Modelling the effect of a booster vaccination on disease epidemiology
Modelling the effect of a booster vaccination on disease epidemiology
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DOI：
10.1007/s00285-005-0356-0
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发表时间：
2006-03-01
影响因子：
1.9
通讯作者：
Summers, AR
中科院分区：
文献类型：
--
作者：
Alexander, ME;Moghadas, SM;Summers, AR
尽管疫苗在显著减少新感染病例和疾病严重程度方面具有有效性，但不完善的疫苗可能无法完全预防感染。这是因为这些疫苗提供的免疫力不完全，并可能随着时间的推移而减弱，尽管初级疫苗接种水平很高，但仍会导致疾病死灰复燃和流行病暴发。为了防止疾病的地方性传播并实现根除，一些国家已经引入了加强疫苗接种计划。这一战略能否最终为全球根除提供条件的问题，在这里通过建立季节性强迫数学模型来解决。该模型的分析根据四个主要参数提供了疾病控制的阈值条件：一级疫苗的覆盖率；疫苗的效力；下降率;以及加强管理的比率。结果表明，如果疫苗只提供暂时的免疫力，那么感染通常不能通过单次接种来根除。此外，有一个加强计划并不一定保证控制疾病，尽管流行病的程度可能会降低。此外，这些发现有力地表明，初级疫苗接种的高覆盖率对于加强战略的成功仍然至关重要。使用麻疹估计参数的模拟说明了模型的预测。
Despite the effectiveness of vaccines in dramatically decreasing the number of new infectious cases and severity of illnesses, imperfect vaccines may not completely prevent infection. This is because the immunity afforded by these vaccines is not complete and may wane with time, leading to resurgence and epidemic outbreaks notwithstanding high levels of primary vaccination. To prevent an endemic spread of disease, and achieve eradication, several countries have introduced booster vaccination programs. The question of whether this strategy could eventually provide the conditions for global eradication is addressed here by developing a seasonally-forced mathematical model. The analysis of the model provides the threshold condition for disease control in terms of four major parameters: coverage of the primary vaccine; efficacy of the vaccine; waning rate; and the rate of booster administration. The results show that if the vaccine provides only temporary immunity, then the infection typically cannot be eradicated by a single vaccination episode. Furthermore, having a booster program does not necessarily guarantee the control of a disease, though the level of epidemicity may be reduced. In addition, these findings strongly suggest that the high coverage of primary vaccination remains crucial to the success of a booster strategy. Simulations using estimated parameters for measles illustrate model predictions.