On evaluating the effectiveness of quarantine measures and forecasting the end of the epidemic


Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Acesso é pago ou somente para assinantes

Resumo

The World today is facing a pandemic and countries are trying to optimize their behavior strategies. The main tool of struggle is quarantine. To model the scale of an epidemic, it is necessary to estimate the reproductive number R0, defined as the expected number of cases of infection. It is proposed to interpret R0 as a denominator of geometric progression, since the most infectious person becomes at the end of the incubation period, which on average is 5 days. During the same time, the number of infected people approximately doubles. The quarantine composed of two incubation periods in China was successful. It took three incubation periods to reduce the number of active cases in Italy. Russia has not yet overcome the predicted boundaries of isolation measures, so the effectiveness of quarantine is in the focus of attention. The effectiveness of quarantine measures can be evaluated by selecting the reduction of R0 as the optimization criterion. The method of bringing the initial data for countries to zero, i.e. to the appearance of a «zero» patient, is shown. Then the effectiveness of the restriction period is estimated in the change of R0 at the edges of the time series, which makes it possible to predict the timing of the end of epidemics.

Texto integral

Acesso é fechado

Sobre autores

Natalia Kontsevaya

Financial University under the Government of the Russian Federation

Email: NVKontsevaya@fa.ru
Cand. Sci. (Econ.), Associate Professor, Assoc. Prof. of the Department DA, DM FT Russian Federation

Bibliografia

  1. James Holland Jones Notes On R0 // Department of Anthropological Sciences Stanford University May 1, 2007 - URL: https://web.stanford.edu/~jhj1/teachingdocs/Jones-on-R0.pdf
  2. Kermack, W.O. and McKendrick, A.G. Contributions to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London, 1927 - A, 115, 700-721.
  3. Большая Медицинская Энциклопедия (БМЭ), под редакцией Петровского Б.В., 3-е издание, т.28: Экономо - Ящур, 1986
  4. Энциклопедический словарь Брокгауза и Ефрона. Россия, Санкт-Петербург, 1890-1907, т. XXII (1897): Опека - Оутсайдер, Оспопрививание - с. 309-316.
  5. Бейли Н. Математика в биологии и медицине // М.: Мир, 1970. 327 с.
  6. Бузин П. Как математика помогает бороться с эпидемиями // URL: https://nplus1.ru/material/2019/12/26/epidemic-math
  7. Гришунина Ю.Б., Контаров Н.А. и др. Моделирование эпидемической ситуации с учетом внешних рисков // Эпидемиология и Вакцинопрофилактика, 2014, 5 (78): 61-66.
  8. Леоненко В.Н. Математическая эпидемиология: учебно-методическое пособие по выполнению лабораторных работ. // СПб.: Университет ИТМО, 2018
  9. Леонов В.П. Применение статистики в статьях и диссертациях по медицине и биологии. Часть II. История биометрии и ее применения в России // Межд. Ж. Мед. Практики , 1999, N 4, с. 7-19.
  10. Сычев И. История мировых эпидемий, ч.3 // URL: https://habr.com/ru/post/399439/

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML


Este site utiliza cookies

Ao continuar usando nosso site, você concorda com o procedimento de cookies que mantêm o site funcionando normalmente.

Informação sobre cookies