Nikolopoulos Konstantinos, Punia Sushil, Schäfers Andreas, Tsinopoulos Christos, Vasilakis Chrysovalantis
Durham University Business School, United Kingdom.
Indian Institute of Technology Delhi, India.
Eur J Oper Res. 2021 Apr 1;290(1):99-115. doi: 10.1016/j.ejor.2020.08.001. Epub 2020 Aug 8.
Policymakers during COVID-19 operate in uncharted territory and must make tough decisions. Operational Research - the ubiquitous 'science of better' - plays a vital role in supporting this decision-making process. To that end, using data from the USA, India, UK, Germany, and Singapore up to mid-April 2020, we provide predictive analytics tools for forecasting and planning during a pandemic. We forecast COVID-19 growth rates with statistical, epidemiological, machine- and deep-learning models, and a new hybrid forecasting method based on nearest neighbors and clustering. We further model and forecast the excess demand for products and services during the pandemic using auxiliary data (google trends) and simulating governmental decisions (lockdown). Our empirical results can immediately help policymakers and planners make better decisions during the ongoing and future pandemics.
新冠疫情期间的政策制定者们处于未知领域，必须做出艰难决策。运筹学——无处不在的“优化科学”——在支持这一决策过程中发挥着至关重要的作用。为此，我们利用截至2020年4月中旬来自美国、印度、英国、德国和新加坡的数据，提供了用于大流行期间预测和规划的预测分析工具。我们使用统计、流行病学、机器学习和深度学习模型，以及一种基于最近邻和聚类的新型混合预测方法来预测新冠疫情的增长率。我们还利用辅助数据（谷歌趋势）并模拟政府决策（封锁），对大流行期间产品和服务的超额需求进行建模和预测。我们的实证结果可以立即帮助政策制定者和规划者在当前及未来的大流行期间做出更好的决策。
