Interindustry transactions recorded at a macro level are simply summations of commodity shipment decisions taken at a micro level. The resulting statistical problem is to obtain minimally biased estimates of commodity flow distributions at the disaggregated level, given various forms of aggregated information. This study demonstrates the application of the entropy-maximizing paradigm in its traditional form, together with recent adaptations emerging from information theory, to the area of spatial and non-spatial input-output analysis. A clear distinction between the behavioural and statistical aspects of entropy modelling is suggested. The discussion of non-spatial input-output analysis emphasizes the rectangular and dynamic extensions of Leontief's original model, and also outlines a scheme for simple aggregation, based on a criterion of minimum loss of information. In the chapters on spatial analysis, three complementary approaches to the estimation of interregional flows are proposed. Since the static formulations cannot provide an accurate picture of the gross interregional flows between any two sectors, Leontief's dynamic framework is adapted to the problem. The study concludes by describing a hierarchical system of models to analyse feasible paths of economic development over space and time. / digitalisering@umu

The ability of Next-Generation Sequencing (NGS) to produce massive quantities of genomic data inexpensively has allowed to study the structure of viral populations from an infected host at an unprecedented resolution. As a result of a high rate of mutation and recombination events, an RNA virus exists as a heterogeneous "swarm". Virologists and computational epidemiologists are widely using NGS data to study viral populations. However, discerning rare variants is muddled by the presence of errors introduced by the sequencing technology. We develop and implement time- and cost-efficient strategy for NGS of multiple viral samples, and computational methods to analyze large quantities of NGS data and to handle sequencing errors. In particular, we present: (i) combinatorial pooling strategy for massive NGS of viral samples; (ii) kGEM and 2SNV — methods for viral population haplotyping; (iii) ShotMCF — a Multicommodity Flow (MCF) based method for frequency estimation of viral haplotypes; (iv) QUASIM — an agent-based simulator of viral evolution taking in account viral variants and immune response.

The aim of my thesis is the construction of estimates of interregional commodity flows for the regions of the Czech Republic, by using a gravity model. Gravity model is based on Newton's law of gravitation. Gravity models can be used in two different information contexts. The first is an information context, when the spatial interaction flows are known a priori, and the model is used to explain the trade flows' behaviour. And the second is an information context in which these interactions are totally unknown a priori and the flows must be estimated. This paper is focused on the second information context. When we estimating commodity flows we need to know the value of exports and imports for individual regions. Estimated interregional commodity flows are the results of this work. Estimated interregional flows are put into the regional input-output tables compiled by the Department of Economic Statistics. Regional input-output tables are arranged so as to reached the equality of resources and use. On the basis of the resulting tables for all regions, I conducted a input-output analysis. Input-output analysis examines the impact of model changes to investment on selected commodities, to estimated interregional flows and selected macroeconomic indicators.