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The current respiratory infectious disease has expanded over the world, posing a serious threat to people's physical and mental health, as well as their lives. Science and technology immediately united to fight against such deadly infectious disease in the past 100 years. Mathematical models have proved invaluable to understand and help control infectious disease epidemics. By simplifying real world phenomena, these models describe, analyze, and predict disease transmission patterns, producing tractable solutions in the face of quickly changing situations. In this Chapter, we firstly summarized the history and development of the mathematical models in infectious diseases. Afterwards, the specific transmission dynamics models with different model structures used in fitting and forecasting the situation of the current respiratory infectious disease were introduced, aiming different analytical objectives including but not limited to parameter estimation, trend prediction and early warning, prevention and control measures effectiveness evaluation, and transmission uncertainty exploration. Summary in values of transmission dynamics models is followed to illustrate their contribution in understanding and combating infectious disease outbreaks. Despite their utility, however, mathematical models are facing several important challenges which, if ignored, would result in biased estimation of the crucial epidemiological parameters, bad fitting of the data, or misinterpretation of the results. In conclusion, mathematical modeling should be one of the most valuable tools to reflect such huge uncertainties or, on the other hand, warn of the worst situation. An appreciation of models' shortcomings not only clarifies why they cannot do but helps anticipate what they can. © People's Medical Publishing House, PR of China 2022.
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In the context of COVID-19, numerous people present their opinions through social networks. It is thus highly desired to conduct sentiment analysis towards COVID-19 tweets to learn the public's attitudes, and facilitate the government to make proper guidelines for avoiding the social unrest. Although many efforts have studied the text-based sentiment classification from various domains (e.g., delivery and shopping reviews), it is hard to directly use these classifiers for the sentiment analysis towards COVID-19 tweets due to the domain gap. In fact, developing the sentiment classifier for COVID-19 tweets is mainly challenged by the limited annotated training dataset, as well as the diverse and informal expressions of user-generated posts. To address these challenges, we construct a large-scale COVID-19 dataset from Weibo and propose a dual COnsistency-enhanced semi-superVIseD network for Sentiment Anlaysis (COVID-SA). In particular, we first introduce a knowledge-based augmentation method to augment data and enhance the model's robustness. We then employ BERT as the text encoder backbone for both labeled data, unlabeled data, and augmented data. Moreover, we propose a dual consistency (i.e., label-oriented consistency and instance-oriented consistency) regularization to promote the model performance. Extensive experiments on our self-constructed dataset and three public datasets show the superiority of COVID-SA over state-of-the-art baselines on various applications. IEEE
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This study aims to explore university students' continuous intention toward online learning during COVID-19 pandemic. A total of 120 students enrolled in online learning were surveyed to collect their perception of an extended model by adding task value to the expectation-confirmation model. Structural equation modeling was employed to verify the hypotheses proposed in this study. The results indicated that task value and technology usefulness were significant predictors of students' continuous intention toward online learning. More specifically, technology usefulness had a direct impact on students' continuous intention, while students' perceived task value played an indirect role in the prediction of their continuous intention. However, the impacts of both confirmation and satisfaction were not statistically significant on students' continuous intention. The results suggest that practitioners and researchers should pay special attention to the technological usefulness of online learning environments and task value, especially task value, in order to enhance students' retention of online learning. This study would contribute to implications to better design and implement online learning. © 2023 IEEE.
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Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81:1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers' markets, medical workers and other key areas and groups, and ensure early detection and timely response.Copyright © 2022 China Tropical Medicine. All rights reserved.
