Dependence between mortality in regions and prevalence of active SARS-COV2 carriers and resources available to public healthcare organizations

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UDC: 
616.9–036.8; 51-76; 519.237
DOI: 
10.21668/health.risk/2020.4.02.eng
Authors: 

V.S. Stepanov

Organization: 

The Central Economics and Mathematics Institute of the Russian Academy of Sciences, 47 Nakhimovskii Ave., Moscow, 117418, Russian Federation

Abstract: 

The paper dwells on certain mathematical models showing how epidemics develop, namely, logistic ones, SIR-model, and some others. There is also a review of articles that focus on such models showing dynamics of incidence with COVID-19 infection. These models are often successfully applied for data collected in a whole country but on a regional level there are difficulties due to peculiarities of calculating mortality figures in Russia. In this case regression models can be useful with their obvious advantage at the initial stage in an epidemic process. They also include exogenous variables that influence mortality, for example, a number of doctors and nurses per a hospital, how well hospitals are equipped with ALV devices, and a number of available beds in them.
Our research goal was to build up a linear regression model that could be used as a basis for estimating regional mortality caused by COVID-19 as well as for more efficient distribution of all the resources mentioned above.
The model is built as per a set of resource parameters including data on «active cases». Preliminary three variables that showed data on resources available to communicable diseases departments in hospitals were transformed into a new single one via linear transformation. Then the model was tested on a training sample containing an endogenous variable on mortality and four factor ones including prevalence of active virus carriers. Regions were included into training data with different lags; they were included into such daily samples when death cases were registered rarely. Then the estimated model was applied with other values. It turned out to be quite efficient in estimating COVID-induced mortality for regions from trainings samples as well as for several others (for certain intervals).
As a result, we built a regression model and estimated its precision; the model showed a relation between mortality in a region and prevalence of active SARS-CoV2 carriers and availability of resources to hospitals in it. It can be useful when these resources are distributed. It can also be used to build SIRD, SEIR, and SEIRF models at a regional level when choosing parameters in them related to mortality. A methodology itself that can be similarly applied for other epidemic processes also deserves certain attention.

