Claudia Furlan

Extreme value methods for modelling historical series of large volcanic magnitudes

Volcanic eruptions are among the most extreme events on earth and it seems natural to make use of the theory of extreme values to improve understanding of volcanic pocesses. The dataset we use is a catalogue of large eruptions over the last two millennia, in which the date of occurrence and magnitude are recorded. The dataset is affected by a recording bias, mostly for eruptions of lower magnitude, though this under-recording process largely disappears in the most recent 400 years. Coles and Sparks modelled these data, via maximum likelihood, using a Poisson process motivated by extreme value theory, with an intensity function that takes into account the recording bias. Nevertheless, the fitted model did not seem entirely consistent with the observed data, since this intensity function does not represent effectively the temporal evolution of the censoring effect in the recording process. The aim of the paper is to provide a more flexible model that might fit better the under-recording process, through an alternative intensity function based on a change-point model. Moreover, the Bayesian context we use allows us to refine some inferential aspects of the return period calculation to improve forecast accuracy.

Hierarchical random effect models for coastal erosion of cliffs in the Holderness coast

Prediction of possible cliff erosion at some future date is fundamental to coastal planning and shoreline management, for example to avoid development in vulnerable areas. Historically, to predict cliff recession rates deterministic methods were used. More recently, recession predictions have been expressed in probabilistic terms. However, to date, only simplistic models have been developed. We consider the cliff erosion along the Holderness Coast. Since 1951 a monitoring program has been started in 118 stations along the coast, providing an invaluable, but often missing, source of information. We build hierarchical random effect models, taking account of the known dynamics of the process and including the missing information.

Pleural Mesothelioma: forecasts of the death toll in the area of Casale Monferrato, Italy

In the city of Casale Monferrato, the largest Italian factory that produced asbestos-cement goods was active from 1907 to 1985. Consequently, asbestos fibers scattered in the surrounding area and caused an enormous number of cases of pleural mesothelioma. Owing to the very long latency of this disease, many subjects have not exhibited its symptoms yet. The aim of this paper is to model and predict the future evolution of the number of deaths due to this disease among residents in the area around that city. The model used here is based on a cellular automata that is assumed to pass through three steps: exposure, contamination, and diagnosis. In this way, forecasts of the future evolution take into account the environmental conditions that changed over the last century because of different levels of plant activity. The model is fitted to annual diagnosis data from 1954 to 2008. Results show that deaths will not end until 2031 and that in the next two decades, at least 505 more subjects will be diagnosed with this disease.

Pre-launch forecasting of a pharmaceutical drug

Purpose The emergence of a pharmaceutical drug as a late entrant in a homogeneous category is a relevant issue for strategy implementation in the pharmaceutical industry. This paper aims to suggest a methodology for making pre-launch forecasts with a complete lack of information for a late entrant. Design/methodology/approach The diffusion process of the emerging entrant is estimated using the diffusion dynamics of pre-existing drugs, after an appropriate assessment of the drug’s entrance point. The authors’ methodology is applied to study the late introduction of a pharmaceutical drug in Italy within the category of ranitidine. Historical data of seven already active drugs in the category are used to assess and estimate ex ante the dynamics of a late entrant (Ulkobrin). Findings The results of applying the procedure to the ranitidine market reveal a high degree of accuracy between the ex post observed values of the late entrant and its ex ante mean predicted trajectory. Moreover, the assessed launch date corresponds to the actual date. Research limitations/implications The category has to be homogeneous to ensure a high degree of similarity among the existing drugs and the late entrant. For this reason, radical innovations cannot be forecast with this methodology. Originality/value The proposed approach contributes to the still challenging research field of pre-launch forecasting by estimating the dynamic features of a homogeneous category and exploiting them for forecasting purposes.