Predictive algorithm for the regional spread of coronavirus infection across the Russian Federation.
Andrey Reshetnikov, Vitalii Berdutin, Alexander Zaporozhtsev, Sergey Romanov, Olga Abaeva, Nadezhda Prisyazhnaya, Nadezhda Vyatkina
Author Information
Andrey Reshetnikov: Institute of Social Sciences, Sechenov First Moscow State Medical University, Moscow, Russian Federation. prisyazhnaya_n_v@staff.sechenov.ru.
Vitalii Berdutin: Contract Department, Federal Budgetary Institution of Healthcare "Volga District Medical Center of the Federal Medical and Biological Agency", Nizhny Novgorod, Russian Federation.
Alexander Zaporozhtsev: Department of Theoretical and Applied Mechanics, Federal State Budgetary Educational Institution of Higher Education "Nizhny Novgorod State Technical University Named After R.E. Alekseev", Nizhny Novgorod, Russian Federation.
Sergey Romanov: Department of Sociology of Medicine, Health Economics, and Health Insurance, Sechenov First Moscow State Medical University, Moscow, Russian Federation.
Olga Abaeva: Department of Sociology of Medicine, Health Economics, and Health Insurance, Sechenov First Moscow State Medical University, Moscow, Russian Federation.
Nadezhda Prisyazhnaya: Institute of Social Sciences, Sechenov First Moscow State Medical University, Moscow, Russian Federation.
Nadezhda Vyatkina: Institute of Social Sciences, Sechenov First Moscow State Medical University, Moscow, Russian Federation.
BACKGROUND: Outbreaks of infectious diseases are a complex phenomenon with many interacting factors. Regional health authorities need prognostic modeling of the epidemic process. METHODS: For these purposes, various mathematical algorithms can be used, which are a useful tool for studying the infections spread dynamics. Epidemiological models act as evaluation and prognosis models. The authors outlined the experience of developing a short-term predictive algorithm for the spread of the COVID-19 in the region of the Russian Federation based on the SIR model: Susceptible (vulnerable), Infected (infected), Recovered (recovered). The article describes in detail the methodology of a short-term predictive algorithm, including an assessment of the possibility of building a predictive model and the mathematical aspects of creating such forecast algorithms. RESULTS: Findings show that the predicted results (the mean square of the relative error of the number of infected and those who had recovered) were in agreement with the real-life situation: σ(I) = 0.0129 and σ(R) = 0.0058, respectively. CONCLUSIONS: The present study shows that despite a large number of sophisticated modifications, each of which finds its scope, it is advisable to use a simple SIR model to quickly predict the spread of coronavirus infection. Its lower accuracy is fully compensated by the adaptive calibration of parameters based on monitoring the current situation with updating indicators in real-time.
