Amidst the continuing spread of the COVID-19 pandemic, timely, data-driven metrics remain critical to track the pandemic's impact and inform policy making. It is critical to use multiple metrics concurrently as they provide complementary insight into relative impact, as in the effective reproduction number (Rt), and absolute impact, as in the number of new cases and deaths in a given population. This tool provides a visualization for these three metrics which are calculated based on Poisson log-linear models. Please see the About tab for more information about the usage of the site and please see our paper for more details about the method.

  • The effective reproduction number (Rt) characterizes the COVID-19 spread rate, defined as the average number of secondary infectious cases produced by a primary infectious case. It's used to define the potential for spread at a specific time. If Rt > 1, the virus will spread out and the disease will become an epidemic; if Rt = 1, the virus will spread locally and the disease is endemic; if Rt < 1, the virus will stop spreading and the disease will disappear eventually.

  • The daily new cases per million and daily new deaths per million quantify the daily new COVID-19 cases and deaths, scaled by the population of a given area.

Change the date, metric, and resolution below. Click on an area in the map to see its metrics over time.

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Select areas to compare their Rt.

Some areas may not appear in the plot for all time points because of insufficient data.

Occasionally, locations may have negative values for observed new daily cases or new daily deaths because of reporting issues.

Some overseas possessions, like Bermuda or French Polynesia, can be found in the Countries selector, not the Subnational one.

Note the Rt is lagged by 7 days.

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Click a column to sort by that metric. Note that Rt is not available beyond Tue January 4, 2022 due to the 7 day lag.


The Rt, case rate, and death rate might also not be shown for certain locations and certain dates if that location had too few total cases or too few new cases on that date. Occasionally, locations may have negative values for new cases or new deaths because of reporting issues. Full table available for download on Github.

Table Column Explanations

  • Location: Location name as a string.
  • Rt, Rt (lwr), Rt (upr): Value of Rt and the lower and upper 95% confidence interval bounds, calculated using the Poisson method. Note that there is a 7-day lag for Rt, so there are no estimates of Rt or its confidence interval in the last 7 days of the data.
  • Case rate, Case rate (lwr), Case rate (upr): Value of the case rate per million population and the lower and upper 95% confidence interval bounds, calculated using the Poisson method.
  • Death rate, Death rate (lwr), Death rate (upr): Value of the death rate per million population and the lower and upper 95% confidence interval bounds, calculated using the Poisson method.
  • Cum. cases: Cumulative number of cases up to and including the date selected.
  • Daily new cases: Number of new COVID-19 cases on the date selected. Note that different locations may define a case differently, e.g. some may only include those confirmed by PCR tests, while others would include those who tested positive on an antibody test and had COVID-19 symptoms.
  • Cum. cases per million: Number of cumulative cases up to and including the date selected per million population. We used the population numbers from the JHU CSSE data to calculate any per capita statistics.
  • Daily new cases per million: Number of new COVID-19 cases on the date selected per million population. Note that different locations may define a case differently, e.g. some may only include those confirmed by PCR tests, while others would include those who tested positive on an antibody test and had COVID-19 symptoms.
  • Cum. deaths: Cumulative number of deaths up to and including the date selected. Note that different locations may define a COVID-19 death differently.
  • Daily new deaths: Number of new COVID-19 deaths on the date selected. Note that different locations may define a COVID-19 death differently.
  • Cum. deaths per million: Cumulative number of deaths up to and including the date selected per million population.
  • Daily new deaths per million: Number of new deaths on the date selected per million population.
  • Population: Population of the location from the JHU CSSE data used to calculate any per capita statistics.

Website Usage

Map tab: This tab shows a map of the Rt, case rate, or death rate by date for various resolutions. Change the date by clicking on the displayed date and using the calendar. Change the geographic resolution (country, states / subnational, US states' counties) using the resolution dropdown menu, and click on a location on the map to see a line graph of the Rt, case rate, and death rate over time. In this plot, the dotted line shows the observed number of new cases or deaths per day, while the solid line and gray band shows the calculated Rt, case rate, or death rate along with a 95% confidence interval. You can scroll to change the zoom of the map and click-drag to move the map around. Locations where Rt, case rate, or death rate could not be calculated are shown as gray in the map (see Limitations for more info).

