Achla Marathe
Professor
University of Virginia
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MIDAS Coordination Center
University of Pittsburgh
A737 Public Health
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Pittsburgh PA 15261
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Online Portal for COVID-19 Modeling Research
Information and Resources for COVID-19 Modeling Research
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Since late December 2019, a new Coronavirus infectious disease (COVID-19) has spread around the world. This global outbreak has been characterized as a pandemic by the World Health Organization. Researchers, students, and others in the Models of Infectious Disease Agent Study (MIDAS) create and use computational models to study transmission dynamics of a broad range of infectious diseases. Many MIDAS members are conducting research on COVID-19 and are contributing to an extraordinary international collection of data and information regarding the outbreak.
For general guidance and information about the global COVID-19 situation, follow information by the World Health Organization.
For guidance on the U.S. situation, see the U.S. CDC COVID-19 website.
For journalists and the general public, see the Software Catalog for a list of dashboards and visualizations to follow the COVID-19 pandemic.
For researchers and others interested in COVID-19 data and parameter estimates, see the Data and Information section.
For questions or ideas regarding COVID-19 modeling research, contact questions@midasnetwork.us.
COVID-19 General Announcements
COVID-19 modeling expert survey seeking participants
Since mid-February, Tom McAndrew and Nicholas Reich at UMass-Amherst have been surveying expert modelers (many from the MIDAS network) about the trajectory of the COVID-19 outbreak. The experts’ responses have been aggregated into “consensus opinions” from the modeling community and shared with decision-makers at CDC and the White House. Recent reports are available here and here. The more participation we have in this effort, the broader a range of views it will represent and we believe the more useful and accurate the results will be. Surveys are sent out weekly, typically have 5-6 items, and require 5-10 minutes to complete. Many thanks to those of you who have participated already! If you are interested in participating in future surveys, please email Tom McAndrew at mcandrew@umass.edu indicating your interest.
High Performance Computing (HPC) Resources Available
The NSF-funded XSEDE HPC resource is available for COVID-19 modeling by infectious disease modelers working together with the US Centers for Disease Control and Prevention, fee of charge. XSEDE provides large HPC capacity funded by the National Science Foundation (NSF). Interested researchers can contact the Principal Investigator of XSEDE, John Towns at the University of Illinois directly (jtowns@ncsa.illinois.edu).
Online Stanford COVID-19 Meeting scheduled for April 1st
The Stanford Human-Centered AI (HAI) institute is organizing a pop-up ONLINE and distributed meeting on “Covid-19: challenges for AI, data science and informatics” for April 1, 2020. Interested researchers and students interested in participating can contact Dr. Russ Altman, Associate Director of HAI (Russ.Altman@stanford.edu).
COVID-19 Funding Announcements
National Science Foundation (NSF) RAPID Funding
NSF General COVID-19 RAPID Funding Announcement
In light of the emergence and spread of the coronavirus disease 2019 (COVID-19) in the United States and abroad, the National Science Foundation (NSF) is accepting proposals to conduct non-medical, non-clinical-care research that can be used immediately to explore how to model and understand the spread of COVID-19, to inform and educate about the science of virus transmission and prevention, and to encourage the development of processes and actions to address this global challenge.
The Office of Advanced Cyberinfrastructure (OAC) within the Directorate for Computer and Information Science and Engineering is inviting RAPID proposals and supplemental funding requests to existing awards that address COVID-19 challenges through data and/or software infrastructure development activities. Such activities would be funded by the Computational and Data-Enabled Science and Engineering (CDS&E) program or the Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program.
NSF Mathematical Biology in the Division of Mathematical Sciences also participates in NSF COVID-19 RAPID funding for mathematical modeling of COVID-19. Contact Zhilan Feng at the NSF Mathematical Biology Program (zfeng@nsf.gov).
Wellcome Trust
Wellcome Trust – Epidemic Preparedness: COVID-19 funding call
This call aims to strengthen the evidence base to better prevent and control coronavirus (COVID-19) epidemics and to increase research and response capacity. It’s part of an existing partnership between Wellcome and the UK Department for International Development (DFID) through the Joint Initiative on Research in Epidemic Preparedness and Response to help low- and middle-income countries prepare for and tackle epidemics. There are no deadlines. Expressions of interest should be submitted as soon as possible.
National Institutes of Health (NIH)
New Notice of Special Interest Funding through NIH
This Notice of Special Interest Funding (NOSI), NOT-AI-20-030, is to highlight the urgent need for research on the 2019 novel Coronavirus (2019-nCoV). NIAID is particularly interested in projects focusing on viral natural history, pathogenicity, transmission, as well as projects developing medical countermeasures and suitable animal models for pre-clinical testing of vaccines and therapeutics against 2019-nCoV through Urgent Competitive Revisions to Existing Supplements of an NIH grant. NIGMS is specifically interested in incorporation of data from the 2019-nCoV into ongoing research efforts to develop predictive models for the spread of Coronaviruses and related pathogens, through administrative supplements within the scope of an existing NIH grant. There are two ways to apply: through PA-18-935, which enables the expansion of the scope of an existing grant; or through PA-18-591, which enables an nCoV supplement if it is within the scope of an existing grant.
Data and Information for COVID-19 Modeling
Disclaimer: Information listed in this section has been extracted from reputable scientific sources by the MIDAS Coordination Center and community members. This information should not be considered as endorsed by the MIDAS Coordination Center or its funding agency, the U.S. National Institutes of Health. Users should decide on the validity of each dataset or parameter estimate as they would for any other source of information used for scientific research.
Dataset Catalog
Public-access data collections with documented metadata. (Click on a collection name to see the collection in GitHub.)
Parameter Estimates - Peer-reviewed
Published estimates of epidemiological characteristics that have been peer-reviewed, encoded by community members and approved by authors. For complete information on each estimate, view this file on GitHub.
| name | value | country | lower bound | upper bound | start date | end date | publication date | source |
|---|---|---|---|---|---|---|---|---|
| Ascertainment rate | 0.092 | Japan | 0.05 | 0.2 | 2020-01-22 | 2020-01-29 | 2020-02-04 | Nishiura H, et al. |
| Incubation period | 5.2 | China | 4.4 | 6 | 2019-12-01 | 2019-02-04 | 2020-02-04 | Lauer SA, et al. |
| Cases exported internationally from wuhan | 461 | China | 227 | 805 | 2019-12-31 | 2020-01-28 | 2020-01-31 | Wu JT, et al. |
| Cases exported internationally from wuhan | 113 | China | 57 | 193 | 2019-12-31 | 2020-01-28 | 2020-01-31 | Wu JT, et al. |
| Cases exported internationally from wuhan | 98 | China | 49 | 168 | 2019-12-31 | 2020-01-28 | 2020-01-31 | Wu JT, et al. |
| Cases exported internationally from wuhan | 111 | China | 56 | 191 | 2019-12-31 | 2020-01-28 | 2020-01-31 | Wu JT, et al. |
| Cases exported internationally from wuhan | 80 | China | 40 | 139 | 2019-12-31 | 2020-01-28 | 2020-01-31 | Wu JT, et al. |
| Cumulative case count | 75815 | China | 37304 | 130330 | 2019-12-01 | 2020-01-25 | 2020-01-31 | Wu JT, et al. |
| Basic reproduction number | 2.24 | China | 1.96 | 2.55 | 2020-01-10 | 2020-01-24 | 2020-01-27 | Zhao S, et al. |
| Proportion of hospitalized cases that die | 0.14 | China | 0.039 | 0.32 | Unspecified | 2020-01-21 | 2020-01-21 | Wu P, et al. |
| Basic reproduction number | 0.3 | China | 0.17 | 0.44 | 2020-01-09 | 2020-01-11 | 2020-01-12 | Wu P, et al. |
Parameter Estimates - Not peer-reviewed
Published estimates of epidemiological characteristics that have not been peer-reviewed, encoded by community members and approved by authors. For complete information on each estimate, view this file on GitHub.
| name | value | country | lower bound | upper bound | start date | end date | publication date | source |
|---|---|---|---|---|---|---|---|---|
| Basic reproduction number | 2.5 | Japan | 2.43 | 2.55 | 2020-01-14 | 2020-03-08 | 2020-03-16 | Sugishita Y, et al. |
| Effective reproduction number | 1.88 | Japan | 1.68 | 2.02 | 2020-01-14 | 2020-03-08 | 2020-03-16 | Sugishita Y, et al. |
| Effective reproduction number | 0.9 | Singapore | 0.7 | 1 | 2020-01-21 | 2020-03-05 | 2020-03-12 | Amna Tari, et al. |
| Epidemic size | 294.4 | Singapore | 101.1 | 1239.7 | 2020-01-21 | 2020-03-05 | 2020-03-12 | Amna Tari, et al. |
| Growth rate | 1.1 | Singapore | 0.4 | 2 | 2020-01-21 | 2020-03-05 | 2020-03-12 | Amna Tari, et al. |
| Overall reproductive number | 0.7 | Singapore | 0.5 | 1 | 2020-01-21 | 2020-03-05 | 2020-03-12 | Amna Tari, et al. |
| Dispersion parameter | 0.4 | Singapore | 0.1 | Inf | 2020-01-21 | 2020-03-05 | 2020-03-12 | Amna Tari, et al. |
| Basic reproduction number | 3.6 | Iran | 3.2 | 4.2 | 2020-02-19 | 2020-02-29 | 2020-03-10 | Muniz-Rodriguez K, et al. |
| Growth rate | 0.85 | Iran | 0.69 | 1 | 2020-02-19 | 2020-02-29 | 2020-03-10 | Muniz-Rodriguez K, et al. |
| Basic reproduction number | 3.58 | Iran | 1.29 | 8.46 | 2020-02-19 | 2020-02-29 | 2020-03-10 | Muniz-Rodriguez K, et al. |
| Doubling time | 1.2 | Iran | 1.05 | 1.44 | 2020-02-19 | 2020-02-29 | 2020-03-10 | Muniz-Rodriguez K, et al. |
| Ascertainment rate | 0.44 | Japan | 0.37 | 0.5 | Unspecified | 2020-28-02 | 2020-03-10 | Omori R, et al. |
| Incubation period | 8.13 | China | 7.37 | 8.91 | 2020-01-12 | 2020-01-23 | 2020-03-10 | Qin J, et al. |
| Incubation period | 8.62 | China | 8.02 | 9.28 | 2020-01-12 | 2020-01-23 | 2020-03-10 | Qin J, et al. |
| Generation interval | 5.2 | Singapore | 3.78 | 6.78 | 2019-12-31 | 2020-02-26 | 2020-03-08 | Ganyani T, et al. |
| Serial interval | 5.21 | Singapore | -3.35 | 13.94 | 2019-12-31 | 2020-02-26 | 2020-03-08 | Ganyani T, et al. |
| Basic reproductive number | 1.27 | Singapore | 1.19 | 1.36 | 2019-12-31 | 2020-02-26 | 2020-03-08 | Ganyani T, et al. |
| Generation interval | 3.95 | China | 3.01 | 4.91 | 2019-12-31 | 2020-02-27 | 2020-03-08 | Ganyani T, et al. |
| Serial interval | 3.95 | China | -4.47 | 12.51 | 2019-12-31 | 2020-02-27 | 2020-03-08 | Ganyani T, et al. |
| Basic reproductive number | 1.59 | China | 1.42 | 1.78 | 2019-12-31 | 2020-02-27 | 2020-03-08 | Ganyani T, et al. |
| Incubation period | 6.6 | Singapore | 1.8 | 11.4 | 2020-01-19 | 2020-02-26 | 2020-03-06 | Tindale LC, et al. |
| Time from symptom onset to hospitalization | 5.9 | Singapore | 0.8 | 11 | 2020-01-19 | 2020-02-26 | 2020-03-06 | Tindale LC, et al. |
| Time from hospitalization to recovery | 13.3 | Singapore | 7.3 | 19.3 | 2020-01-19 | 2020-02-26 | 2020-03-06 | Tindale LC, et al. |
| Incubation period | 5.4 | China | 0.9 | 9.9 | 2020-01-21 | 2020-02-22 | 2020-03-06 | Tindale LC, et al. |
| Time from symptom onset to case confirmation | 5.2 | China | 1 | 9.4 | 2020-01-21 | 2020-02-22 | 2020-03-06 | Tindale LC, et al. |
| Case fatality rate | 2.2 | China | Unspecified | Unspecified | 2020-01-21 | 2020-02-22 | 2020-03-06 | Tindale LC, et al. |
| Incubation period | 7.1 | Singapore | 6.1 | 8.3 | 2020-01-19 | 2020-02-26 | 2020-03-06 | Tindale LC, et al. |
| Incubation period | 5.46 | Singapore | 1.34 | 11.1 | 2020-01-19 | 2020-01-31 | 2020-03-06 | Tindale LC, et al. |
| Incubation period | 7.27 | Singapore | 1.31 | 17.3 | 2020-02-01 | 2020-02-26 | 2020-03-06 | Tindale LC, et al. |
| Incubation period | 9 | China | 7.92 | 10.2 | 2020-01-21 | 2020-02-22 | 2020-03-06 | Tindale LC, et al. |
| Incubation period | 6.9 | China | 2 | 12.7 | 2020-01-21 | 2020-01-31 | 2020-03-06 | Tindale LC, et al. |
| Incubation period | 12.4 | China | 5.4 | 19 | 2020-02-01 | 2020-02-22 | 2020-03-06 | Tindale LC, et al. |
| Serial interval | 4.56 | Singapore | 2.69 | 6.42 | 2020-01-19 | 2020-02-26 | 2020-03-06 | Tindale LC, et al. |
| Serial interval | 4.22 | China | 3.43 | 5.01 | 2020-01-21 | 2020-02-22 | 2020-03-06 | Tindale LC, et al. |
| Incubation period | 4.8 | China | 4.2 | 5.4 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 20.3 | China | 19.4 | 21.3 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 21.2 | China | 20.2 | 22.3 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 17.5 | China | 15.3 | 20 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 19.1 | China | 15.8 | 22.9 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 19.2 | China | 17.5 | 21 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 19.2 | China | 18 | 20.5 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 21.6 | China | 20 | 23.4 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 22.4 | China | 20.8 | 24.1 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 22.9 | China | 21.2 | 24.7 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 22.5 | China | 19.1 | 26.3 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 20.1 | China | 19 | 21.3 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 20.3 | China | 19.5 | 21.1 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 28.3 | China | 25.3 | 31.6 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 19.3 | China | 17.9 | 20.9 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 21.2 | China | 20.4 | 22 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to recovery | 21.2 | China | 18 | 24.9 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Secondary attack rate | 14.9 | China | 12.1 | 18.2 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Secondary attack rate | 9.6 | China | 7.9 | 11.8 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Serial interval | 6.3 | China | 5.2 | 7.6 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Observed reproductive number | 0.4 | China | 0.3 | 0.5 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to isolation | 2.7 | China | 2.1 | 3.3 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to isolation | 4.6 | China | 4.1 | 5 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to pcr confirmation | 3.18 | China | 2.65 | 3.76 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Time from symptom onset to pcr confirmation | 5.46 | China | 4.99 | 5.92 | 2020-01-14 | 2020-02-12 | 2020-03-04 | Qifang B, et al. |
| Basic reproduction number | 4.8 | China | 3.36 | 6.67 | 2020-01-11 | 2020-01-16 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 4.6 | China | 3.56 | 5.65 | 2020-01-11 | 2020-01-17 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 5.14 | China | 4.25 | 6.03 | 2020-01-11 | 2020-01-18 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 6.09 | China | 5.02 | 7.16 | 2020-01-11 | 2020-01-19 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 7.09 | China | 5.84 | 8.35 | 2020-01-11 | 2020-01-20 | 2020-03-03 | Anastassopoulou C, et al. |
| Symptomatic case fatality ratio | 2.94 | China | 2.9 | 3 | 2020-01-11 | 2020-02-10 | 2020-03-03 | Anastassopoulou C, et al. |
| Recovery rate | 16 | China | 13 | 20 | 2020-01-11 | 2020-02-10 | 2020-03-03 | Anastassopoulou C, et al. |
| Infection rate | 0.191 | China | 0.19 | 0.192 | 2020-01-11 | 2020-02-10 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 2.6 | China | Unspecified | Unspecified | 2020-01-11 | 2020-02-10 | 2020-03-03 | Anastassopoulou C, et al. |
| Cumulative case count | 180000 | China | 45000 | 760000 | 2020-01-11 | 2020-02-29 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 3.25 | China | 2.37 | 4.14 | 2020-01-11 | 2020-01-16 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 3.13 | China | 2.5 | 3.76 | 2020-01-11 | 2020-01-17 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 3.42 | China | 2.91 | 3.93 | 2020-01-11 | 2020-01-18 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 3.96 | China | 3.36 | 4.57 | 2020-01-11 | 2020-01-19 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 4.58 | China | 3.81 | 5.35 | 2020-01-11 | 2020-01-20 | 2020-03-03 | Anastassopoulou C, et al. |
| Mortality rate | 0.147 | China | 0.144 | 0.15 | 2020-01-11 | 2020-02-10 | 2020-03-03 | Anastassopoulou C, et al. |
| Recovery rate | 6 | China | 5 | 7 | 2020-01-11 | 2020-02-10 | 2020-03-03 | Anastassopoulou C, et al. |
| Infection rate | 0.319 | China | 0.318 | 0.32 | 2020-01-11 | 2020-02-10 | 2020-03-03 | Anastassopoulou C, et al. |
| Basic reproduction number | 2 | China | Unspecified | Unspecified | 2020-01-11 | 2020-02-10 | 2020-03-03 | Anastassopoulou C, et al. |
| Cumulative case count | 8000000 | China | 720000 | 37000000 | 2020-01-11 | 2020-02-29 | 2020-03-03 | Anastassopoulou C, et al. |
| Case fatality rate | 0.8-0.9 | China | Unspecified | Unspecified | 2020-01-21 | 2020-03-01 | 2020-03-03 | Rui Q, et al. |
| Case fatality rate | 5.4 | China | Unspecified | Unspecified | 2020-01-21 | 2020-03-01 | 2020-03-03 | Rui Q, et al. |
| Basic reproduction number | 2.2 | Cruise Ship | 2.1 | 2.4 | 2020-01-20 | 2020-02-20 | 2020-03-02 | Zhao S, et al. |
| Dispersion | 44 | Cruise Ship | 6 | 88 | 2020-01-20 | 2020-02-20 | 2020-03-02 | Zhao S, et al. |
| Cumulative case count | 3066 | Cruise Ship | 2046 | 3441 | 2020-01-20 | 2020-02-20 | 2020-03-02 | Zhao S, et al. |
| Doubling time | 4.6 | Cruise Ship | 3 | 9.3 | 2020-01-20 | 2020-02-20 | 2020-03-02 | Zhao S, et al. |
| Effective reproduction number | 1.5 | South Korea | 1.4 | 1.6 | 2020-01-20 | 2020-02-26 | 2020-03-02 | Shim E, et al. |
| Growth rate | 0.6 | South Korea | 0.5 | 0.7 | 2020-01-20 | 2020-02-26 | 2020-03-02 | Shim E, et al. |
| Cumulative case count | 79467 | China | 71576 | 93855 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 64261 | China | 58223 | 77895 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 46607 | China | 40812 | 57678 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 13906 | China | 12748 | 16092 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 1410 | China | 1324 | 1550 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 1265 | China | 1204 | 1364 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 1369 | China | 1271 | 1559 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 1136 | China | 1050 | 1279 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 1100 | China | 1021 | 1273 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 1123 | China | 948 | 1146 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 429 | China | 395 | 486 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 356 | China | 334 | 388 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 365 | China | 346 | 393 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Cumulative case count | 430 | China | 407 | 461 | 2020-01-21 | 2020-02-14 | 2020-02-20 | Fu X, et al. |
| Proportion of hospitalized cases admitted to icu | 0.05 | China | Unspecified | Unspecified | 2019-12-31 | 2020-02-18 | 2020-02-18 | Zhou G, et al. |
| Cumulative case count | 2868.7 | China | 1129.7 | 4607.7 | 2019-12-31 | 2020-01-23 | 2020-02-18 | Zhou G, et al. |
| Cumulative case count | 52185.4 | China | 20564 | 83806.8 | 2020-01-23 | 2020-02-12 | 2020-02-18 | Zhou G, et al. |
| Cumulative case count | 45000 | China | Unspecified | Unspecified | 2019-12-31 | 2020-02-12 | 2020-02-18 | Wan K, et al. |
| Basic reproduction number | 1.44 | China | 1.4 | 1.47 | 2020-01-22 | 2020-02-12 | 2020-02-18 | Wan K, et al. |
| Cumulative case count | 453 | China | Unspecified | Unspecified | 2019-12-08 | 2020-01-22 | 2020-02-18 | Zhang B, et al. |
| Cumulative case count | 6333 | China | Unspecified | Unspecified | 2020-01-23 | 2020-02-13 | 2020-02-18 | Zhang B, et al. |
| Cumulative case count | 24333 | China | Unspecified | Unspecified | 2020-02-14 | Unspecified | 2020-02-18 | Zhang B, et al. |
| Effective reproduction number | 3.6 | China | Unspecified | Unspecified | 2019-12-08 | 2020-01-22 | 2020-02-18 | Zhang B, et al. |
| Effective reproduction number | 1.55 | China | Unspecified | Unspecified | 2020-01-23 | 2020-02-13 | 2020-02-18 | Zhang B, et al. |
| Effective reproduction number | 1.08 | China | Unspecified | Unspecified | 2020-02-14 | Unspecified | 2020-02-18 | Zhang B, et al. |
| Cumulative case count | 7710 | China | Unspecified | Unspecified | 2020-01-10 | 2020-01-22 | 2020-02-18 | Zhang B, et al. |
| Cumulative case count | 18832 | China | Unspecified | Unspecified | 2020-01-23 | 2020-02-13 | 2020-02-18 | Zhang B, et al. |
| Cumulative case count | 21342 | China | 21057 | 21629 | 2020-02-14 | Unspecified | 2020-02-18 | Zhang B, et al. |
| Effective reproduction number | 3.4 | China | Unspecified | Unspecified | 2020-01-10 | 2020-01-22 | 2020-02-18 | Zhang B, et al. |
| Effective reproduction number | 0.83 | China | Unspecified | Unspecified | 2020-01-23 | 2020-02-13 | 2020-02-18 | Zhang B, et al. |
| Effective reproduction number | 0.59 | China | Unspecified | Unspecified | 2020-02-14 | Unspecified | 2020-02-18 | Zhang B, et al. |
| Cumulative case count | 7793 | China | Unspecified | Unspecified | 2020-01-23 | 2020-02-03 | 2020-02-18 | Zhang B, et al. |
| Cumulative case count | 13384 | China | Unspecified | Unspecified | 2020-02-04 | Unspecified | 2020-02-18 | Zhang B, et al. |
| Effective reproduction number | 3.3 | China | Unspecified | Unspecified | 2020-01-23 | 2020-02-03 | 2020-02-18 | Zhang B, et al. |
| Effective reproduction number | 0.63 | China | Unspecified | Unspecified | 2020-02-04 | Unspecified | 2020-02-18 | Zhang B, et al. |
| Time from symptom onset to reporting | 5.1 | China | 3.5 | 7.5 | 2019-12-31 | 2020-01-24 | 2020-02-17 | Jung S, et al. |
| Time from symptom onset to death | 15.2 | China | 13.1 | 17.7 | 2019-12-31 | 2020-01-24 | 2020-02-17 | Jung S, et al. |
| Cumulative case count | 6924 | China | 4885 | 9211 | 2019-12-08 | 2020-01-24 | 2020-02-17 | Jung S, et al. |
| Proportion of symptomatics that die | 5.30% | China | 3.50% | 7.50% | 2019-12-08 | 2020-01-24 | 2020-02-17 | Jung S, et al. |
| Basic reproduction number | 2.1 | China | 2 | 2.2 | 2019-12-08 | 2020-01-24 | 2020-02-17 | Jung S, et al. |
| Cumulative case count | 19289 | China | 10901 | 30158 | 2019-12-08 | 2020-01-24 | 2020-02-17 | Jung S, et al. |
| Proportion of symptomatics that die | 8.40% | China | 5.30% | 12.30% | 2019-12-08 | 2020-01-24 | 2020-02-17 | Jung S, et al. |
| Basic reproduction number | 3.2 | China | 2.7 | 3.7 | 2019-12-08 | 2020-01-24 | 2020-02-17 | Jung S, et al. |
| Cumulative case count | 4090 | China | 3975 | 4206 | 2019-11-30 | 2020-01-11 | 2020-02-13 | Lin Q, et al. |
| Cumulative case count | 56833 | China | 55242 | 58449 | 2019-11-30 | 2020-02-09 | 2020-02-13 | Lin Q, et al. |
| Reporting rate | 1.41% | China | 1.37% | 1.45% | 2019-11-30 | 2020-01-11 | 2020-02-13 | Lin Q, et al. |
| Reporting rate | 29.74% | China | 28.92% | 30.60% | 2019-11-30 | 2020-02-09 | 2020-02-13 | Lin Q, et al. |
| Cumulative case count | 110000 | China | 40000 | 310000 | 2020-01-29 | 2020-02-02 | 2020-02-13 | Zhao H, et al. |
| Infection rate | 1.10% | China | 0.40% | 3.10% | 2020-01-29 | 2020-02-02 | 2020-02-13 | Zhao H, et al. |
| Basic reproduction number | 7.05 | China | 6.11 | 8.18 | 2019-12-31 | 2020-01-23 | 2020-02-13 | Mizumoto K, et al. |
| Effective reproduction number | 3.24 | China | 3.16 | 3.32 | 2020-01-23 | 2020-02-13 | 2020-02-13 | Mizumoto K, et al. |
| Cumulative case count | 983006 | China | 759575 | 1296258 | 2019-12-31 | 2020-02-13 | 2020-02-13 | Mizumoto K, et al. |
| Crude infection fatality ratio | 0.07% | China | 0.05% | 0.09% | 2019-12-31 | 2020-02-13 | 2020-02-13 | Mizumoto K, et al. |
| Time delay adjusted infection fatality ratio | 0.23% | China | 0.17% | 0.30% | 2019-12-31 | 2020-02-13 | 2020-02-13 | Mizumoto K, et al. |
| Proportion of symptomatics that die | 0.0406 | China | Unspecified | Unspecified | 2019-12-31 | 2020-02-13 | 2020-02-13 | Mizumoto K, et al. |
| Cumulative case count | 3491 | China | 1924 | 7360 | 2019-12-01 | 2020-01-22 | 2020-02-11 | Chinazzi M, et al. |
| Cumulative case count | 58956 | China | 40760 | 87471 | 2019-12-01 | 2020-01-22 | 2020-02-11 | Chinazzi M, et al. |
| Ascertainment rate | 0.1959 | China | 0.1436 | 0.3558 | 2019-12-01 | 2020-02-05 | 2020-02-11 | Chinazzi M, et al. |
| Incubation period | 4.2 | China | 3.5 | 5.1 | 2019-12-31 | 2020-02-10 | 2020-02-11 | Sanche S, et al. |
| Time from hospitalization to discharge | 11.5 | China | 8 | 17.3 | 2019-12-31 | 2020-02-10 | 2020-02-11 | Sanche S, et al. |
| Time from hospitalization to death | 11.2 | China | 8.7 | 14.9 | 2019-12-31 | 2020-02-10 | 2020-02-11 | Sanche S, et al. |
| Growth rate | 0.29 | China | 0.21 | 0.37 | 2019-12-31 | 2020-01-29 | 2020-02-11 | Sanche S, et al. |
| Cumulative case count | 4100 | China | 2423 | 6178 | 2019-12-31 | 2020-01-18 | 2020-02-11 | Sanche S, et al. |
| Cumulative case count | 18700 | China | 7141 | 38663 | 2020-01-19 | 2020-01-23 | 2020-02-11 | Sanche S, et al. |
| Growth rate | 0.14 | China | 0.12 | 0.15 | 2020-01-30 | 2020-02-10 | 2020-02-11 | Sanche S, et al. |
| Proportion of symptomatics that die | 0.18 | China | 0.11 | 0.81 | 2019-12-31 | 2020-02-05 | 2020-02-10 | Dorigatt I, et al. |
| Time from symptom onset to death | 22.3 | China | 18 | 82 | 2019-12-31 | 2020-01-21 | 2020-02-10 | Dorigatt I, et al. |
| Cumulative case count | 24000 | China | 13000 | 44000 | 2019-12-31 | 2020-01-31 | 2020-02-10 | Dorigatt I, et al. |
| Ascertainment rate | 0.0012 | China | 0.0009 | 0.0015 | 2020-01-01 | 2020-01-22 | 2020-02-09 | Cao Z, et al. |
| Cumulative case count | 35454 | China | Unspecified | Unspecified | Unspecified | 2020-01-23 | 2020-02-09 | Cao Z, et al. |
| Basic reproduction number | 1.48 | China | Unspecified | Unspecified | 2019-12-17 | 2020-02-06 | 2020-02-08 | Hermanowic, et al. |
| Effective reproduction number | 1.06 | China | Unspecified | Unspecified | 2019-12-17 | 2020-02-08 | 2020-02-08 | Hermanowic, et al. |
| Basic reproduction number | 2.4 – 2.5 | China | Unspecified | Unspecified | 2019-12-17 | 2020-02-06 | 2020-02-08 | Hermanowic, et al. |
| Cumulative case count | 65000 | China | Unspecified | Unspecified | 2019-12-17 | 2020-02-06 | 2020-02-08 | Hermanowic, et al. |
| Doubling time | 2.4 | China | Unspecified | Unspecified | 2020-01-20 | 2020-02-02 | 2020-02-06 | Muniz-Rodriguez K, et al. |
| Proportion of hospitalized cases that die | 33 | China | 29 | 37 | 2019-12-10 | 2020-01-27 | 2020-02-04 | Famular, et al. |
| Ascertainment rate | 1 | China | 0.6 | 2 | 2019-12-10 | 2020-01-27 | 2020-02-04 | Famular, et al. |
| Cumulative case count | 58000 | China | Unspecified | Unspecified | 2019-12-31 | 2020-01-31 | 2020-01-31 | Gardner L. |
| Cumulative case count | 95933 | China | Unspecified | Unspecified | 2019-12-31 | 2020-01-28 | 2020-01-30 | Ming W, et al. |
| Cumulative case count | 31200 | China | 23400 | 40400 | 2019-12-31 | 2020-01-29 | 2020-01-29 | Vespignani A. |
| Incubation period | 4.6 | China | 3.3 | 5.7 | 2019-12-31 | 2020-01-25 | 2020-01-28 | Linton NM, et al. |
| Incubation period | 5 | China | 4.1 | 5.8 | 2019-12-31 | 2020-01-25 | 2020-01-28 | Linton NM, et al. |
| Time from symptom onset to hospitilization | 2.7 | China | 1.6 | 4.1 | 2019-12-31 | 2020-01-25 | 2020-01-28 | Linton NM, et al. |
| Time from symptom onset to death | 13.8 | China | 11.8 | 16 | 2019-12-31 | 2020-01-25 | 2020-01-28 | Linton NM, et al. |
| Time from hospitilization to death | 8.3 | China | 6.4 | 10.5 | 2019-12-31 | 2020-01-25 | 2020-01-28 | Linton NM, et al. |
| Time from symptom onset to isolation | 2.9 | China | Unspecified | Unspecified | 2019-12-29 | 2020-01-23 | 2020-01-26 | Liu T, et al. |
| Proportion of symptomatics that die | 0.03 | China | Unspecified | Unspecified | 2019-12-29 | 2020-01-23 | 2020-01-26 | Liu T, et al. |
| Incubation period | 5.4 | China | 4.2 | 6.7 | 2019-12-10 | 2020-01-25 | 2020-01-26 | Famular, et al. |
| Basic reproduction number | 2.6 | China | 1.5 | 3.5 | Unspecified | 2020-01-18 | 2020-01-24 | Imai N, et al. |
| Date of emergence | Unspecified | China | 2020-11-19 | 2020-12-19 | Unspecified | Unspecified | 2020-01-23 | Neher R, et al. |
| Basic reproduction number | 2.2 | China | 1.4 | 3.8 | 2019-11-27 | 2020-01-18 | 2020-01-23 | Riou J, et al. |
| Basic reproduction number | Unspecified | China | 1.5 | 3.5 | 2019-11-01 | 2020-01-01 | 2020-01-23 | Neher R, et al. |
| Distribution of secondary cases | 0.54 | China | 0.014 | 6.95 | 2019-11-27 | 2020-01-18 | 2020-01-23 | Riou J, et al. |
| Cumulative case count | 4000 | China | 1000 | 9700 | 2020-01-18 | 2020-01-18 | 2020-01-22 | Imai N, et al. |
Software Catalog
Listing of software created by community members, with documented metadata. (Click on a software name to open the software webpage.)
| software type | software name | Creator(s) |
|---|---|---|
| Visualization | Alerts for Novel Coronavirus (2019-nCoV) | HealthMap |
| Visualization | Novel Coronavirus (2019-nCoV) | Brownstein J, et al. |
| Dashboard | Distribution of new coronavirus pneumonia | China Centers for Disease Control |
| Visualization | Coronavirus Map: Tracking the Spread of the Outbreak | Lai R KK, et al. |
| Dashboard | Coronavirus COVID-19 Global Cases by Johns Hopkins CSSE | Gardner L, et al. |
| Visualization | Realtime interactive map of coronavirus 2019-nCov global distribution | Malishev M |
| Dashboard | COVID-19 Surveillance Dashboard | University of Virignia |
| Dashboard | COVID-19 Cases and Clusters Outside of China | University of Virignia |
| Dashboard | COVID-19 | European Centre for Disease Prevention and Control |
| Dashboard | Novel Coronavirus (COVID-19) Infection Map | Zhao B, et al. |
| Data processing | Stats of the ‘2019-nCov’ Cases | Yu G, et al. |
| Modeling | NobBS: Nowcasting by Bayesian Smoothing | McGough S, et al. |
| Dashboard | 2019nCoV-Dashboard | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia |
| Modeling | A stochastic model for the transmission of 2019-nCov in Wuhan | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia |
| Modeling | Estimation of the effective reproduction number of COVID-19 outside China | The Center for the Ecology of Infectious Diseases (CEID) at the University of Georgia |
| Modeling | Extended state-space SIR epidemiological models | Song Lab, et al. |
| Dashboard | COVID-19 Italy – Situation monitoring | Italian Department of Civil Protection |
| Data processing | scrapR | Kucharski A |
COVID-19 GitHub Repository
The MIDAS Network has created a GitHub Repository for COVID-19 modeling research. The repository includes data and information, listed on this website, in computational form, mostly structured CSV files and rich metadata.
Directly access content on the COVID-19 GitHub repository:
Published estimates of epidemiological characteristics, encoded by community members and approved by authors.
Listing of software created by community members, with documented metadata.
Reports, pre-prints, and peer-reviewed papers relevant to COVID-19 modeling, organized by country or by topic. All documents are also listed in a downloadable format.
How to Participate
Mailing List:
Any infectious disease scientist, practitioner, or student interested in COVID-19 modeling research can join this mailing list. Subscription requests will be reviewed and approved by a moderator.
Contribute to the MIDAS COVID-19 Repository:
We encourage community members to contribute resources to the repository and thus support the overall COVID-19 research effort. See the information for contributors section on the GitHub repository for guidance on how to contribute material to the repository. Contributions can be made via pull request or by submitting an issue. Contact questions@midasnetwork.us for any questions or ideas for improvements, or to send/request any material to be included.
Join the Slack COVID-19 Workspace:
A Slack workspace is being maintained by Caitlin Rivers (caitlin.rivers@gmail.com). The workspace includes various channels: general, importation, news, random, publications, forecasting, data, and announcements.
Review COVID-19 Preprints on Outbreak Science Rapid PREreview Platform:
This rapid pre-review platform enables the community to give feedback on pre-prints coming out with modeling and other analyses of the COVID-19 and other outbreaks. Visit the platform here.
For questions or concerns, contact the MIDAS Coordination Center via questions@midasnetwork.us.
MIDAS Members Working on COVID-19
Alessandro Vespignani
Professor
Northeastern University
Alison Galvani
Professor
Yale University
Ana Pastore y Piontti
Associate Research Scientist
Northeastern University
Andreas Handel
Associate Professor
University of Georgia, College of Public Health, Department of Epidemiology & Biostatistics
Ben Cowling
Professor and Head
Hong Kong University
Caitlin Rivers
Assistant Professor
Johns Hopkins University
Caroline Buckee
Associate Professor
Harvard University
Cecile Viboud
Staff Scientist
U.S. Department of Health and Human Services
Cynthia Chen
Professor
University of Washington
Donald Burke
Distinguished University Professor of Health Science and Policy
University of Pittsburgh
Eben Kenah
Associate Professor
Ohio State University
Eric Lofgren
Assistant Professor
Washington State University
Evan Ray
Assistant Professor
Mount Holyoke College
Gregory Hart
Research Scientist
Institute for Disease Modeling
Ira Longini
Professor
University of Florida
Jin Wang
Professor and UNUM Chair of Excellence
University of Tennessee Chattanooga
John Drake
Distingued Research Professor
University of Georgia
Joseph Wu
Professor
Hong Kong University
Justin Lessler
Associate Professor
Johns Hopkins University
Katelyn Gostic
Postdoctoral Researcher
University of Chicago
Laura White
Associate Professor
Boston University
Lauren Childs
Assistant Professor
Virginia Tech
Lauren Meyers
Professor
University of Texas at Austin
M Halloran
Faculty Member
Fred Hutchinson Cancer Research Center
Madhav Marathe
Professor
University of Virginia
Marc Lipsitch
Professor
Harvard University
Marisa Eisenberg
Associate Professor
University of Michigan
Matteo Chinazzi
Associate Research Scientist
Northeastern University
Matthew Biggerstaff
Epidemiologist
U.S. Department of Health and Human Services
Mauricio Santillana Guzman
Assistant Professor
Harvard University
Michael Johansson
Biologist
U.S. Department of Health and Human Services
Neil Ferguson
Department Head
Imperial College London
Paige Miller
PhD Candidate
University of Georgia
Paul Edlefsen
Staff Scientist
Fred Hutchinson Cancer Research Center
Pragati Prasad
ORISE Fellow
U.S. Department of Health and Human Services
Rachel Slayton
Modeling Unit Lead
U.S. Department of Health and Human Services
Roni Rosenfeld
Professor
Carnegie Mellon University
Sara Del Valle
Deputy Group Leader
Los Alamos National Laboratory
Sarah Cobey
Associate Professor
University of Chicago
Sarah Rae Wannier
PhD Student
University of California San Francisco
Seth Blumberg
PostDoc
University of California San Francisco
Shan Liu
Associate Professor
University of Washington
Simon Cauchemez
Head of Structure
Institut Pasteur
Stephen Eubank
Professor and Deputy Director
University of Virginia
Steven Riley
Professor of Infectious Disease Dynamics
Imperial College London
Steven Weaver
Senior Programming Analyst
Temple University
Travis Porco
Professor
University of California San Francisco
Trevor Bedford
Associate Member
Fred Hutchinson Cancer Research Center
Yang Yang
Associate Professor
University of Florida