The crisis cloud's silver linings: The effects of hope and gratitude on employee burnout and engagement

A crisis such as the COVID-19 pandemic has a tremendous impact on organisations and their employees. Building on the job demands–resources model, conservation of resources theory and the broaden-and-build theory of positive emotions, we examined the influence of job stressors on employee burnout, as well as how positive emotions can help employees thrive in tough times. We collected data from 503 Australian employees during the transition period of the COVID-19 crisis, when the country had reached a high vaccination rate and was starting to prepare to return to pre-crisis normal. Our findings show that financial insecurity has a direct impact on employee burnout, whereas a health threat has only an indirect effect. Further, our findings highlight the importance of positive emotions. Hope for the post-crisis future was found to buffer the negative impact of financial insecurity and reduce employee burnout, and feeling gratitude at work was found to mitigate the effects of burnout and enhance employee engagement even when employees are emotionally exhausted.

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT.

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE), AUC, precision and F1-score achieved by our method are 91.79%, 93.05%, 89.95%, 96.35%, 93.10% and 93.07%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Role of vaccine in fighting the variants of COVID-19.

In this paper, we investigate the effectiveness of COVID-19 vaccination in controlling the infectivity and mortality of the SARS-CoV-2. Two major variants Delta and Omicron are investigated respectively. The main method used in the research is the multifractal detrended fluctuation analysis (MF-DFA). We use Δ α as the evaluation of control effectiveness. In the transmission stages of Delta and Omicron, we observe whether Δ α shows a downward trend by gradually expanding the length of time series. Vaccine effectiveness is evaluated using a time series of newly diagnosed patients and newly reported deaths. Data samples are taken from 9 different countries. According to the obtained results, the vaccine controls infectivity and mortality of the virus in the Delta transmission stage, but infectivity control is less effective than mortality. In the Omicron transmission stage, the immune effect of the vaccine is not obvious, which may be related to the high infectivity of Omicron. However, the vaccine is still effective in controlling mortality. We also find that the immune effect of vaccine on Omicron was lower than that of Delta. Finally, we observe that the immune effect of the vaccine in 'Poland' was abnormal. By analyzing the vaccination curve, we conclude that in 'Poland', when the growth rate of vaccination rate slowed down, the immune effect of the vaccine was very poor in terms of pathogenicity and lethality. Therefore, we suggest that all countries should continue to strengthen the vaccination rate. A higher or faster growth rate of vaccination rate will help control the infectivity and mortality rate, especially in the effectiveness of controlling mortality. Our research can be used to evaluate the effectiveness of vaccines for epidemic prevention and control, the formulation of epidemic prevention measures and vaccination policies for different countries with respect to their current pandemic situation accordingly.

Feasible Distributed Energy Supply Options for Household Energy Use in China from a Carbon Neutral Perspective.

This contribution firstly proposed the concept of annual average power generation hours and analyzed per capita energy consumption, carbon emission, and the human development index from a macro perspective. On this basis, we compared the average household electrical energy consumption of urban and rural residents based on the data from CGSS-2015 from a micro perspective. The results show the positive correlation between carbon emissions per capita and the human development index and China's regional imbalance characteristics between household electricity consumption and renewable energy distribution. Therefore, the distributed energy supply system is proposed as an effective complement to centralized power generation systems and is the key to synergizing human development and carbon emissions in China. Moreover, we analyzed the characteristics of distributed energy supply systems in the context of existing energy supply systems, pointing out the need to fully use solar energy and natural gas. Finally, two types of typical distributed energy supply systems are proposed for satisfying the household energy requirements in remote or rural areas of western and the eastern or coastal areas of China, respectively. Two typical distributed energy systems integrate high-efficiency energy conversion, storage, and transfer devices such as electric heat pumps, photovoltaic thermal, heat and electricity storage, and fuel cells.

An Agile Systems Modeling Framework for Bed Resource Planning During COVID-19 Pandemic in Singapore.

