QUANTIFYING EPIDEMIC SPREAD THROUGH GROWTH TRAJECTORIES, RECOVERY DYNAMICS, AND TEMPORAL FORECASTING
DOI:
https://doi.org/10.69980/2db0cx07Keywords:
epidemic growth, recovery dynamics, COVID-19, time-series forecasting, ARIMAAbstract
Temporal variations of the incidence, recovery and mortality rates and the burden of active disease determine how an epidemic progresses. These indicators can be incorporated into forecasting to give a more comprehensive picture of the dynamics of an epidemic than cumulative case numbers. This study quantified epidemic spread through growth trajectories, recovery dynamics, associations among major indicators, and short-term temporal forecasting using longitudinal COVID-19 data from India. Daily cases of confirmed, recovered, and deceased patients were analyzed using a retrospective time-series design. The daily incidence, active cases, growth rate, recovery rate, case fatality rate and seven-day moving averages were calculated. Monthly patterns and Spearman correlations were examined, while ARIMA and Holt damped-trend models were evaluated using a chronological 80:20 training-testing split. The mean number of confirmed cases was 33,019.78 per day, and the recovery rate was 97.30% at the end of the study. Active cases peaked at 1,021,053 before declining substantially. The daily growth rate showed a strong inverse association with the recovery rate (ρ = −0.968). Holt damped trend achieved lower MAE (7,540.36) and RMSE (9,346.85) than ARIMA(2,1,3). Combining epidemic growth, recovery, association patterns and forecasting offers a unified picture for characterizing temporal evolution of an epidemic and assessing short-term predictive performance.
References
1. AuBartolomeo, N., Trerotoli, P., & Serio, G. (2021). Short-term forecast in the early stage of the COVID-19 outbreak in Italy. Application of a weighted and cumulative average daily growth rate to an exponential decay model. Infectious Disease Modelling, 6, 212-221.
2. Benvenuto, D., Giovanetti, M., Vassallo, L., Angeletti, S., & Ciccozzi, M. (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in brief, 29, 105340.
3. Chen, Z. L., Zhang, Q., Lu, Y., Guo, Z. M., Zhang, X., Zhang, W. J., ... & Lu, J. H. (2020). Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China. Chinese medical journal, 133(09), 1044-1050.
4. Chyon, F. A., Suman, M. N. H., Fahim, M. R. I., & Ahmmed, M. S. (2022). Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning. Journal of virological methods, 301, 114433.
5. Derakhshan, M., Ansarian, H. R., & Ghomshei, M. (2021). Temporal variations in COVID-19: an epidemiological discussion with a practical application. Journal of International Medical Research, 49(8), 03000605211033208.
6. Drake, J. M., Brett, T. S., Chen, S., Epureanu, B. I., Ferrari, M. J., Marty, É., ... & Rohani, P. (2019). The statistics of epidemic transitions. PLoS computational biology, 15(5), e1006917.
7. Gross, B., Zheng, Z., Liu, S., Chen, X., Sela, A., Li, J., ... & Havlin, S. (2020). Spatio-temporal propagation of COVID-19 pandemics. Europhysics Letters, 131(5), 58003.
8. Hay, J. A., Kennedy-Shaffer, L., Kanjilal, S., Lennon, N. J., Gabriel, S. B., Lipsitch, M., & Mina, M. J. (2021). Estimating epidemiologic dynamics from cross-sectional viral load distributions. Science, 373(6552), eabh0635.
9. Khajanchi, S., & Sarkar, K. (2020). Forecasting the daily and cumulative number of cases for the COVID-19 pandemic in India. Chaos: An interdisciplinary journal of nonlinear science, 30(7).
10. Kim, S., Jeong, Y. D., Byun, J. H., Cho, G., Park, A., Jung, J. H., ... & Jung, I. H. (2020). Evaluation of COVID-19 epidemic outbreak caused by temporal contact-increase in South Korea. International Journal of Infectious Diseases, 96, 454-457.
11. Kochańczyk, M., Grabowski, F., & Lipniacki, T. (2020). Dynamics of COVID-19 pandemic at constant and time-dependent contact rates. Mathematical Modelling of Natural Phenomena, 15, 28.
12. Kumar, N., & Susan, S. (2020, July). COVID-19 pandemic prediction using time series forecasting models. In 2020 11th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1-7). IEEE.
13. Loeffler-Wirth, H., Schmidt, M., & Binder, H. (2020). Covid-19 transmission trajectories–monitoring the pandemic in the worldwide context. Viruses, 12(7), 777.
14. Mustavee, S., Agarwal, S., Enyioha, C., & Das, S. (2022). A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility. Nonlinear Dynamics, 109(2), 1233-1252.
15. Perone, G. (2020). An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy. MedRxiv, 2020-04.
16. Post, L., Culler, K., Moss, C. B., Murphy, R. L., Achenbach, C. J., Ison, M. G., ... & Oehmke, J. F. (2021). Surveillance of the second wave of COVID-19 in Europe: longitudinal trend analyses. JMIR public health and surveillance, 7(4), e25695.
17. Sahai, A. K., Rath, N., Sood, V., & Singh, M. P. (2020). ARIMA modelling & forecasting of COVID-19 in top five affected countries. Diabetes & metabolic syndrome: clinical research & reviews, 14(5), 1419-1427.
18. Salgotra, R., Singh, S., Singh, U., Saha, S., & Gandomi, A. H. (2021). COVID-19: Time series datasets India versus world (Version 37) [Data set]. Mendeley Data. https://doi.org/10.17632/tmrs92j7pv.37
19. Tkachenko, A. V., Maslov, S., Elbanna, A., Wong, G. N., Weiner, Z. J., & Goldenfeld, N. (2021). Time-dependent heterogeneity leads to transient suppression of the COVID-19 epidemic, not herd immunity. Proceedings of the National Academy of Sciences, 118(17), e2015972118.
20. Watson, G. L., Xiong, D., Zhang, L., Zoller, J. A., Shamshoian, J., Sundin, P., ... & Ramirez, C. M. (2021). Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model. PLoS computational biology, 17(3), e1008837.
21. Xu, B., Gutierrez, B., Mekaru, S., Sewalk, K., Goodwin, L., Loskill, A., ... & Kraemer, M. U. (2020). Epidemiological data from the COVID-19 outbreak, real-time case information. Scientific data, 7(1), 106.
22. Yang, H., Zhang, S., Liu, R., Krall, A., Wang, Y., Ventura, M., & Deflitch, C. (2020, December). Epidemic informatics and control: A review from system informatics to epidemic response and risk management in public health. In INFORMS International Conference on Service Science (pp. 1-58). Cham: Springer International Publishing.
