study data. The results are summarized as follows and have lead to the following contributions. Firstly, the results present the current status of semiotic research in UI design and evaluation and highlight the importance of considering semiotic concepts in UI design and evaluation. Secondly, the thesis explores interface sign ontologies (i.e., sets of concepts and skills that a user should know to interpret the meaning of interface signs) by providing a set of ontologies used to interpret the meaning of interface signs, and by providing a set of features related to ontology mapping in interpreting the meaning of interface signs. Thirdly, the thesis explores the value of integrating semiotic concepts in usability testing. Fourthly, the thesis proposes a semiotic framework (Semiotic Interface sign Design and Evaluation – SIDE) for interface sign design and evaluation in order to make them intuitive for end users and to improve web usability. The SIDE framework includes a set of determinants and attributes of user-intuitive interface signs, and a set of semiotic heuristics to design and evaluate interface signs. Finally, the thesis assesses (a) the quality of the SIDE framework in terms of performance metrics (e.g., thoroughness, validity, effectiveness, reliability, etc.) and (b) the contributions of the SIDE framework from the evaluators’ perspective.

Quantitative Magnetic Resonance Imaging of the Liver and Heart In Iron Overload

Systemic iron overload (IO) is considered a principal determinant in the clinical outcome of different forms of IO and in allogeneic hematopoietic stem cell transplantation (alloSCT). However, indirect markers for iron do not provide exact quantification of iron burden, and the evidence of iron-induced adverse effects in hematological diseases has not been established. Hepatic iron concentration (HIC) has been found to represent systemic IO, which can be quantified safely with magnetic resonance imaging (MRI), based on enhanced transverse relaxation. The iron measurement methods by MRI are evolving. The aims of this study were to implement and optimise the methodology of non-invasive iron measurement with MRI to assess the degree and the role of IO in the patients. An MRI-based HIC method (M-HIC) and a transverse relaxation rate (R2*) from M-HIC images were validated. Thereafter, a transverse relaxation rate (R2) from spin-echo imaging was calibrated for IO assessment. Two analysis methods, visual grading and rSI, for a rapid IO grading from in-phase and out-of-phase images were introduced. Additionally, clinical iron indicators were evaluated. The degree of hepatic and cardiac iron in our study patients and IO as a prognostic factor in patients undergoing alloSCT were explored. In vivo and in vitro validations indicated that M-HIC and R2* are both accurate in the quantification of liver iron. R2 was a reliable method for HIC quantification and covered a wider HIC range than M-HIC and R2*. The grading of IO was able to be performed rapidly with the visual grading and rSI methods. Transfusion load was more accurate than plasma ferritin in predicting transfusional IO. In patients with hematological disorders, the prevalence of hepatic IO was frequent, opposite to cardiac IO. Patients with myelodysplastic syndrome were found to be the most susceptible to IO. Pre-transplant IO predicted severe infections during the early post-transplant period, in contrast to the reduced risk of graft-versus-host disease. Iron-induced, poor transplantation results are most likely to be mediated by severe infections.

Initialization of Continuous Nonlinear Models Using Extended Kalman Filter

The two main objectives of Bayesian inference are to estimate parameters and states. In this thesis, we are interested in how this can be done in the framework of state-space models when there is a complete or partial lack of knowledge of the initial state of a continuous nonlinear dynamical system. In literature, similar problems have been referred to as diffuse initialization problems. This is achieved first by extending the previously developed diffuse initialization Kalman filtering techniques for discrete systems to continuous systems. The second objective is to estimate parameters using MCMC methods with a likelihood function obtained from the diffuse filtering. These methods are tried on the data collected from the 1995 Ebola outbreak in Kikwit, DRC in order to estimate the parameters of the system.

