TB Modelling Group, TB Centre and Centre for the Mathematical Modelling of Infectious Diseases, Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Department of Global Health & Development, Faculty of Public Health & Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom.
PLoS Comput Biol. 2022 Apr 22;18(4):e1010002. doi: 10.1371/journal.pcbi.1010002. eCollection 2022 Apr.
We investigated the effects of updating age-specific social contact matrices to match evolving demography on vaccine impact estimates. We used a dynamic transmission model of tuberculosis in India as a case study. We modelled four incremental methods to update contact matrices over time, where each method incorporated its predecessor: fixed contact matrix (M0), preserved contact reciprocity (M1), preserved contact assortativity (M2), and preserved average contacts per individual (M3). We updated the contact matrices of a deterministic compartmental model of tuberculosis transmission, calibrated to epidemiologic data between 2000 and 2019 derived from India. We additionally calibrated the M0, M2, and M3 models to the 2050 TB incidence rate projected by the calibrated M1 model. We stratified age into three groups, children (<15y), adults (≥15y, <65y), and the elderly (≥65y), using World Population Prospects demographic data, between which we applied POLYMOD-derived social contact matrices. We simulated an M72-AS01E-like tuberculosis vaccine delivered from 2027 and estimated the per cent TB incidence rate reduction (IRR) in 2050 under each update method. We found that vaccine impact estimates in all age groups remained relatively stable between the M0-M3 models, irrespective of vaccine-targeting by age group. The maximum difference in impact, observed following adult-targeted vaccination, was 7% in the elderly, in whom we observed IRRs of 19% (uncertainty range 13-32), 20% (UR 13-31), 22% (UR 14-37), and 26% (UR 18-38) following M0, M1, M2 and M3 updates, respectively. We found that model-based TB vaccine impact estimates were relatively insensitive to demography-matched contact matrix updates in an India-like demographic and epidemiologic scenario. Current model-based TB vaccine impact estimates may be reasonably robust to the lack of contact matrix updates, but further research is needed to confirm and generalise this finding.
我们研究了根据不断变化的人口结构更新特定年龄组社会接触矩阵对疫苗效果估计的影响。我们以印度的结核病动力学传播模型为例进行了研究。我们构建了四种随时间递增更新接触矩阵的方法，其中每种方法都结合了前一种方法：固定接触矩阵（M0）、保留接触互惠性（M1）、保留接触 assortativity（M2）和保留个体平均接触数（M3）。我们更新了结核病传播的确定性房室模型的接触矩阵，该模型根据 2000 年至 2019 年从印度获得的流行病学数据进行了校准。我们还根据校准的 M1 模型预测的 2050 年结核病发病率对 M0、M2 和 M3 模型进行了校准。我们使用世界人口展望人口数据将年龄划分为三组，儿童（<15 岁）、成年人（≥15 岁，<65 岁）和老年人（≥65 岁），在这三组之间，我们应用了 POLYMOD 衍生的社会接触矩阵。我们模拟了从 2027 年开始接种 M72-AS01E 样结核病疫苗，并根据每种更新方法估计了 2050 年的结核病发病率降低率（IRR）。我们发现，在 M0-M3 模型之间，所有年龄组的疫苗效果估计值相对稳定，而不论疫苗的目标年龄组如何。在针对成年人的疫苗接种后，观察到的最大影响差异为 7%，在老年人中，我们观察到的 IRR 分别为 19%（置信区间为 13-32）、20%（置信区间为 13-31）、22%（置信区间为 14-37）和 26%（置信区间为 18-38），分别在 M0、M1、M2 和 M3 更新之后。我们发现，在印度样人口和流行病学情况下，基于模型的结核病疫苗效果估计对与人口结构匹配的接触矩阵更新相对不敏感。目前基于模型的结核病疫苗效果估计可能对缺乏接触矩阵更新具有相当的稳健性，但需要进一步研究来证实和推广这一发现。
