Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves

Mathematical Assessment of the Role of Human Behavior Changes on SARS-CoV-2 Transmission Dynamics in the United States

The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020-June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. This study suggests that, as more newly-infected individuals become asymptomatically-infectious, the overall level of positive behavior change can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

Monitoring epidemic processes under political measures

Statistical modeling of epidemiological curves to capture the course of epidemic processes and to implement a signaling system for detecting significant changes in the process is a challenging task, especially when the process is affected by political measures. As previous monitoring approaches are subject to various problems, we develop a practical and flexible tool that is well suited for monitoring epidemic processes under political measures. This tool enables monitoring across different epochs using a single statistical model that constantly adapts to the underlying process, and therefore allows both retrospective and on-line monitoring of epidemic processes. It is able to detect essential shifts and to identify anomaly conditions in the epidemic process, and it provides decision-makers a reliable method for rapidly learning from trends in the epidemiological curves. Moreover, it is a tool to evaluate the effectivity of political measures and to detect the transition from pandemic to endemic. This research is based on a comprehensive COVID-19 study on infection rates under political measures in line with the reporting of the Robert Koch Institute covering the entire period of the pandemic in Germany.

Innovative applications of artificial intelligence in zoonotic disease management

Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.

Integrated approaches to COVID-19 emergency response in fragile, conflict-affected and vulnerable settings: a public health policy brief

In the absence of fully effective measures to prevent and treat COVID-19, the limited access to and hesitancy about vaccines, the prolongation of the on-going pandemic is likely. This underscores the need to continue to respond and maintain preparedness, preferably using a more sustainable approach. A sustainable management is particularly important in fragile, conflict-affected and vulnerable countries of sub-Saharan Africa given several peculiar challenges. This Viewpoint proposes policy options to guide transitioning from current COVID-19 emergency response interventions to longer-term and more sustainable responses in such settings. In the long term, a shift in policy from a vertical to a more effective approach should integrate response coordination, surveillance, case management, risk communication and operational support, among other elements, for better results. We call on public health policymakers, partners and donors to support full implementation of these policy options in a holistic manner to encompass all emerging public health threats.

A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning

Objective

Coronavirus disease 2019 demonstrated the inconsistencies in adequately responding to biological threats on a global scale due to a lack of powerful tools for assessing various factors in the formation of the epidemic situation and its forecasting. Decision support systems have a role in overcoming the challenges in health monitoring systems in light of current or future epidemic outbreaks. This paper focuses on some applied examples of logistic planning, a key service of the Earth Cognitive System for Coronavirus Disease 2019 project, here presented, evidencing the added value of artificial intelligence algorithms towards predictive hypotheses in tackling health emergencies.

Methods

Earth Cognitive System for Coronavirus Disease 2019 is a decision support system designed to support healthcare institutions in monitoring, management and forecasting activities through artificial intelligence, social media analytics, geospatial analysis and satellite imaging. The monitoring, management and prediction of medical equipment logistic needs rely on machine learning to predict the regional risk classification colour codes, the emergency rooms attendances, and the forecast of regional medical supplies, synergically enhancing geospatial and temporal dimensions.

Results

The overall performance of the regional risk colour code classifier yielded a high value of the macro-average F1-score (0.82) and an accuracy of 85%. The prediction of the emergency rooms attendances for the Lazio region yielded a very low root mean square error (<11 patients) and a high positive correlation with the actual values for the major hospitals of the Lazio region which admit about 90% of the region's patients. The prediction of the medicinal purchases for the regions of Lazio and Piemonte has yielded a low root mean squared percentage error of 16%.

Conclusions

Accurate forecasting of the evolution of new cases and drug utilisation enables the resulting excess demand throughout the supply chain to be managed more effectively. Forecasting during a pandemic becomes essential for effective government decision-making, managing supply chain resources, and for informing tough policy decisions.

