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.

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).

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).

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:

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

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.

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.

- Added daily new cases for LGA.

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

- Added crude incidence rate based on currently active cases.
- Added
`leaflet`

map for confirmed COVID-19 cases for local government areas (LGA).

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

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

- Minor changes to content and plot.

- 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.

- Fixed
`crontab`

schedule. - Fixed non-loading Shiny app issue by coding the new
`Interstate`

transmission source.

- 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.

- 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 \(R_{eff}\).

*American Journal of Epidemiology*, *178*(9), 1505–1512.

*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*.

*Scientific Reports*, *9*(1), 1–12.

*International Journal of Infectious Diseases*.

*Epidemics*, *22*, 29–35.

*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.