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Characterizing the epidemiological dynamics of COVID-19 using a non-parametric framework


Affiliations
1 Department of Computer Science, Ravenshaw University, Cuttack 753 003, India
 

The recently evolved family of coronaviruses known as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) or COVID-19 has spread across the world at a critically alarming rate, thereby causing a global health emergency. Several nations have been adversely affected in terms of both social and economic aspects. Hence there is an utmost need to control the transmission rate of the virus by incorporating stringent control measures. In this article, a non-parametric framework for characterizing the epidemiological behaviour of COVID-19 is suggested. Several statistical analysis tests have been conducted on the time series data acquired for four countries to derive a relationship between the three considered cases, viz. the new incidence of COVID-19, new deaths, and new sample testing facilities. Further, considering the dynamical behaviour of the sample data, a smoothing spline approach is implemented to obtain a better analysis of the observations. Subsequently, autocorrelation function is used to study the degree of correlation for each considered case for specific time lags. Finally, the non-parametric kernel density estimate is adopted for obtaining a robust characterization of the underlying distributions pertaining to each case considered in this study. Hence these observations lead to the development of an efficient prediction framework that can be implemented for analysing the epidemiological behaviour of the COVID-19 pandemic.

Keywords

Autocorrelation function, COVID-19, epidemiological dynamics, non-parametric statistical tests.
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  • Wang, Li-Sheng, Wang, Yi-Ru, Ye, Da-Wei and Qing-Quan, L., A review of the 2019 novel coronavirus (COVID-19) based on current evidence. Int. J. Antimicrob. Agents, 2020, 61(2), 105,948.

  • Rademaker, M., Baker, C., Foley, P., Sullivan, J. and Wang, C., Advice regarding COVID-19 and use of immunomodulators, in patients with severe dermatological diseases. Australiasm J. Dermatol., 2020, 61(2), 158–159.

  • Yang, P. and Wang, X., COVID-19: a new challenge for human beings. Cell. Mol. Immunol., 2020, 17(5), 555–557.

  • Kampf, G., Todt, D., Pfaender, S. and Steinmann, E., Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents. J. Hosp. Infect., 2020, 104(3), 246–251.

  • Hebel, J. R. and McCarter, R., Study Guide to Epidemiology and Biostatistics, Jones & Bartlett Publishers, Burlington, Massachusetts, 2011.

  • Sahoo, H., Senapati, D., Thakur, I. S. and Naik, U. C., Integrated bacteria–algal bioreactor for removal of toxic metals in acid mine drainage from iron ore mines. Bioresour. Technol. Rep., 2020, 11, 100,422.

  • Senapati, D. and Karmeshu, Generation of cubic power-law for high frequency intra-day returns: maximum tsallis entropy framework. Digit. Signal Process., 2016, 48, 276–284.

  • Bebortta, S. et al., Evidence of power-law behavior in cognitiveiot applications. Neural Comput. Appl., 2020, 32, 1–13.

  • Bebortta, S., Kumar Singh, A. K., Mohanty, S. and Senapati, D., Characterization of range for smart home sensors using tsallis’ entropy framework. In Advanced Computing and Intelligent Engineering, Springer, Singapore, 2020, pp. 265–276.

  • Nayak, G., Singh, A. K. and Senapati, D., Computational modeling of non-Gaussian option price using non-extensive tsallis’ entropy framework. Comput. Econ., 2020, 57, 1–19.

  • Mukherjee, T., Singh, A. K. and Senapati, D., Performance evaluation of wireless communication systems over Weibull/q-lognormal shadowed fading using tsallis’ entropy framework. Wireless Pers. Commun., 2019, 106(2), 789–803.

  • Fernandes, A. A. R. et al., Comparison of curve estimation of the smoothing spline nonparametric function path based on PLS and PWLS in various levels of heteroscedasticity. In IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2019, vol. 546, p. 052,024.

  • Schultz, M. B., Vera, D. and Sinclair, D. A., Can artificial intelligence identify effective COVID-19 therapies? EMBO Mol. Med., 2020, 12(8), e12817.

  • Chen, L. et al., Disease progression patterns and risk factors associated with mortality in deceased patients with COVID-19 in Hubei Province, China. Immunity Inflamm. Dis., 2020, 8(4), 584–594.

  • Bebortta, S., Panda, M. and Panda, S., Classification of pathological disorders in children using random forest algorithm. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), IEEE, 2020, pp. 1–6.

  • Dimri, V. P., Ganguli, S. S. and Srivastava, R. P., Understanding trend of the COVID-19 fatalities in India. J. Geol. Soc. India, 2020, 95(6), 637.

  • Reddy, V. K. R. and Zhang, L., Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons Fractals, 2020, p. 109864.

  • Tang, B., Bragazzi, N. L., Li, Q., Tang, S., Xiao, Y. and Wu, J., An updated estimation of the risk of transmission of the novel coronavirus (2019-nCoV). Infect. Dis. Model., 2020, 5, 248–255.

  • Tang, B., Wang, X., Li, Q., Bragazzi, N. L., Tang, S., Xiao, Y. and Wu, J., Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. J. Clin. Med., 2020, 9(2), 462.

