Vai alla pagina in italianoversione italiana

Automated data analysis and forecast of COVID-19 (coronavirus) outbreak for Italy and World countries

COVID-19 (coronavirus) Data analysis and forecast for Italy and World countries
online since May 2020, with automatic daily update

Author: Massimiliano d'Aquino
Visit the documentation page


NEW! Since end of July 2020 the mathematical model is able to monitor multiple waves of coronavirus spreading (both for World Countries and Italian Regions)

This is an experimental implementation of an automatic tool for daily worldwide monitoring of the COVID-19 outbreak (infectious respiratory disease caused by SARS-CoV-2, a novel type of coronavirus).
The analysis is performed everyday for single World countries as well as for Italy, single Italian Regions and single Italian Provinces.
You can select them by using the dropdown menus below.
Everytime this page is (re)loaded, the latest up-to-date forecast diagrams for World and Italy will appear.
All of the forecasts reported in the figures below have RMS error below 10% with respect to actual data available. I apologize if you should see some strange results, which might be mainly due to unsupervised computing or anomalies in datasets (you can check the issues for JHU CSSE data (for World countries) and the list of issues for DPC data for Italy).
Worldwide data are taken from the JHU CSSE Database and updated daily (generally everyday after 7:00am, Italian time).
Data for Italian Regions and Provinces are taken from the official Department of Civil Protection (DPC) database (generally everyday bewteen 6:00pm and 7:00pm, Italian time).

This service is offered for free to the community.



For World countries: select the nation to analyze data and forecast for COVID19 spread



Scale:


For Italy: select the Italian region to analyze data and forecast for COVID19 spread



Scale:


For Italy's Provinces: select the Italian Province to analyze data and forecast for COVID19 spread.
Please, notice that for Provinces only the total number of confirmed cases is available in the DPC dataset.


Top graph reports the cumulative data for Confirmed, Infected, Recovered and Deaths.
Middle graph reports the daily changes (difference with respect to the previous day) for Confirmed, Infected, Recovered and Deaths.
Bottom graph reports the evolution of the effective reproduction number Rt, which should drop below 1 (horizontal green line) to let the epidemic vanish in the long term.
In all graphs, dots refer to available data, lines to model forecasts.
Legend for color interpretation in COVID-19 forecast diagrams
Quantities predicted by the mathematical model visible in the above figures
Quantity Description
C, I, R, D, HO, IC respectively: confirmed cases, infected, recovered, deaths, hospitalized with symptoms, hospitalized in intensive care.
Cmax expected maximum number of infected people at the saturation of contagions t > tsat_C.
tsat_C expected day of contagion saturation when C(tsat_C) = 99% Cmax.
tmax_C expected day of maximum growth rate (maximum number of positive cases per day).
dC last daily change (difference with respect to the day before) of total positive.
dC7 number of confirmed cases over the last 7 days.
i7_100k number of confirmed cases over the last 7 days per 100000 inhabitants (pop = total population).
dCmax expected maximum daily change for total positive at time t = tmax_C.
Rmax expected maximum number of recovered people at the saturation time t > tsat_R.
tsat_R expected day of recovered saturation when R(tsat_R) = 99% Rmax.
tmax_R expected day of maximum growth rate (maximum number of recovered per day).
dR last daily change (difference with respect to the day before) of recovered people.
dRmax expected maximum daily change for recovered people at time t = tmax_R.
Dmax expected maximum number of deceased people at the saturation time t > tsat_D.
tsat_D expected day of deaths saturation when D(tsat_D) = 99% Dmax.
tmax_D expected day of maximum growth rate (maximum number of deaths per day).
dD last daily change (difference with respect to the day before) of deceased people.
dDmax expected maximum daily change for deceased people at time t = tmax_D.
Imax maximum number of active cases (positive people) at day tmax_I.
tmax_I expected day of maximum active cases (positive people) people per day.
tzero_I expected day of positive cases when 99% of recovered is reached (t > tsat_R), almost zero active cases.
dI last daily change (difference with respect to the day before) of active cases (positive people).
dImax expected maximum daily change for active cases (positive people) at time t = tmax_I.
DT1w_C, DT1w_I, DT1w_R, DT1w_D doubling time (days) for C, I, R, D, HO, IC computed over last 7 days.
DT2w_C, DT2w_I, DT2w_R, DT2w_D doubling time (days) for C, I, R, D, HO, IC computed over last 14 days.
DT1m_C, DT1m_I, DT1m_R, DT1m_D doubling time (days) for C, I, R, D, HO, IC computed over last 30 days.