Background: The COVID-19 pandemic has had a major impact on health systems globally. The sufficiency of hospitals' bed resource is a cornerstone for access to care which can significantly impact the public health outcomes. Objective: We describe the development of a dynamic simulation framework to support agile resource planning during the COVID-19 pandemic in Singapore. Materials and Methods: The study data were derived from the Singapore General Hospital and public domain sources over the period from 1 January 2020 till 31 May 2020 covering the period when the initial outbreak and surge of COVID-19 cases in Singapore happened. The simulation models and its variants take into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes in Singapore. Results: The models were calibrated against historical data for the Singapore COVID-19 situation. Several variants of the resource planning model were rapidly developed to adapt to the fast-changing COVID-19 situation in Singapore. Conclusion: The agility in adaptable models and robust collaborative management structure enabled the quick deployment of human and capital resources to sustain the high level of health services delivery during the COVID-19 surge.

Does the "Delta Variant" affect the nonlinear dynamic characteristics of SARS-CoV-2 transmission?

In this paper, we analyzed the difference of nonlinear dynamic characteristics of SARS-CoV-2 transmission caused by 'Delta Variant'. We selected the daily new diagnostic data of SARS-CoV-2 from 15 countries. Four different kinds of complexity metrics such as Kolmogorov complexity, Higuchi's Hurst exponent, Shannon entropy, and multifractal degrees were selected to explore the features of information content, persistence, randomness, multifractal complexity. Afterwards, Student's t-tests were performed to assess the presence of differences of these nonlinear dynamic characteristics for periods before and after "Delta Variant" appearance. The results of two-tailed Student's t-test showed that for all the nonlinear dynamic characteristics, the null hypothesis of equality of mean values were strongly rejected for the two periods. In addition, by one-tailed Student's t-test, we concluded that time series in "Delta period" exhibit higher value of Kolmogorov complexity and Shannon entropy, indicating a higher level of information content and randomness. On the other hand, the Higuchi's Hurst exponent in "Delta period" was lower, which showed the weaker persistent in this period. Moreover, the multifractal specturm width after "Delta" emergence were reduced, representing a more stable multifractality. The sources for the formation of multifractal features are also investigated.

Multifactorial influences underpinning a decision on COVID-19 vaccination among healthcare workers: a qualitative analysis.

COVID-19 vaccination in healthcare workers (HCW) is essential for improved patient safety and resilience of health systems. Despite growing body of literature on the perceptions of COVID vaccines in HCWs, existing studies tend to focus on reasons for 'refusing' the vaccines, using surveys almost exclusively. To gain a more nuanced understanding, we explored multifactorial influences underpinning a decision on vaccination and suggestions for decision support to improve vaccine uptake among HCWs in the early phase of vaccination rollout. Semi-structured interviews were undertaken with thirty-three HCWs in Singapore. Transcribed data was thematically analyzed. Decisions to accept vaccines were underpinned by a desire to protect patients primarily driven by a sense of professional integrity, collective responsibility to protect others, confidence in health authorities and a desire to return to a pre-pandemic way of life. However, there were prevailing concerns with respect to the vaccines, including long-term benefits, safety and efficacy, that hampered a decision. Inadequate information and social media representation of vaccination appeared to add to negative beliefs, impeding a decision to accept while low perceived susceptibility played a moderate role in the decision to delay or decline vaccination. Participants made valuable suggestions to bolster vaccination. Our findings support an approach to improving vaccine uptake in HCWs that features routine tracking and transparent updates on vaccination status, use of institutional platforms for sharing of experience, assuring contingency management plans and tailored communications to emphasize the duty of care and positive outlook associated with vaccination.

A Pre-Vaccination Baseline of SARS-CoV-2 Genetic Surveillance and Diversity in the United States.