  • Adhikary, S., Chaturvedi, S., Chaturvedi, S. K. and Banerjee, S., COVID-19 spreading prediction and impact analysis by using artificial intelligence for sustainable global health assessment. In Advances in Environmental Engineering Management, Springer, Cham, 2021, pp. 375–386.

  • Hsiang, S. et al., The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature, 2020, 584(7820), 262–267.

  • Robertson, L. S., COVID-19 confirmed cases and fatalities in 883 US counties with a population of 50,000 or more: predictions based on social, economic, demographic factors and shutdown days. medRxiv, 2020.

  • Block, P. et al., Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nature Hum. Behav., 2020, 1–9.

  • Liu, J. et al., Impact of meteorological factors on the COVID-19 transmission: a multi-city study in China. Sci. Total Environ., 2020, 26, 138513.

  • Yuan, Z., Ji, J., Zhang, T., Liu, Y., Zhang, X., Chen, W. and Xue, F., A novel chi-square statistic for detecting group differences between pathways in systems epidemiology. Stat. Med., 2016, 35(29), 5512–5524.

  • Lin, D. et al., Evaluations of the serological test in the diagnosis of 2019 novel Coronavirus (SARS-COV-2) infections during the COVID-19 outbreak. Eur. J. Clin. Microbiol. Infect. Dis., 2020, 39, 1–7.

  • Magagnoli, J. et al., Outcomes of hydroxychloroquine usage in United States veterans hospitalized with COVID-19. Med, 2020.

  • Hu, S. et al., Weakly supervised deep learning for COVID-19 infection detection and classification from CT images. IEEE Access, 2020, 8, 118869–118883.

  • Farrow, D. C. et al., A human judgment approach to epidemiological forecasting. PLOS Comput. Biol., 2017, 13(3), e1005248.

  • Hudson, J., Fielding, S. and Ramsay, C. R., Methodology and reporting characteristics of studies using interrupted time series design in healthcare. BMC Med. Res. Methodol., 2019, 19(1), 137.

  • Walczak, B., Wavelets in Chemistry, Elsevier, 2000.

  • Satija, U., Ramkumar, B. and Sabarimalai Manikandan, M., Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE J. Biomed. Health Informat., 2017, 22(3), 722–732.

  • Kühn, I., Incorporating spatial autocorrelation may invert observed patterns. Divers. Distribut., 2007, 13(1), 66–69.

  • Dormann, C. F. et al., Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 2007, 30(5), 609–628.

  • Irvine, M. A. and Hollingsworth, T. D., Kernel-density estimation and approximate bayesian computation for flexible epidemiological model fitting in python. Epidemics, 2018, 25, 80–88.

  • Pereira-Franchi, E. P. L. et al., Molecular epidemiology of methicillin-resistant Staphylococcus aureus in the Brazilian primary health care system. Trop. Med. Int. Health, 2019, 24(3), 339–347.

  • Brooks, L. C., Farrow, D. C., Hyun, S., Tibshirani, R. J. and Rosenfeld, R., Nonmechanistic forecasts of seasonal influenza with iterative one-week-ahead distributions. PLOS Comput. Biol., 2018, 14(6), e1006134.

  • Tsay, R. S., Anal. Financial Time Series, John Wiley, 2005, vol. 543.

  • Choong, M. K., Charbit, M. and Yan, H., Autoregressive modelbased missing value estimation for DNA microarray time series data. IEEE Trans. Inform. Technol. Biomed., 2009, 13(1), 131–137.


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  • Characterizing the epidemiological dynamics of COVID-19 using a non-parametric framework

Abstract Views: 301  |  PDF Views: 131

Authors

Sujit Bebortta
Department of Computer Science, Ravenshaw University, Cuttack 753 003, India
Dilip Senapati
Department of Computer Science, Ravenshaw University, Cuttack 753 003, India

Abstract


The recently evolved family of coronaviruses known as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) or COVID-19 has spread across the world at a critically alarming rate, thereby causing a global health emergency. Several nations have been adversely affected in terms of both social and economic aspects. Hence there is an utmost need to control the transmission rate of the virus by incorporating stringent control measures. In this article, a non-parametric framework for characterizing the epidemiological behaviour of COVID-19 is suggested. Several statistical analysis tests have been conducted on the time series data acquired for four countries to derive a relationship between the three considered cases, viz. the new incidence of COVID-19, new deaths, and new sample testing facilities. Further, considering the dynamical behaviour of the sample data, a smoothing spline approach is implemented to obtain a better analysis of the observations. Subsequently, autocorrelation function is used to study the degree of correlation for each considered case for specific time lags. Finally, the non-parametric kernel density estimate is adopted for obtaining a robust characterization of the underlying distributions pertaining to each case considered in this study. Hence these observations lead to the development of an efficient prediction framework that can be implemented for analysing the epidemiological behaviour of the COVID-19 pandemic.

Keywords


Autocorrelation function, COVID-19, epidemiological dynamics, non-parametric statistical tests.

References





DOI: https://doi.org/10.18520/cs%2Fv122%2Fi7%2F790-800