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Objective To investigate the impact of the outbreak of coronavirus disease 2019 (COVID-19) as an intervention factor on residency training at different stages, and look into the enhancement effect of post-graduation medical training program based on competency of residency training, so as to provide reference for the optimization of medical education at the postgraduate stage. Methods After the initial success of COVID-19 prevention and control, 169 clinical postdoctoral trainess(clinical postdocs) and 515 graduate students specializing in clinical medicine(professional postdocs) were surveyed by an anonymous online questionnaire. To analyze the differences of cognition and self- evaluation of core competence between the two groups. Results There were 141 valid questionnaires collected from clinical postdocs (83.43%, 141/169) and 264 valid questionnaires collected from professional postdocs (51.26%, 264/515). In both groups, more than 85% of the students agreed or strongly agreed that they had a deeper understanding of the profession of doctors during the epidemic. The results of competency self-evaluation showed that, except for the items of "self-improvement", the self-evaluation scores of clinical postdoctoral students on other items were significantly higher than those of professional postdoctoral students (all P <0.05). Conclusions COVID-19, as a factor of emergency intervention, can improve the competency cognition of residents. The core-competency based post-graduation medical education model can comprehensively improve the students' comprehensive ability, which is an effective training program for residents. It is suggested that the vocational planning education for residents should be paid attention to in the stage of college education, and a new mode of college education that is closely combined with the post-graduation education should be further explored.Copyright © 2021 Thomson Reuters and Contributors.
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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread worldwide and threatened human's health. With the passing of time, the epidemiology of coronavirus disease 2019 evolves and the knowledge of SARS-CoV-2 infection accumulates. To further improve the scientific and standardized diagnosis and treatment of maternal SARS-CoV-2 infection in China, the Chinese Society of Perinatal Medicine of Chinese Medical Association commissioned leading experts to develop the Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection under the guidance of the Maternal and Child Health Department of the National Health Commission. This recommendations includes the epidemiology, diagnosis, management, maternal care, medication treatment, care of birth and newborns, and psychological support associated with maternal SARS-CoV-2 infection. It is hoped that the recommendations will effectively help the clinical management of maternal SARS-CoV-2 infection.Copyright © Wolters Kluwer Health, Inc. All rights reserved.
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This paper investigates spatiotemporal dynamics of the effects of urban form on the Covid-19 spread within local communities in Salt Lake County, Utah, in the United States. We identify three types of communities-minority, traditional urban and suburban, and new suburban-and three stages throughout March 2020-September 2021, reflecting the initial, outbreak, and recovery stages. While the traditional urban and suburban communities experience the least risk of Covid-19, minority communities are severely impacted in the initial and outbreak stages, and remote suburban communities are primarily affected in the outbreak and recovery stages. The regression further reveals the role of urban form in the pandemic. High-density urban land use is the main density factor contributing to the disease's spread. In the initial stage, mobility factors such as street connectivity and walkability contribute to the local spread, while land use mixture is the catalyst in the outbreak stage. A comprehensive compact development might offset these negative effects on local public health, and its contribution to local resilience in the recovery stage is also confirmed. Thus, compact development is still valuable for building urban resilience, and proper planning and policies can offset the potential adverse effects of pandemics.
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Due to the impact of the COVID-19 pandemic, many students are unable to attend face-to-face courses, Therefore, in this case, distance education should be promoted to replace face-to-face education. However, because of the imbalance of education in different regions, such as the imbalance of education resources between rural and urban areas, the quality of distance education may not be guaranteed. Therefore, in China and some regions, there have been efforts made to carry out blended synchronous classroom attempts. In hybrid synchronous classroom situations, teachers' workloads have increased, and it is difficult to fully understand students' learning efficiency and class participation. We use deep learning to identify the behaviors of teachers and students in a blended synchronous classroom-based situation, aiming to automate the analysis of classroom videos, which can help teachers in classroom reflection and summary in a blended synchronous classroom or face-to-face classroom. In the behavior recognition of students and teachers, we combine the head, hand, and body posture information of teachers and students and add the feature pyramid (FPN) and convolutional block attention module (CBAM) for comparative experiments. Finally, S–T (student–teacher) analysis and engagement analysis were carried out on the identification results. © 2023 by the authors.