COVID-19 vaccines were first administered on 15 December 2020, marking an important transition point for the spread of SARS-CoV-2 in the United States (U.S.). Prior to this point in time, the virus spread to an almost completely immunologically naïve population, whereas subsequently, vaccine-induced immune pressure and prior infections might be expected to influence viral evolution. Accordingly, we conducted a study to characterize the spread of SARS-CoV-2 in the U.S. pre-vaccination, investigate the depth and uniformity of genetic surveillance during this period, and measure and otherwise characterize changing viral genetic diversity, including by comparison with more recently emergent variants of concern (VOCs). In 2020, SARS-CoV-2 spread across the U.S. in three phases distinguishable by peaks in the numbers of infections and shifting geographical distributions. Virus was genetically sampled during this period at an overall rate of ~1.2%, though there was a substantial mismatch between case rates and genetic sampling nationwide. Viral genetic diversity tripled over this period but remained low in comparison to other widespread RNA virus pathogens, and although 54 amino acid changes were detected at frequencies exceeding 5%, linkage among them was not observed. Based on our collective observations, our analysis supports a targeted strategy for worldwide genetic surveillance as perhaps the most sensitive and efficient means of detecting new VOCs.

Resuming elective surgery after COVID-19: A simulation modelling framework for guiding the phased opening of operating rooms.

OBJECTIVE: To develop a 2-stage discrete events simulation (DES) based framework for the evaluation of elective surgery cancellation strategies and resumption scenarios across multiple operational outcomes. MATERIALS AND METHODS: Study data was derived from the data warehouse and domain knowledge on the operational process of the largest tertiary hospital in Singapore. 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 were extracted for the study. A clustering approach was used in stage 1 of the modelling framework to develop the groups of surgeries that followed distinctive postponement patterns. These clusters were then used as inputs for stage 2 where the DES model was used to evaluate alternative phased resumption strategies considering the outcomes of OR utilization, waiting times to surgeries and the time to clear the backlogs. RESULTS: The tool enabled us to understand the elective postponement patterns during the COVID-19 partial lockdown period, and evaluate the best phased resumption strategy. Differences in the performance measures were evaluated based on 95% confidence intervals. The results indicate that two of the gradual phased resumption strategies provided lower peak OR and bed utilizations but required a longer time to return to BAU levels. Minimum peak bed demands could also be reduced by approximately 14 beds daily with the gradual resumption strategy, whilst the maximum peak bed demands by approximately 8.2 beds. Peak OR utilization could be reduced to 92% for gradual resumption as compared to a minimum peak of 94.2% with the full resumption strategy. CONCLUSIONS: The 2-stage modelling framework coupled with a user-friendly visualization interface were key enablers for understanding the elective surgery postponement patterns during a partial lockdown phase. The DES model enabled the identification and evaluation of optimal phased resumption policies across multiple important operational outcome measures. LAY ABSTRACT: During the height of the COVID-19 pandemic, most healthcare systems suspended their non-urgent elective surgery services. This strategy was undertaken as a means to expand surge capacity, through the preservation of structural resources (such as operating theaters, ICU beds, and ventilators), consumables (such as personal protective equipment and medications), and critical healthcare manpower. As a result, some patients had less-essential surgeries postponed due to the pandemic. As the first wave of the pandemic waned, there was an urgent need to quickly develop optimal strategies for the resumption of these surgeries. We developed a 2-stage discrete events simulation (DES) framework based on 34,025 unique cases over 43 operating rooms (ORs) and 18 surgical disciplines performed from 1 January 2019 to 31 May 2020 captured in the Singapore General Hospital (SGH) enterprise data warehouse. The outcomes evaluated were OR utilization, waiting times to surgeries and time to clear the backlogs. A user-friendly visualization interface was developed to enable decision makers to determine the most promising surgery resumption strategy across these outcomes. Hospitals globally can make use of the modelling framework to adapt to their own surgical systems to evaluate strategies for postponement and resumption of elective surgeries.

