Simulation Study on Airflow Organization and Environment in Reconstructed Fangcang Shelter Hospital Based on CFD

With frequent outbreaks of COVID-19, the rapid and effective construction of large-space buildings into Fangcang shelter hospitals has gradually become one of the effective means to control the epidemic. Reasonable design of the ventilation system of the Fangcang shelter hospital can optimize the indoor airflow organization, so that the internal environment can meet the comfort of patients and at the same time can effectively discharge pollutants, which is particularly important for the establishment of the Fangcang shelter hospital. In this paper, through the reconstruction of a large-space gymnasium, CFD software is used to simulate the living environment and pollutant emission efficiency of the reconstructed Fangcang shelter hospital in summer under different air supply temperatures, air supply heights and exhaust air volume parameters. The results show that when the air supply parameters are set to an air supply height of 4.5 m, an air supply temperature of 18 °C, and an exhaust air volume of a single bed of 150 m3/h, the thermal comfort can reach level I, and the ventilation efficiency for pollutants can reach 69.6%. In addition, the ventilation efficiency is 70.1% and 70.3% when the exhaust air volume of a single bed is continuously increased to 200 and 250 m3/h, which can no longer effectively improve the pollutant emission and will cause an uncomfortable blowing feeling to patients. © 2023 by the authors.

Non-stationary spatio-temporal point process modeling for high-resolution COVID-19 data

Most COVID-19 studies commonly report figures of the overall infection at a state- or county-level. This aggregation tends to miss out on fine details of virus propagation. In this paper, we analyze a high-resolution COVID-19 dataset in Cali, Colombia, that records the precise time and location of every confirmed case. We develop a non-stationary spatio-temporal point process equipped with a neural network-based kernel to capture the heterogeneous correlations among COVID-19 cases. The kernel is carefully crafted to enhance expressiveness while maintaining model interpretability. We also incorporate some exogenous influences imposed by city landmarks. Our approach outperforms the state-of-the-art in forecasting new COVID-19 cases with the capability to offer vital insights into the spatio-temporal interaction between individuals concerning the disease spread in a metropolis.

Exploring Information Retrieval for Personalized Teaching Support

The outbreak of Covid-19 challenged the education system and caused more disconnections than ever between instructors, students, and content. Having instructors use personal information relevant to their students within a lesson would create a more personalized lesson that could resonate with the students and facilitate their participation in the classroom. However, teaching is already a complex and challenging job as teachers must multitask in delivering content and fulfilling students' needs. To encourage and support instructors to integrate the personal experiences of students during their lessons, we propose an approach based on a speech-recognition-based personal information retrieval pipeline. We designed and developed PRIS, a personalized, real-time teaching support system, as an exemplar of the approach. This paper presents a small-scale within-subjects study comparing the use of PRIS and typical notecards to assess the impact of the proposed approach on teaching. Results showed that the PRIS condition has better usability, imposes lower cognitive load on the teacher, and leads to more frequent personalized teaching behaviors compared to the notecard condition. We discuss the implications for the design of personalized teaching support systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Worn Face Mask Testing to Diagnose SARS-CoV-2 Infection in Hemodialysis Patients

Background: Hemodialysis (HD) patients are at increased risk for COVID-19 infection, hospitalization, and mortality. Early COVID-19 diagnosis is thus critical to mitigate SARS-CoV-2 spread and improving patients' health outcomes. Generally, nasopharyngeal (NP) specimens are considered the most sensitive biological samples to diagnose SARS-CoV-2 infections. However, NP swabbing is considered uncomfortable by most patients, and it requires health professionals, thus impacting its cost-effectiveness. In a previous proof-of-principle study, we demonstrated that face masks worn by in-center HD patients can harbor SARS-CoV-2. In this Kidney-X funded study, we determined efficiency of face mask testing by comparing results to saliva specimen collected from same individuals. Method(s): Disposable 3-layer masks were provided to each subject at the time of entering the dialysis center. Masks were collected 4 hours after worn. Saliva was collected using Salivette kit at the time of mask collection. RT-PCR based testing were performed using Thermo Fisher COVID-19 Combo Kit (A47814). Result(s): We collected 179 pairs of saliva/masks, 114 from 42 dialysis staff and patients without recent COVID-19 infection (control group), and 65 from 30 HD patients with COVID-19, diagnosed by NP RT-PCR (COVID-19 group). Patients provided 1 to 7 sample pairs on average 11+/-8 days (0 to 36) after COVID-19 diagnosis. Thirty-one of the 65 sample pairs were SARS-CoV-2 positive either in the saliva or the mask samples (26 positive saliva;20 positive masks). Saliva and mask testing sensitivities were 84% and 65% with a mean cycle threshold (CT) of 31.8 and 32.2, respectively. Fifteen pairs tested positive for both worn masks and saliva. Mask and saliva CT values did not differ significantly. Of note, in 5 sample pairs saliva tested negative while masks tested positive. In the control group, all 114 saliva samples tested negative;one mask tested weakly positive, resulting in saliva and mask testing specificities of 100% and 99%, respectively. S gene dropout was observed in all positive samples, indicating Omicron BA.1 infection. Conclusion(s): While the sensitivity of mask testing is less compared to saliva testing, its operational ease, lack of patient discomfort, seamless repeatability, and lower costs make it a viable option for SARS-CoV-2 screening.

