Despite the progress in medical data collection the actual burden of SARS-CoV-2 remains unknown due to under-ascertainment of cases. This was apparent in the acute phase of the pandemic and the use of reported deaths has been pointed out as a more reliable source of information, likely less prone to under-reporting. Since daily deaths occur from past infections weighted by their probability of death, one may infer the total number of infections accounting for their age distribution, using the data on reported deaths.
View Article and Find Full Text PDFMultiplex panel tests identify many individual pathogens at once, using a set of component tests. In some panels the number of components can be large. If the panel is detecting causative pathogens for a single syndrome or disease then we might estimate the burden of that disease by combining the results of the panel, for example determining the prevalence of pneumococcal pneumonia as caused by many individual pneumococcal serotypes.
View Article and Find Full Text PDFBackgroundUnderstanding the relative vaccine effectiveness (rVE) of new COVID-19 vaccine formulations against SARS-CoV-2 infection is a public health priority. A precise analysis of the rVE of monovalent and bivalent boosters given during the 2022 spring-summer and autumn-winter campaigns, respectively, in a defined population remains of interest.AimWe assessed rVE against hospitalisation for the spring-summer (fourth vs third monovalent mRNA vaccine doses) and autumn-winter (fifth BA.
View Article and Find Full Text PDFThis paper is concerned with the application of recent statistical advances to inference of infectious disease dynamics. We describe the fitting of a class of epidemic models using Hamiltonian Monte Carlo and variational inference as implemented in the freely available Stan software. We apply the two methods to real data from outbreaks as well as routinely collected observations.
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