Chrisovalantis Malesios

Modeling sheep pox disease from the 1994-1998 epidemic in Evros Prefecture, Greece.

Sheep pox is a highly transmissible disease which can cause serious loss of livestock and can therefore have major economic impact. We present data from sheep pox epidemics which occurred between 1994 and 1998. The data include weekly records of infected farms as well as a number of covariates. We implement Bayesian stochastic regression models which, in addition to various explanatory variables like seasonal and environmental/meteorological factors, also contain serial correlation structure based on variants of the Ornstein-Uhlenbeck process. We take a predictive view in model selection by utilizing deviance-based measures. The results indicate that seasonality and the number of infected farms are important predictors for sheep pox incidence.

Efficiency analysis of forestry journals: Suggestions for improving journals’ quality

In this paper we attempt to assess the impact of journals in the field of forestry, in terms of bibliometric data, by providing an evaluation of forestry journals based on data envelopment analysis (DEA). In addition, based on the results of the conducted analysis, we provide suggestions for improving the impact of the journals in terms of widely accepted measures of journal citation impact, such as the journal impact factor (IF) and the journal h-index. More specifically, by modifying certain inputs associated with the productivity of forestry journals, we have illustrated how this method could be utilized to raise their efficiency, which in terms of research impact can then be translated into an increase of their bibliometric indices, such as the h-index, IF or eigenfactor score.

An early warning indicator for monitoring infectious animal diseases and its application in the case of a sheep pox epidemic

We describe an early warning indicator for monitoring infectious diseases. The approach is illustrated with data from a major sheep pox epidemic, which occurred in the Evros prefecture of Greece, from August 2013 until the end of 2014. We present the epidemic data and identify factors affecting this major epidemic using a suitable methodological framework for the sequential analysis of such outbreaks. Environmental conditions and seasonality affect the spread of the epidemic: autumn seems to enhance disease occurrence and higher average temperatures facilitate disease spread. The on-line prediction of the disease potential is investigated through an early warning system that is based on the probability of the disease going extinct. The temporal stability of this indicator is extensively explored.

Measuring the robustness of the journal h-index with respect to publication and citation values: A Bayesian sensitivity analysis

Braun, Glanzel, and Schubert (2006) recommended using the h-index as an alternative to the journal impact factor (IF) to qualify journals. In this paper, a Bayesian-based sensitivity analysis is performed with the aid of mathematical models to examine the behavior of the journal h-index to changes in the publication/citation counts of journals. Sensitivity of the h-index was most apparent for changes in the number of citations, revealing similar patterns of behavior for almost all models and independently to the field of research. In general, the h-index was found to be robust to changes in citations up to approximately the 25th percentile of the citation distribution, inflating its value afterwards.

A Decision Support System Web—Application for the Management of Forest Road Network

The present study contributes to the development of an online FRMP (Forest Road Management Platform) that aims to assist in the management of forest road network in a holistic way. This is achieved by the proposed methodology which serves as a database using geoprocessing and geospatial technologies for the handling, and the identification of critical issues in the infrastructure of forest road networks, visualization of forest roads, and the optimization of the management of the forest road network by proposing alternative strategies. In this paper, the development of the decision making web-tool, and present examples to demonstrate effectively its application and resulting advantages are described. The developed web-application may provide assistance to various forest organizations in the management of forest road networks and associated problems in an effective and sustainable way.

Bayesian epidemic models for spatially aggregated count data

Epidemic data often possess certain characteristics, such as the presence of many zeros, the spatial nature of the disease spread mechanism, environmental noise, serial correlation and dependence on time-varying factors. This paper addresses these issues via suitable Bayesian modelling. In doing so, we utilize a general class of stochastic regression models appropriate for spatio-temporal count data with an excess number of zeros. The developed regression framework does incorporate serial correlation and time-varying covariates through an Ornstein-Uhlenbeck process formulation. In addition, we explore the effect of different priors, including default options and variations of mixtures of g-priors. The effect of different distance kernels for the epidemic model component is investigated. We proceed by developing branching process-based methods for testing scenarios for disease control, thus linking traditional epidemiological models with stochastic epidemic processes, useful in policy-focused decision making. The approach is illustrated with an application to a sheep pox dataset from the Evros region, Greece. Copyright

A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data

Malaria constitutes an important cause of human mortality. After 2009 Greece experienced a resurgence of malaria. Here, we develop a model-based framework that integrates entomological, geographical, social and environmental evidence in order to guide the mosquito control efforts and apply this framework to data from an entomological survey study conducted in Central Greece. Our results indicate that malaria transmission risk in Greece is potentially substantial. In addition, specific districts such as seaside, lakeside and rice field regions appear to represent potential malaria hotspots in Central Greece. We found that appropriate maps depicting the basic reproduction number, R0, are useful tools for informing policy makers on the risk of malaria resurgence and can serve as a guide to inform recommendations regarding control measures.

Market and Economic Development in Bulgaria

The present paper tests empirically the existence of a causal relationship between the economic growth and the development in the banking and stock market in ex transition economies, recently member states of the EU and especially the case of Bulgaria. The Johansen cointegration test indicated a sole relationship between the banking sector, the stock market and the economic growth, while the application of the Granger causality/block exogeneity test indicated a bilateral relationship between the economic growth and the development in the stock market, as well as between the economic growth and the development in banking sector. Finally, no casual relationship was confirmed between the development in credit and stock market.