Global Scientific Responses
- Longbing Cao and Wenfeng Hou. How Have Global Scientists Responded to Tackling COVID-19? Full technical report, pp. 1-125, University of Technology Sydney, 2022. Access the report at medRxiv and its associated COVID-19 global scientist response dataset and results at Kaggle. The report and data provide comprehensive literature analyses and results about the global scientific response to the COVID-19 pandemic.
- Longbing Cao and Qing Liu. COVID-19 Modeling: A Review, 1-103, 2021. Accessible at medRxiv or SSRN. This review provides a comprehensive review of COVID-19 modeling including epidemiological modeling, AI, data science, machine learning and deep learning, statistical and mathematical modeling, and simulation methods, etc.
The following two comprehensive and systematic literature review reports summarize how the global scientists have responded to the COVID-19 pandemic:
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has quickly emerged into a pandemic within a short period.
During the pandemic, multiple variants of the virus that causes COVID-19 have emerged. In the United Kingdom (UK), a new variant with an unusually large number of mutations is affecting ordinary lives. This variant spreads more easily and quickly than other variants. This variant was first detected in September 2020 and is now highly prevalent in London and southeast England. It has been detected in numerous countries around the world, including the United States and Canada.
In South Africa, another variant has emerged independently of the variant detected in the UK. This variant, originally detected in early October 2020, shares some mutations with the variant detected in the UK. This variant seems to spread more easily and quickly than other variants. Now, cases caused by this variant have been detected outside of South Africa. Currently, there is no evidence that it causes more severe illness or increases the risk of death.
Another variant recently emerged in Nigeria. There is no evidence that this variant causes more severe illness or increases the spread of COVID-19 in Nigeria.
- Mild symptoms or none at all
Some people become infected but only have very mild symptoms or none at all.
- Common symptoms
The most common symptoms of COVID-19 are fever, dry cough, fatigue, persistent pain or pressure in the chest, shortness of breath.
Other symptoms that are less common and may affect some patients include: runny nose, loss of taste or smell, nasal congestion, conjunctivitis (also known as red eyes), sore throat, headache, muscle or joint pain, different types of skin rash, nausea or vomiting, diarrhea, chills or dizziness, irritability, confusion, reduced consciousness (sometimes associated with seizures), anxiety, depression, or sleep disorders.
Children tend to have abdominal symptoms and skin changes or rashes, and sometimes vomiting and diarrhea.
- Severe symptoms
More severe and rare neurological complications include strokes, brain inflammation, delirium and nerve damage.
- A high temperature
- A new, continuous cough
- A loss or change to the patients’ sense of smell or taste
- More children, and pregnant and post-partum women
- More frequently presented renal and gastrointestinal symptoms
- More often treated with non-invasive mechanical ventilation and corticoids, and less often with invasive mechanical ventilation, conventional oxygen therapy and anticoagulants
- School closing
- Workplace closing
- Cancel public events
- Restrictions on gathering size
- Close public transport
- Stay at home requirements
- Restrictions on internal movement
- Restrictions on international movement
- Income support
- Debt/contract relief for households
- Fiscal measures
- Giving international support
- Public information campaign
- Testing policy
- Contact tracing
- Emergency investment in healthcare
- Investment in Covid-19 vaccines
- Facial coverings
- Other responses
Most people infected with the COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. 80% of patients have mild to moderate symptoms. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. COVID-19 can affect different people in different ways:
2. Symptoms of the second waves:
3. Difference between COVID-19 and flu:
|Differences||Virus family||SARS-CoV-2||Any of several different types and strains of influenza viruses|
|Epidemiology||More easily, more serious, longer time (from two days to two weeks) and higher contagiousness||Less time(one to four days)|
|Treatment||Currently only available in intravenous form||Oral antiviral medications|
|Vaccine||Partly available, in development||Available|
|Similarities||Symptoms||Fever, cough, body aches, and sometimes vomiting and diarrhea|
|Spread||Transmit the virus to other people nearby|
|Treatment||Both are treated by addressing symptoms|
|Prevention||Mask-wearing; hand washing; staying home|
To help slow down the spread of COVID-19, many countries or regions propose advice to the public. OxCGRT organizes the current official suggestions into four categories:
2. Economic response
3. Health systems
With the spread of COVID-19 on a global scale, some datasets related to COVID-19 cases, policies implemented against COVID-19, economic measures taken on the impact of COVID-19, and so on have been made public worldwide to tackle COVID-19. The following four types of datasets may be helpful for the COVID-19 modeling. Note: Some datasets overlap with each other.