By clicking “Show More”, a heatmap and forest plot will be displayed. The heatmap shows the values of the selected metric as colored blocks over time in the selected geographic resolution. The forest plot shows the estimated value (as a point) and 95% confidence interval (as a bar) of the selected metric on the selected day. Locations where the metric could not be calculated are shown as gray in the heatmap and not shown in the forest plot (see Limitations for more info).

Compare tab: Select states / provinces / subnational units, US counties, and countries to compare their Rt over time. You can select a location by using the dropdown menu. You can also type the name of the location. Multiple locations for each category (states, counties, and countries) can be chosen. Additionally, choose which metrics to display and toggle display of the confidence interval for the Rt, case rate, and death rate. After you click submit, the results will be displayed as a series of line plots. Some areas may not appear in the plot because of insufficient data (see Limitations for more info).

Table tab: This tab shows a table of Rts for the chosen date and resolution, as well as the number of new cases, new case rate, cumulative number of cases, number of new deaths, new death rate, and cumulative number of deaths. The columns displayed in the table can be changed by clicking the dialog box under “Select Columns for Table”. Click the “Reset Columns” button to reset the columns to the default configuration, and click the “Download Table” button to download the table as a csv file. This table is by default sorted in descending order of case rate, but the sorting can be changed by clicking a column header. Locations where Rt could not be calculated are not shown in the table (see Limitations for more info).

Downloading Plots / Maps / Rt

To download a plot, you can use the Download buttons, or alternatively right-click on a plot and select “Save Image As…” Right now we do not have a way to save a map, but in the meantime you can take a screenshot. To download the information shown in the tables, please see the Rt table CSV on our Github page or use the Table tab.

If you'd like the shapefiles with metrics information merged that we used for our maps, they are saved as an RDS file on our Github.

Method Description

We calculate and report the daily effective reproduction number (Rt), case rate, and death rate to characterize the COVID-19 spread rate. The Rt is defined as the expected number of secondary infectious cases produced by a primary infectious case. Rt is used to determine the potential for epidemic spread at a specific time t under the control measures in place (Figure 1, Inglesby, T.V., 2020, reproduced below). If Rt > 1, the virus will spread out and the disease will become an epidemic; if Rt = 1, the virus will spread locally and the disease is endemic; if Rt < 1, the virus will stop spreading and the disease will disappear eventually.

Figure 1: Explanation of Rt

Figure credit: Thomas V. Inglesby, MD (Inglesby, T.V., 2020)

We developed a method based on Poisson log-linear models to estimate the Rt, case rate, and death rate. Briefly, we can model the expected number of new cases per day as Rt times the infectivity potential, which is a weighted sum of cases in the previous days. We can use a Poisson or negative binomial generalized linear model (GLM) to model Rt as a function of covariates. Specifically, we model Rt as a B-spline of time to smooth out weekly trends in reporting. Using the estimated coefficients and standard errors from the GLM, we can obtain estimates and confidence intervals for Rt. This method can be extended to calculate the case rate and death rate by modeling the expected number of new cases or deaths per day as the case or death rate times the population.

Our method requires the following data:

  • Daily new cases and deaths: we used data from Johns Hopkins University Center for Systems Science and Engineering (JHU-CSSE) Coronavirus Resource Center (Dong, E., et al, 2020).

  • The input parameter values of the distribution of the disease serial interval: We used a Gamma distribution with a mean of 5.2 days and a standard deviation of 5.1 days (He, X., et al, 2020).

For a full description of our method, please see our paper:

Rt Lag Adjustment

Because the number of reported cases on a particular day does not represent the number of people who contracted COVID-19 on that day, the Rt curve needs to be adjusted to account for the fact that people contract COVID-19 before their case gets counted. As a heuristic, we assume that there is a 7-day lag from the time a person contracts COVID-19 until they are reported as a case, so we shift the Rt curve back 7 days to reflect this. This assumes an average incubation period of 7 days, which includes an average latent period of 3 days and an average presymptomatic period 2 days (He, X., et al, 2020), plus an additional delay of two days to account for the time between getting tested and receiving a test result. Subjects are infectious during the presymptomatic period and are likely to test positive. This assumption also considers that with the increasing testing capacity, more presymptomatic and asymptomatic subjects are being tested. The length of delay is likely to vary between individuals, regions, and over time. See the Limitation section for further discussion.