SARS-CoV-2 Saliva Testing to Determine Viral Shedding Duration in Hemodialysis Patients

Background: Hemodialysis (HD) patients are vulnerable to COVID-19. Early detection of COVID-19 in dialysis clinics informs isolation and infection control policies. Saliva testing is an alternative to nasopharyngeal swab to detect SARS-CoV-2. The understanding of viral shedding in HD patients is limited. We explore viral shedding duration in HD patients and determine its correlation with immunosuppression. Method(s): Eligible patients diagnosed with COVID-19, confirmed by nasal swab RTPCR within 2 weeks of COVID-19 diagnosis, were recruited. They were given Salivette Saliva Collection kits and instructed to chew a cotton swab for 60 seconds. Result(s): 30 COVID-19 positive patients participated (Table 1). Each patient provided up to 7 saliva samples. 65 samples were collected for an average of 11+/-8 days (range 0-36) after diagnosis. 26 samples showed at least one COVID-19 target gene (N, ORF1ab) with cycle threshold <38 cycles. 12 patients had at least 1 positive sample, and 23 patients had at least 1 negative sample. Of the 23 patients who had at least one negative sample, median days to first negative sample is 9 days (range 0-36). For the 7 patients who only had positive samples, median days to last positive sample is 9 days (range 0-36). There is no observed difference between vaccinated (n=24) and vaccinated patients (n=6). 6 out of 30 patients took immunosuppressants such as Tacrolimus, Hydroxychloroquine, and Mycophenolate sodium. Median days to turn negative (or use last positive date if negative results never achieved) was 15 days for immunocompromised group and 8 days for nonimmunocompromised group (Fig.1) Conclusion(s): Immunocompromised HD patients shed COVID-19 virus for a significantly longer period. While our study did not explore the shedding of viable SARS-CoV-2, a longer isolation should be considered in immunosuppressed HD patients. Studies on shedding of viable SARS-CoV-2 are warranted in immunocompromised HD patients to inform policies regarding isolation and contact tracing protocols, and vaccination strategies.

Association Between Sleep and COVID-19 Vaccination Response in Hemodialysis Patients

Background: Hemodialysis (HD) patients are less likely to mount a response to the COVID-19 vaccination (CoVac). Poor sleep is associated with blunted vaccination response in the general population. We aim to explore the association between CoVac and sleep quality (SQ) in HD patients. Method(s): Patients from 3 HD clinics were enrolled if they were >=18 years and able to give written consent. Patients were administered the Insomnia Severity Index (ISI) and the Pittsburg Sleep Quality Index (PSQI). Blood specimen were collected after the primary series of COVID-19 vaccination. SARS-CoV-2 neutralization antibodies (nAB) were assayed using the GenScript SARS-CoV-2 Surrogate Virus Neutralization Test Kit (Cat#L00847-A). nAB titers are presented as Unit/ml on a natural log scale. PSQI scores of >5 were categorized as poor SQ and <=5 as good SQ. ISI scores were grouped as no clinically significant insomnia (NI;score 0-7), subthreshold insomnia (SI;score 8-14), and clinical insomnia (CI;score 14-28). T-test and ANOVA analysis were performed on PSQI and ISI scores, respectively, to determine the statistical association between SQ and nAB levels Results: 58 patients were included (60+/-9 years old, HD vintage 4.7+/-4.5 years, 62% male, 66% Black, 21% Hispanic). In the PSQI, 72% (n=42) had poor SQ. In the ISI, 52% = NI, 31% = SI, and 17% CI. Box plots of nAB levels with median and IQR are shown in Fig. 1. There is no association between SQ and nAB levels. Conclusion(s): There is no association between SQ and CoVac response. Given the immune dysfunction in this population, any modifying effect SQ has on CoVac, as observed in the general population, is unlikely. Other methods of improving CoVac response in this vulnerable population should be explored. (Figure Presented).

