What Madrid and Spain can learn from the Lodi vs Bergamo in Italy: taking action quickly is the big key to curb the coronavirus
For the past few weeks, to understand what the coronavirus really represented and the consequences we were exposing ourselves to, we have had to resort to events that occurred more than 100 years ago. A 'Spanish Flu', the last time humanity faced an epidemic with characteristics similar to the current one. But that is over.
As we are clearing the data of the current pandemic, we are seeing in real time how history repeats itself over and over again. Now, to see the devastating effect of delaying measures (or that they are not aggressive enough) we do not have to go to Philadelphia and St. Louis in the early twentieth century, just travel to the Italian provinces of Bergamo and Lodi just a few days ago.
Italy: the curve that did not flatten
On February 21, 2020, Italy confirmed the first case of coronavirus in Codogno. From that moment on, the Lombard province of Lodi became the epicenter of the largest European outbreak of COVID-19 and the figures for all the regions of northern Italy began to grow very rapidly. Two days later, on February 23, the Government of Rome imposed a decree that closed ten municipalities of Lodi and one of Padova.
The cases in the province of Bergamo started on February 24, but, unlike the previous case, no equivalent measures or restrictions were imposed so quickly. That same day, Lombardy and Veneto announced the closure of schools, institutes and universities and began to cancel public events. It was not until March 8 when the Italian government approved a closure of the Lombard region (including Bergamo) similar to that experienced in the province of Lodi.
Jenn Dowd, Melinda Mills and their team from the University of Oxford collected the data of the evolution of both provinces and when comparing them they discovered that, as we can see in the graph above, we were seeing the phenomenon of "curve flattening" in vivo and live. In Lodi, where the measures were applied quickly and 'aggressively', it was able to control the growth of cases. In Bergamo, however, the situation shot up. But is this just anecdotal data or are there reasons to think that speed of action is critical?
Was acting so important really fast?
That's the question asked by Shengije Lai and Andrew J. Talem of the University of Southampton. The researchers used models of human movement, evolution of infectious disease outbreaks in cities in mainland China, and containment of epidemics to map an epidemiological model with which to estimate the impact of delaying or advancing the introduction of measures such as early detection, isolation of the infected and movement restrictions.
According to his estimates, China had around 114,325 coronavirus cases at the end of February 2020. Even if it seems a huge number, what the model indicates is that this figure would have been 67 times higher if the measures that had been taken had not been taken. they took at the time they were taken. However, it also tells us that, if the interventions could have been implemented just one week earlier, the infections would have fallen by 66%. If it had been three weeks earlier, the drop would have been close to 95% of those infected.
The researchers acknowledge that the model is purely epidemiological. In other words, it does not take into account political factors or strategies to reduce the social or economic damage that this type of measure may have. However, the conclusions are clear. "From a purely scientific point of view, implementing interventions as soon as possible is the best way to slow the spread and reduce the size of the outbreak," said Tatem.
Along the same lines, a team from Northeastern University and several Italian research centers led by Alessandro Vespignani used various models of disease transmission to project the impact of travel limitations on the national and international spread of the epidemic.
Chinazzi et al. (2020) The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak DOI: 10.1126 / science.aba9757
According to his calculations, when the travel ban to Wuhan was implemented on January 23, 2020, most Chinese cities had already received many infected travelers. For this reason, this measure delayed the epidemic in the rest of mainland China by only 3-5 days, but it had "a more marked effect on an international scale, where case imports fell by almost 80% until mid-February."
However, as we are seeing these days, that 80% reduction was not enough. What the model tells us is that, if we cannot reduce the transmission of the virus (through measures such as social distancing), prohibiting travel or closing borders only gives us a little more time. As can be seen in the graph above, only reducing the transmission speed (r = 1.00, r = 0.75 and r = 0.25) has a significant impact on the evolution of the epidemic.
And in this the timing is essential. Returning to the Lai and Talem model of Wuhan, the conclusions tell us that, if the social isolation measures had been delayed one, two or three more weeks, the number of cases would have shot up three, seven and 18 times respectively. In other words, with the available evidence, taking rapid action was (and is) the great key to stopping the coronavirus.
What China and the rest of the world have learned
With all this information on the table, it seems legitimate to ask whether the graph showing the different evolution of the Chinese provinces (except Hubei) and the rest of the world is also a case of "flattening the curve". . As we have analyzed these days, China has not only applied draconian measures in Hubei, but many of them have applied throughout the country.
Instead, most countries in the world have delayed (or are delaying) stringent social distancing measures until local outbreaks are out of control. Something that, if we listen to the most current epidemiological models, only ends up deepening the size and intensity of the epidemic. Strictly speaking, it is still too early to anticipate that conclusion, but the truth is that looking at the data it is difficult not to think about it.
Image | Protezione Civile Department