Shankar Manjari, Hartner Anna-Maria, Arnold Callum R K, Gayawan Ezra, Kang Hyolim, Kim Jong-Hoon, Gilani Gemma Nedjati, Cori Anne, Fu Han, Jit Mark, Muloiwa Rudzani, Portnoy Allison, Trotter Caroline, Gaythorpe Katy A M
Medical Research Council Centre for Global Infectious Disease Analysis, Imperial College London, London, UK.
Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute, Wildau, Germany.
BMC Infect Dis. 2024 Dec 1;24(1):1371. doi: 10.1186/s12879-024-10243-0.
Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns in disease spread, simulate control options to assist public health authorities in decision-making, and longer-term operational and financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines are one of the most-cost effective outbreak response interventions, with the potential to avert significant morbidity and mortality through timely delivery. Models can contribute to the design of vaccine response by investigating the importance of timeliness, identifying high-risk areas, prioritising the use of limited vaccine supply, highlighting surveillance gaps and reporting, and determining the short- and long-term benefits. In this review, we examine how models have been used to inform vaccine response for 10 VPDs, and provide additional insights into the challenges of outbreak response modelling, such as data gaps, key vaccine-specific considerations, and communication between modellers and stakeholders. We illustrate that while models are key to policy-oriented outbreak vaccine response, they can only be as good as the surveillance data that inform them.
数学模型是协助应对疫情的既定工具。它们有助于刻画疾病传播中的复杂模式，模拟控制方案以协助公共卫生当局进行决策以及开展长期的运营和财务规划。在疫苗可预防疾病（VPDs）方面，疫苗是最具成本效益的疫情应对干预措施之一，有潜力通过及时接种避免大量发病和死亡。模型可通过研究及时性的重要性、识别高风险地区、优先使用有限的疫苗供应、突出监测差距和报告以及确定短期和长期效益，为疫苗应对的设计提供帮助。在本综述中，我们研究了模型如何被用于为10种疫苗可预防疾病的疫苗应对提供信息，并对疫情应对建模的挑战提供更多见解，如数据缺口、特定疫苗的关键考虑因素以及建模者与利益相关者之间的沟通。我们表明，虽然模型是面向政策的疫情疫苗应对的关键，但它们的效果仅取决于为其提供信息的监测数据。
