ABSTRACT
Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal locations for public NAT sites presents a significant challenge, due to the varying number of sites required in different regions, and the substantial influences of population, the population heterogeneity, and daily dynamics, on the effectiveness of fixed location schemes. To address this issue, this study proposes a data-driven framework based on classical location-allocation models and bi-objective optimization models. The framework optimizes the number and location of NAT sites, while balancing various cost constraints and adapting to population dynamics during different periods of the day. The bi-objective optimization process utilizes the Knee point identification (KPI) algorithm, which is computationally efficient and does not require prior knowledge. A case study conducted in Shenzhen, China, demonstrates that the proposed framework provides a broader service coverage area and better accommodates residents' demands during different periods, compared to the actual layout of NAT sites in the city. The study's findings can facilitate the rapid planning of primary healthcare facilities, and promote the development of sustainable healthy cities.