Limitations

Rt, case rate, and death rate estimation becomes unstable when there are only a few new cases per day, when there is a large spike in cases in a single day or when the total number of cases is small. We calculate Rt at the county level for the US, and counties can have populations from the thousands to the millions. Because of low population size or lack of testing or reporting, many counties in the US as well as many countries with underdeveloped healthcare infrastructure may not have many cases. Therefore, we do not show the Rt value on dates when the number of total cases is below 50 or when the average number of new cases within the previous 7 days is below 10; we do not show the case rate or death rate values when the number of total cases or deaths is below 50 or when the average number of new cases or deaths in the previous 7 days is below 1. We also provide these metrics at different resolutions so we can aggregate data from areas with few cases or deaths.

Our calculation of these metrics is dependent on the number of reported daily new cases. We use the number of reported cases as a proxy for the number of actual cases. In some instances, the number of reported cases is likely to be lower than the number of actual cases because of reporting issues or lack of COVID-19 testing. In other instances, local authorities may report cases from several days on the same day; for example, they may not report many cases on weekends but report many on Mondays. The reported cases in some regions include cases using both PCR and antibody tests, where PCR tests detect incidence cases (currently infected cases) and antibody tests detect prevalence cases (previously infected cases). Including both may result in double counting. These data issues can cause bias in point estimates and confidence interval estimates. Therefore, we need to be cautious about interpreting these metrics for any particular region and time and take into account how reliable the case numbers are.

The metric Rt is defined at the time of infection and estimated using daily reported cases using our modification of EpiEstim model (Cori, et al, 2013). This model makes several assumptions including serial interval parameters, length of case reporting delay, and constant ascertainment rate over time. The length of case reporting delay is likely to vary between subjects, regions and over time. Rts, case rates, and death rates are estimated assuming the model is correctly specified. If the model is misspecified, the estimates and the confidence interval estimates may be biased. One can perform a tailored analysis by modifying model assumptions so they can be more suitable for a given region and a given time interval. For example, if a region has a longer length of reporting delay, the lag adjustment of Rt should be increased. Sensitivity analysis is valuable to examine the robustness of model assumptions. Future research is needed to develop advanced methods to address these limitations.

Interpretation, Using Metrics to Guide Reopening, and the Need for Multiple Metrics

Rt should not be used in isolation, and should be used as one of several metrics, such as case rate and death rate, to measure the extent of the epidemic in a region and consider when making reopening decisions. Specifically, Rt measures the transmission rate, i.e, how rapid the spread is on a given day. It is thus a relative measure on the multiplicative scale. Absolute measures such as the number of new cases per day or daily case rate should also be considered. For example, we can consider Montana and Texas on 6/28 and New York on 5/18. We assume Rt is lagged from the daily case data by 7 days, thus we will use the number of cases until 7/5 and 5/18, respectively in calculations.

  • Montana: Rt of 1.3, 95% CI (1.05 - 1.60), 45 new cases on 7/5.

  • Texas: Rt of 1.16, 95% CI (1.02 - 1.31), 4265 new cases on 7/5.

  • New York: Rt of 0.79, 95% CI (0.73 - 0.85), 1498 new cases on 5/18.

Montana has the highest Rt but doesn't have that many new cases. That's because in the previous week it had around 30 cases, so having 45 new cases represents a large jump, relative to 30 cases.

Texas has Rt above 1, which means the disease is spreading. It has a lower Rt compared to Montana but still has a substantial number of new cases. This means that the pandemic is spreading less quickly in Texas than in Montana. However, it would not be right to say that Texas is doing “better” than Montana and rely solely on the Rt metric, as there are a much larger number of newly infected people in Texas given its much higher case count. The pandemic has infected more people in Texas than in Montana.

For New York on 5/11, the Rt was below 1. This means that the number of daily cases decreased; however, on that day New York had one of the highest number of new cases out of all 50 states. This means the interventions being put into place were reducing the spread of the disease, but the number of newly infected subjects was still large given NY had a larger number of cases to start with.

In summary, one needs to look at both relative measures such as Rt and absolute measures such as the number of new cases per day or daily case rate. Rt can tell us where the trajectory of the disease is heading while the number of new cases per day and daily case rate can tell us the size of the infected population, the number of lives the disease has infected, and the number of people who may need medical attention or need to be isolated. The discussed limitations of Rt should be kept in mind. In addition, other metrics such as number of deaths, the number of hospitalizations, hospital capacity, and adherence to mask wearing, social distancing, quarantine and isolation, should be considered as well, to evaluate health care capacity and the extent of the implementation of intervention measures.