Impact of media trust and personal epidemic experience on epidemic prevention behaviors in the context of COVID-19: A cross-sectional study based on protection motivation theory

Objective

This study aimed to elucidate the impact of media trust on epidemic prevention motivation and behaviors based on the Protection Motivation Theory (PMT) and to evaluate the moderation effect of personal epidemic experience, which focused on the differences in two groups with or without epidemic experience.

Methods

The exogenous constructs and PMT model and scale were constructed through literature analysis, and a web-based questionnaire survey was conducted among 428 individuals aged above 18 years in China. Statistical analysis and hypothesis testing were performed in SPSS 26 and SmartPLS 3.

Results

Traditional media trust accounted for the largest weight in media trust (w = 0.492, p-value < 0.001), followed by social media (w = 0.463, p-value < 0.001), and interpersonal communication (w = 0.290, p-value < 0.001). Media trust was positively and significantly related to both threat appraisal (β = 0.210, p-value < 0.001) and coping appraisal (β = 0.260, p-value < 0.001). Threat appraisal (β = 0.105, p-value < 0.05) and coping appraisal (β = 0.545, p-value < 0.001) were positively and significantly related to epidemic prevention motivation, which positively and significantly related to epidemic prevention behaviors (β = 0.492, p-value < 0.001). The R² values of epidemic prevention motivation and behavior are 0.350 and 0.240, respectively, indicating an acceptable explanation. Multiple-group analysis revealed five significant differences in paths between the two groups, indicating personal epidemic experience acting as a slight moderator on these paths.

Conclusion

Traditional media trust and social media trust were the important elements in COVID-19 prevention and control, and public health departments and governments should ensure the accuracy and reliability of information from traditional and social media. Simultaneously, the media should balance threat information and efficacy information in order to generate the public's prevention motivation and behaviors.

Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa

East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial intelligence-driven ensemble model in East Africa. The dataset, which spans two years, was divided into training and verification datasets. To predict the mortality, three steps were conducted, which included a sensitivity analysis, the modelling of four single AI-driven models, and development of four ensemble models. Four dominant input variables were selected to conduct the single models. Hence, the coefficients of determination of ANFIS, FFNN, SVM, and MLR were 0.9273, 0.8586, 0.8490, and 0.7956, respectively. The non-linear ensemble approaches performed better than the linear approaches, and the ANFIS ensemble was the best-performing ensemble approach that boosted the predicting performance of the single AI-driven models. This fact revealed the promising capability of ensemble models for predicting the daily mortality due to COVID-19 in other parts of the globe.

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

This systematic review aims to study and classify machine learning models that predict pandemics' evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region's criticality and optimize hospitals' approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Scopus, SpringerLink, Web of Science, and Wiley Online Library between 1 January 2020 and 31 July 2022. We divided the interventions into similarities between cumulative COVID-19 real cases and machine learning prediction models' ability to track pandemics trending. We included 45 studies that rated low to high risk of bias. The standardized mean differences (SMD) for the two groups were 0.18, 95% CI, with interval of [0.01, 0.35], I 2 =0, and p value=0.04. We built a taxonomic analysis of the included studies and determined two domains: pandemics trending prediction models and geolocation tracking models. We performed the meta-analysis and data synthesis and got low publication bias because of missing results. The level of certainty varied from very low to high. By submitting the 45 studies on the risk of bias, the levels of certainty, the summary of findings, and the statistical analysis via the forest and funnel plots assessments, we could determine the satisfactory statistical significance homogeneity across the included studies to simulate the progress of the pandemics and help the healthcare authorities to take preventive decisions.

