### Organization
However, UAV flights are challenged by limitations of high wind instability, which reduces the overall model accuracy. Effective UAV designs for sanitization (disinfectant spray) are explored in [25], where a quadcopter UAV with a 2200 KV operating characteristic is designed over a proportional integral derivative (PID) controller to achieve superior torque control and high-altitude conditions. The quadcopter can carry 200 milliliters of disinfect spray, which is insufficient for large regions. [PERSON] et al. [26] presented the role of 6G networks to handle rapid response units that can provide required resources to patients during disasters and epidemic control. The authors explored the UAV latency assessment and connection density assessment for a controller base station (BS) and presented key technologies of 6G to operate in hospital environments, remote sensing, and responsive disaster control units. However, the challenges of topology mobility (ad hoc wireless network formation) and ubiquitous coverage are considered major issues in the design of the responsive unit. [PERSON] et al. [27] presented the 6G vision for edge intelligence models. As 6G supports E2E latency of \(<\)1 ms and over-the-air latency of 100 us, it becomes a potential candidate for applications such as autonomous vehicles, UAV communication, and holographic communication supported through AR and VR. A reference use case of UAV-enabled 6G-assisted edge intelligence is discussed to fight the COVID-19 pandemic regarding social distancing and sanitization. [PERSON] et al. [28] proposed a UAV-based delivery ecosystem for the COVID-19 pandemic for timely aid. Furthermore, a convolutional neural network (CNN) architecture is proposed inside the hospital setup for chest X-ray images. A deep transfer learning architecture is exploited and patients are classified into positive and negative classes. An accuracy of 94.42% is reported for X-ray classification. Based on the classification, timely medical deliveries are orchestrated through an optimal path-planning UAV swarm network. Authors in [29] presented radar-based UAVs for respiratory pattern detection of COVID-19 from remote UAV swarms. The radar senses the respiration rate and displacement associated with tachypnea. An adaptive filter method is applied with noise-cancellation features to improve the sensing accuracy. [PERSON] et al. [30] discussed the potential use cases of 5G and Internet-of-Things (IoT) for COVID-19. The presented use cases include contact tracing, delivery supply chains, and real-time information sharing. The technical landscape and open challenges are discussed to leverage effective solutions to monitor COVID-19 patterns and present useful suggestions for daily tasks to the common public. In COVID-19 detection models, [PERSON] et al. [32] proposed an image processing scheme for COVID-19 RT-PCR tests, named _CD19-Net_, which includes chest X-ray images. A deep learning (DL)-based model is designed on the Inceptionv4 architecture with a multi-class support vector machine (SVM) classifier, where the images are scaled and fed to the model. A detection accuracy of 96.24% is reported in the study on a 4-type classification and is further enhanced to 95.51% for three classes and 98.1% for two classes. The scheme is beneficial for places where there is a shortage of COVID-19 kits and demand is high. Authors in [33] proposed a machine-learning-based time series analysis to predict COVID-19 deaths using the NueralProphet model. The work employs exponential Gaussian distribution over a forecast period of 1 January 2020 to 16 July 2021 and predicts the impact on the death rate during the second wave of COVID-19. [PERSON] et al. [34] proposes a convolutional neural network (CNN)-based X-ray classification, where a dual-layer pipeline is followed. In the first phase, the CNN model extracts useful chest features, which are fed to a Bayesian optimizer function to fine-tune the CNN hyper-parameters based on the selected objectives.