Faculty of Science, Energy and Environment, King Mongkut's University of Technology North Bangkok, Rayong Campus, Rayong, 21120, Thailand.
Biophysics Group, Department of Physics, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand; ThEP Center, CHE, 328 Si Ayutthaya Road, Bangkok, 10400, Thailand.
Comput Biol Med. 2017 Aug 1;87:162-168. doi: 10.1016/j.compbiomed.2017.05.031. Epub 2017 Jun 2.
In this work, a mathematical model for describing diphtheria transmission in Thailand is proposed. Based on the course of diphtheria infection, the population is divided into 8 epidemiological classes, namely, susceptible, symptomatic infectious, asymptomatic infectious, carrier with full natural-acquired immunity, carrier with partial natural-acquired immunity, individual with full vaccine-induced immunity, and individual with partial vaccine-induced immunity. Parameter values in the model were either directly obtained from the literature, estimated from available data, or estimated by means of sensitivity analysis. Numerical solutions show that our model can correctly describe the decreasing trend of diphtheria cases in Thailand during the years 1977-2014. Furthermore, despite Thailand having high DTP vaccine coverage, our model predicts that there will be diphtheria outbreaks after the year 2014 due to waning immunity. Our model also suggests that providing booster doses to some susceptible individuals and those with partial immunity every 10 years is a potential way to inhibit future diphtheria outbreaks.
本工作提出了一个描述泰国白喉传播的数学模型。基于白喉感染的过程，人群被分为 8 个流行病学类别，即易感者、有症状传染性者、无症状传染性者、具有完全自然获得性免疫力的带菌者、具有部分自然获得性免疫力的带菌者、具有完全疫苗诱导免疫力的个体和具有部分疫苗诱导免疫力的个体。模型中的参数值要么直接从文献中获得，要么根据现有数据进行估计，要么通过敏感性分析进行估计。数值解表明，我们的模型可以正确描述 1977 年至 2014 年泰国白喉病例的下降趋势。此外，尽管泰国的 DTP 疫苗覆盖率很高，但我们的模型预测，由于免疫力下降，2014 年后仍将发生白喉暴发。我们的模型还表明，每隔 10 年为一些易感者和部分免疫者提供加强剂量可能是抑制未来白喉暴发的一种方法。
