COVID‐19 pandemic affects people in various ways and continues to spread globally. Researches are ongoing to develop vaccines and traditional methods of Medicine and Biology have been applied in diagnosis and treatment. Though there are success stories of recovered cases as of November 10, 2020, there are no approved treatments and vaccines for COVID‐19. As the pandemic continues to spread, current measures rely on prevention, surveillance, and containment. In light of this, emerging technologies for tackling COVID‐19 become inevitable. Emerging technologies including geospatial technology, artificial intelligence (AI), big data, telemedicine, blockchain, 5G technology, smart applications, Internet of Medical Things (IoMT), robotics, and additive manufacturing are substantially important for COVID‐19 detecting, monitoring, diagnosing, screening, surveillance, mapping, tracking, and creating awareness. Therefore, this study aimed at providing a comprehensive review of these technologies for tackling COVID‐19 with emphasis on the features, challenges, and country of domiciliation. Our results show that performance of the emerging technologies is not yet stable due to nonavailability of enough COVID‐19 dataset, inconsistency in some of the dataset available, nonaggregation of the dataset due to contrasting data format, missing data, and noise. Moreover, the security and privacy of people's health information is not totally guaranteed. Thus, further research is required to strengthen the current technologies and there is a strong need for the emergence of a robust computationally intelligent model for early differential diagnosis of COVID‐19.

COVID-19 pandemic expedites the development of digital technologies to tackle the spread of the virus. Several digital interventions have been deployed to reduce the catastrophic impact of the pandemic and observe preventive measures. However, the adoption and utilisation of these technologies by the affected populace has been a daunting task. Therefore, this study carried out exploratory investigation of the factors influencing the behavioural intention (BI) of people to accept COVID-19 digital tackling technologies (CDTT) using the UTAUT (Unified Theory of Acceptance and Use of Technology) framework. The study applied principal components analysis and multiple regression analysis for hypotheses testing. The study revealed that performance expectancy (PE), facilitating conditions (FC) and social influence (SI) are the best predictors of people's BI to accept CDTT. Also, organizational influence and benefit (OIB) and government expectancy and benefits (GEB) influence the people's BI. However, variables such as age, gender and voluntariness to use CDTT have no significance to influence BI because the CDTT is still nascent and not easily accessible. The results show that the decision-makers and regulators should consider inciting variables such as PE, FC, SI, OIB and GEB, that motivate the acceptance and use of CDTT. Furthermore, the populace must be sensitized to the availability and use of CDTT in all communities. Also, the path diagram and hypothesis testing results for CDTT acceptance and use, will help government and private organizations in planning and responding to the digitalization of COVID-19 protective measures and hence revise the COVID-19 health protection regulation.

The continuous COVID-19 upsurge and emerging variants present unprecedented challenges in many health systems. Many regulatory authorities have instituted the mandatory use of face masks especially in public places where massive contact of people is frequent and inevitable, particularly inside public transport facilities, sports arenas, shopping malls and workplaces. However, compliance and adherence to proper wearing of face masks have been difficult due to various reasons including diversified mask types, different degrees of obstructions, various variations, balancing various model detection accuracy or errors and deployment requirements, angle of view, deployment of detection model on computers with limited processing power, low-resolution images, facial expression, and lack of real-world dataset. Therefore, this study aimed at providing a comprehensive review of artificial intelligence models that have been used to detect face masks. The study revealed that deep learning models such as the Inceptionv3 convolutional neural network achieved 99.9% accuracy in detecting COVID-19 face masks. We deducted that most of the datasets used to detect face masks are created artificially, do not represent the real-world environments which ultimately affect the precision accuracy of the model when deployed in the real world. Hence there is a need for sharing real-world COVID-19 face mask images for modelling deep learning techniques. The study also revealed that deeper and wider deep learning architectures with increased training parameters, such as inception-v4, Mask R-CNN, Faster R-CNN, YOLOv3, Xception, and DenseNet are not yet implemented to detect face masks.

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Zimbabwe is among the countries affected with the coronavirus disease (COVID‐19) and implemented several infection control and measures such as social distancing, contact tracing, regular temperature checking in strategic entry and exit points, face masking among others. The country also implemented recursive national lockdowns and curfews to reduce the virus transmission rate and its catastrophic impact. These large‐scale measures are not easy to implement, adhere to and subsequently difficult to practice and maintain which lead to imperfect public compliance, especially if there is a significant impact on social and political norms, economy, and psychological wellbeing of the affected population. Also, emerging COVID‐19 variants, porous borders, regular movement of informal traders and sale of fake vaccination certificates continue to threaten impressive progress made towards virus containment. Therefore, several emerging technologies have been adopted to strengthen the health system and health services delivery, improve compliance, adherence and maintain social distancing. These technologies use health data, symptoms monitoring, mobility, location and proximity data for contact tracing, self‐isolation, and quarantine compliance. However, the use of emerging technologies has been debatable and contentious because of the potential violation of ethical values such as security and privacy, data format and management, synchronization, over‐tracking, over‐surveillance and lack of proper development and implementation guidelines which impact their efficacy, adoption and ultimately influence public trust. Therefore, the study proposes ethical framework for using emerging technologies to contain the COVID‐19 pandemic. The framework is centered on ethical practices such as security, privacy, justice, human dignity, autonomy, solidarity, beneficence, and non‐maleficence.