The Cyclicity of coronavirus cases: "Waves" and the "weekend effect".

INTRODUCTION: Medical statistics is one of the "milestones" of current medical systems. It is the foundation for many protocols, including medical care systems, government recommendations, epidemic planning, etc. At this time of global COVID-19, credible data on epidemic spread can help governments make better decisions. This study's aim is to evaluate the cyclicity in the number of daily diagnosed coronavirus patients, thus allowing governments to plan how to allocate their resources more effectively. METHODS: To assess this cycle, we consider the time series of the first and second differences in the number of registered patients in different countries. The spectral densities of the time series are calculated, and the frequencies and amplitudes of the maximum spectral peaks are estimated. RESULTS: It is shown that two types of cycles can be distinguished in the time series of the case numbers. Cyclical fluctuations of the first type are characterized by periods from 100 to 300 days. Cyclical fluctuations of the second type are characterized by a period of about seven days. For different countries, the phases of the seven-day fluctuations coincide. It is assumed that cyclical fluctuations of the second type are associated with the weekly cycle of population activity. CONCLUSIONS: These characteristics of cyclical fluctuations in cases can be used to predict the incidence rate.

A new modelling of the COVID 19 pandemic.

А model of coronavirus incidence is proposed. Process of disease development is represented as analogue of first- and second order phase transition in physical systems. The model is very simple in terms of the data necessary for the calculations. To verify the proposed model, only data on the current incidence rate are required. However, the determination coefficient of model R2 is very high and exceeds 0.95 for most countries. The model permits the accurate prediction of the pandemics dynamics at intervals of up to 10 days. The ADL(autoregressive distributed lag)-model was introduced in addition to the phase transition model to describe the development of the disease at the exponential phase.The ADL-model allows describing nonmonotonic changes in relative infection over the time, and providing to governments and health care decision makers the possibility to predict the outcomes of their decisions on public health.

Seasonal variations of air pollutant concentrations within Krasnoyarsk City.

This paper examines the significant differences in seasonal variations of criteria pollutant concentrations in various parts of a large urban area. These differences are caused by the microclimatic heterogeneity of the city and show the influence of breeze and orographic-type circulations on urban air pollution. The temperature heterogeneity of Krasnoyarsk territory during the winter leads to an increase of 150% in CO air pollution levels in the central part of city. During the summer the orographical heterogeneity of Krasnoyarsk City leads to increases of up to 400% in air pollution for different areas.