Performance analysis of software run-time behaviour using 3D-visualisation

Monimutkaisen tietokonejärjestelmän suorituskykyoptimointi edellyttää järjestelmän ajonaikaisen käyttäytymisen ymmärtämistä. Ohjelmiston koon ja monimutkaisuuden kasvun myötä suorituskykyoptimointi tulee yhä tärkeämmäksi osaksi tuotekehitysprosessia. Tehokkaampien prosessorien käytön myötä myös energiankulutus ja lämmöntuotto ovat nousseet yhä suuremmiksi ongelmiksi, erityisesti pienissä, kannettavissa laitteissa. Lämpö- ja energiaongelmien rajoittamiseksi on kehitetty suorituskyvyn skaalausmenetelmiä, jotka edelleen lisäävät järjestelmän kompleksisuutta ja suorituskykyoptimoinnin tarvetta. Tässä työssä kehitettiin visualisointi- ja analysointityökalu ajonaikaisen käyttäytymisen ymmärtämisen helpottamiseksi. Lisäksi kehitettiin suorituskyvyn mitta, joka mahdollistaa erilaisten skaalausmenetelmien vertailun ja arvioimisen suoritusympäristöstä riippumatta, perustuen joko suoritustallenteen tai teoreettiseen analyysiin. Työkalu esittää ajonaikaisesti kerätyn tallenteen helposti ymmärrettävällä tavalla. Se näyttää mm. prosessit, prosessorikuorman, skaalausmenetelmien toiminnan sekä energiankulutuksen kolmiulotteista grafiikkaa käyttäen. Työkalu tuottaa myös käyttäjän valitsemasta osasta suorituskuvaa numeerista tietoa, joka sisältää useita oleellisia suorituskykyarvoja ja tilastotietoa. Työkalun sovellettavuutta tarkasteltiin todellisesta laitteesta saatua suoritustallennetta sekä suorituskyvyn skaalauksen simulointia analysoimalla. Skaalausmekanismin parametrien vaikutus simuloidun laitteen suorituskykyyn analysoitiin.

Marketing culture of Finnish research libraries : an analysis of marketing attitude, knowledge and behaviour

The aim of the present dissertation is to investigate the marketing culture of research libraries in Finland and to understand the awareness of the knowledge base of library management concerning modern marketing theories and practices. The study was based onthe notion that a leader in an organisation can have large impact on its culture. Therefore, it was considered important to learn about the market orientation that initiates at the top management and flows throughout the whole organisationthus resulting in a particular kind of library culture. The study attempts to examine the marketing culture of libraries by analysing the marketing attitudes, knowledge (underlying beliefs, values and assumptions), behaviour (market orientation), operational policies and activities, and their service performance (customer satisfaction). The research was based on the assumption that if the top management of libraries has market oriented behaviour, then their marketing attitudes, knowledge, operational policies and activities and service performance should also be in accordance. The dissertation attempts to connect all these theoretical threads of marketing culture. It investigates thirty three academic and special libraries in the south of Finland. The library director and three to ten customers from each library participated as respondents in this study. An integrated methodological approach of qualitative as well as quantitative methods was used to gain knowledge on the pertinent issues lying behind the marketing culture of research libraries. The analysis of the whole dissertation reveals that the concept of marketing has very varied status in the Finnish research libraries. Based on the entire findings, three kinds of marketing cultures were emerged: the strong- the high fliers; the medium- the brisk runners; and the weak- the slow walkers. The high fliers appeared to be modern marketing believers as their marketing approach was customer oriented and found to be closer to the emerging notions of contemporary relational marketing. The brisk runners were found to be traditional marketing advocates as their marketing approach is more `library centred¿than customer defined and thus is in line of `product orientation¿ i.e. traditional marketing. `Let the interested customers come to the library¿ was appeared to be the hallmark of the slow walkers. Application of conscious market orientation is not reflected in the library activities of the slow walkers. Instead their values, ideology and approach to serving the library customers is more in tuneof `usual service oriented Finnish way¿. The implication of the research is that it pays to be market oriented which results in higher customer satisfaction oflibraries. Moreover, it is emphasised that the traditional user based service philosophy of Finnish research libraries should not be abandoned but it needs to be further developed by building a relational based marketing system which will help the libraries to become more efficient and effective from the customers¿ viewpoint. The contribution of the dissertation lies in the framework showing the linkages between the critical components of the marketing culture of a library: antecedents, market orientation, facilitators and consequences. The dissertationdelineates the significant underlying dimensions of market-oriented behaviour of libraries which are namely customer philosophy, inter-functional coordination,strategic orientation, responsiveness, pricing orientation and competition orientation. The dissertation also showed the extent to which marketing attitudes, behaviour, knowledge were related and impact of market orientation on the serviceperformance of libraries. A strong positive association was found to exist between market orientation and marketing attitudes and knowledge. Moreover, it also shows that a higher market orientation is positively connected with the service performance of libraries, the ultimate result being higher customer satisfaction. The analysis shows that a genuine marketing culture represents a synthesis of certain marketing attitudes, knowledge and of selective practices. This finding is particularly significant in the sense that it manifests that marketing culture consists of a certain sets of beliefs and knowledge (which form a specific attitude towards marketing) and implementation of a certain set of activities that actually materialize the attitude of marketing into practice (market orientation) leading to superior service performance of libraries.

