ABSTRACT
Purpose: The aim of the article is to present a real-time assessment of the effectiveness of pandemic management in a selected country, here - on the example of Poland. Design/Methodology/Approach: It is hypothesized that the territorial distribution of deaths due to the pandemic should be similar to the population in voivodships. The Gini index is an established method of measuring this uniformity. A similar hypothesis applies to the uniform distribution of vaccines. Findings: The knowledge about uneven distribution should be an incentive for decision-makers to take preventive measures to reduce disproportions between regions according to the indicator taking into account the number of deaths per million inhabitants and similarly the number of vaccinations. The result is a ranking ofprovinces from worst to best in terms of fighting the pandemic. Practical Implications: Such a classification should change the attitudes of the central authorities towards local decision-makers. Originality/Value: The bi-criterion proposed by the authors includes a component related to vaccinations (with a plus) and deaths (with a minus). The more vaccinations and fewer deaths per million inhabitants, the better for a given region.
ABSTRACT
There are many discussions in the media about an interval (delay) from the time of the infections to deaths. Apart from the curiosity of the researchers, defining this time interval may, under certain circumstances, be of great organizational and economic importance. The study considers an attempt to determine this difference through the correlations of shifted time series and a specific bootstrapping that allows finding the distance between local maxima on the series under consideration. We consider data from Poland, the USA, India and Germany. The median of the difference's distribution is quite consistent for such diverse countries. The main conclusion of our research is that the searched interval has rather a multimodal form than unambiguously determined.
ABSTRACT
We investigate the problem of mathematical modeling of new corona virus (COVID-19) in Poland and tries to predict the upcoming wave. A Gaussian mixture model is proposed to characterize the COVID-19 disease and to predict a new / future wave of COVID-19. This prediction is very much needed to prepare for medical setup and continue with the upcoming program. Specifically, data related to the new confirmed cases of COVID-19 per day are considered, and then we attempt to predict the data and statistical activity. A close match between actual data and analytical data by using the Gaussian mixture model shows that it is a suitable model to present new cases of COVID-19. In addition, it is thought that there are N waves of COVID-19 and that information for each future wave is also present in current and previous waves as well. Using this concept, predictions of a future wave can be made.
ABSTRACT
This paper presents models of the spread of SARS-CoV-2 coronavirus in individual countries and globally in 2020 based on the statistical characteristics of the spread in the given countries or regions (in particular, in Hubei province). Through modeling, we attempt to achieve a goal which is of vital interest to societies in a pandemic catastrophe, and to answer the question of what stage of spread the epidemic has reached in a given country. The country classifier we developed is based on the relative variability indicator of the confirmed cases variable. This classification indicator is compared with a set of data-driven thresholds, the crossing of which determines the degree of spread of the epidemic in a given country. The article was written between April 2020, when the pandemic had been suppressed in China and was raging in Europe and the USA, and August 2020, as a new wave of local resumed outbreaks appeared in many countries. We contend that the spread phases are predictable based on statistical similarity. There are four phases of epidemic spread: growth, duration, suppression and re-outbreak. The authors' Matlab software, which allows simulations of the spread of coronavirus in any country based on data published by CSSE, is available in the public GitHub repository.