Assessing the ratio between new Covid-19 cases and new tests for Sars-Cov-2 in Italy by fractal investigation

Main Article Content

Ugo Indraccolo

Keywords

Covid-19, Italy, Fractal analysis

Abstract

Aim: processing the heterogeneous data on the Italian Covid-19 epidemic by fractal investigation on the trend curve of the ratio between new Covid19 cases/new Sars-Cov-2 tests. Methods: New cases of Covid-19 disease and new tests were calculated from raw data freely available on the Italian governing website. The effectiveness of Italian government Decrees aiming to obtain lock-down was assessed by fractal investigation. Self-similarity parameters of presumed fractal shapes obtained 6 days after each Decree were estimated, when possible. Self-organized criticality was also assessed to check for chaos involvement in disturbing the fractal shapes. Shapes were then compared and were used to estimate the number of new tests for Sars-Cov-2 that Italy would be able to perform. Results: The full lock-down changed the biocomplexity of the Covid-19 epidemic in Italy. If the biocomplexity of Covid-19 did not change after the lock-down, Italy should have been able to perform at least 25490 tests daily (±8940) on average, while real data show that a larger number of tests were done (p<0.001) (thereby obtaining the lowering of contagions). If the same biocomplexity was observed before full lock down, Italy would be able to perform 7088 tests daily (±5163) on average, while real data show that a lower number of tests were done (p=0.029) (thereby observing the worsening of contagions). Conclusion: in case of heterogeneous data, fractal investigation would be prove useful for assessing and estimating trends.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Abstract 638 | PDF Downloads 128

References

1 Guan WJ, Ni ZY, Hu Y, et al. Clinical characteristics of Coronavirus disease 2019 in China. N Engl J Med 2020; 382:1708-20. doi: 10.1056/NEJMoa2002032
2 Phan LT, Nguyen TV, Luong QC, et al. Importation and human-to-human transmission of a novel Coronavirus in Vietnam. N Engl J Med 2020; 382: 872-4. doi: 10.1056/NEJMc2001272
3 Ng OT, Marimuthu K, Chia PY, et al. SARS-CoV-2 infection among travelers returning from Wuhan, China. N Engl J Med 2020; 382: 1476-8. doi: 10.1056/NEJMc2003100
4 Lipsitch M, Swerdlow DL, Finelli L. Defining the epidemiology of Covid-19 – Studies needed. N Engl J Med 2020; 382: 1194-6. doi: 10.1056/NEJMp2002125
5 Odone A, Delmonte D, Scognamiglio T, Signorelli C. COVID-19 deaths in Lombardy, Italy: data in context. Lancet Public Health 2020; S2468-2667(20)30099-2. doi: 10.1016/S2468-2667(20)30099-2.
6 Signorelli C, Scognamiglio T, Odone A. COVID-19 in Italy: impact of containment measures and prevalence estimates of infection in the general population. Acta Biomed 2020; 91 (3-S): 175-9. doi: 10.23750/abm.v91i3-S.9511
7 Remuzzi A, Remuzzi G. COVID-19 and Italy: what next? Lancet 2020; 395: 1225-8. doi: 10.1016/S0140-6736(20)30627-9
8 Del Buono MG, Iannaccone G, Camilli M, Del Buono R, Aspromonte N. The Italian outbreak of COVID-19: conditions, contributors, and concerns. Mayo Clin Proc 2020; 95: 1116-8. doi: 10.1016/j.mayocp.2020.04.003
9 Fattorini D, Regoli F. Role of the chronic air pollution levels in the Covid-19 outbreak risk in Italy. Environ Pollut 2020; 264: 114732. doi: 10.1016/j.envpol.2020.114732.
10 He X, Lau EHY, Wu P, et al. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med 2020; 26: 672-5. doi: 10.1038/s41591-020-0869-5
11 Halfmann PJ, Hatta M, Chiba S, et al. Transmission of SARS-CoV-2 in domestic cats. N Engl J Med 2020 doi: 10.1056/NEJMc2013400.
12 Tobías A. Evaluation of the lockdowns for the SARS-CoV-2 epidemic in Italy and Spain one month follow up. Sci Total Environ 2020; 725: 138539. doi: 10.1016/j.scitotenv.2020.138539
13 Gatto M, Bertuzzo E, Mari L, et al. Spread and dynamics of the COVID-19 epidemic in Italy: effects of emergency containment measures. Proc Natl Acad Sci U S A 2020; 117: 10484-91. doi: 10.1073/pnas.2004978117
14 Petersen E, Gökengin D. SARS-CoV-2 epidemiology and control, different scenarios for Turkey. Turk J Med Sci 2020; 50(SI-1): 509-14. doi: 10.3906/sag-2003-260
15 Wang W, Xu Y, Gao R, et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA 2020; 323: 1843-4. doi: 10.1001/jama.2020.3786.
16 Glattre E, Nygård JF. Fractal meta-analysis and ‘causality’ embedded in complexity: advanced understanding of disease etiology. Nonlinear Dynamics Psychol Life Sci 2004; 8: 315-44.
17 Baldado M, Padua R, Adanza JG, Panduyos JB. Statistical analysis of fractal observations: applications in education and poverty estimation. SDSSU Multidisciplinary Research Journal 2013; 1: 41-9. https://smrj.sdssu.edu.ph/index.php/SMRJ/article/view/84/81
18 Bak P, Tang C, Wiesenfeld K. Self-organized criticality: an explanation of the 1/f noise. Phys Rev Lett 1987; 59: 381-4. doi: 10.1103/PhysRevLett.59.381
19 Saba H, Miranda JGV, Moret MA. Self-organized critical phenomenon as a q-exponential decay – Avalanche epidemiology of dengue. Physica A 2014; 413: 205-11. https://doi.org/10.1016/j.physa.2014.06.045
20 Lauer SA, Grantz KY, Bi Q, et al. The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: estimation and application. Ann Intern Med 2020; 172: 577-82. doi: 10.7326/M20-0504
21 Sebastiani G, Massa M, Riboli E. Covid-19 epidemic in Italy: evolution, projection and impact of government measures. Eur J Epidemiol 2020; 35: 341-5. doi: 10.1007/s10654-020-00631-6
22 Giangreco G. Case fatality rate analysis of Italian COVID-19 outbreak. J Med Virol 2020; 92: 919-23. doi: 10.1002/jmv.25894.

Similar Articles

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 > >> 

You may also start an advanced similarity search for this article.