Angel N. Desai, MD, is a Clinical Infectious Disease Research Fellow, Division of Infectious Diseases, Brigham & Women's Hospital, Boston, MA.
Moritz U. G. Kraemer, DPhil, is in the Department of Zoology, University of Oxford, UK; Computational Epidemiology group, Boston Children's Hospital, Boston, MA; and Harvard Medical School, Harvard University, Boston, MA.
Health Secur. 2019 Jul/Aug;17(4):268-275. doi: 10.1089/hs.2019.0022.
Infectious disease outbreaks play an important role in global morbidity and mortality. Real-time epidemic forecasting provides an opportunity to predict geographic disease spread as well as case counts to better inform public health interventions when outbreaks occur. Challenges and recent advances in predictive modeling are discussed here. We identified data needs in the areas of epidemic surveillance, mobility, host and environmental susceptibility, pathogen transmissibility, population density, and healthcare capacity. Constraints in standardized case definitions and timely data sharing can limit the precision of predictive models. Resource-limited settings present particular challenges for accurate epidemic forecasting due to the lack of granular data available. Incorporating novel data streams into modeling efforts is an important consideration for the future as technology penetration continues to improve on a global level. Recent advances in machine-learning, increased collaboration between modelers, the use of stochastic semi-mechanistic models, real-time digital disease surveillance data, and open data sharing provide opportunities for refining forecasts for future epidemics. Epidemic forecasting using predictive modeling is an important tool for outbreak preparedness and response efforts. Despite the presence of some data gaps at present, opportunities and advancements in innovative data streams provide additional support for modeling future epidemics.
传染病疫情在全球发病率和死亡率中起着重要作用。实时疫情预测提供了一个机会，可以预测地理疾病传播以及病例数，以便在疫情发生时更好地为公共卫生干预提供信息。本文讨论了预测建模中的挑战和最新进展。我们确定了在疫情监测、流动性、宿主和环境易感性、病原体传染性、人口密度和医疗保健能力等领域的数据需求。标准化病例定义和及时数据共享方面的限制可能会限制预测模型的准确性。由于资源有限的环境中缺乏可用的详细数据，因此对准确的疫情预测提出了特别的挑战。将新型数据流纳入建模工作是未来的一个重要考虑因素，因为技术普及在全球范围内不断提高。机器学习的最新进展、建模者之间的增加合作、使用随机半机械模型、实时数字疾病监测数据和开放数据共享为未来疫情的预测提供了机会。使用预测模型进行疫情预测是疫情准备和应对工作的重要工具。尽管目前存在一些数据空白，但创新数据流的机会和进展为未来疫情的建模提供了额外的支持。