The Effect of “Wuhan Closure” on the COVID-19 Pandemic in China

In this study, we analyzed daily records of newly diagnosed cases in Wuhan, Hubei excluding Wuhan (HEW), and China excluding Hubei (CEH) to investigate the impact of the new coronavirus outbreak in Wuhan on cities around it and throughout China. We used multifractal detrended cross-correlation analysis (MF-DXA) method to investigate the correlations between the daily number of patients in Wuhan and HEW as well as in Wuhan and CEH. We concluded that the cross-correlations between the daily number of patients in Wuhan and HEW were higher than those between the daily number of patients in Wuhan and CEH because the multifractal features of Wuhan and HEW are greater than those of Wuhan and CEH. We also found that the “Wuhan closure” conducted on January 23 resulted in a decrease in cross-correlations between Wuhan and CEH.

Constructing Sports Promotion Models for an Accessibility and Efficiency Analysis of City Governments

This study provides a systematic analysis of sports promotion efficiency in 22 administrative districts in Taiwan from 2011 to 2018. We first considered sports behavior and sports information promotion and connected the multiple intermediate products using network DEA, used the public performance and outputs to measure the total efficiency of sports promotion in the 22 administrative districts, and then established the final input–output indicators. The long-term tracking of sports promotion efficiency shows that, while Taipei and Taoyuan experienced upward trends, the other 20 administrative districts saw declining trends. We also used truncated regression to identify 14 environmental variables that affected the efficiency of sports promotion in the 22 administrative districts from 2016 to 2018, with the results showing that funding, satisfaction with life, and average BMI in each administrative district were significant factors, revealing the latest trends in and measurements of governance in terms of government accessibility.

Liver injury in COVID-19: Detection, pathogenesis, and treatment.

In the early December 2019, a novel coronavirus named severe acute respiratory syndrome coronavirus 2 was first reported in Wuhan, China, followed by an outbreak that spread around the world. Numerous studies have shown that liver injury is common in patients with coronavirus disease 2019 (COVID-19), and may aggravate the severity of the disease. However, the exact cause and specific mechanism of COVID-associated liver injury needs to be elucidated further. In this review, we present an analysis of the clinical features, potential mechanisms, and treatment strategies for liver injury associated with COVID-19. We hope that this review would benefit clinicians in devising better strategies for management of such patients.

Prediction of Re-positivity for Coronavirus Nucleic Acid Among COVID-19 Patients in the Recovery Phase.

Background and Objectives: Although the pathogenesis and treatment of coronavirus disease 2019 (COVID-19) have been gradually revealed, the risk for re-emergence of coronavirus nucleic acids in recovered patients remains poorly understood. Hence, this study evaluated the risk predictors associated with re-positivity for virus nucleic acid. Methods: Between February 1 and March 20, 2020, we retrospectively reviewed the clinical epidemiological data of 129 COVID-19 patients who were treated at Zhongxiang People's Hospital of Hubei Province in China. Subsequently, a risk prediction model for the re-positivity of virus nucleic acid was developed, and a receiver operating characteristic (ROC) curve was drawn for further validation. Results: In this study, the rate of re-positivity for virus nucleic acid was 17.8% (23/129) where all re-positivity cases were asymptomatic. The median time interval from discharge to nucleic acid re-positivity to discharge after being cured again was 11.5 days (range: 7-23 days). Multivariate logistic regression analysis showed that leukocytopenia [odds ratio (OR) 7.316, 95% confidence interval (CI) 2.319-23.080, p = 0.001], prealbumin < 150 mg/L (OR 4.199, 95% CI 1.461-12.071, p = 0.008), and hyperpyrexia (body temperature >39°C, OR 4.643, 95% CI 1.426-15.117, p = 0.011) were independent risk factors associated with re-positivity. The area under the ROC curve was 0.815 (95% CI, 0.729-0.902). Conclusion: COVID-19 patients with leukocytopenia, low prealbumin level, and hyperpyrexia are more likely to test positive for virus nucleic acid after discharge. Timely and effective treatment and appropriate extension of hospital stays and quarantine periods may be feasible strategies for managing such patients.

The effectiveness of public health interventions against COVID-19: Lessons from the Singapore experience.

