Publications by authors named "K K Avilov"

Previous work has failed to fit classic SEIR epidemic models satisfactorily to the prevalence data of the famous English boarding school 1978 influenza A/H1N1 outbreak during the children's pandemic. It is still an open question whether a biologically plausible model can fit the prevalence time series and the attack rate correctly. To construct the final model, we first used an intentionally very flexible and overfitted discrete-time epidemiologic model to learn the epidemiological features from the data.

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Background: During the COVID-19 pandemic, many countries used lockdowns as a containment measure. While lockdowns successfully contributed to slowing down the contagion, the related mobility restrictions were reportedly associated with an increased risk of major depressive and anxiety disorders. We aimed to quantify the trade-off between the quality-adjusted life years (QALY) gain due to lower COVID-19 incidence as a result of a lockdown and QALY loss due to lockdown-induced mental disorders.

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Little is known about the dynamics of the early stages of untreated active pulmonary tuberculosis: unknown are both the rates of progression and the model "scheme". The "parallel" scheme assumes that infectiousness of tuberculosis cases is effectively predefined at the onset of the disease, and the "serial" scheme considers all cases to be non-infectious at the onset, with some of them later becoming infectious. Our aim was to estimate the progression of the early stages of pulmonary tuberculosis using data from a present-day population.

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Allergy to ragweed pollen and other weeds is a global problem due to the rapid spread of these grasses around the world. In addition, pollen of short ragweed (Ambrosia, Amb) and mugwort (Artemisia, Art) - one of the main causes of respiratory allergy - seasonal allergic rhinoconjunctivitis (hay fever) with/without bronchial asthma patients living in the South of Russia. Epidemiological studies on the prevalence of Allergy to Amb and Art among patients living in Chechen Republic, absent.

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Objective: To estimate tuberculosis (TB) incidence and case detection rate (CDR) using routine TB surveillance data only.

Methods: A mathematical model of the case detection process, representing competition between disease progression and case finding, is proposed. The model describes disease progression as a two-stage process (bacillary and non-bacillary TB), and so relates the proportion of bacillary TB cases on detection to the effectiveness of detection.

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