- Cases dataset
- COVID Intel Database:
Consisting of the number of confirmed cases and deaths of covid-19 worldwide. The data is updated daily.
The data includes the name of a country, the WHO region, cases (cumulative total), cases (cumulative total per 1 million population), cases (newly reported in last 7 days), cases (newly reported in last 24 hours), deaths (cumulative total), deaths (cumulative total per 1 million population), deaths (newly reported in last 7 days), deaths (newly reported in last 24 hours), and transmission classification. Data types are divided into numeric and textual data.
Additionally, other eleven aspects are involved in this dataset, such as hospitalizations, vaccinations, mortality risks, and so on.
- Our World in Data:
Focusing on the tests of COVID-19 a country has done. Data types are divided into numeric and textual data.
- Polices dataset
- The dataset is of interest to epidemiologists who wish to link government measures worldwide to the developments of the number of cases.
- The dataset is also of interest to social scientists interested in the impact of other factors, e.g., democracy or institutions, on the rigidity and the timing of the measures taken.
- The coding of the economic measures is also useful to relate economic interventions with economic outcomes such as the gross domestic product or national financial market indices.
- Mobility dataset
- Research dataset
- Biomedical data
- Case statistics data
- Competition data
- Research article data
- COVID-19 Open Research Dataset
- Database of research articles(WHO)
- Database of research articles(U.S. National Library of Medicine)
- Other data
- Characterizing and predicting the COVID-19 epidemic and transmission.
- Diagnosis, case identification and contact tracing
- Modeling the efficacy of pharmaceutical and nonpharmaceutical interventions
- Understanding pathology for drug development
- Modeling the resurgence and mutation
- Modeling influence and impact
- COVID-19 mathematical and statistical analysis.
- COVID-19 machine learning by classic methods
- COVID-19 epidemiological modeling including compartmental models
- COVID-19 deep learning
- COVID-19 simulation
- COVID-19 influence and impact modeling
- Literature review of COVID-19 definitions and descriptions of the pathology, infection, and treatment of COVID-19
- Machine learning research towards combating COVID-19: virus detection, spread prevention, and medical assistance:
- Early signs, preventing the spread
- Contact tracing
- Statistical forecasting: susceptible-infected-recovered models (SIR Models) and their variants
- Machine leaning approaches (ML): such as modified autoencoder (MAE), epidemiological data of COVID-19 SEIR (susceptible-exposed-infectious-recovered) model, SARS-CoV data pre-training by an LSTM model, etc.
- Social media analysis
- Review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19
- Review of the positive and negative effects of COVID-19 from various aspects such as education, religion, environment, and economy
- Artificial intelligence (AI) in action: addressing the COVID-19 pandemic with Natural Language Processing (NLP)
- Survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic
- Survey on applications of Artificial Intelligence in fighting against COVID-19
- Review of automated detection and forecasting of COVID-19 using Deep Learning techniques
- Survey of data-driven analytical models of COVID-19 for epidemic prediction, clinical diagnosis, policy effectiveness and contact tracing
- Survey of data-driven modeling for different stages of pandemic response
-  Longbing Cao and Qing Liu. COVID-19 Modeling: A Review, 1-103, 2021. Accessible at arXiv or SSRN
-  Longbing Cao and Qing Liu. How control and relaxation interventions and virus mutations influence the resurgence of COVID-19, medRxiv, doi: https://doi.org/10.1101/2021.08.31.21262897, 1-29, 2021.
-  Qing Liu and Longbing Cao. Modeling COVID-19 uncertainties evolving over time and density-dependent social reinforcement and asymptomatic infections, Sci Rep, 12(1):5891, 1-14, 2022, PubMed.
