DOI: 10.37421/2736-657X. 2023.7.191
DOI: 10.37421/2736-657X.2023.7.172
DOI: 10.37421/2736-657X.2023.7.171
G Jacques Richardson* , and R Walter Erdelen
The authors peer into the future, after COVID-19 has passed (if ever). The growing human population remains the top concern-what systems analysts call a ‘wicked’ challenge and not soon resolvable. Returning to 2019 or doing nothing at all will not work obliging us to adopt a duo of interlinked strategies. First, reduce pressures on the environment; second, mend our socioeconomic habits, envisaging a world that was not before. Changes must be significant. We discuss eventual mechanisms to support the United Nations in efforts to make sustainability the key characteristic of our future on planet Earth. The wastrel’s reckless ways must cede to reason. How life is lived will decide if Homo sapiens can keep its home on Earth—or even continue to exist. Courage should prove key to a saner future. This may require the casting of new forms of multi-scale governance of our highly interrelated hyper-network at the level of Earth system. We also consider new arrangements for leadership such as multi-nation options, a regional setup, or a multilateral organizational scheme such as the UN’s. Further, paradigm change is suggested, linked to basic reform of the UN itself, various schemes for transformative change, a democratic world parliament or a cohabitation model. A Great Reset mechanism for the time during and after COVID-19 should there be such an era may facilitate the deep changes we need. In sum, profound reflections over the right developmental trajectory are essential for our future in ‘our’ time: the Anthropocene.
Edward Dadyan
The analyst often must deal with data that represents the history of changes in various objects over time, with time series. They are the ones that are most interesting from the point of view of many analysis tasks, and especially forecasting. For analysis tasks, time counts are of interest-values recorded at some, usually equidistant, points in time. Counts can be taken at various intervals: in a minute, an hour, a day, a week, a month, or a year, depending on how much detail the process should be analyzed. In time series analysis problems, we are dealing with discrete time, when each observation of a parameter forms a time frame. We can say the same about the behavior of COVID-19 over time. This paper solves the problem of predicting COVID-19 diseases in Moscow and the Russian Federation using neural networks. This approach is useful when it is necessary to overcome difficulties related to non-stationarity, incompleteness, unknown distribution of data, or when statistical methods are not completely satisfactory. The problem of forecasting is solved using the analytical platform Deductor Studio, developed by specialists of Intersoft Lab of the Russian Federation. When solving this problem, we used mechanisms for clearing data from noise and anomalies, which ensured the quality of building a forecast model and obtaining forecast values for tens of days ahead. The principle of time series forecasting was also demonstrated: import, seasonal detection, cleaning, smoothing, building a predictive model, and predicting COVID-19 diseases in Moscow and the Russian Federation using neural technologies for twenty days ahead.
Lu xan , Ba lin
With the long-term outbreak of the COVID-19 around the world, identi- fying high-risk areas is becoming a new research boom. In this paper, we propose a novel regression method namely Regular Linear Kernel Regression (RLKR) for COVID-19 high-risk areas exploration. We explain in detail how the canonical linear kernel regression method is linked to the identification of high-risk areas for COVID-19. Furthermore, the consistence condition of Kernel Selection, which is closely related to the identification of high-risk areas, is given with two mild assumptions. Finally, the RLKR method was verified by simulation experiments and applied for COVID-19 high-risk area Exploration
DOI: 10.37421/2736-657X.2022.6.152
Dengue and co-infection with typhoid infection is increasingly recognized as one of the world's emerging infectious diseases. We have appraised Complete Blood Count (CBC) parameters and serological NS1, IgG/IgM rapid test data along with survey questionnaire of 314 suspected dengue and typhoid patients with Acute Febrile Illness (AFI) symptoms patients from the different villages of Sonepat district, Haryana to predict dengue and co-infection with typhoid model. Among those suspected patients, 50 dengue positive (14 primaries and 37 secondary infections) in age groups 10-39 years, 86 typhoid positive (64 primaries and 22 secondary infections) in age groups 10-49 years, 8 co-infection cases in age groups 10-29 year and 40-49 years mostly were reported respectively. As per bayesian analysis model and logistic regression model, TLC<4000 cells/cmm (leukopenia) of dengue, MCH>32 pg of typhoid and MCV<83 fL of co-infection was mostly statistically significant (p<0.05) among different clinical parameters with high ROC value (area ± SE) with 61-71% accuracy of disease diagnosis evaluation. We identified important CBC parameters to qualify the distinction of dengue, typhoid and co-infection patients with AFI and for more confirmation, a further investigation should be designed for early diagnosis and treatment for the patients.
Virology: Current Research received 166 citations as per Google Scholar report
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