A Bayesian estimate of the COVID-19 infection fatality rate in Brazil based on a random seroprevalence survey

We infer the infection fatality rate (IFR) of SARS-CoV-2 in Brazil by combining three datasets. We compute the prevalence via the population-based seroprevalence survey EPICOVID19-BR, which tested 89000 people in 3 stages over a period of 5 weeks. This randomized survey selected people of 133 cities (accounting for 35.5% of the Brazilian population) and tested them for IgM/IgG antibodies making use of a rapid test. We estimate the time delay between the development of antibodies and subsequent fatality using the public SIVEP-Gripe dataset. The number of fatalities is obtained using the public Painel Coronavírus dataset. We obtain the IFR via Bayesian inference for each survey stage and 27 federal states. In particular, we include the effect of fading IgG levels by marginalizing over the time T after contagion at which the test gives a negative result. We adopt a flat broad prior on the interval [40, 80] days. We infer a country-wide average IFR of 0.85% (95% CI: 0.76–0.99%).

Bayesian Estimate of Parameters for ARMA Model Forecasting

This paper presents a Bayesian approach to finding the Bayes estimator of parameters for ARMA model forecasting under normal-gamma prior assumption with a quadratic loss function in mathematical expression. Obtaining the conditional posterior predictive density is based on the normal-gamma prior and the conditional predictive density, whereas its marginal conditional posterior predictive density is obtained using the conditional posterior predictive density. Furthermore, the Bayes estimator of parameters is derived from the marginal conditional posterior predictive density.

ОЦЕНКА ОШИБОК КОРРЕЛЯЦИОННО-ЭКСТРЕМАЛЬНОЙ НАВИГАЦИИ ПО КАРТЕ АНОМАЛИЙ СИЛЫ ТЯЖЕСТИ НА ОСНОВЕ ТРАЕКТОРНЫХ ИЗМЕРЕНИЙ С БОРТА АВТОНОМНОГО ПОДВОДНОГО РОБОТА

Оценки точности подводной навигации на основе данных гравиметрических измерений с борта автономного подводного робота непосредственно связаны с вопросами картографирования аномалий поля силы тяжести (АСТ) с помощью высокоточных малоразмерных гравиметров. В работе продолжены исследования, направленные на повышение точности навигации по карте АСТ с возможностью реализации алгоритмов навигационной коррекции в реальном времени. Для решения задачи исследован новый метод оценки ошибок корреляционно-экстремальной навигации, основанный на анализе отношения главного и бокового пиков корреляционного функционала. Особенность предлагаемого метода, в отличие от известного байесовского подхода, заключается в возможности его использования при реализации бортовых алгоритмов, а также и при предварительной оценке информативности карты поля для планирования маршрутов. Результаты вычислительных экспериментов показывают, что при определенных условиях исследуемая оценка близка к расчетной байесовской оценке.The accuracy assessment of underwater navigationbased on gravimetric measurements, acquired from theautonomous underwater vehicle, is directly related to issuesof mapping the local gravitational field (LGF) usinghigh-precision small-sized gravimeters. The paper presentsresearch aimed to improve the accuracy of navigation usingthe map of LGF, which is suitable for the implementationof algorithms of real-time navigation correction. Addressedto this problem, the new method based on the analysis ofthe sidelobe level of the matching function was studied forestimating the error of correlation-extreme navigation. Thefeature of the proposed method, in contrast with the wellknownBayesian approach, lies in the opportunity of its implementationin onboard algorithms and utilization for thepreliminary assessment of informational content of the fieldmap for route planning. The results of computational experimentsshow that under certain conditions, the studiedestimates are near the calculated Bayesian estimate.

Reconciling Cover Thickness Estimates in Cloncurry Region in Queensland, Australia using Bayesian Estimate Fusion

<p>The Cloncurry region lies in NW of Queensland and includes the Mount Isa Inlier, one of the most highly endowed metallogenic provinces in Australia, which has a long history of mining and exploration. The area is covered by the Jurassic-Cretaceous Carpentaria and Eromanga Basin sediments with the Mount Isa Inlier outcropping to the West and South. The fully concealed Millungera Basin underlies younger basins to the East. In order to de-risk further mineral exploration in this region it is important to know the thickness of cover. There are a variety of geophysical data available that can be used to estimate cover thickness. The point depth estimates of cover are derived from geophysical data using different inference methods. In order to create a map, these individual depth estimates must be reconciled/interpolated. The conventional interpolation methods do not produce the most optimal solution since these methods don’t easily account for discrepancies in the geophysical data distribution, resolution of the data and consequently variable accuracy of the cover thickness depth estimates. Also, most of these techniques do not produce an uncertainty estimate of the result. We have developed a Bayesian estimate fusion method that accounts for the variable data inaccuracies of the point cover thickness estimates which produces a map of cover thickness and its uncertainty. Additionally, the method uses non-intersecting drill holes, which were not usually utilised to create a map of the cover thickness. The method deals with outliers, by differentiating between the point depth estimates related to the cover-basement interface and the false positives that might be coming from the intrasedimentary units or the deeper basement. Lastly, the method incorporates existing fault information which allows to better capture sharp cover thickness changes.</p>