Impact Assessment of COVID-19 Severity on Environment, Economy and Society towards Affecting Sustainable Development Goals

The COVID-19 pandemic has affected every sector in the world, ranging from the education sector to the health sector, administration sector, economic sector and others in different ways. Multiple kinds of research have been performed by research centres, education institutions and research groups to determine the extent of how huge of a threat the COVID-19 pandemic poses to each sector. However, detailed analysis and assessment of its impact on every single target within the 17 Sustainable Development Goals (SDGs) have not been discussed so far. We report an assessment of the impact of COVID-19 effect towards achieving the United Nations SDGs. In assessing the pandemic effects, an expert elicitation model is used to show how the COVID-19 severity affects the positive and negative impact on the 169 targets of 17 SDGs under environment, society and economy groups. We found that the COVID-19 pandemic has a low positive impact in achieving only 34 (20.12%) targets across the available SDGs and a high negative impact of 54 targets (31.95%) in which the most affected group is the economy and society. The environmental group is affected less;rather it helps to achieve a few targets within this group. Our elicitation model indicates that the assessment process effectively measures the mapping of the COVID-19 pandemic impact on achieving the SDGs. This assessment identifies that the COVID-19 pandemic acts mostly as a threat in enabling the targets of the SDGs.

Detection of the SARS-CoV-2 Delta Variant in the Transboundary Rivers of Yunnan, China

Ruili and Longchuan, two border counties in southwestern China, are facing epidemic control challenges due to the high rate of COVID-19 infections originating from neighboring Myanmar. Here, we aimed to establish the applicability of wastewater and environmental water surveillance of SARS-CoV-2 and conduct whole-genome sequencing (WGS) to trace the possible infection origin. In August 2021, total 72 wastewater and river water samples were collected from 32 sampling sites. SARS-CoV-2 ORF1ab and N genes were measured by RT-qPCR. We found that 19 samples (26.39%) were positive, and the viral loads of ORF1ab and N genes were 6.62 × 102–2.55×105 and 1.86 × 103–2.32 × 105 copies/L, respectively. WGS further indicated the sequences in two transboundary river samples, and one hospital wastewater sample belonged to the delta variant, suggesting that the infection source might be areas with high COVID-19 delta variant incidence in Southeast Asia (e.g., Myanmar). We reported for the first time the detection and quantification of SARS-CoV-2 RNA in the transboundary rivers of Myanmar–China. Our findings demonstrate that wastewater and environmental water may provide independent and nonintrusive surveillance points to monitor the global spread of emerging COVID-19 variants of concern, particularly in high-risk regions or border areas with considerable epidemic challenges and poor wastewater treatment facilities.

Robust traveling salesman problem with drone: balancing risk and makespan in contactless delivery

The spread of COVID-19 outbreak has promoted truck-drone delivery from trials to commercial applications in end-to-end contactless solutions. To fully integrate truck-drone delivery in contactless solutions, we introduce the robust traveling salesman problem with a drone, in which a drone makes deliveries and returns to the truck that is moving on its route under uncertainty. The challenge is to find, for each customer location in truck-drone routing, an assignment to minimize the expected makespan. Apart from the complexity of this problem, the risk of synchronization failure associated with uncertain travel time should be also considered. The problem is first formulated as a robust model, and a novel efficient frontier heuristic is proposed to solve this model. By coupling the implicit adaptive weighting with epsilon-constraint methods, the heuristic generates a series of scalarized single-objective problems, where the goal is to minimize expected makespan under the constraint of synchronization risk. The experiment results show that the robust (near-)optimal solutions offer a considerable reduction in risk, yet only hint at a small increase in makespan. The heuristic in the present study is effective to construct approximations of Pareto frontier and allows for assignment decisions in a priori or a posteriori manner. © 2022 The Authors. International Transactions in Operational Research © 2022 International Federation of Operational Research Societies.