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In order to comprehensively understand the epidemiological situation of bovine coronavirus (BCoV) in beef cattle herds in Jilin Province, blood, nasal swabs, fecal swabs and tissue organs of clinically diseased and dead cattle were collected in different seasons from 12 counties and cities in the east, central and western regions of Jilin Province, using serological and molecular diagnostic testing techniques to conduct an epidemiological investigation of BCoV in the The epidemiological situation of BCoV in some areas of Jilin Province. A total of 1 298 clinical serum samples, 462 clinical samples (including fecal samples, liver, lung, spleen, trachea and other tissue samples) were collected, and PCR detection of clinical samples was performed by applying commercial BCoV antibody detection kits to detect serum antibodies and a novel detection technique of nano-PCR, and sequencing and analysis of positive results detected by nucleic acid. The results showed that the serum positive rate of BCoV antibodies was 1.08%, and the positive rate of clinical samples such as feces and liver was 21.10%. The BCoV prevalent strain in the investigated area was more than 99% homologous to the prevalent strain in Sichuan, China, after sequencing analysis. This study provides a comprehensive survey of BCoV prevalence in central Jilin Province, which enriches the epidemiological survey data of bovine coronavirus and lays the foundation for guiding the prevention and control of bovine coronavirus.Copyright © 2023 Acta Veterinaria et Zootechnica Sinica. All rights reserved.
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Background: Little is known about the induction of mucosal Ab after the 3rd dose. We reported that two doses of BNT162b2 induced mucosal Abs as early as 14-days after the 1st dose. As BNT162b2 only provides the RNA encoding a full-length spike (S) protein, a mixed-vaccine regime with a vaccine that provides inactivated but intact viral particles was executed in some countries to expand the diversity of SARS-CoV-2 Abs. Aim and objectives: To examine the mucosal and plasma Ab induction in vaccine recipients receiving their 3rd vaccine dose with single or mixed vaccine type. Method(s): 46 healthy subjects who had BNT162b2 (B) or CoronaVac (C) in a sequence of either BBB, BBC, CCC or CCB were recruited for a longitudinal sampling of nasal fluid and blood. The S1-specific Ab and neutralizing Ab against SARS-CoV-2 VOCs were measured. Result(s): All BBB recipients (n=28) had nasal specific S1-IgA and IgG after two doses, and the Abs lasted six months and were readily induced after the 3rd dose. In BBC recipients (n=4), though they had prior induction of nasal Abs after two doses of B, the inactivated vaccine did not boost their nasal Abs. In CCC recipients (n=5), there was no induction on nasal Abs. If they adopted the CCB regime (CCB, n=11), they acquired nasal Ab after the 3rd dose. The nasal neutralizing antibodies against the wild type were boosted in 20/28 of the BBB recipients and induced in 8/11 of the CCB recipients but not in CCC or BBC recipients. Lastly, all 46 subjects had a boosted specific S1-IgA and S1IgG in plasma. Conclusion(s): Our findings highlighted the uniqueness of BNT162b2 in induing nasal Ab regardless of the vaccination history.
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A number of medical experts have become famous overnight in China since the outbreak of COVID-19. This research investigates four representative Chinese scientists by employing search analytics of the Baidu index (from December 2019 to May 2020) and content analysis of answers and commentaries on the Zhihu website (from January 2020 to May 2020). We find that the four scientists present different images and spark unprecedented publicity. In particular, the key to the transformation from scientists into public intellectuals is to demonstrate moral responsibility in public images, or to realize humorous and effective communication with the public. The birth of celebrity scientists has not only reshaped the public's traditional perception of scientists but also played a crucial role in the governance of pandemic risks by guiding the public's behavior and offering scientific ways to cope with risks. © 2023 National Science and Technology Council, Taiwan.