Visual customer segmentation and behavior analysis : a SOM-based approach

Ett ämne som väckt intresse både inom industrin och forskningen är hantering av kundförhållanden (CRM, eng. Customer Relationship Management), dvs. en kundorienterad affärsstrategi där företagen från att ha varit produktorienterade väljer att bli mera kundcentrerade. Numera kan kundernas beteende och aktiviteter lätt registreras och sparas med hjälp av integrerade affärssystem (ERP, eng. Enterprise Resource Planning) och datalager (DW, eng. Data Warehousing). Kunder med olika preferenser och köpbeteende skapar sin egen ”signatur” i synnerhet via användningen av kundkort, vilket möjliggör mångsidig modellering av kundernas köpbeteende. För att få en översikt av kundernas köpbeteende och deras lönsamhet, används ofta kundsegmentering som en metod för att indela kunderna i grupper utgående från deras likheter. De mest använda metoderna för kundsegmentering är analytiska modeller konstruerade för en viss tidsperiod. Dessa modeller beaktar inte att kundernas beteende kan förändras med tiden. I föreliggande avhandling skapas en holistisk översikt av kundernas karaktär och köpbeteende som utöver de konventionella segmenteringsmodellerna även beaktar dynamiken i köpbeteendet. Dynamiken i en kundsegmenteringsmodell innefattar förändringar i segmentens struktur och innehåll, samt förändringen av individuella kunders tillhörighet i ett segment (s.k migrationsanalyser). Vardera förändringen modelleras, analyseras och exemplifieras med visuella datautvinningstekniker, främst med självorganiserande kartor (SOM, eng. Self-Organizing Maps) och självorganiserande tidskartor (SOTM), en vidareutveckling av SOM. Visualiseringen anteciperas underlätta tolkningen av identifierade mönster och göra processen med kunskapsöverföring mellan den som gör analysen och beslutsfattaren smidigare. Asiakkuudenhallinta (CRM) eli organisaation muuttaminen tuotepainotteisesta asiakaskeskeiseksi on herättänyt mielenkiintoa niin yliopisto- kuin yritysmaailmassakin. Asiakkaiden käyttäytymistä ja toimintaa pystytään nykyään helposti tallentamaan ja varastoimaan toiminnanohjausjärjestelmien ja tietovarastojen avulla; asiakkaat jättävät jatkuvasti piirteistään ja ostokäyttäytymisestään kertovia tietojälkiä, joita voidaan analysoida. On tavallista, että asiakkaat poikkeavat toisistaan eri tavoin, ja heidän mieltymyksensä kuten myös ostokäyttäytymisensä saattavat olla hyvinkin erilaisia. Asiakaskäyttäytymisen monimuotoisuuteen ja tuottavuuteen paneuduttaessa käytetäänkin laajalti asiakassegmentointia eli asiakkaiden jakamista ryhmiin samankaltaisuuden perusteella. Perinteiset asiakassegmentoinnin ratkaisut ovat usein yksittäisiä analyyttisia malleja, jotka on tehty tietyn aikajakson perusteella. Tämän vuoksi ne monesti jättävät huomioimatta sen, että asiakkaiden käyttäytyminen saattaa ajan kuluessa muuttua. Tässä väitöskirjassa pyritäänkin tarjoamaan holistinen kuva asiakkaiden ominaisuuksista ja ostokäyttäytymisestä tarkastelemalla kahta muutosvoimaa tiettyyn aikarajaukseen perustuvien perinteisten segmentointimallien lisäksi. Nämä kaksi asiakassegmentointimallin dynamiikkaa ovat muutokset segmenttien rakenteessa ja muutokset yksittäisten asiakkaiden kuulumisessa ryhmään. Ensimmäistä dynamiikkaa lähestytään ajallisen asiakassegmentoinnin avulla, jossa visualisoidaan ajan kuluessa tapahtuvat muutokset segmenttien rakenteissa ja profiileissa. Toista dynamiikkaa taas lähestytään käyttäen nk. segmenttisiirtymien analyysia, jossa visuaalisin keinoin tunnistetaan samantyyppisesti segmentistä toiseen vaihtavat asiakkaat. Visualisoinnin tehtävänä on tukea havaittujen kaavojen tulkitsemista sekä helpottaa tiedonsiirtoa analysoijan ja päättäjien välillä. Visuaalisia tiedonlouhintamenetelmiä, kuten itseorganisoivia karttoja ja niiden laajennuksia, käytetään osoittamaan näiden menetelmien hyödyllisyys sekä asiakkuudenhallinnassa yleisesti että erityisesti asiakassegmentoinnissa.

