An SEIR Model with Time-Varying Coefficients for Analyzing the SARS-CoV-2 Epidemic.

In this study, we propose a time-dependent susceptible-exposed-infected-recovered (SEIR) model for the analysis of the SARS-CoV-2 epidemic outbreak in three different countries, the United States, Italy, and Iceland using public data inherent the numbers of the epidemic wave. Since several types and grades of actions were adopted by the governments, including travel restrictions, social distancing, or limitation of movement, we want to investigate how these measures can affect the epidemic curve of the infectious population. The parameters of interest for the SEIR model were estimated employing a composite likelihood approach. Moreover, standard errors have been corrected for temporal dependence. The adoption of restrictive measures results in flatten epidemic curves, and the future evolution indicated a decrease in the number of cases.

Accumulation of trace elements in feathers of the Kentish plover Charadrius alexandrinus.

A non-invasive study of trace element accumulation in tail feathers of the Kentish plover (Charadrius alexandrinus) was performed along the coastline of the northern littoral strip of the Venice Lagoon, with the aim to verify whether contamination may be a factor affecting conservation status of Kentish plover populations. Body burdens in feathers of 11 trace elements including toxic metals/metalloids and essential elements (As, Cd, Co, Cr, Cu, Hg, Ni, Pb, Se, V, Zn) were quantified by ICP-MS, then concentrations were normalized to feather's age calculated using ptilochronology in order to obtain daily deposition rates. Mercury emerged as a major threat to the conservation of the species, since average feather concentration was clearly above the adverse-effect threshold associated with impairment in the reproductive success in a number of bird species. Also Cd and Se occurred at levels that may impact on the conservation status of the studied species at local scale, even if to a lesser extent than Hg. Gender-related differences in trace element accumulation emerged only for As, although for this element the risks associated to environmental exposure seem to be negligible.

Structural decomposition of decadal climate prediction errors: A Bayesian approach.

Decadal climate predictions use initialized coupled model simulations that are typically affected by a drift toward a biased climatology determined by systematic model errors. Model drifts thus reflect a fundamental source of uncertainty in decadal climate predictions. However, their analysis has so far relied on ad-hoc assessments of empirical and subjective character. Here, we define the climate model drift as a dynamical process rather than a descriptive diagnostic. A unified statistical Bayesian framework is proposed where a state-space model is used to decompose systematic decadal climate prediction errors into an initial drift, seasonally varying climatological biases and additional effects of co-varying climate processes. An application to tropical and south Atlantic sea-surface temperatures illustrates how the method allows to evaluate and elucidate dynamic interdependencies between drift, biases, hindcast residuals and background climate. Our approach thus offers a methodology for objective, quantitative and explanatory error estimation in climate predictions.