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Background: COVID-19 vaccines are important for patients with heart failure (HF) to prevent severe outcomes but the safety concerns could lead to vaccine hesitancy. This study aimed to investigate the safety of two COVID-19 vaccines, BNT162b2 and CoronaVac, in patients with HF. Methods: We conducted a self-controlled case series analysis using the data from the Hong Kong Hospital Authority and the Department of Health. The primary outcome was hospitalization for HF and the secondary outcomes were major adverse cardiovascular events (MACE) and all hospitalization. We identified patients with a history of HF before February 23, 2021 and developed the outcome event between February 23, 2021 and March 31, 2022 in Hong Kong. Incidence rate ratios (IRR) were estimated using conditional Poisson regression to evaluate the risks following the first three doses of BNT162b2 or CoronaVac. Findings: We identified 32,490 patients with HF, of which 3035 were vaccinated and had a hospitalization for HF during the observation period (BNT162b2 = 755;CoronaVac = 2280). There were no increased risks during the 0–13 days (IRR 0.64 [95% confidence interval 0.33–1.26];0.94 [0.50–1.78];0.82 [0.17–3.98]) and 14–27 days (0.73 [0.35–1.52];0.95 [0.49–1.84];0.60 [0.06–5.76]) after the first, second and third doses of BNT162b2. No increased risks were observed for CoronaVac during the 0–13 days (IRR 0.60 [0.41–0.88];0.71 [0.45–1.12];1.64 [0.40–6.77]) and 14–27 days (0.91 [0.63–1.32];0.79 [0.46–1.35];1.71 [0.44–6.62]) after the first, second and third doses. We also found no increased risk of MACE or all hospitalization after vaccination. Interpretation: Our results showed no increased risk of hospitalization for HF, MACE or all hospitalization after receiving BNT162b2 or CoronaVac vaccines in patients with HF. Funding: The project was funded by a Research Grant from the Food and Health Bureau, The Government of the Hong Kong Special Administrative Region (Ref. No. COVID19F01). F.T.T.L. (Francisco T.T. Lai) and I.C.K.W. (Ian C.K. Wong)'s posts were partly funded by the D24H;hence this work was partly supported by AIR@InnoHK administered by Innovation and Technology Commission. © 2022 The Authors
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Understanding the interactions among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets, especially the role of climate change in this system is of great significance for policy makers, energy producers/consumers and relevant investors. The present paper aims to quantify the time-varying connectedness effects among the four factors by using the TVP-VAR based extensions of both time- and frequency-domain connectedness index measurements proposed by Antonakakis et al. (2020) and Ellington and Barunik (2021) [8,48]. The empirical results suggest that, firstly, the average total connectedness among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets is not so strong for the heterogenous fundamentals underlying them. Nevertheless, the time-varying total connectedness fluctuates fiercely through May 2005 to September 2021, varying from about 8% to 30% and rocket to very high levels during the global subprime mortgage crisis and the COVID-19 pandemic. Furthermore, the total connectedness mainly centers on the short-term frequency, i.e., 1–3 months. Secondly, climate change is generally the leading information contributor among the four factors, although not particularly strong, and its leading role also performs mainly on the short-term frequency (1–3 months). Thirdly, renewable energy stock market and crude oil market show tight interactions between them and they are the two major bridges of information exchanges across various time frequencies (horizons) in this system. Finally, we confirm the evidence that the primary net connectedness contributor and receiver switch frequently across different time frequencies, implying that it is extremely essential for policy makers, energy producers/consumers and investors to make time-horizon-specific regulatory, production/purchasing or investment decisions when facing the uncertain effects of climate change on the interactions among carbon emission allowance, crude oil and renewable energy stock markets. © 2022 Elsevier Ltd
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Background: Online learning is currently adopted by educational institutions worldwide to provide students with ongoing education during the COVID-19 pandemic. However, online learning has seen students lose interest and become anxious, which affects learning performance and leads to dropout. Thus, measuring students' engagement in online learning has become imperative. It is challenging to recognize online learning engagement due to the lack of effective recognition methods and publicly accessible datasets. Methods: This study gathered a large number of online learning videos of students at a normal university. Engagement cues were used to annotate the dataset, which was constructed with three levels of engagement: low engagement, engagement, and high engagement. Then, we introduced a bi-directional long-term recurrent convolutional network (BiLRCN) for online learning engagement recognition in video. Result: An online learning engagement dataset has been constructed. We evaluated six methods using precision and recall, where BiLRCN obtained the best performance. Conclusions: Both category balance and category similarity of the data affect the performance of the results;it is more appropriate to consider learning engagement as a process-based evaluation;learning engagement can provide intervention strategies for teachers from a variety of perspectives and is associated with learning performance. Dataset construction and deep learning methods need to be improved, and learning data management also deserves attention. © 2022 by the authors.
