Regression Models Predicting the Number of Deaths from the New Coronavirus Infection
Predicting the development of epidemic infection caused by the COVID-19 coronavirus is a matter of the utmost urgency for health care and effective anti-epidemic measures. Given the rapidly changing initial information and the ambiguous quality of data coming from various sources, it is important to quickly optimize the existing prognostic models by using more sophisticated algorithms.
The aim of the study is to test the originally developed mathematical algorithms for predicting the development of the COVID-19 epidemic process.
Materials and Methods. To assess the situation in China, Italy, and the USA, we used the information from Russian- and English-language sources available in official websites. The generally accepted descriptive statistics were used; mathematical modeling was based on linear regression. Statistical data processing was performed using the IBM SPSS Statistics 24.0 and R (RStudio) 3.6.0.
Results. We found significant differences not only in the incidence rate of COVID-19 in the countries in question, but also in the death rate. The risk of death associated with COVID-19 is high due to the high number of severe clinical cases of the disease reported from these countries.
Two preliminary regression models were created. The first, initial model was based on the increase in new cases of infection — this factor was significantly associated with the outcome; the regression coefficient was 0.02 (95% CI 0.01–0.03). In the second, expanded model, in addition to the increase in new cases, the increase in the number of severe forms of infection was also considered; the regression coefficients were 0.017 (95% CI 0.012–0.022) and 0.01 (95% CI 0.008–0.011), respectively. Adding the second variable contributed to a more accurate description of the available data by the model.
Conclusion. The developed regression models for infection control and predicting the number of lethal outcomes can be successfully used under conditions of spreading diseases from the group of “new infections” when primary data received from various sourced are changing rapidly and updates of the information are continually required. In addition, our initial model can produce a preliminary assessment of the situation, and the expanded model can increase the accuracy and improve the analytic algorithm.
- Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., Cheng Z., Yu T., Xia J., Wei Y., Wu W., Xie X., Yin W., Li H., Liu M., Xiao Y., Gao H., Guo L., Xie J., Wang G., Jiang R., Gao Z., Jin Q., Wang J., Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395(10223): 497–506, https://doi.org/10.1016/s0140-6736(20)30183-5.
- Verity R., Okell L.C., Dorigatti I., Winskill P., Whittaker C., Imai N., Cuomo-Dannenburg G., Thompson H., Walker P.G.T., Fu H., Dighe A., Griffin J.T., Baguelin M., Bhatia S., Boonyasiri A., Cori A., Cucunubá Z., FitzJohn R., Gaythorpe K., Green W., Hamlet A., Hinsley W., Laydon D., Nedjati-Gilani G., Riley S., van Elsland S., Volz E., Wang H., Wang Y., Xi X., Donnelly C.A., Ghani A.C., Ferguson N.M. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis 2020, https://doi.org/10.1016/s1473-3099(20)30243-7.
- Wu J.T., Leung K., Bushman M., Kishore N., Niehus R., de Salazar P.M., Cowling B.J., Lipsitch M., Leung G.M. Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China. Nat Med 2020, https://doi.org/10.1038/s41591-020-0822-7.
- World Health Organization. Coronavirus disease (COVID-19) outbreak situation. URL: https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
- Rasporyazhenie Pravitel’stva Rossiyskoy Federatsii ot 30 yanvarya 2020 g. No.140-r “O vremennom ogranichenii dvizheniya cherez punkty propuska na otdel’nykh uchastkakh gosudarstvennoy granitsy Rossiyskoy Federatsii s Kitayskoy Narodnoy Respublikoy” [Order of the Government of the Russian Federation dated January 30, 2020 No.140-r “On the temporary restriction of traffic through checkpoints at certain sections of the state border of the Russian Federation with the People’s Republic of China”].
- Postanovlenie Glavnogo gosudarstvennogo sanitarnogo vracha Rossiyskoy Federatsii ot 30 marta 2020 g. No.9 “O dopolnitel’nykh merakh po nedopushcheniyu rasprostraneniya COVID-19” [Decree of the Chief State Sanitary Doctor of the Russian Federation dated March 30, 2020 No.9 “On additional measures to prevent the spread of COVID-19”].
- Ministry of Health of the Russian Federation. Profilaktika, diagnostika i lechenie novoy koronavirusnoy infektsii (COVID-19). Vremennye metodicheskie rekomendatsii. Versiya 4 (27.03.2020) [Prevention, diagnosis and treatment of new coronavirus infection (COVID-19). Temporary guidelines. Version 4 (March 27, 2020)]. URL: https://static-3.rosminzdrav.ru/system/attachments/ attaches/000/049/881/original/COVID19_recomend_v4.pdf.
- Ministry of Health of the Russian Federation. URL: https://www.rosminzdrav.ru/.
- World Health Organization. Coronavirus disease 2019 (COVID-19). Situation report — 72. URL: https://www.who.int/docs/default-source/ coronaviruse/situation-reports/20200401-sitrep-72- covid-19.pdf?sfvrsn=3dd8971b_2.
- Ministry of Health of the Russian Federation. Informatsionnyy resurs o COVID-19 [Information resource about COVID-19]. URL: https://covid19.rosminzdrav.ru/.
