Overview

This is a developmental R Shiny app designed to provide some tools and analyses to monitor the COVID-19 situation in the state of Victoria in Australia. It is important to note that none of the estimates produced in the application are official. Simulations and output produced from this tool is for educational purposes only and should not be used for decision-making.

Reproduction Number

The basic reproduction number (\(R_{0}\)) is the fundamental epidemiological estimate for measuring the transmissibility of an epidemmic (Heesterbeek & Dietz, 1996). It is generally seen as the number of infections that is caused by a reference cases in a completely susceptible population. The limtation to the \(R_{0}\) estimate is that it does not reflect the time lapsed in an epidemic (Ng & Wen, 2019).

The time-varying reproduction number is estimated in this app to provide a measure of transmissibility over a time series (\(R_{t}\)). This uses the EpiEstim package (Cori, Ferguson, Fraser, & Cauchemez, 2013) to quantify the transmissibility of the COVID-19 outbreak in Victoria using past incidence data.

The estimation of the \(R_{t}\) accounts for the incidence of imported cases. The mean and standard deviation (in days) for the serial interval of COVID-19, by default, is set as 4.7 days and 2.9 days. This is adopted from Nishiura, Linton, & Akhmetzhanov (2020).

Incidence Projections

The Projections tab allows for simulations of future COVID-19 incidence. These require an assumed reproduction number and distribution of the serial interval. For further details about the forecasting process, take a look at Nouvellet et al. (2018).

Incidence Rates

Crude incidence rates for confirmed COVID-19 cases in the population are calculated for simplicity and are not adjusted for any variable(s). The equation for calculating crude incidence rate is described below:

\(Crude Rate = \frac{Confimed Cases}{Population} \cdot 10000\)

Disclaimer

Note that the Doherty Institute (Price et al., 2020) has produced time-varying reproduction number estimates and these appear to be very similar to the time-varying estimates announced previously by the Chief Medical Officer of Australia. The estimates produced are independent of those estimates and are intended to be illustrative. I have no affiliation with Doherty Institute and official information announced by the Australian Government should be referred to for public health information. Please do not use this app for official public health monitoring and information.

Projected cases are only illustrative. No one can precisely predict when an outbreak occurs and as such, projections generated cannot and should not be used for planning purposes.

References

Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S. (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology, 178(9), 1505–1512.

Heesterbeek, J., & Dietz, K. (1996). The concept of ro in epidemic theory. Statistica Neerlandica, 50(1), 89–110.

Ng, T.-C., & Wen, T.-H. (2019). Spatially adjusted time-varying reproductive numbers: Understanding the geographical expansion of urban dengue outbreaks. Scientific Reports, 9(1), 1–12.

Nishiura, H., Linton, N. M., & Akhmetzhanov, A. R. (2020). Serial interval of novel coronavirus (covid-19) infections. International Journal of Infectious Diseases.

Nouvellet, P., Cori, A., Garske, T., Blake, I. M., Dorigatti, I., Hinsley, W., … others. (2018). A simple approach to measure transmissibility and forecast incidence. Epidemics, 22, 29–35.

Price, D., Shearer, F. M., Meehan, M. T., McBryde, E., Moss, R., Golding, N., … McCaw, J. M. (2020). Early analysis of the australian covid-19 epidemic. https://www.doherty.edu.au/uploads/content_doc/COVID_19_early_epidemic_analysis_Doherty.pdf.

Time-varying reproduction number (Rt) for Victoria

Notes

Note that the daily incidence data is automatically scraped every day. The underlying data set is managed by the contributors of the covid19data.com.au website.

Why Rt?

The basic reproduction number, R0 tells us a story about the transmission potential for an outbreak. However, this number is likely to change with time. Hence, the Rt reflects the transmission potential and takes into account time and imported cases.

Generally, a persistent Rt of less than 1 indicates that the infection will cease eventually (Swerdlow & Finelli, 2020).

Estimation parameters

By default, the mean and standard deviation for the serial interval is adopted from Nishiura et al. (2020). This is respectively 4.7 days and 2.9 days.

Confirmed cases by LGA

Notes

Please allow a few seconds for the map to render.