BACKGROUND: In dealing with community spread of COVID-19, two active interventions have been attempted or advocated-containment, and mitigation. Given the extensive impact of COVID-19 globally, there is international interest to learn from best practices that have been shown to work in controlling community spread to inform future outbreaks. This study explores the trajectory of COVID-19 infection in Singapore had the government intervention not focused on containment, but rather on mitigation. In addition, we estimate the actual COVID-19 infection cases in Singapore, given that confirmed cases are publicly available. METHODS AND FINDINGS: We developed a COVID-19 infection model, which is a modified SIR model that differentiate between detected (diagnosed) and undetected (undiagnosed) individuals and segments total population into seven health states: susceptible (S), infected asymptomatic undiagnosed (A), infected asymptomatic diagnosed (I), infected symptomatic undiagnosed (U), infected symptomatic diagnosed (E), recovered (R), and dead (D). To account for the infection stages of the asymptomatic and symptomatic infected individuals, the asymptomatic infected individuals were further disaggregated into three infection stages: (a) latent (b) infectious and (c) non-infectious; while the symptomatic infected were disaggregated into two stages: (a) infectious and (b) non-infectious. The simulation result shows that by the end of the current epidemic cycle without considering the possibility of a second wave, under the containment intervention implemented in Singapore, the confirmed number of Singaporeans infected with COVID-19 (diagnosed asymptomatic and symptomatic cases) is projected to be 52,053 (with 95% confidence range of 49,370-54,735) representing 0.87% (0.83%-0.92%) of the total population; while the actual number of Singaporeans infected with COVID-19 (diagnosed and undiagnosed asymptomatic and symptomatic infected cases) is projected to be 86,041 (81,097-90,986), which is 1.65 times the confirmed cases and represents 1.45% (1.36%-1.53%) of the total population. A peak in infected cases is projected to have occurred on around day 125 (27/05/2020) for the confirmed infected cases and around day 115 (17/05/2020) for the actual infected cases. The number of deaths is estimated to be 37 (34-39) among those infected with COVID-19 by the end of the epidemic cycle; consequently, the perceived case fatality rate is projected to be 0.07%, while the actual case fatality rate is estimated to be 0.043%. Importantly, our simulation model results suggest that there about 65% more COVID-19 infection cases in Singapore that have not been captured in the official reported numbers which could be uncovered via a serological study. Compared to the containment intervention, a mitigation intervention would have resulted in early peak infection, and increase both the cumulative confirmed and actual infection cases and deaths. CONCLUSION: Early public health measures in the context of targeted, aggressive containment including swift and effective contact tracing and quarantine, was likely responsible for suppressing the number of COVID-19 infections in Singapore.

Impact of COVID-19 on acute isolation bed capacity and nursing workforce requirements: A retrospective review.

AIM: To understand the impact of COVID-19 on isolation bed capacity requirements, nursing workforce requirements and nurse:patient ratios. BACKGROUND: COVID-19 created an increased demand for isolation beds and nursing workforce globally. METHODS: This was a retrospective review of bed capacity, bed occupancy and nursing workforce data from the isolation units of a tertiary hospital in Singapore from 23 January 2020 to 31 May 2020. R v4.0.1 and Tidyverse 1.3.0 library were used for data cleaning and plotly 4.9.2.1 library for data visualization. RESULTS: In January to March 2020, isolation bed capacity was low (=<203 beds). A sharp increase in bed capacity was seen from 195 to 487 beds during 25 March to 29 April 2020, after which it plateaued. Bed occupancy remained lower than bed capacity throughout January to May 2020. After 16 April 2020, we experienced a shortage of 1.1 to 70.2 nurses in isolation wards. Due to low occupancy rates, nurse:patient ratio remained acceptable (minimum nurse:patient ratio = 0.26). CONCLUSION: COVID-19 caused drastic changes in isolation bed capacity and nursing workforce requirements. IMPLICATIONS FOR NURSING MANAGEMENT: Building a model to predict nursing workforce requirements during pandemic surges may be helpful for planning and adequate staffing.