A Review of the Potential of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading

The spread of SARS-CoV-2 can be considered one of the most complicated patterns with a large number of uncertainties and nonlinearities. Therefore, analysis and prediction of the distribution of this virus are one of the most challenging problems, affecting the planning and managing of its impacts. Although different vaccines and drugs have been proved, produced, and distributed one after another, several new fast-spreading SARS-CoV-2 variants have been detected. This is why numerous techniques based on artificial intelligence (AI) have been recently designed or redeveloped to forecast these variants more effectively. The focus of such methods is on deep learning (DL) and machine learning (ML), and they can forecast nonlinear trends in epidemiological issues appropriately. This short review aims to summarize and evaluate the trustworthiness and performance of some important AI-empowered approaches used for the prediction of the spread of COVID-19. Sixty-five preprints, peer-reviewed papers, conference proceedings, and book chapters published in 2020 were reviewed. Our criteria to include or exclude references were the performance of these methods reported in the documents. The results revealed that although methods under discussion in this review have suitable potential to predict the spread of COVID-19, there are still weaknesses and drawbacks that fall in the domain of future research and scientific endeavors.

Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey

The year 2020 experienced an unprecedented pandemic called COVID-19, which impacted the whole world. The absence of treatment has motivated research in all fields to deal with it. In Computer Science, contributions mainly include the development of methods for the diagnosis, detection, and prediction of COVID-19 cases. Data science and Machine Learning (ML) are the most widely used techniques in this area. This paper presents an overview of more than 160 ML-based approaches developed to combat COVID-19. They come from various sources like Elsevier, Springer, ArXiv, MedRxiv, and IEEE Xplore. They are analyzed and classified into two categories: Supervised Learning-based approaches and Deep Learning-based ones. In each category, the employed ML algorithm is specified and a number of used parameters is given. The parameters set for each of the algorithms are gathered in different tables. They include the type of the addressed problem (detection, diagnosis, or detection), the type of the analyzed data (Text data, X-ray images, CT images, Time series, Clinical data,...) and the evaluated metrics (accuracy, precision, sensitivity, specificity, F1-Score, and AUC). The study discusses the collected information and provides a number of statistics drawing a picture about the state of the art. Results show that Deep Learning is used in 79% of cases where 65% of them are based on the Convolutional Neural Network (CNN) and 17% use Specialized CNN. On his side, supervised learning is found in only 16% of the reviewed approaches and only Random Forest, Support Vector Machine (SVM) and Regression algorithms are employed.

Identifying Country-Level Risk Factors for the Spread of COVID-19 in Europe Using Machine Learning

Coronavirus disease 2019 (COVID-19) has resulted in approximately 5 million deaths around the world with unprecedented consequences in people’s daily routines and in the global economy. Despite vast increases in time and money spent on COVID-19-related research, there is still limited information about the factors at the country level that affected COVID-19 transmission and fatality in EU. The paper focuses on the identification of these risk factors using a machine learning (ML) predictive pipeline and an associated explainability analysis. To achieve this, a hybrid dataset was created employing publicly available sources comprising heterogeneous parameters from the majority of EU countries, e.g., mobility measures, policy responses, vaccinations, and demographics/generic country-level parameters. Data pre-processing and data exploration techniques were initially applied to normalize the available data and decrease the feature dimensionality of the data problem considered. Then, a linear ε-Support Vector Machine (ε-SVM) model was employed to implement the regression task of predicting the number of deaths for each one of the three first pandemic waves (with mean square error of 0.027 for wave 1 and less than 0.02 for waves 2 and 3). Post hoc explainability analysis was finally applied to uncover the rationale behind the decision-making mechanisms of the ML pipeline and thus enhance our understanding with respect to the contribution of the selected country-level parameters to the prediction of COVID-19 deaths in EU.

A Bioinformatics Tool for Predicting Future COVID-19 Waves Based on a Retrospective Analysis of the Second Wave in India: Model Development Study

Background

Since the start of the COVID-19 pandemic, health policymakers globally have been attempting to predict an impending wave of COVID-19. India experienced a devastating second wave of COVID-19 in the late first week of May 2021. We retrospectively analyzed the viral genomic sequences and epidemiological data reflecting the emergence and spread of the second wave of COVID-19 in India to construct a prediction model.

Objective

We aimed to develop a bioinformatics tool that can predict an impending COVID-19 wave.

Methods

We analyzed the time series distribution of genomic sequence data for SARS-CoV-2 and correlated it with epidemiological data for new cases and deaths for the corresponding period of the second wave. In addition, we analyzed the phylodynamics of circulating SARS-CoV-2 variants in the Indian population during the study period.

