Tel Aviv University Biomathematics Unit, Faculty Life Science, Tel-Aviv University, Ramt aviv, P.O. 39040, Israel.
J Theor Biol. 2012 Jul 21;305:88-95. doi: 10.1016/j.jtbi.2012.02.031. Epub 2012 Mar 23.
Seasonality strongly affects the transmission and spatio-temporal dynamics of many infectious diseases, and is often an important cause for their recurrence. However, there are many open questions regarding the intricate relationship between seasonality and the complex dynamics of infectious diseases it gives rise to. For example, in the analysis of long-term time-series of childhood diseases, it is not clear why there are transitions from regimes with regular annual dynamics, to regimes in which epidemics occur every two or more years, and vice-versa. The classical seasonally-forced SIR epidemic model gives insights into these phenomena but due to its intrinsic nonlinearity and complex dynamics, the model is rarely amenable to detailed mathematical analysis. Making sensible approximations we analytically study the threshold (bifurcation) point of the forced SIR model where there is a switch from annual to biennial epidemics. We derive, for the first time, a simple equation that predicts the relationship between key epidemiological parameters near the bifurcation point. The relationship makes clear that, for realistic values of the parameters, the transition from biennial to annual dynamics will occur if either the birth-rate (μ) or basic reproductive ratio (R(0)) is increased sufficiently, or if the strength of seasonality (δ) is reduced sufficiently. These effects are confirmed in simulations studies and are also in accord with empirical observations. For example, the relationship may explain the correspondence between documented transitions in measles epidemics dynamics and concomitant changes in demographic and environmental factors.
季节变化强烈影响着许多传染病的传播和时空动态,并且常常是其复发的重要原因。然而,季节变化与由此产生的传染病复杂动态之间的复杂关系仍存在许多悬而未决的问题。例如,在儿童疾病的长期时间序列分析中,尚不清楚为什么会出现从具有规律年度动态的规律向每两年或更长时间发生一次流行的规律转变,反之亦然。经典的季节性强制 SIR 传染病模型为这些现象提供了一些见解,但由于其内在的非线性和复杂动态,该模型很少适用于详细的数学分析。通过进行合理的近似,我们对受强制的 SIR 模型的阈值(分岔)点进行了分析研究,在该点上,年度流行和两年流行之间存在着转变。我们首次推导出了一个简单的方程,该方程预测了分岔点附近关键传染病参数之间的关系。该关系明确指出,如果出生率 (μ) 或基本繁殖数 (R(0)) 增加足够多,或者季节性 (δ) 强度降低足够多,则会从两年流行向年度流行转变。这些效应在模拟研究中得到了证实,并且与经验观察结果一致。例如,该关系可能解释了麻疹流行动态记录中的转变与人口和环境因素的伴随变化之间的对应关系。