It is not safe to fully reopen without restrictions when the number of cases is still large even when Rt is below 1, because those large number of cases could go on to infect others. When Rt is sufficiently below 1 and the number of new cases is sufficiently small for two weeks, to prevent resurgence reopening still needs to proceed with control measures in place, such as mask wearing, social distancing, and test-trace-isolate (Hao et al, 2020; Powell, 2020). Carefully planned multi-phased reopening with close monitoring of new cases would be desirable.

In the News

Screenshot of Nature article

The Harvard site produces numbers for US counties—which can range from thousands to millions of inhabitants—but one of its creators, Xihong Lin, says that hyperlocal data come with big uncertainties. The researchers don't calculate an Rt for a county unless there are ten cases, Lin says. And she stresses that policymakers should not use them in isolation, but only alongside other measures such as the total number of cases and whether it is increasing. “When making recommendations, it's definitely important to look at the whole picture and not just rely on Rt,” she says. Used properly, the data could help public-health officials to identify hot spots of infection to prioritize resources such as testing, she says.

Citation and Code Availability

Shi, A.*, Gaynor, S. M.*, Quick, C. & Lin, X. Multi-resolution characterization of the COVID-19 pandemic: A unified framework and open-source tool. In submission.

Credits

This website and the associated Rt analysis was developed by Xihong Lin's Group in the Department of Biostatistics at the Harvard Chan School of Public Health.

  • Website development: Andy Shi

  • Rt Calculation: Andy Shi, Sheila Gaynor, Corbin Quick

  • Helpful discussions: Xihao Li, Hui Li, Zilin Li, Derek Shyr

  • Principal Investigator: Xihong Lin

  • Special thanks to Evan Sarmiento and the team at Harvard Institute of Quantitative Social Science (IQSS) for help with hosting.

Contact Us

  • If you have a question or feedback about the website, please write to us at linlab.covid19.analysis@gmail.com.

  • If you found a bug on the website, please create an issue on Github.

  • If you have a bug fix or new feature to add, please create a pull request on Github.

References

  1. Inglesby, T.V., 2020. Public health measures and the reproduction number of SARS-CoV-2. JAMA, 323(21), pp.2186-2187. doi: 10.1001/jama.2020.7878

  2. Cori, A., Ferguson, N.M., Fraser, C. and Cauchemez, S., 2013. A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology, 178(9), pp.1505-1512. doi: https://doi.org/10.1093/aje/kwt133

  3. Thompson, R.N., Stockwin, J.E., van Gaalen, R.D., Polonsky, J.A., Kamvar, Z.N., Demarsh, P.A., Dahlqwist, E., Li, S., Miguel, E., Jombart, T. and Lessler, J., 2019. Improved inference of time-varying reproduction numbers during infectious disease outbreaks. Epidemics, 29, p.100356. doi: 10.1016/j.epidem.2019.100356

  4. Wallinga, J. and Teunis, P., 2004. Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology, 160(6), pp.509-516. doi: 10.1093/aje/kwh255

  5. Zhang, J., Litvinova, M., Wang, W., Wang, Y., Deng, X., Chen, X., Li, M., Zheng, W., Yi, L., Chen, X. and Wu, Q., 2020. Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study. The Lancet Infectious Diseases. doi: 10.1016/S1473-3099(20)30230-9

  6. Dong, E., Du, H. and Gardner, L., 2020. An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases, 20(5), pp.533-534. doi:10.1016/S1473-3099(20)30120-1

  7. He, X., Lau, E.H., Wu, P., Deng, X., Wang, J., Hao, X., Lau, Y.C., Wong, J.Y., Guan, Y., Tan, X. and Mo, X., 2020. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature Medicine, 26(5), pp.672-675. doi:10.1038/s41591-020-0869-5

  8. Hao, X., Cheng, S., Wu, D., Wu, T., Lin, X. and Wang, C., 2020. Full-spectrum dynamics of the coronavirus disease outbreak in Wuhan, China: a modeling study of 32,583 laboratory-confirmed cases. medRxiv. doi:https://doi.org/10.1101/2020.04.27.20078436

  9. Powell, A. (2020). Pandemic threatens to veer out of control in U.S., public health experts say.. Harvard Gazette.