Results

Our prediction analysis showed that the first signs of the arrival of the second wave could be seen by the end of January 2021, about 2 months before its peak in May 2021. By the end of March 2021, it was distinct. B.1.617 lineage variants powered the wave, most notably B.1.617.2 (Delta variant).

Conclusions

Based on the observations of this study, we propose that genomic surveillance of SARS-CoV-2 variants, complemented with epidemiological data, can be a promising tool to predict impending COVID-19 waves.

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since of 2020. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

Application of artificial intelligence in COVID-19 medical area: a systematic review

BACKGROUND

Coronavirus disease 2019 (COVID-19) has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition. This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics.

METHODS

We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic.

RESULTS

We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice.

DISCUSSION

This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians' role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.

Beliefs Toward Smoking and COVID-19, and the Pandemic Impact on Smoking Behavior and Quit Intention: Findings from a Community-Based Cross-Sectional Study in Jordan

BACKGROUND

The relationship between smoking and coronavirus disease-2019 (COVID-19) is still topical with mixed epidemiological evidence. However, the pandemic may affect people's beliefs toward smoking as well as their smoking behavior and quit intentions. Considering high smoking rates in Jordan, our current study aimed to assess the following domains in a community-based sample from Jordan: (i) the beliefs that surround smoking/vaping and COVID-19 and (ii) the pandemic impact on smoking behavior and quit intention.

METHODS

A cross-sectional study was conducted in Jordan from March 9 to March 16, 2021, utilizing a web-based structured questionnaire. The questionnaire comprised 13 items on sociodemographic, health, and smoking profiles, 14 items to assess beliefs surrounding COVID-19 and the use of combustible cigarettes (CCs), waterpipe (WP), and electronic cigarettes (ECs), and 12 items to assess the pandemic impact on smoking behavior and quit intention.

RESULTS

Of 2424 survey respondents who participated in our study, there were 1163 never-smokers, 1044 current smokers, and 217 ex-smokers. The mean age of participants was 35.2 years (SD: 11.06). Most participants have reported anti-smoking beliefs with around 72.9% believed that WP smoking is related to the risk of contracting COVID-19. Also, 71.7% believed that smoking CC may worsen the COVID-19 clinical course, while 74.1% of respondents believed that smoking has no protective effect against COVID-19. During the pandemic, about 28.1% and 19.3% of current smokers reported increased or reduced smoking, respectively. Besides, 459 current smokers have expressed their plans/intention to quit smoking during the pandemic, of whom 27.5% (n = 126) confirmed that the driving force for their decision is a COVID-19-related reason, such as self-protection (n = 123) and protection of family members (n = 121) which were the most cited reasons. Also, around 63 participants have successfully ceased smoking during the pandemic. However, only 22 of them reported that the main driving motivation of their successful quit attempt was the COVID-19 pandemic.

CONCLUSION

Most participants' beliefs and attitudes were against smoking during the pandemic. Nevertheless, the double-edged effect of the pandemic on smoking habits should be carefully considered, and reliable anti-smoking measures should be strengthened and sustained in the country.

A predictive analytics model for COVID-19 pandemic using artificial neural networks

The COVID-19 pandemic spread rapidly around the world and is currently one of the most leading causes of death and heath disaster in the world. Turkey, like most of the countries, has been negatively affected by COVID-19. The aim of this study is to design a predictive model based on artificial neural network (ANN) model to predict the future number of daily cases and deaths caused by COVID-19 in a generalized way to fit different countries’ spreads. In this study, we used a dataset between 11 March 2020 and 23 January 2021 for different countries. This study provides an ANN model to assist the government to take preventive action for hospitals and medical facilities. The results show that there is an 86% overall accuracy in predicting the mortality rate and 87% in predicting the number of cases.

