Olinky Ronen, Huppert Amit, Stone Lewi
Biomathematics Unit, Faculty of Life Sciences, Tel Aviv University, Ramat Aviv 69978, Israel.
J Math Biol. 2008 Jun;56(6):827-39. doi: 10.1007/s00285-007-0140-4. Epub 2007 Nov 8.
Driven by seasonality, many common recurrent infectious diseases are characterized by strong annual, biennial and sometimes irregular oscillations in the absence of vaccination programs. Using the seasonally forced SIR epidemic model, we are able to provide new insights into the dynamics of recurrent diseases and, in some cases, specific predictions about individual outbreaks. The analysis reveals a new threshold effect that gives clear conditions for the triggering of future disease outbreaks or their absence. The threshold depends critically on the susceptibility S (0) of the population after an outbreak. We show that in the presence of seasonality, forecasts based on the susceptibility S (0) are more reliable than those based on the classical reproductive number R (0) from the conventional theory.
在季节性因素的驱动下,许多常见的复发性传染病的特点是,在没有疫苗接种计划的情况下,每年、每两年会出现强烈的波动,有时甚至会出现不规则的振荡。使用季节性强迫的SIR流行病模型,我们能够对复发性疾病的动态提供新的见解,并且在某些情况下,能够对个别疫情爆发做出具体预测。分析揭示了一种新的阈值效应,它为未来疾病爆发或不爆发给出了明确的条件。该阈值关键取决于疫情爆发后人群的易感性S(0)。我们表明,在存在季节性的情况下,基于易感性S(0)的预测比基于传统理论中的经典繁殖数R(0)的预测更可靠。