Telemedicine improves Anesthesia Pre-operative Evaluation Appointment Adherence: A Retrospective Analysis

Background/Introduction: Amidst the COVID-19 pandemic, the sudden demand for virtual medical visits drove the drastic expansion of telemedicine across all medical specialties. Current literature demonstrates limited knowledge on the impact of telehealth on appointment adherence particularly in preoperative anesthesia evaluations. We hypothesized that there would be increased completion of preoperative anesthesia appointments in patients who received telemedicine visits. Methods: We performed a retrospective cohort study of adult patients at UCLA who received preoperative anesthesia evaluations by telemedicine or in-person within the Department of Anesthesiology and Perioperative Medicine from January to September 2021 and assessed appointment adherence. The primary outcome was incidence of appointment completion. The secondary outcomes included appointment no show and cancellations. Patient demographic characteristics including sex, age, ASA physical status class, race, ethnicity, primary language, interpreter service requested, patient travel distance to clinic, and insurance payor were also evaluated. Demographic characteristics, notably race and ethnicity, are presented as captured in the electronic health record and we recognize its limitations and inaccuracies in illustrating how people identify. Patient reported reasons for cancellations were also reviewed and categorized into thematic groups by two physicians. Statistical comparison was performed using independent samples t test, Pearson's chi-square, and Fischer's exact test. Results: Of 1332 patients included in this study, 956 patients received telehealth visits while 376 patients received in-person preoperative anesthesia evaluations. Compared to the in-person group, the telemedicine group had more appointment completions (81.38% vs 76.60%, p = 0.0493). There were fewer cancellations (12.55% vs 19.41%, p = 0.0029) and no statistical difference in appointment no-shows (6.07% vs 3.99%, p = 0.1337) in the telemedicine group (Figure 1). Compared to the in-person group, patients who received telemedicine evaluations were younger (55.81 ± 18.38 vs 65.97 ± 15.19, p < 0.001), less likely American Indian and Alaska Native (0.31% vs 1.60%, p = 0.0102), more likely of Hispanic or Latino ethnicity (16.63% vs 12.23%, p = 0.0453), required less interpreter services (4.18% vs 9.31%, p = 0.0003), had more private insurance coverage (53.45% vs 37.50%, p < 0.0001) and less Medicare coverage (37.03% vs 50.53%, p < 0.0001). Main reasons for cancellation included patient request, surgery rescheduled/cancelled/already completed, and change in method of appointment. Conclusions: In 2021, preoperative anesthesia evaluation completion was greater in patients who received telemedicine appointments compared to those who received in-person evaluations at UCLA. We also demonstrate potential shortcomings of telemedicine in serving patients who are older, require interpreter services, or are non-privately insured. Knowledge of these factors can provide feedback to improve access and equity to telehealth for patients from all backgrounds, particularly during the COVID pandemic as virtual evaluations increase. (Table Presented).

Exploring Social Media Misinformation in the COVID-19 Pandemic Using a Convolutional Neural Network

Misinformation is rampant in the modern information age and understanding how social media misinformation diffuses can provide vital insight on how to combat it. With social media becoming a major information source, it is increasingly important to address this concern. Social media misinformation has negatively impacted healthcare response in the past and may have played a major role in how to respond to COVID-19. Understanding how misinformation diffuses through online social networks can provide help healthcare and government entities information on how to mitigate the associated negative impact. This paper proposes a data set as criterion for identifying pandemic specific misinformation and develops a Convolution Neural Network model and. A case study is then conducted to illustrate how diffusion can be explored using labelled misinformation. The work shows a decrease of COVID-19 misinformation over time and a pattern that does not depend on regional geographic location. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Systematic Review on Technologies and Applications in Smart Campus: A Human-Centered Case Study

The smart revolution has penetrated in a wide range of applications. Smart campus, as the high-end form of education systems, deploys cutting-edge information and communication technologies to enhance the effectiveness and efficiency of campus services. Under the pandemic of COVID-19, smart campus has shown unprecedented importance owing to its remote, personalized, and ubiquitous features. All these factors have made smart campus an ongoing intense research topic in recent years, whereas existing reviews on smart campus were conducted in earlier years and thus an update is imperatively needed to investigate and summarize the emerging knowledge, technologies, and applications in this context. This paper conducts a systematic review on smart campus technologies and applications, and then strategically classifying them into different domains to investigate the current research pattern. Moreover, adhering to the human-centered principle of smart campus development, a human-centered case study has been carried out and presented in this paper to evaluate the consistency and adherence of current research trend to the stakeholders needs and interests. Author

