Application of distribution-delay models to estimating the hospitalized mortality rate of covid-19 according to delay effect of hospitalizations counts
Abstract
Introduction:The COVID-19 infectious epidemic has become a serious worry all over the world, including Iran. The high outbreak of disease ranked Iran as second in Asia and 11th in the world. Given the growing progress of this epidemic in infecting and killing individuals, it is essential to forecast the delay effect of the number of hospitalized upon the hospitalized mortality rate.
Methods: In this study, we used the daily Hospitalization cases of COVID-19 of IRAN for the period of 15-May 2020 to 5-Oct 2020 which were obtained from the online database. Five distribution delay models were compared for estimating and forecasting.
Results: Based on measurement errors DDM selected as the best model for forecasting the number of death. According to this model, the long-run effects show that observing the effect of hospitalization counts on death counts takes an average of five days and the long-run hospitalized mortality rate was 12%.
Conclusion: The overall hospitalized mortality rate of COVID-19 in Iran is less than the global rate of 15%. The mean of delay effect of daily hospitalization on mortality is approximately 5 days. Our findings showed distributed delay model (DDM) has better performance in the forecasting of the future behavior of the Coronavirus mortality, and providing to government and health care decision- makers the possibility to predict the outcomes of their decision on public health.
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2. da Silva CAG. A CLASSIFICATION STUDY OF BRAZIL ‘S FEDERATIVE UNITS FOR COVID-BASED IN HIDDEN MARKOV MODEL: THE NEW EPICENTER OF SARS-COV-VIRUS IN THE WORLD.
3. Takian A, Raoofi A, Kazempour-Ardebili S. COVID-19 battle during the toughest sanctions against Iran. Lancet (London, England). 2020;395(10229):1035.
4. Adhikari SP, Meng S, Wu Y-J, Mao Y-P, Ye R-X, Wang Q-Z, et al. Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: a scoping review. Infectious diseases of poverty. 2020;9(1):1-12.
5. https://www.worldometers.info/coronavirus/?utm_campaign=homeAdvegas1?
6. Grasselli G, Pesenti A, Cecconi M. Critical care utilization for the COVID-19 outbreak in Lombardy, Italy: early experience and forecast during an emergency response. Jama. 2020;323(16):1545-6.
7. Ferguson N, Laydon D, Nedjati-Gilani G, Imai N, Ainslie K, Baguelin M, et al. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Imperial College London. 2020;10:77482.
8. Liu Y, Gayle AA, Wilder-Smith A, Rocklöv J. The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of travel medicine. 2020.
9. Docherty AB, Harrison E, Green C. Features of 16,749 hospitalised UK patients with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol. medRxiv. The Preprint Server for Health Science. 2020;28.
10. White DB, Lo B. A framework for rationing ventilators and critical care beds during the COVID-19 pandemic. Jama. 2020;323(18):1773-4.
11. Anderson RM, Heesterbeek H, Klinkenberg D, Hollingsworth TD. How will country-based mitigation measures influence the course of the COVID-19 epidemic? The Lancet. 2020;395(10228):931-4.
12. Nyasulu J, Pandya H. The effects of coronavirus disease 2019 pandemic on the South African health system: A call to maintain essential health services. African Journal of Primary Health Care & Family Medicine. 2020;12(1).
13. Hellewell J, Abbott S, Gimma A, Bosse NI, Jarvis CI, Russell TW, et al. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health. 2020.
14. Konarasinghe K. Modeling COVID-19 Epidemic of USA, UK and Russia. Journal of New Frontiers in Healthcare and Biological Sciences. 2020;1(1):1-14.
15. Konarasinghe KMUB. Modeling COVID -19 Epidemic of India and Brazil. Journal of New Frontiers in Healthcare and Biological Sciences. 2020;1.
16. Soukhovolsky V, Kovalev A, Pitt A, Kessel B. A new modelling of the COVID 19 pandemic. Chaos, Solitons & Fractals. 2020:110039.
17. Almon S. The distributed lag between capital appropriations and expenditures. Econometrica: Journal of the Econometric Society. 1965:178-96.
18. Schwartz J. The distributed lag between air pollution and daily deaths. Epidemiology. 2000;11(3):320-6.
19. Zanobetti A, Wand MP, Schwartz J, Ryan LM. Generalized additive distributed lag models: quantifying mortality displacement. Biostatistics. 2000;1(3):279-92.
20. Muggeo VM, Hajat S. Modelling the non-linear multiple-lag effects of ambient temperature on mortality in Santiago and Palermo: a constrained segmented distributed lag approach. Occupational and Environmental Medicine. 2009;66(9):584-91.
21. Gasparrini A. Distributed lag linear and non-linear models in R: the package dlnm. Journal of statistical software. 2011;43(8):1.
22. Demirhan H. dLagM: An R package for distributed lag models and ARDL bounds testing. Plos one.
2020;15(2):e0228812.
23. R. Ravines R, M. Schmidt A, S. Migon HJASMiB, Industry. Revisiting distributed lag models through a Bayesian perspective. 2006;22(2):193-210.
24. Demirhan H. dLagM: Time Series Regression Models with Distributed Lag Models. R package version. 2019;1:12.
25. Tuomisto JT, Yrjölä J, Kolehmainen M, Bonsdorff J, Pekkanen J, Tikkanen TJm. An agent-based epidemic model REINA for COVID-19 to identify destructive policies. 2020.
26. Clifford SJ, Klepac P, Van Zandvoort K, Quilty BJ, Eggo RM, Flasche S, et al. Interventions targeting air travellers early in the pandemic may delay local outbreaks of SARS-CoV-2. 2020.
27. Shen M, Peng Z, Guo Y, Xiao Y, Zhang LJm. Lockdown may partially halt the spread of 2019 novel coronavirus in Hubei province, China. 2020.
28. Tang B, Bragazzi NL, Li Q, Tang S, Xiao Y, Wu JJIdm. An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov). 2020;5:248-55.
29. Allali M, Portecop P, Carlès M, Gibert DJm. Prediction of the time evolution of the COVID-19 disease in Guadeloupe with a stochastic evolutionary model. 2020.
30. Ediriweera DS, de Silva NR, Malavige NG, de Silva HJJm. AN EPIDEMIOLOGICAL MODEL TO AID DECISION-MAKING FOR COVID-19 CONTROL IN SRI LANKA. 2020.
31. Shen JJm. A Recursive Bifurcation Model for Predicting the Peak of COVID-19 Virus Spread in United States and Germany. 2020.
| Files | ||
| Issue | Vol 6 No 4 (2020) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v6i4.5676 | |
| Keywords | ||
| COVID-19, mortality rate, hospitalization, Distribution Delay Model (DDM) Forecasting | ||
| Rights and permissions | |
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
How to Cite
1.
Rastaghi sedighe, Akbari Shark N, Saki A. Application of distribution-delay models to estimating the hospitalized mortality rate of covid-19 according to delay effect of hospitalizations counts. JBE. 2021;6(4):241-250.
