OVERVIEW (GERMANY)
Row
confirmed
171,879death
7,569 (4.4%)det
498,117 (34.5%)IFR
1.5%Row
Daily cumulative cases by type (Germany only)
SIR MODEL
Column
SIR model Covid-19 Germany (2020-04-11)
SIR Model results for Germany
SIR Model
An SIR model is an epidemiological model that computes the theoretical number of people infected with a contagious illness in a closed population over time. The model divides the population into compartments: Susceptible, Infectious, Recovered. Between S and I, the transition rate βI/N, where β is the average number of contacts per person per time, multiplied by the probability of disease transmission in a contact between a susceptible and an infectious subject, and I/N is the fraction of contact occurrences that involve an infectious individual.Between I and R, the transition rate is γ (simply the rate of recovery or mortality,that is, γ = 1/D, where D is the duration of the infection. This analysis is done for the purpose of learning the basics of SIR modelling and the dynamics of the spread of a pandemic. See more details here and for its example for covid-19 here. 
Data and assumptions
The analysis is done on 12th of April. The covid-19 case data used in this modelling are from the 15th of March, which is around the time the first restriction measures were put in place, until the 11th of April.(The raw data is pulled from Johns Hopkins University Center for Systems Science and Engineering).
Population of Germany: 83149300 (Destatis)
Based on this source; Mild symptomps= 80.9%, Severe cases= 13.8%, Intensive care= 4.7%. The case fatality rate = 3%, the infection fatality rate = 1.1%.
Model statistics- Peak of the pandemic: Beginning of May- Ro: 1.159939- Maximum number of infected: 837038- Total severe cases: 115613- Total number of cases in intensive care: 39341- Total deaths: 25111GLOBAL TREND
Column
Total confirmed cases and deaths (World-wide)
Trajectories: Outbreak growth after 100 cases (log scale)
PROGRESSION
Column
Daily case progression as % of daily cases and linear trend line
Daily death progression as % of daily deaths and linear trend line
COVID-19 DAILY
Column
Daily new confirmed cases (2020-05-10) - Top 10 Countries
Daily new deaths (2020-05-10) - Top 10 Countries
Column
Daily new confirmed cases over time - Germany
Daily new deaths over time - Germany
MAP
World map of cases (use + and - icons to zoom in/out)
PATIENTS
Column
Seriousness of symptomps (Source: Statista, China Center for DC&P)
Covid-19 patients 2020-04-12, Germany (Source: RKI)
About
The Coronavirus Dashboard: the case of Germany
This Coronavirus dashboard: the case of Germany provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Germany as well as comparison to some other countries. This dashboard is built with R using the R Markdown framework by Bcogan.
Code
This dashboard is adapted from here and here.
Data
The input data for this dashboard is the dataset available from the {coronavirus} R package.The data and dashboard are updated on a daily basis.The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.
Projected cases: The forecast is calculated on the 31st of March based on the logarithmic scale of the data between 16-31 March. The applied linear regression on the log scale is used to interpolate the raw data as an exponential model.
Estimated number of infected: The actual number of people who are infected is roughly estimated based on the calculated case growth rate and an assumed period of 5 days for symptoms to develop. It is also assumed that only 50% of infected people are symptomatic according to this source. Detection rate:Ppercentage of confirmed cases in estimated total infections. This sourceuses a different method for estimating detection rate.
Infection fatality rate: Case fatality rate is unreliable during an outbreak. See more detailed information about Infection fatality rate and case fatality rate here.
Progression: Progression rate for daily number of cases and deaths (second derivative of the daily number of cases and deaths) gives us information about the acceleration or deceleration rate of the daily cases and deaths. If the this rate is positive, that is if growth of the new cases per day is accelerating, the outbreak is exponentially growing. If the rate of variation is negative, even if the number of cases is still growing, the growth of the daily new cases is slowing down. For example, in the graph for the confirmed case progression; on 21st of March there were 10% more cases than the day before in Italy, and 47% more deaths than the day before. In Germany on the same day, there were 48% less cases than the day before, and 26% less deaths.See more info here.
The Map tab was contributed by Art Steinmetz on this pull request.
Update
The data is as of Sunday May 10, 2020