CLINICAL CHARACTERISTICS AND RISK OF DEATH OF 194 PATIENTS WITH SEVERE COVID-19

Background and Purpose: Corona Virus Disease 2019 (COVID-19) is a highly contagious disease which continuously and rapidly circulating around the world now. The patients with severe COVID-19 have relatively high mortality. Therefore, there is an urgent need for methods to assess mortality risk in patients with COVID-19 accurately. Materials and methods: We conducted a retrospective study focusing on the clinical characteristics of 194 confirmed cases of severe COVID-19. Personal information, clinical data and laboratory information of patients with COVID-19 were collected by consulting case records so as to investigate the risk of death related to COVID-19. Results: In the 194 patients with COVID-19, there was no difference in prevalence between men and women. Comorbidities (such as hypertension, cerebral infarction) associated with severe clinical features and mortality are prevalent in non-survivors. 86.1% of patients with severe COVID-19 had fever and 46.9% coughed, and the proportion of chest tightness, airlessness and dyspnea in non-survivors was significantly higher than that in survivors. There were multiple laboratory indicator differences between survivors and non-survivors. Non-survivors had significantly lower lymphocyte count (including T lymphocyte). Changes in liver (aspartate aminotransferase, AST), kidney [Urea, creatinine (Cr)], and heart [lactate dehydrogenase (LDH), creatine kinase (CK), B-type natriuretic peptide (BNP)] damage markers, coagulation, and inflammation indicators in severe patients were related to their risk of death. Multivariable logistic regression model revealed that age (OR 1.082, 95% CI 1.024-1.357), interleukin-6 (IL-6). (OR 1.568, 95% CI 1.149-2.138), D-dimer (OR 1.327, 95% CI 1.087-1.621) were associated significantly with risk of death, whereas CD4 count was associated with a lower risk (OR 0.972, 95% CI 0.953-0.992). Conclusion: This study found that age, IL-6, D-dimer and CD4 counts are closely related to mortality risk in patients with severe COVID-19, and they are useful in assessing the prognosis of patients.

Roles of Artificial Intelligence and Extended Reality Development in the Post-COVID-19 Era

This paper explores recent roles of artificial intelligence and extended reality development during the coronavirus pandemic and then predicts their significant roles in the post-COVID-19 era in an interdisciplinary manner. To begin with, we investigate roles of artificial intelligence in tackling coronavirus during the outbreak since 2020 until today. It has been effectively used for many ways, such as forecasting the spread of COVID-19 on multimodal data using data analytics, preliminary diagnosis the virus disease from specific symptoms using machine learning, and analyzing big data from social media platforms to accurately prevent the spread of virus. At the same time, due to rapid advancement in recent immersive technology and extended reality is a very popular research topic in computer science, we discuss roles of extended reality which has been extensively used during the virus outbreak for various purposes, such as supporting for businesses and education and helping the medical and health care workers. For instance, it can used for supporting psychological recovery from medical treatment for virus patients, reducing the face-to-face interactivity of physicians with the symptomatic patients, and helping people with the use of telemedicine. Next, we present a new summary of integrated roles of artificial intelligence and extended reality development in the post-COVID-19 era in an interdisciplinary perspective. Moreover, we suggest possible directions of artificial intelligence in extended reality which can be used to guide the design of the next-generation human-computer interaction applications in the future. © 2021, Springer Nature Switzerland AG.

