The study on the early warning period of varicella outbreaks based on logistic differential equation model

Epidemiol Infect. 2019 Jan:147:e70. doi: 10.1017/S0950268818002868.

Abstract

Chickenpox is a common acute and highly contagious disease in childhood; moreover, there is currently no targeted treatment. Carrying out an early warning on chickenpox plays an important role in taking targeted measures in advance as well as preventing the outbreak of the disease. In recent years, the infectious disease dynamic model has been widely used in the research of various infectious diseases. The logistic differential equation model can well demonstrate the epidemic characteristics of epidemic outbreaks, gives the point at which the early epidemic rate changes from slow to fast. Therefore, our study aims to use the logistic differential equation model to explore the epidemic characteristics and early-warning time of varicella. Meanwhile, the data of varicella cases were collected from first week of 2008 to 52nd week of 2017 in Changsha. Finally, our study found that the logistic model can be well fitted with varicella data, besides the model illustrated that there are two peaks of varicella at each year in Changsha City. One is the peak in summer-autumn corresponding to the 8th-38th week; the other is in winter-spring corresponding to the time from the 38th to the seventh week next year. The 'epidemic acceleration week' average value of summer-autumn and winter-spring are about the 16th week (ranging from the 15th to 17th week) and 45th week (ranging from the 44th to 47th week), respectively. What is more, taking warning measures during the acceleration week, the preventive effect will be delayed; thus, we recommend intervene during recommended warning weeks which are the 15th and 44th weeks instead.

Keywords: Logistic equation; mathematical model; ordinary differential equation; varicella; warning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chickenpox / epidemiology*
  • China / epidemiology
  • Disease Outbreaks*
  • Humans
  • Logistic Models