Chaotic Behaviour of Active Magnetic Bearing System by Time Series Analysis

Chaotic behaviour is one of the hardest problems that can happen in nonlinear dynamical systems with severe nonlinearities. It makes the system's responses unpredictable. It makes the system's responses to behave similar to noise. In some applications it should be avoided. One of the approaches to detect the chaotic behaviour is nding the Lyapunov exponent through examining the dynamical equation of the system. It needs a model of the system. The goal of this study is the diagnosis of chaotic behaviour by just exploring the data (signal) without using any dynamical model of the system. In this work two methods are tested on the time series data collected from AMB (Active Magnetic Bearing) system sensors. The rst method is used to nd the largest Lyapunov exponent by Rosenstein method. The second method is a 0-1 test for identifying chaotic behaviour. These two methods are used to detect if the data is chaotic. By using Rosenstein method it is needed to nd the minimum embedding dimension. To nd the minimum embedding dimension Cao method is used. Cao method does not give just the minimum embedding dimension, it also gives the order of the nonlinear dynamical equation of the system and also it shows how the system's signals are corrupted with noise. At the end of this research a test called runs test is introduced to show that the data is not excessively noisy.

On Bayesian predictive modeling : with applications on the analysis of chromatography data in forensic investigations

Med prediktion avses att man skattar det framtida värdet på en observerbar storhet. Kännetecknande för det bayesianska paradigmet är att osäkerhet gällande okända storheter uttrycks i form av sannolikheter. En bayesiansk prediktiv modell är således en sannolikhetsfördelning över de möjliga värden som en observerbar, men ännu inte observerad storhet kan anta. I de artiklar som ingår i avhandlingen utvecklas metoder, vilka bl.a. tillämpas i analys av kromatografiska data i brottsutredningar. Med undantag för den första artikeln, bygger samtliga metoder på bayesiansk prediktiv modellering. I artiklarna betraktas i huvudsak tre olika typer av problem relaterade till kromatografiska data: kvantifiering, parvis matchning och klustring. I den första artikeln utvecklas en icke-parametrisk modell för mätfel av kromatografiska analyser av alkoholhalt i blodet. I den andra artikeln utvecklas en prediktiv inferensmetod för jämförelse av två stickprov. Metoden tillämpas i den tredje artik eln för jämförelse av oljeprover i syfte att kunna identifiera den förorenande källan i samband med oljeutsläpp. I den fjärde artikeln härleds en prediktiv modell för klustring av data av blandad diskret och kontinuerlig typ, vilken bl.a. tillämpas i klassificering av amfetaminprover med avseende på produktionsomgångar.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Bayesian analysis of SEIR epidemic models