-  Longbing Cao and Wenfeng Hou. How have global scientists responded to tackling COVID-19?, 1-125, Full Technical Report, 2022. Accessible at medRxiv, and data at Kaggle.
-  Longbing Cao. AI in combating COVID-19. IEEE Intell. Syst. 37:2, 3-13, 2022
- Flaxman S, Mishra S, Gandy A, Unwin HJ, Mellan TA, Coupland H, Whittaker C, Zhu H, Berah T, Eaton JW, Monod M. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature. 2020 Aug;584(7820):257-61.
- Wikipedia contributors, “Coronavirus disease 2019,” Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=Coronavirus_disease_2019&oldid=999546416 (accessed January 11, 2021).
- Centers for Disease Control and Prevention contributors, “New COVID-19 Variants” Centers for Disease Control and Prevention,https://www.cdc.gov/coronavirus/2019-ncov/transmission/variant.html
- World Health Organization contributors, “Coronavirus” World Health Organization,https://www.who.int/health-topics/coronavirus#tab=tab_1
- Oxford COVID-19 Government Response Tracker,https://covidtracker.bsg.ox.ac.uk/
- SIRM, “COVID-19-BSTI Imaging Database,” 2020. [On-line],https://www.bsti.org.uk/training-and-education/covid-19-bsti-imaging-database/
- Coronavirus latest: From the new strain, to whether you can catch Covid twice,https://www.bhf.org.uk/informationsupport/heart-matters-magazine/news/coronavirus-and-your-health/coronavirus-second-wave
- Simona Iftimie, Ana F. López-Azcona, et al.First and second waves of coronavirus disease-19: A comparative study in hospitalized patients in Reus, Spain , medRxiv,10 Dec.2020,
- How Do the Symptoms of COVID-19 Differ from Those of Cold and Flu?https://www.britannica.com/story/how-do-the-symptoms-of-covid-19-differ-from-those-of-cold-and-flu
- Coronavirus disease (COVID-19), https://www.who.int/emergencies/diseases/novel-coronavirus-2019/question-and-answers-hub/q-a-detail/coronavirus-disease-covid-19
- Symptoms of Coronavirus,https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
- Coronavirus Symptoms: Frequently Asked Questions,https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/coronavirus-symptoms-frequently-asked-questions
- Coronavirus disease (COVID-19),https://www.google.com/covid19/
- Coronavirus disease 2019 vs. the Flu,https://www.hopkinsmedicine.org/health/conditions-and-diseases/coronavirus/coronavirus-disease-2019-vs-the-flu
- Wiersinga, W. Joost, et al. “Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review.” Jama 324.8 (2020): 782-793.
- Shahid, Osama, et al. “Machine Learning Research Towards Combating COVID-19: Virus Detection, Spread Prevention, and Medical Assistance.” arXiv preprint arXiv:2010.07036 (2020).
- Agbehadji, Israel Edem, et al. “Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing.” International journal of environmental research and public health 17.15 (2020): 5330.
- Rahimi, Iman, Fang Chen, and Amir H. Gandomi. “A review on COVID-19 forecasting models.” Neural Computing and Applications (2021): 1-11.
- Alamoodi, Abdullah, et al. “Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review.” Expert systems with applications (2020): 114155.
- Mohamadou, Youssoufa, Aminou Halidou, and Pascalin Tiam Kapen. “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19.” Applied Intelligence 50.11 (2020): 3913-3925.
- Shakil, Mohammad Hassan, et al. “COVID-19 and the environment: A critical review and research agenda.” Science of the Total Environment (2020): 141022.
- Rajkumar, Ravi Philip. “COVID-19 and mental health: A review of the existing literature.” Asian journal of psychiatry 52 (2020): 102066.
- Nicola, Maria, et al. “The socio-economic implications of the coronavirus and COVID-19 pandemic: a review.” International journal of surgery (2020).
- Pan, Daniel, et al. “The impact of ethnicity on clinical outcomes in COVID-19: a systematic review.” EClinicalMedicine 23 (2020): 100404.
- Chen, Qingyu, et al. “Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP).” arXiv preprint arXiv:2010.16413 (2020).
