Author: Mariano Supino; Alberto d'Onofrio; Federico Luongo; Giovanni Occhipinti; Alma Dal Co
Title: World governments should protect their population from COVID-19 pandemic using Italy and Lombardy as precursor Document date: 2020_3_27
ID: 5war06j2_25
Snippet: The effects of the confinement measures become evident with some delay. Specifically, we expect the effect on the number of ICU patients to appear within about two weeks (i.e. the maximum incubation time), and on the number of deaths to appear in about three weeks (i.e. the time from infection to death) (2) . At the current moment, 21 days have passed from the national lockdown, so we expect to see effects on both the number of ICU patients and d.....
Document: The effects of the confinement measures become evident with some delay. Specifically, we expect the effect on the number of ICU patients to appear within about two weeks (i.e. the maximum incubation time), and on the number of deaths to appear in about three weeks (i.e. the time from infection to death) (2) . At the current moment, 21 days have passed from the national lockdown, so we expect to see effects on both the number of ICU patients and deaths. Because of the saturation of the ICUs in several Italian regions, the number of ICU patients in Italy currently underestimates the number of cases that would require intensive care. Therefore, we analyze the data of Italy excluding the regions where the ICUs have saturated. This leaves us with 15 of the 20 regions and excludes about 28% of the Italian population. We find that the recent growth of ICU patients and deaths is consistent with a linear growth, rather than an exponential growth, suggesting that the lockdown measures have effectively reduced the spread of the infection (Fig. 6) , as it has been for Hubei region (1). Specifically, for the number of ICU patients, we obtain a good fit using an exponential curve (ICU patients(t) ∠exp[r t], t = days) up to five days after the lockdown (equal to the median incubation time of COVID-19 (9)), and a line (ICU patients(t) ∠b t, t = days) for later datapoints, up to fifteen days after the lockdown. We suggest that a logistic curve might represent well the overall trend of ICU patients in time (Fig. 6a) . For the number of deaths in time, we obtain a good fit using an exponential curve up to eleven days after the lockdown, and a line for later datapoints (Fig. 6b) .
Search related documents:
Co phrase search for related documents- case number and death infection time: 1, 2, 3, 4
- case number and death number: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
- case number and exponential curve: 1, 2, 3, 4
- case number and exponential growth: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
- case number and exponential growth linear growth: 1, 2
- case number and good fit: 1, 2, 3, 4
- case number and ICU line: 1
- case number and ICU patient: 1, 2
- death ICU patient and ICU patient: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
- death infection and exponential growth: 1, 2, 3, 4, 5, 6, 7, 8, 9
- death infection and ICU patient: 1, 2, 3, 4
- death infection time and exponential growth: 1
- death number and exponential curve: 1
- death number and exponential growth: 1, 2, 3, 4, 5, 6, 7, 8
- death number and good fit: 1, 2, 3
- death number and Hubei region: 1
- delay evident and exponential curve: 1
- delay evident and exponential growth: 1
- exponential growth and Hubei region: 1, 2
Co phrase search for related documents, hyperlinks ordered by date