Prevention and control of COVID-19 based on Spark and spatial big data

The novel coronavirus pneumonia is a major public health emergency with fast transmission rate, wide infection range and great difficulty in prevention and control, which poses challenges to the urban governance system and governance capacity. At this moment, it is particularly significant to get the track of people's movements. And at the same time, trajectory, as a typical spatio-temporal data, has been more and more used in subject researches such as road change detection, travel pattern exploration and urban hotspot analysis in recent years. In this paper, based on Spark and GeoSpark technology, real-time monitoring of the whereabouts of the community, schools and other personnel is carried out, in order to generate action tracks. At the same time, the deep learning algorithm is used to classify and warn the danger level of the trajectory of the people who are about to go in or go out of the residential district, schools, etc. It provides strong support for the public security, health and epidemic command and other government departments to achieve scientific prevention and control, intelligent prevention and control. The results show that spark can achieve high throughput and fault-tolerant real-time stream data processing. Geospark processes large-scale spatial data on the basis of spark, and can create point, line, surface and other spatial data structures based on longitude and latitude information. At the same time, the semi supervised learning model based on recurrent neural network is used to classify and early warn the danger level of personnel trajectories. The experiment randomly selected 2000 users from districts and schools in Chengdu, and divided the experimental data set into training set and verification set in the proportion of 8:2. The best performance of the trained model is 96.2%. © 2021 IEEE.

Data-related and methodological obstacles to determining associations between temperature and COVID-19 transmission

More and more studies have evaluated the associations between ambient temperature and coronavirus disease 2019 (COVID-19). However, most of these studies were rushed to completion, rendering the quality of their findings questionable. We systematically evaluated 70 relevant peer-reviewed studies published on or before 21 September 2020 that had been implemented from community to global level. Approximately 35 of these reports indicated that temperature was significantly and negatively associated with COVID-19 spread, whereas 12 reports demonstrated a significantly positive association. The remaining studies found no association or merely a piecewise association. Correlation and regression analyses were the most commonly utilized statistical models. The main shortcomings of these studies included uncertainties in COVID-19 infection rate, problems with data processing for temperature, inappropriate controlling for confounding parameters, weaknesses in evaluation of effect modification, inadequate statistical models, short research periods, and the choices of research areal units. It is our viewpoint that most studies of the identified 70 publications have had significant flaws that have prevented them from providing a robust scientific basis for the association between temperature and COVID-19.

Bioinformatics analysis of hematopoietic injury and potential therapeutic drugs associated with SARS-CoV-2 infection

Objective To analyze and predict hematopoietic injury caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and potential therapeutic drugs, and to provide theoretical basis for clinical treatment of the hematopoietic injury. Methods The gene expression omnibus (GEO) database was used to screen the whole genome expression data related to SARS-CoV-2 infection. The R language package was used for differential expression analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis. The core genes were screened by protein-protein interaction (PPI) network analysis using STRING online analysis website. Then the self-developed apparent precision therapy prediction platform (EpiMed) was used to analyze diseases, drugs and related target genes. Results A total of 222 differential genes were screened, including 172 up-regulated and 50 down-regulated. GO enrichment analysis suggested that gene is mainly related to type I interferon response, cell cycle regulation, inflammatory cell migration, innate immune response, secretion of blood particles and vesicles, chemokines and their receptors. KEGG enrichment analysis suggested that gene is mainly related to viral infection, myocardial injury, complement and coagulation cascade, cell chemotaxis, platelet activation, acute inflammation, immune response, cellular signal transduction and so on. Ten core genes such as STAT1, IL-6, IRF7, TNF, MX1, ISG15, IFIH1, IRF9, DDX58 and GBP1were screened by PPI network analysis. EpiMed screened 10 drugs with potential intervention effects, including Rabdosia rubescens, sirolimus, glucocorticoid, Houttuynia cordata, Polygonum multiflorum, Red peony, tretinoin, Glycyrrhiza, cyclosporine A, fluvastatin and so on. Conclusions SARS-CoV-2 infection can damage the hematopoietic system by changing the expression of a series of genes. The potential intervention drugs screened from this have certain reference significance for the basic and clinical research of coronavirus disease 2019 (COVID-19). © 2020 People's Military Medical Press. All rights reserved.

Research progress on nucleic acid detection in COVID-19

The prevention and control of novel coronavirus pneumonia caused by 2019 novel coronavirus is at a critical period. Nucleic acid detection, as the definite diagnosis tool, plays an important role in rapid diagnosis, therapeutic efficacy, epidemic prevention and control. However, the disease is outbreak, and the time of nucleic acid detection in clinical application is short. So the insufficient method verification and clinical evaluation has been made. "False negative" is observed in clinical practice, and the result of nucleic acid detection is not matched with the clinical diagnosis. Therefore, it is urgent to optimize the current methods and improve detection sensitivity. Based on latest studies of 2019 novel coronavirus, this article reviews the current status and application prospects of nucleic acid detection. Also, this article provides references for clinicians and researchers.