Towards Health System Resiliency: An Agile Systems Modelling Framework for Bed Resource Planning During COVID-19 (preprint)

Background: We describe the development of a dynamic simulation modelling framework to support agile resource planning during the COVID-19 pandemic. The framework takes into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes to deal with the ever-changing outbreak scenarios.Methods: A specific use case based on short-term bed resource planning is described within the proposed framework. The simulation model was calibrated against historical data for the Singapore COVID-19 situation. The time period for model calibration was from 1st April till 30th April 2020. The model was used to project for bed resource needs over the period from 1st May 2020 till 31st May 2020. Multivariate sensitivity analysis was also conducted for ICU and general isolation bed demand, length-of-stay (LOS), and age-adjusted conversion rates across different care needs. The unmet needs under various scenarios were also evaluated for planning purposes.Results: Several variants of the agile resource planning model were developed to adapt to the fast-changing COVID-19 situation in Singapore. The use case demonstrated an agile adaptation of the model to account for previously unexpected scenarios. The rapid evolution of the pandemic locally revealed streams of new infections that arose from two distinct sources. The model projections were calibrated with the latest data for short-term projections. The agility in flexing plans and collaborative management structures to rapidly deploy human and capital resources to surge the level of care during the COVID-19 pandemic have proven utility in guiding the allocation of scarce healthcare resources and helped system resiliency.Conclusions: The rapidly evolving COVID-19 pandemic in Singapore has necessitated the development of an agile and adaptable modelling framework that can be quickly calibrated to changes both from demand and supply. The modelling framework is able to deploy systems modelling concepts in a holistic manner. This facilitates the evaluation of complex cause-and-effect relationships. A robust collaborative framework, coupled with the availability of in-depth domain knowledge and accurate and updated data availability ensures a model is realistic, timely and useful. 

Toilets dominate environmental detection of severe acute respiratory syndrome coronavirus 2 in a hospital.

Respiratory and fecal aerosols play confirmed and suspected roles, respectively, in transmitting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). An extensive environmental sampling campaign of both toilet and non-toilet environments was performed in a dedicated hospital building for patients with coronavirus disease 2019 (COVID-19), and the associated environmental factors were analyzed. In total, 107 surface samples, 46 air samples, two exhaled condensate samples, and two expired air samples were collected within and beyond four three-bed isolation rooms. The data of the COVID-19 patients were collected. The building environmental design and the cleaning routines were reviewed. Field measurements of airflow and CO2 concentrations were conducted. The 107 surface samples comprised 37 from toilets, 34 from other surfaces in isolation rooms, and 36 from other surfaces outside the isolation rooms in the hospital. Four of these samples were positive, namely two ward door handles, one bathroom toilet seat cover, and one bathroom door handle. Three were weakly positive, namely one bathroom toilet seat, one bathroom washbasin tap lever, and one bathroom ceiling exhaust louver. Of the 46 air samples, one collected from a corridor was weakly positive. The two exhaled condensate samples and the two expired air samples were negative. The fecal-derived aerosols in patients' toilets contained most of the detected SARS-CoV-2 in the hospital, highlighting the importance of surface and hand hygiene for intervention.

Coronavirus disease 2019 (COVID-19): an evidence map of medical literature.

BACKGROUND: Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises. METHODS: In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps. RESULTS: The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16). CONCLUSIONS: Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises.

Coronavirus Disease 2019 (COVID-19): An Evidence Map of Medical Literature (preprint)

Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of May 2020, gaps in the existing literature remain unidentified and, hence, unaddressed. In this paper, we summarise the medical literature on COVID-19 between 1 January and 24 March 2020 using evidence maps and bibliometric analysis in order to systematically identify gaps and propose areas for valuable future research. The examined COVID-19 medical literature originated primarily from Asia and focussed mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health research, the use of novel technologies and artificial intelligence, research on the pathophysiology of COVID-19 within different body systems, and research on indirect effects of COVID-19 on the care of non-COVID-19 patients. Research collaboration at the international level was limited although improvements may aid global containment efforts.