Wadhwa Ankita, Thakur Manish Kumar
Department of Computer Science Engineering and IT, Jaypee Institute of Information Technology, A-10 Sector 62, Noida, UP, 201309, India.
Appl Geogr. 2022 Jul;144:102719. doi: 10.1016/j.apgeog.2022.102719. Epub 2022 May 24.
A novel virus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly become a pandemic called Coronavirus disease 2019 (COVID-19). According to the World Health Organization, COVID-19 was first detected in Wuhan city in December 2019 and has affected 216 countries with 9473214 confirmed cases and 484249 deaths globally as on June 26th, 2020. Also, this outbreak continues to grow in many countries like the United States of America (U.S.), Brazil, India, and Russia. To ensure rapid surveillance and better decision-making by government authorities in different countries, it is vital to identify alive and emerging hotspots within a country promptly. State-of-the-art methods based on space-time scan statistics (like SaTScan) are not geographically robust. Also, due to the enumeration of many Spatio-temporal cylinders, the computation cost of Spatio-temporal SaTScan (ST-SaTScan) is very high. In the applications like COVID-19 where we need to detect the emerging hotspots daily as soon as the new count of cases gets updated, ST-SaTScan seems inefficient. Therefore, this paper proposes a Particle Swarm Optimizer-based scheme to timely detect geographically robust, alive, and emerging COVID-19 hotspots in a country. Timely detection can help government officials design better control strategies like increasing testing in hotspots, imposing stricter containment rules, or setting up temporary hospital beds. Performance of ST-SaTScan and proposed scheme have been analyzed for four worst-hit U.S. states for the incubation period of 14 days between June 11th, 2020, and June 24th, 2020. Results indicate that the proposed scheme detects hotspots of a higher likelihood ratio (a measure to indicate the significance of hotspot) than ST-SaTScan in significantly less time. We also applied the proposed scheme to detect the emerging COVID-19 hotspots in all states of the U.S. During the study period, the proposed scheme has detected 104 emerging COVID-19 hotspots.
一种名为严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的新型病毒迅速引发了一场名为2019冠状病毒病（COVID-19）的全球大流行。据世界卫生组织称，COVID-19于2019年12月首次在武汉市被发现，截至2020年6月26日，已影响216个国家，全球确诊病例达9473214例，死亡484249例。此外，在美国、巴西、印度和俄罗斯等许多国家，疫情仍在持续蔓延。为确保不同国家的政府当局能够迅速进行监测并做出更好的决策，及时识别一个国家内现存和新出现的热点地区至关重要。基于时空扫描统计的先进方法（如SaTScan）在地理上并不稳健。此外，由于要列举许多时空柱体，时空SaTScan（ST-SaTScan）的计算成本非常高。在COVID-19这样的应用中，我们需要在每日新增病例数更新后尽快检测出新出现的热点地区，ST-SaTScan似乎效率低下。因此，本文提出了一种基于粒子群优化器的方案，以及时检测一个国家内地理上稳健、现存且新出现的COVID-19热点地区。及时检测有助于政府官员制定更好的控制策略，如增加热点地区的检测、实施更严格的防控措施或设置临时病床。针对美国受灾最严重的四个州，分析了ST-SaTScan和所提方案在2020年6月11日至2020年6月24日14天潜伏期内的性能。结果表明，所提方案在显著更短的时间内检测出的热点地区的似然比（一种表明热点地区显著性的指标）高于ST-SaTScan。我们还应用所提方案检测了美国所有州新出现的COVID-19热点地区。在研究期间，所提方案检测出了104个新出现的COVID-19热点地区。
