##Public and Private Leaderboard

To have a public leaderboard for this forecasting task, we will be using data from 7 days before to 7 days after competition launch. Only use data prior to 2020-03-26 for predictions on the public leaderboard period. Use up to and including the most recent data for predictions on the private leaderboard period.

- **Public Leaderboard Period** - 2020-03-26 - 2020-04-08
- **Private Leaderboard Period** - 2020-04-09 - 2020-05-07

##Evaluation

Submissions are evaluated using the column-wise
[root mean squared  logarithmic error](https://www.kaggle.com/wiki/RootMeanSquaredLogarithmicError)
.
The RMSLE for a single column calculated as
$$\sqrt{\frac{1}{n} \sum_{i=1}^n (\log(p_i + 1) - \log(a_i+1))^2 },
$$
where:
\\(n\\) is the total number of observations
\\(p_i\\) is your prediction
\\(a_i\\) is the actual value
\\(\log(x)\\) is the natural logarithm of \\(x\\)
The final score is the mean of the RMSLE over all columns (in this case, 2).
## Submission File

*We understand this is a serious situation, and in no way want to trivialize the human impact this crisis is causing by predicting fatalities. Our goal is to provide better methods for estimates that can assist medical and governmental institutions to prepare and adjust as pandemics unfold*.

For each `ForecastId` in the test set, you'll predict the *cumulative* COVID-19 cases and fatalities to date. The file should contain a header and have the following format:

    ForecastId,ConfirmedCases,Fatalities
    1,10,0
    2,10,0
    3,10,0
    etc.

You will get the `ForecastId` for the corresponding date and location from the `test.csv` file.