- Sufian, Abu, et al. “A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic.” Journal of Systems Architecture 108 (2020): 101830.
- Chen, Jianguo, et al. “A survey on applications of artificial intelligence in fighting against covid-19.” arXiv preprint arXiv:2007.02202 (2020).
- Shoeibi, Afshin, et al. “Automated detection and forecasting of covid-19 using deep learning techniques: A review.” arXiv preprint arXiv:2007.10785 (2020).
- Mao, Ying, Susiyan Jiang, and Daniel Nametzˆ. “Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey.” (2020).
- Adiga, Aniruddha, et al. “Data-driven modeling for different stages of pandemic response.” Journal of the Indian Institute of Science (2020): 1-15.
This dataset consists of two parts, daily cases and deaths (COVID Intel Database), testing data (Our World in Data).
The dataset for the 2019 Novel Coronavirus Visual Dashboard is operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE), also supported by the ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL). This dataset mainly consists of the confirmed cases or deaths of COVID-19.
The data sources are composed of aggregated data sources, US data sources at the state (Admin1) or county/city (Admin2) level, and Non-US data sources at the country/region (Admin0) or state/province (Admin1) level.
The Oxford COVID-19 Government Response Tracker (OxCGRT) is a program established by the Blavatnik School of Government at the University of Oxford, UK. Data on seventeen different indicators of government response are recorded. These include containment, economic, and health system policies. Non-quantitative indicators are converted to ordinal scales based on a series of criteria. These are combined into a number of response indices on a quantitative scale.
The Oxford team collects information on common policy responses, scores the stringency of such measures, and aggregates these into a Stringency Index.
The data is collected from publicly available information by a cross-disciplinary Oxford University team of academics and students from every part of the world, led by the Blavatnik School of Government.
The team collects publicly available information on a number of indicators of government response. The first seven indicators (S1-S7), taking policies such as school closures, travel bans, etc., are recorded on an ordinal scale; the remainder are financial indicators such as fiscal or monetary measures.
This dataset contains key characteristics about the data described in the Data Descriptor Response2covid19, a dataset of government responses to COVID-19 all around the world.
The Apple Mobility Data is anonymised and aggregated, which was harvested from users of the mobile phone application Apple Maps and made publicly available by Apple. All data are presented relative to a baseline established on Jan 13, 2020, with days defined as midnight to midnight PST. There are generally marked differences by day of week (e.g. weekend effects), and likely be affected by seasonal mobility changes. These data are generally made available with a one-to-two-day delay and are used in accordance with the Apple’s Terms and Conditions.
Google has provided anonymously aggregated mobility data from users of the mobile phone application Google Maps. Data are informed by visits and length of stay at locations and are available from users that share their location history with Google. All data are presented relative to a baseline, which is the median value, for the day of the week, for the five week period between Jan 3 and Feb 6 2020. Due to this, the data are less influenced by day of week biases but are influenced by normal differences in seasonal usage. This data are generally made available with approximately a one week delay, and are used in accordance with Google’s Terms and Conditions.
During the COVID-19 outbreak, a large number of publications have been produced to tackle COVID-19. The relevant research can be categorized into different families. Figure 1 summarizes the learning objectives and methods of COVID-19 modeling. Figure 2 demonstrates the word clouds of 160k WHO COVID-19 literature and 12k on modeling.
Figure 1. A transdisciplinary research landscape of COVID-19 modeling. Blue: modeling methods; Green: business objectives and tasks 
(a) Word cloud of all WHO COVID-19 literature
(b) Word cloud of modeling-specific WHO COVID-19 literature
1. Objectives of COVID-19 Modeling
The main business problems, objectives and tasks explored in COVID-19 modeling include:
2. COVID-19 Modeling Methods
Many transdisciplinary modeling methods have been or are useful for modeling COVID-19, e.g.
3. COVID-19 Surveys
Here we list some literature review of COVID-19 research.
4. Our Work on COVID-19 Modeling
Our team has been working on integrating data- and model-driven COVID-19 modeling, and review on the global scientific research on quantifying COVID-19, including: