2019 Coronavirus Tracker - News
Latest News and Updates
Stochastic model for Georgia
The 2019 Coronavirus Tracker now includes a stochastic model for the COVID-19 outbreak in the state of Georgia. https://2019-coronavirus-tracker.com/stochastic-GA.
Effect of early intervention
CEID has released an anlysis of the effect of early intervention on its 2019 Coronavirus Tracker. The analysis of intervention timing and outbreak size by province in China clearly demonstrates the importance of early intervention.
Based on a study of government intervention in China following the Initial outbreak of COVID-19 in Wuhan, Dr. Drake found a very strong correlation (90%) between how early a lockdown was imposed in a province and the ultimate severity of the outbreak there. The results show that early action yields critical gains, with every delay of 3.8 days leading to a tenfold increase in cases.
Details: https://2019-coronavirus-tracker.com/early-intervention.
Estimating final outbreak size
The CEID Coronavirus Working Group has developed a model to provide insight into the potential final size of the ongoing COVID-19 outbreak. The authors adapt a model previously developed for the recent Ebola outbreak.
In the model, multiple local outbreaks of a hypothetical disease are sparked (using what we know of epidemic characteristics of COVID-19). The model assumes containment improves over time due to better diagnostics and patient isolation, until the time that infectious individuals are in the community is shortened enough to halt spread. The rate of improvement in time-to-isolation, called the “Learning Rate”, has a strong effect on the final outbreak size, with rapid response greatly reducing the final outbreak size.
The model is scaled up to the size of a global epidemic by probabilistically incorporating the sparking of new outbreaks across the globe, resulting in a predicted distribution of ultimate outbreak sizes. The full model is useful in providing best-case, worst-case and average-case scenarios under a range of assumptions, especially before regional data is fully collected.
The global model shows how rapid response and limiting sparking are key to preventing pandemic. According to the model, average-case final outbreak sizes can vary over several orders of magnitude as a result of both how quickly local containment is achieved and how likely infectious individuals are to seed new outbreaks elsewhere.
Model details and estimates are available at https://2019-coronavirus-tracker.com/final-size.
Assessment of mass screening and testing
The CEID Coronavirus Working Group has released an assessment of the impact of symptom-based mass screening and testing intervention (MSTI) during novel disease outbreaks to help understand how current mass testing and screening may be affecting the COVID-19 outbreak in China.
China is currently using a strategy of symptom-based mass screening and testing intervention (MSTI) for the COVID-19 outbreak. MSTI can identify a large fraction of infected individuals during an infectious disease outbreak. However, MSTI might lead to increased transmission if not properly implemented. The new study investigates which conditions MSTI is beneficial, comparing theoretical helpfulness of MSTI during outbreaks of COVID-19, Measles and Ebola.
Because COVID-19 shows general respiratory symptoms that are similar to common infections, MSTI might quickly overload the health system. MSTI might also lead directly to a worsening of the outbreak due to increased transmission at testing sites. This study presents a method for balance the risks and benefits of MSTI given known caharacteristics of novel pathogens.
Estimates of case fatality rate added
Estimates of the case fatality rate CFR have been added to the Epidemic Characteristics page. The case fatality rate is the death rate among those with COVID-19 disease.
Effective reproduction number outside China updated
Estimates of the effective reproduction number Reff outside of China have been updated to account for heterogeneity in transmission. See R-effective outside China for details. Reeff is the average number of secondary infections, taking into account immunity in the population and containment efforts. When Reff < 1.0 containment occurs.
Virus causing COVID-19 disease named “SARS-CoV-2”
The virus causing the current outbreak of COVID-19 has been officially named “Severe acute respiratory syndrome coronavirus 2” (SARS-CoV-2). For the first several weeks of the outbreak, the virus had been informally referred to as 2019-nCoV.
A manuscript describing the work of the ICTV Coronaviridae Study Group reports that the virus belongs to the existing species “Severe acute respiratory syndrome-related coronavirus”, and as such, is a sister to existing SARS-CoVs.
The official disease name, COVID-19, which was announced February 11, 2020, should not be confused with the official name of the virus itself, henceforth known as SARS-CoV-2.
R effective outside China
The CEID Coronavirus Working Group has developed a method to estimate the effective reproduction number Reff of COVID-19 outside China. The Effective Reproduction Number is the expected number of secondary cases to arise from an arbitrary case, taking into account immunity and containment efforts.
Inside of China, the related quantity “Basic Reproduction Number” (R0), which does not take into account comtainment or immunity, has been estaimated at around 2, meaning on average an infected person would infect 2 other people in a population without immunity. This should be a basic property of the virus.
In contrast, Reff is expected to change over the course of an outbreak due to increasing immuity and containment efforts. Containment will occur when Reff < 1.
As of February 16, 2020, a number of cases of COVID-19 have been exported outside of China. Some of these have led to secondary transmission, while others have not. Some important questions include:
- Is the current level of vigilance adequate for detection and containment?
- What potential is there for sustained transmission in these settings?
CEID has now introduced a method to estimate the average effective reproduction number of cases exported outside China, usingrelying on certain assumptions. The method and first estimates are detailed here: https://2019-coronavirus-tracker.com/reff-outside.html
Novel coronavirus disease renamed COVID-19
The 2019 novel coronavirus disease has received its official name from the World Health Organization.
The new disease name, “COVID-19,” is a contraction of “Corona Virus Disease 2019”, indicating that the outbreak begain in 2019, and is caused by a novel virus in the coronavirus family.
The novel virus itself is still unnamed, and is being referred to as 2019-nCov. ICTV will be the official authority on naming the virus.
Announcing the Coronavirus Tracker
Based on micrographs of related SARS and MERS coronaviruses. (Image credit: Chloe Parker)
The UGA Center for the Ecology of Infectious Diseases (CEID) launches its new Coronavirus Tracker today. The tracker is the work of CEID’s Coronavirus Working Group, convened to provide timely, data-driven situation awareness about the 2019 novel coronavirus outbreak.
Due to the lack of prior data during the emergence of a novel pathogen, the ability to respond quickly and effectively depends on the timely assessment of information collected in real time. On January 24, 2020, the CEID formed the Coronavirus Working Group, a group of about 20 scientists with data science expertise in data manipulation and interpretation, visualization, GIS, machine learning, computational statistics, and dynamical modeling.
The Working Group’s 2019 Coronavirus Tracker provides data-driven, information rich situation awareness about the rapidly changing conditions of the current outbreak.
Activities include:
- Mapping the spatial spread of the virus within China and abroad
- Developing models for understanding the early stages of transmission and extrapolation to future events
- Estimating key parameters related to transmission
- Compiling clinical and epidemiological information to aid in the development and interpretation of model outputs as well as understanding of events as they are observed
- Assessing the effectiveness of public health interventions on containment