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One of the ultimate goals of the Green Economy is to move away from dependence on fossil energy, thereby achieving a sustainable development of a resource-saving and environment-friendly society. Thus, whether Green Economy stocks can hedge the risks of fossil energy markets, especially for natural gas market during recent crisis periods, is of great importance for both policy makers and portfolio managers. This paper identifies the time-varying connectedness and hedging effects of twelve NASDAQ OMX Green Economy sector stocks on NYMEX natural gas futures during three major turmoil events, i.e., European debt crisis, COVID-19 pandemic, and recent Russia-Ukraine conflict. The empirical results show that various Green Economy sector stocks can provide gratifying hedge effectiveness on the market risk of natural gas futures, and some of them can even perform similarly to gold and USD. Moreover, NASDAQ OMX Green Economy sector stocks offer better hedge effectiveness during recent Russia-Ukraine conflict than those of them in the periods of European debt crisis and COVID-19 pandemic. Finally, the Sharpe ratio results further show the important but time-varying roles of Green Economy sector stocks in hedging risks of natural gas market. © 2023 Elsevier Inc.
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Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. IEEE
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This paper proposes an automatic system to monitor the health status of the individuals in an estate such as their blood pressure values, their blood glucose value, their blood oxygen value, their heart rate and their respiratory rate. In particular, the system consists of an intelligent watch, a mobile application, a central server and a medical platform. The intelligent watch acquires five photoplethysmograms (PPGs) via different photo sensors with different wavelengths and transmits the PPGs to the mobile via a bluetooth transmitter. The mobile application allows the inputs of the reference values of these health indices of the individuals and displays the estimated values. Also, it sends the PPGs and these reference values to the central server. The central server estimates the health indices. The medical platform consists of a team of medical officers. They monitor the health indices of the individuals and provide the medical advices. This system can detect the occurrence of the sudden decay of the health status of the individuals. Hence, it can reduce the death rate due to the spread of the new diseases such as the COVID19. © 2022 IEEE.
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Introduction: Differential equations governed compartmental models are known for their ability to simulate epidemiological dynamics and provide highly accurate descriptive and predictive results. However, identifying the corresponding parameters of flow from one compartment to another in these models remains a challenging task. These parameters change over time due to the effect of interventions, virus variation and so on, thus time-varying compartmental models are required to reflect the dynamics of the epidemic and provide plausible results.Methods: In this paper, we propose an Euler iteration augmented physics-informed neural networks(called Euler-PINNs) to optimally integrates real-world reported data, epidemic laws and deep neural networks to capture the dynamics of COVID-19. The proposed Euler-PINNs method integrates the differential equations into deep neural networks by discretizing the compartmental model with suitable time-step and expressing the desired parameters as neural networks. We then define a robust and concise loss of the predicted data and the observed data for the epidemic in question and try to minimize it. In addition, a novel activation function based on Fourier theory is introduced for the Euler-PINNs method, which can deal with the inherently stochastic and noisy real-world data, leading to enhanced model performance.Results and Discussion: Furthermore, we verify the effectiveness of the Euler-PINNs method on 2020 COVID-19-related data in Minnesota, the United States, both qualitative and quantitative analyses of the simulation results demonstrate its accuracy and efficiency. Finally, we also perform predictions based on data from the early stages of the outbreak, and the experimental results demonstrate that the Euler-PINNs method remains robust on small dataset.