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

The COVID-19 pandemic has widely spread with an increasing infection rate through more than 200 countries. The governments of the world need to record the confirmed infectious, recovered, and death cases for the present state and predict the cases. In favor of future case prediction, governments can impose opening and closing procedures to save human lives by slowing down the pandemic progression spread. There are several forecasting models for pandemic time series based on statistical processing and machine learning algorithms. Deep learning has been proven as an excellent tool for time series forecasting problems. This paper proposes a deep learning time-series prediction model to forecast the confirmed, recovered, and death cases. Our proposed network is based on an encoding–decoding deep learning network. Moreover, we optimize the selection of our proposed network hyper-parameters. Our proposed forecasting model was applied in Saudi Arabia. Then, we applied the proposed model to other countries. Our study covers two categories of countries that have witnessed different spread waves this year. During our experiments, we compared our proposed model and the other time-series forecasting models, which totaled fifteen prediction models: three statistical models, three deep learning models, seven machine learning models, and one prophet model. Our proposed forecasting model accuracy was assessed using several statistical evaluation criteria. It achieved the lowest error values and achieved the highest R-squared value of 0.99. Our proposed model may help policymakers to improve the pandemic spread control, and our method can be generalized for other time series forecasting tasks.

The untold story of the COVID-19 pandemic: perceptions and views towards social stigma and bullying in the shadow of COVID-19 illness in Jordan

Stigmatization towards COVID-19 patients can lead to negative outcomes like social exclusion and bullying, and it may hinder the willingness of people to undergo testing. This study aimed to measure and explore the perception of stigmatization and bullying towards COVID-19 patients in Jordan. This was a web-based cross-sectional survey. Participants were recruited from social media platforms employing a snowball convenience sampling. The perception of bullying, beliefs regarding social consequences of infection, views on measures towards violators of patients' privacy, and how to reduce the stigma were assessed by self-reported measures. 397 participants returned completed questionnaires. The majority of respondents believed that COVID-19 patients in Jordan are getting bullied (n = 255, 64.3%) and over 80% believed that people enjoy sharing identities, or news about COVID-19 patients. Although most respondents had adequate knowledge regarding transmission/prevention of COVID-19, they believed that all or some of the COVID-19 patients practiced something wrong to get infected (n = 358, 90.2%). Moreover, 86.9% of respondents reported that people in Jordan were crossing their lines with bullying behaviors towards COVID-19 patients. However, these negative views would not discourage most respondents to get tested and follow the government's instructions if they or any of their acquaintances were suspected to be infected. Our study sheds the light on a high degree of stigma and bullying of COVID-19 patients during the early stage of the pandemic in Jordan. Hence, there is a need to develop and implement effective anti-stigma/anti-bullying campaigns that refute the misperception, raise public knowledge about COVID-19, and spread encouraging messages.

The Experiences of Nurses and Physicians Caring for COVID-19 Patients: Findings from an Exploratory Phenomenological Study in a High Case-Load Country

Various changes have affected health services delivery in response to the repercussions of the COVID-19 pandemic, and this may exhibit unprecedented effects on healthcare workers (HCWs). This study aimed to explore the lived experience of physicians and nurses caring for patients with COVID-19 in Jordan. An interpretative phenomenology approach was used, and sampling was purposively performed. Data were collected through semi-structured interviews using an online meeting platform (Zoom®). Interviews were audio-recorded, transcribed verbatim, and analyzed. The data were obtained from 26 physicians and nurses caring for patients with COVID-19. The mean age of the participants was 29.41 years (SD = 2.72). Six main themes and 17 subthemes were identified: (i) emotional reactions; (ii) preparation; (iii) source of support; (iv) extreme workload; (v) occupational challenges, and (vi) work-related concerns. The results showed that nurses and physicians caring for COVID-19 patients in Jordan were experiencing mental and emotional distress and were practicing under inadequate work conditions. This distress could be multifactorial with personal, organizational, or cultural origins. Our findings may guide policymakers to consider the potential factors that significantly affect working environment in healthcare settings, the physical and mental wellbeing of HCWs, and the required professional training that can help in enhancing resilience and coping strategies amidst crises.