Keywords: 
regression model, mortality estimation, COVID-19, coronavirus infection, logistic equitation, SEIR, SIR, ALV
Stepanov V.S. Dependence between mortality in regions and prevalence of active SARS-COV2 carriers and resources available to public healthcare organizations. Health Risk Analysis, 2020, no. 4, pp. 12–22. DOI: 10.21668/health.risk/2020.4.02.eng
References: 
  1. Zhmerenetskii K.V., Sazonova E.N., Voronina N.V., Tomilka G.S., Sen'kevich O.A., Gorokhovskii V.S., D'yachenko S.V., Kol'tsov I.P., Kutsyi M.B. COVID-19: scientific facts. Dal'nevostochnyi meditsinskii zhurnal, 2020, no. 1, pp. 5–22 (in Russian).
  2. Danilova I.A. Morbidity and mortality from COVID-19. The problem of data comparability. Demograficheskoe obozrenie, 2020, vol. 7, no. 1, pp. 6–26 (in Russian).
  3. Drapkina O.M., Samorodskaya I.V., Sivtseva M.G., Kakorina E.P., Briko N.I., Cherkasov S.N., Tsinzerling V.A., Mal'kov P.G. COVID-19: urgent questions for estimating morbidity, prevalence, case fatality rate and mortality rate. Kardiovaskulyarnaya terapiya i profilaktika, 2020, vol. 19, no. 3, pp. 302–309 (in Russian).
  4. Ivanov S. Mortality from COVID-19 against the backdrop of other twentieth century mortality bursts. Demograficheskoe obozrenie, 2020, vol. 7, no. 2, pp. 143–151 (in Russian).
  5. Kozlovskii S., Boldyrev O. Mozhno li predskazat' razvitie pandemii koronavirusa? Ob"yasnyaem na primere Rossii [Can we predict how coronavirus pandemic will develop? Let us explain using Russia as an example]. BBC Russia. Available at: https://www.bbc.com/russian/features-52762747 (30.07.2020).
  6. Glava Rosstata rasskazal, kak schitayut zhertv COVID-19 [Head of Rosstat has told how victims of COVID-19 are calculated in the country]. BBC Russia. Available at: https://www.bbc.com/russian/features-53156041 (02.08.2020).
  7. Koronavirus v Rossii: Infografika [Coronavirus in Russia: graphic data]. Mediazona, 2020. Available at: https://zona.media/coronagraph (30.07.2020).
  8. Boyarintsev V.V., Pal'min R.S., Pal'min S.A., Pertsev S.F. Nique for predicting parameters of the epidemic process caused by COVID-19. Kremlevskaya meditsina. Klinicheskii vestnik, 2020, no. 2, pp. 14–21 (in Russian).
  9. Kondrat'ev M.A. Forecasting methods and models of disease spread. Komp'yuternye issledovaniya i modelirovanie, 2013, vol. 5, no. 5, pp. 863–882 (in Russian).
  10. Magpantay F.M.G., Kosovalić N., Wu J. An age-structured population model with state-dependent delay: derivation and numerical integration. SIAM Journal on Numerical Analysis, 2014, vol. 52, no. 2, pp. 735–756. DOI: 10.1137/120903622
  11. Khrapov P.V., Loginova A.A. Comparative analysis of the mathematical models of the dynamics of the coronavirus COVID-19 epidemic development in the different countries. International Journal of Open Information Technologies, 2020, vol. 8, no. 5, pp. 17–22.
  12. Minaev V.A., Sychev M.P., Vaits E.V., Bondar' K.M. System-dynamic modeling of network information operations. Inzhenernye tekhnologii i sistemy, 2019, vol. 29, no. 1, pp. 20–39 (in Russian).
  13. Drugova O.V., Pavlov E.A., Bavrina A.P., Blagonravova A.S., Saperkin N.V., Kovalishena O.V. Statistic and dynamic aspects of the prediction of the COVID-19 spread in Nizhny Novgorod region. Meditsinskii al'manakh, 2020, no. 2 (63), pp. 27–36 (in Russian).
  14. Tamm M.V. COVID-19 in Moscow: prognoses and scenarios. Farmakoekonomika. Sovremennaya farmakoekonomika i farmakoepidemiologiya, 2020, vol. 13, no. 1, pp. 43–51. DOI: 10.17749/2070-4909.2020.13.1.43-51
  15. Matveev A.V. The mathematical modeling of the effective measures against the COVID-19 spread. Natsional'naya bezopasnost' i strategicheskoe planirovanie, 2020, vol. 1, no. 29, pp. 23–39 (in Russian).
  16. Godio A., Pace F., Vergnano A. SEIR modeling of the Italian epidemic of SARS-CoV-2 using computational swarm intelligence. Int. J. Environ. Res. & Public Health, 2020, vol. 17, no. 10, pp. 3535. DOI: 10.3390/ijerph17103535
  17. Peng L., Yang W., Zhang D., Zhuge C., Hong L. Epidemic analysis of COVID-19 in China by dynamical modeling. Med Rxiv Epidemiol, 2020, 11 p. (in Russian).
  18. Pengpeng S., Shengli C., Peihua F. SEIR Transmission dynamics model of 2019 nCoV coronavirus with considering the weak infectious ability and changes in latency duration. Med Rxiv, 2020, no. 20, 5 p. DOI: 10.1101/2020.02.16.20023655
  19. Nikitina A.V., Lyapunova I.A., Dudnikov E.A. Study of the spread of viral diseases based on modifications of the SIR model. Computational mathematics and information technologies, 2020, vol. 1, no. 1, pp. 19–30. DOI: 10.23947/2587-8999-2020-1-1-19-30