Note that the data on confirmed cases by LGA is automatically scraped every day. This data is sourced from the Department of Health and Human Services (DHHS) media releases.

Important notes:

  • Residential location is the residential address provided when the case is notified.
  • This is not where they were infected and may not be where the case currently resides.
  • Numbers are correct as at the time the DHHS releases the information but are subject to change as cases are followed up and data is analysed.
  • Active cases are defined as someone who has tested positive, is currently in isolation and being monitored by the Department and who has not yet recovered.
  • Regional summary of confirmed cases

    Daily new cases for selected local government area (LGA)

    Projected confirmed cases

    Notes

    This section allows you to project future daily incidence of COVID-19 cases in Victoria. This uses an assumed reproduction number (R0). The daily incidence uses a Poisson process determined by a daily infectiousness. For further details about the forecasting process , see Nouvellet et al. (2017).

    Note that the simulations use past daily incidence of COVID-19 cases in Victoria.

    Estimation parameters

    By default, the mean and standard deviation for the serial interval is adopted from Nishiura et al. (2020). This is respectively 4.7 days and 2.9 days.

    About

    Change History

    26 July 2020

    • Added daily new cases for LGA.

    28 June 2020

    • Minor fix to data scraper for LGA-level COVID-19 cases.
    • Minor fix to data scraper from covid19.com.au.

    7 June 2020

    • Added crude incidence rate based on currently active cases.
    • Added leaflet map for confirmed COVID-19 cases for local government areas (LGA).

    29 May 2020

    • Fixed scraper to find position number of “VIC”.

    20 May 2020

    • Added brief information about projections.
    • Renamed the first tab.
    • Added projections tab.

    11 May 2020

    • Minor changes to content and plot.

    9 May 2020

    • Modified caption for the \(R_{t}\) plot.
    • Minor changes to the bibliography.
    • Added rangeslider for the \(R_{t}\) plot.
    • Added more information to the sidebar for reproduction tab.

    6 May 2020

    • Fixed crontab schedule.
    • Fixed non-loading Shiny app issue by coding the new Interstate transmission source.

    3 May 2020

    • Added more information to Introduction tab.
    • Added rangeslider for the Victorian daily COVID-19 cases.
    • Time-varying reproduction number now accounts for imported cases.
    • Added estimation parameters for EpiEstim::estimate_R() in the time-varying reproduction number.
    • Added plotly for estimated time-varying reproduction number in Victoria.

    2 May 2020

    • Extract/scraper for covid19data.com.au.
    • Add import of John Hopkins University COVID-19 data.
    • Changed glyph icon for the effective reproduction number tab.
    • Added a new tab for monitoring the effective reproduction number \(R_{eff}\).

    References

    Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S. (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology, 178(9), 1505–1512.

    Heesterbeek, J., & Dietz, K. (1996). The concept of ro in epidemic theory. Statistica Neerlandica, 50(1), 89–110.

    Jenness, S. M., Goodreau, S. M., & Morris, M. (2018). EpiModel: An r package for mathematical modeling of infectious disease over networks. Journal of Statistical Software, 84.

    Ng, T.-C., & Wen, T.-H. (2019). Spatially adjusted time-varying reproductive numbers: Understanding the geographical expansion of urban dengue outbreaks. Scientific Reports, 9(1), 1–12.

    Nishiura, H., Linton, N. M., & Akhmetzhanov, A. R. (2020). Serial interval of novel coronavirus (covid-19) infections. International Journal of Infectious Diseases.

    Nouvellet, P., Cori, A., Garske, T., Blake, I. M., Dorigatti, I., Hinsley, W., … others. (2018). A simple approach to measure transmissibility and forecast incidence. Epidemics, 22, 29–35.

    Price, D., Shearer, F. M., Meehan, M. T., McBryde, E., Moss, R., Golding, N., … McCaw, J. M. (2020). Early analysis of the australian covid-19 epidemic. https://www.doherty.edu.au/uploads/content_doc/COVID_19_early_epidemic_analysis_Doherty.pdf.

    Swerdlow, D. L., & Finelli, L. (2020). Preparation for possible sustained transmission of 2019 novel coronavirus: Lessons from previous epidemics. Jama, 323(12), 1129–1130.