This thesis concerns the analysis of epidemic models. We adopt the Bayesian paradigm and develop suitable Markov Chain Monte Carlo (MCMC) algorithms. This is done by considering an Ebola outbreak in the Democratic Republic of Congo, former Zaïre, 1995 as a case of SEIR epidemic models. We model the Ebola epidemic deterministically using ODEs and stochastically through SDEs to take into account a possible bias in each compartment. Since the model has unknown parameters, we use different methods to estimate them such as least squares, maximum likelihood and MCMC. The motivation behind choosing MCMC over other existing methods in this thesis is that it has the ability to tackle complicated nonlinear problems with large number of parameters. First, in a deterministic Ebola model, we compute the likelihood function by sum of square of residuals method and estimate parameters using the LSQ and MCMC methods. We sample parameters and then use them to calculate the basic reproduction number and to study the disease-free equilibrium. From the sampled chain from the posterior, we test the convergence diagnostic and confirm the viability of the model. The results show that the Ebola model fits the observed onset data with high precision, and all the unknown model parameters are well identified. Second, we convert the ODE model into a SDE Ebola model. We compute the likelihood function using extended Kalman filter (EKF) and estimate parameters again. The motivation of using the SDE formulation here is to consider the impact of modelling errors. Moreover, the EKF approach allows us to formulate a filtered likelihood for the parameters of such a stochastic model. We use the MCMC procedure to attain the posterior distributions of the parameters of the SDE Ebola model drift and diffusion parts. In this thesis, we analyse two cases: (1) the model error covariance matrix of the dynamic noise is close to zero , i.e. only small stochasticity added into the model. The results are then similar to the ones got from deterministic Ebola model, even if methods of computing the likelihood function are different (2) the model error covariance matrix is different from zero, i.e. a considerable stochasticity is introduced into the Ebola model. This accounts for the situation where we would know that the model is not exact. As a results, we obtain parameter posteriors with larger variances. Consequently, the model predictions then show larger uncertainties, in accordance with the assumption of an incomplete model.

Development and Effect Analysis of the Asteri Consultative Auditing Process - Safety and Security Management in Educational Institutions

The Finnish legislation requires for a safe and secure learning environment. However, the comprehensive, risk based safety and security management (SSM) and the management commitment in the implementation and development of the SSM are not mentioned in the legislation. Multiple institutions, operators and researchers have studied and developed safety and security in educational institutions over the past decade. Typically the approach has been fragmented and without bringing up the importance of the comprehensive SSM. The development needs of the safety and security operations in universities have been studied. However, in universities of applied sciences (UASs) and in elementary schools (ESs), the performance level, strengths and weaknesses of the comprehensive SSM have not been studied. The objective of this study was to develop the comprehensive, risk based SSM of educational institutions by developing the new Asteri consultative auditing process and study its effects on auditees. Furthermore, the performance level in the comprehensive SSM in UASs and ESs were studied using Asteri and the TUTOR model developed by the Keski-Uusimaa Department for Rescue Services. In addition, strengths, development needs and differences were identified. In total, 76 educational institutions were audited between the years 2011 and 2014. The study is based on logical empiricism, and an observational applied research design was used. Auditing, observation and an electronic survey were used for data collection. Statistical analysis was used to analyze the collected information. In addition, thematic analysis was used to analyze the development areas of the organizations mentioned by the respondents in the survey. As one of the main contributions, this research presents the new Asteri consultative auditing process. Organizations with low performance levels on the audited subject benefit the most from the Asteri consultative auditing process. Asteri may be usable in many different types of audits, not only in SSM audits. As a new result, this study provides new knowledge on attitudes related to auditing. According to the research findings, auditing may generate negative attitudes and the auditor should take them into account when planning and preparing for audits. Negative attitudes can be compensated by producing added value, objectivity and positivity for the audit and, thus, improve the positive effects of auditing on knowledge and skills. Moreover, as the results of this study shows, auditing safety and security issues do not increase feelings of insecurity, but rather increase feelings of safety and security when using the new Asteri consultative auditing process with the TUTOR model. The results showed that the SSM in the audited UASs was statistically significantly more advanced than that in the audited ESs. However, there is still room for improvement in the ESs and the UASs as the approach to the SSM was fragmented. It can be assumed that the majority of Finnish UASs and ESs do not likely meet the basic level of the comprehensive, risk based the SSM.