  The objective of this work is to predict the spread of COVID-19 starting from
observed data, using a forecast method inspired by probabilistic weather
prediction systems operational today.
  Results show that this method works well for China: on day 25 we could have
predicted well the outcome for the next 35 days. The same method has been
applied to Italy and South Korea, and forecasts for the forthcoming weeks are
included in this work. For Italy, forecasts based on data collected up to today
(24 March) indicate that number of observed cases could grow from the current
value of 69,176, to between 101k-180k, with a 50% probability of being between
110k-135k. For South Korea, it suggests that the number of observed cases could
grow from the current value of 9,018 (as of the 23rd of March), to values
between 8,500 and 9,300, with a 50% probability of being between 8,700 and
8,900.
  We conclude by suggesting that probabilistic disease prediction systems are
possible and could be developed following key ideas and methods from weather
forecasting. Having access to skilful daily updated forecasts could help taking
better informed decisions on how to manage the spread of diseases such as
COVID-19.
