Sequential Resource Planning Decisions in an Epidemic Based on an Innovative Spread Model

Abstract

Making rapid decisions in intervention resource planning is crucial for mitigating morbidity, mortality, and costs to the societies during epidemic outbreaks. This study presents a data-driven optimization approach for multiperiod resources planning, based on a sequential decision framework, considering up-to-date information and uncertainty in the spread of epidemics. In this method, a new <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SEI^{3}H^{2}RD$ </tex-math></inline-formula> spread model is constructed to generate the most potential scenarios of an epidemic, based on all the historical information, and risk-averse stochastic programming was proposed to arrive at an optimal resource planning solution. The data-based numerical experiments demonstrate that our approach could control the epidemic by reducing the infected cases and deaths with similar or fewer resources than in the reality. In addition, we also find that the risk-averse design of the objective was able to take a steadier approach to resource planning helped avoid large fluctuations in resource allocations compared to a risk-neutral design. The other insight obtained from these experiments was that a moderate decision interval along with a planning horizon, which is slightly larger than the decision interval, would be a good choice for the sequential planning problem. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In the event of an outbreak of a new infectious disease, the uncertainty of the epidemic parameters limits the ability of the model to provide accurate predictions. However, decision-makers cannot wait for more information to alleviate this uncertainty and must immediately make decisions to control the epidemic. This article proposes a sequential decision-making approach with an innovative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$SEI^{3}H^{2}RD$ </tex-math></inline-formula> spread model and a risk-averse scenario-based stochastic programming to help decision-makers arrive at real-time decisions. We illustrate the performance of this approach using the COVID-19 outbreak in Wuhan. The results show that our model predictions closely fit the real outbreak data and prove that our decision approach could reduce the cost of using the available resources and achieve the goal of controlling the epidemic. The proposed modeling framework can be adopted to study other infectious diseases and provide tangible policy recommendations for controlling outbreaks of such diseases.