  20. Liu Z., Magal P., Seydi O., G.F. Webb Predicting the Cumulative Number of Cases for the COVID-19 Epidemic in China From Early Data. Populations and Evolution, 2020, vol. 1, no. 10, 10 p. DOI: 10.20944/preprints202002.0365.v1
  21. Ndaïrou F., Area I., Nieto J.J., Torres D.F.M. Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos, Solitons & Fractals, 2020, vol. 135, no. 6, pp. 109846. DOI: 10.1016/j.chaos.2020.109846
  22. Kurkin A.A., Kurkina O.E., Pelinovskii E.N. Logistic models of epidemic growth. Trudy NGTU im. R.E. Alekseeva, 2020, no. 2 (129), pp. 9–18 (in Russian).
  23. Fabiano N., Radenović S.N. On COVID-19 diffusion in Italy: data analysis and possible outcome. Vojnotehnički glasnik, 2020, vol. 68, no. 2, pp. 216–224. DOI: 10.5937/vojtehg68-25948
  24. Cherniha R., Davydovych V. A mathematical model for the coronavirus COVID-19 outbreak. ArXiv, 2020. Available at: https://arxiv.org/abs/2004.01487v2 (25.07.2020).
  25. Kol'tsova E.M., Kurkina E.S., Vasetskii A.M. Mathematical modeling of the spread of COVID-19 in Moscow. Computational nanotechnology, 2020, vol. 7, no. 1, pp. 99–105 (in Russian).
  26. Melik-Guseinov D.V., Karyakin N.N., Blagonravov A.S., Klimko V.I., Bavrina A.P., Drugova O.V., Saperkin N.V., Kovalishena O.V. Regression models predicting the number of deaths from the new coronavirus infection. Sovremennye tekhnologii v meditsine, 2020, vol. 12, no. 2, pp. 6–13 (in Russian).
  27. Lakman I.A., Agapitov A.A., Sadikova L.F., Chernenko O.V., Novikov S.V., Popov D.V., Pavlov V.N., Gareeva D.F. [et al.]. COVID-19 mathematical forecasting in the Russian Federation. Arterial'naya gipertenziya, 2020, vol. 26, no. 3, pp. 288–294 (in Russian).
  28. Fontes E. Modelirovanie v COMSOL Multiphysics rasprostraneniya virusa COVID-19 [Modeling the spread of the COVID-19 virus in COMSOL Multiphysics]. COMSOL, 2020. Available at: https://www.comsol.ru/blogs/modeling-the-spread-of-covid-19-with-comsol-... (30.07.2020) (in Russian).
  29. Getz W.M., Dougherty E.R. Discrete stochastic analog of Erlang epidemic models. J. of Biological Dynamics, 2018, vol. 12, no. 1, pp. 16–38. DOI: 10.1080/17513758.217.1401677
  30. The CDC portal: Forecasts of total deaths at the USA. Centers for Disease Control and Prevention, 2020. Available at: https://www.cdc.gov/coronavirus/2019-ncov/covid-data/forecasting-us.html (15.07.2020).
  31. My izuchili 858 vashikh voprosov pro koronavirus [We have examined 858 questions you have about coronavirus]. Meduza, 2020. Available at: https://www.meduza.io/feature/2020/05/13 (15.07.2020) (in Russian).
  32. Ofitsial'nyi internet-resurs po voprosam koronavirusa [The official web-site on issues related to coronavirus]. Stopkoronavirus.rf, 2020. Available at: https://стопкоронавирус.рф (17.07.2020) (in Russian).
  33. Apukhtina Yu., Zobova S. Issledovanie o tom, skol'ko bol'nichnykh koek mozhet spasti rossiiskuyu meditsinu [Research on a number of available beds in hospitals that can save public healthcare in Russia]. Proekt, 2020. Available at: https://www.proekt.media/research/koronavirus-regiony (01.07.2020) (in Russian).
  34. Sokolov A. Gotovo li rossiiskoe zdravookhranenie k bor'be s koronavirusom [Is public healthcare in Russia ready to fight coronavirus?]. Vedomosti, 2020. Available at: https://www.vedomosti.ru/society/articles/2020/04/09/827471-gotovo-rossi... (02.12.2020) (in Russian).
  35. Free and occupied beds for COVID-19 patients in Germany. Coronavis, 2020. Available at: https://coronavis.dbvis.de/en/ (01.07.2020).
  36. Murray C.J.L. Forecasting COVID‑19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months. MedRxiv, 2020, no. 30, 26 p. DOI: 10.1101/2020.03.27.20043752
  37. COVID caseload calculator C5V. Weill Cornell Medicine, 2020. Available at: https://phs.weill.cornell.edu/cornell-covid-caseload-calculator-c5v (01.08.2020).
  38. Aivazian S.A. Quality of life and living standards analysis: an econometric approach. Berlin/Boston, De Gruyter Publ., 2016, 399 p. DOI: 10.1515/9783110316254
  39. Stepanov V.S. integral indicator of the living conditions at the Crimea Republic and some other territories: dependence from factorial variables. Vestnik TsEMI, 2019, no. 2, pp. 8. DOI: 10.33276/S265838870004976-6
  40. Bol'shev L.N., Smirnov N.V. Tablitsy matematicheskoi statistiki [Mathematical statistics tables]. 3-th edition. Moscow, Nauka Publ., 1983, 416 p. (in Russian).
  41. S aprelya v koronavirusnykh bol'nitsakh umerlo ne men'she 74,9 tysyach chelovek. Kak region skryvayut eti dannye [Not less than 74.9 thousand people have died in coronavirus hospitals since April. How do regions manage to hide these data?]. Mediazona, 2020. Available at: https://zona.media/news/2020/11/30/75k (02.12.2020).
Received: 
18.08.2020
Accepted: 
23.11.2020
Published: 
30.12.2020

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