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Objective To analyze the epidemiological characteristics of community transmission of the coronavirus disease 2019 (COVID-19) caused by four imported cases in Hebei Province, and to provide a scientific basis for the prevention and control of the disease. Methods Descriptive epidemiological methods were used to analyze the epidemiological characteristics of four community-transmitted COVID-19 outbreaks reported in the China Disease Control and Prevention Information System from January 1, 2020 to December 31, 2021 in Hebei Province. Results From January 1, 2020 to December 31, 2021, four community-transmitted COVID-19 outbreaks caused by imported COVID-19 occurred in Hebei Province, respectively related of Hubei (Wuhan) Province, Beijing Xinfadi market, Overseas cases and Ejina banner of Inner Mongolia Autonomous Region. Total of 1 656 cases (1 420 confirmed cases and 236 asymptomatic cases) were reported, including 375 cases in phase A (From January 22 to April 16, 2020), and phase B (from June 14 to June 24, 2020) 27 cases were reported, with 1 116 cases reported in the third phase (Phase C, January 2 to February 14, 2021), and 138 cases reported in the fourth phase (Phase D, October 23 to November 14, 2021). The 1 656 cases were distributed in 104 counties of 11 districts (100.00%), accounting for 60.46% of the total number of counties in the province. There were 743 male cases and 913 female cases, with a male to female ratio of 0.81∶1. The minimum age was 13 days, the maximum age was 94 years old, and the average age (median) was 40.3 years old. The incidence was 64.01% between 30 and 70 years old. Farmers and students accounted for 54.41% and 14.73% of the total cases respectively. Of the 1 420 confirmed cases, 312 were mild cases, accounting for 21.97%;Common type 1 095 cases (77.11%);There was 1 severe case and 12 critical cases, accounting for 0.07% and 0.85%, respectively. 7 patients died from 61.0 to 85.7 years old. The mean (median) time from onset to diagnosis was 1.9 days (0-31 days), and the mean (median) time of hospital stay was 15 days (1.5-56 days). Conclusions Four times in Hebei province COVID-19 outbreak in scale, duration, population, epidemic and type of input source, there are some certain difference, but there are some common characteristics, such as the outbreak occurs mainly during the legal holidays or after starting and spreading epidemic area is mainly in rural areas, aggregation epidemic is the main mode of transmission, etc. To this end, special efforts should be made to strengthen the management of people moving around during holidays, and strengthen the implementation of epidemic prevention and control measures in places with high concentration of people. To prevent the spread of the epidemic, we will step up surveillance in rural areas, farmers′ markets, medical workers and other key areas and groups, and ensure early detection and timely response. © 2022 China Tropical Medicine. All rights reserved.
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The outbreak of the pandemic COVID 19 has caused great impact to the world economy. This paper aims to investigate its impact on micro firms and small and medium enterprises (SMEs) in Taiwan, these firms contribute one third of the local economy but are generally lack of scale and resources. This paper investigated 3 questions: First, impacts of COVID-19 on micro firms and SMEs. Second, how micro firms and SMEs deal with problems caused by the pandemic. Third, a special focus is on the digital tools micro firms and SMEs implement to come through to post pandemic era. Through this analysis we seek to understand the digital capabilities of the micro firms and SMEs and how they may work towards digital transformation and meet the future challenges.A questionnaire was used to collect data about the impact of the pandemic and what measurements micro firms and SMEs take to deal with the impact. Then a proposal of digital transformation for micro firms and SMEs are formed. This paper contributes to the analysis of digital capabilities of micro and SMEs, and how they may migrate to a solution of digital transformation. © 2022 PICMET.