The "Scientific Commentary" on Covid-19 measures (21 MAR 2020)

Here is an English translation of the widely reported (and hotly debated by the Spanish government; some media call it a Catalan separatist measure!) "Scientific Commentary" on measures to curb the Covid-19 pandemic in Spain. Please send me corrections (especially the authors!)
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Source: https://estaticos.elperiodico.com/resources/pdf/5/7/1584808832275.pdf
Also available here: https://estudiscatalans.blogspot.com/2020/03/comentario-cientifico.html  

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The COVID-19 situation in Spain                  21 MARCH 2020                          Scientific commentary

The COVID-19 situation in Spain

 Scientific commentary

IT IS INDISPENSABLE TO DECREE A COMPLETE LOCKDOWN
	Mathematical simulations warn that the current measures of the system will lead to a collapse of the health system around 25 March. Given the projections and the experience of other countries, is necessary to anticipate to the situation and is not reasonable to wait before reacting. To avert the collapse of the health system there is only one option: to intensify the lockdown and mobility restriction measures.

We, the undersigned, are members of the scientific community, including experts that work in the fields of public health, epidemiology, infectology, microbiology, molecular biology, the dynamics and propagation of epidemics, and other fields that are essential for the understanding of the spread of COVID-19 and the effects of the control measures. We express our concern because the actions taken to date, although in the right direction, have been insufficient to achieve the current exponential growth becoming sub-exponential. Considering the rhythm of current growth, based on the basic reproduction number (R0), we can expect that the number of cases not to be alterable. The high proportion of asymptomatic people and the outstanding asymptomatic transmisibility are some of the barriers to slow the increase in the number of cases. Moreover, it is worth highlighting that the age structure of the Spanish population (18% of the population is aged 65 or more) notably increases the risk of complications and mortality. In conclusion, the existing data suggest that the measures adopted to date will not be sufficient to prevent the collapse of the health system and, in consequence, the morbility and mortality of COVID-19.

By definition, the real scope of the epidemic only can be calculated in hindsight. During the epidemic phase, decisions need to be taken on the based of existing data as to the trends in several countries that have adopted varying measures, and in combination with mathematical models of other illnesses. Experts in the dynamics and propagation of epidemics in the field of infectious diseases have made estimates for Spain using multiple mathematical models and all cases, under various hypotheses, converge in that there will be a large volume of cases in the coming weeks. To be specific, the URV and UNIZAR models (Illustration 1 and Appendix) show the evolution of the number of patients that will need to be hospitalised in intensive care units (ICU) on account of COVID-19 in three stages, depending on the kind of intervention that is applied:

• Stage 1: With no restriction on mobility.

• Stage 2: With restriction of partial mobility (which allows 50% workforce mobility) – current situation.

• Stage 3: With total restriction of mobility (does not allow workforce mobility, save in services of basic necessities) – recommended situation.

The risk of our health system of health ― defined as the availability to access UCIs ― overstretching is very high. The interventions of control of the transmission #move of the risk to cross the ceiling of collapse sanitary in the next days. With a partial mobility restriction, like the current one, we estimate that it the overstretching point of the health system will be reached around 25 March. This could be averted if the restriction on mobility were total and decreed without delay.

On the basis of the above, and of the principle of precaution, it would be convenient to increase the number and the intensity of preventive non-farmacological interventions, the effect of which is well attested. Mathematical projections show that to make it possible to reduce the R0 in an effective way, a combination of multiple interventions is necessary, including the isolation of cases (AC), the quarantine of contacts (CC), the social distancing of people aged 70 or more, the social distancing of all the population (D100) and the closing of schools and universities (CE). The maximum level is the lockdown or total confinement, that prevents the population from going to work. It is very important to take these measures before the health services capacity gets overstretched; ideally from 2 to 3 weeks beforehand. We plead that the Government acts with maximum celerity and decision in the application of epidemic control measures that are conclusive in minimising the impact of the pandemia in the Spanish population. We suggest the following measures, which could be replaced or complemented by others of a similar nature:

Differentiation by geographical zone:

1- A Zones: Lockdown of the geographic areas with the main outbreaks of the virus and total confinement of the residents/citizens, excepting only the essential basic services (including hospitals, health and research centres), for a period of at least 15-21 days. In particular, this has to be adopted in a special way in communities with more than 25 cases/million inhabitants: Madrid (132 cases/million inhabitants), Castile and Leon (27), Castile-La Mancha (39), La Rioja (132), the Basque Country (44), Navarra (59) and Catalonia (27).

2- B Zones: Partial lockdown (50% of labour activity allowed and 25% of transport) in the rest of Spain, with special monitoring of the growth rate of new cases in these zones to decide whether to raise them to A Zones if the case arises.

3- Mobility between zones: total interruption of non-essential interurban traffic of passengers and of domestic flight connections, maritime and peninsular railways by a minimum period of 15 days (until 4 April roughly).

Differentiation by phase in time, alonfside the geographical differentiation:

1- In a first phase, of attack (3 weeks)

a. Combined strategies of total lockdown, labour interruption and the social distance, together with the intensification of the use of diagnostic testing in suspected cases, gives good results according to recent studios (Lin, Harvard). Mobility and contact between people have to be reduced as much as possuibler (that is to say, by banning all journeys and all non-basic economic activity).

b. The setting-up of a channel for the purchase and supply of protection equipment, currently insufficient, for health workers who are greatly exposed to contagions and is liable to infect. Recent studies show that SARS-CoV-2 can propagate by the infecting of eyelashes and hair.

c. Making available hotels for the isolation of cases in the general population and among health workers, so that they do not contribute to infect their family core and, at the same time, to protect health workers.

2- In a second phase, of sustainability (2 months),

    a. Increase of the capacity of the laboratories for the undertaking of the PCR diagnostic tests for all residents with symptoms.

    b. Creation and set-up of a universal mobile application for the self-reporting of observations and suspicions of CoVID-19 by the population.

    c. Early containment action focalized in areas with increases of cases detected by PCR or by the population's self-reports.

    d. Make epidemic data available to the scientific community so as to get support from artificial intelligence, simulation and epidemiology.

   e. Creation of a core of support from the different groups that coordinate an integral, objective and transparent scientific response.

Family name, first name   / institutional affiliation   /  Position

Appendix 1 – The Mathematical Model

The model that we are using is a new version of a family of epidemiological models in discrete time, that has been especially modified to represent the transmission dynamics of SARS-COV-2, the virus that causes the COVID-19 illness. The model attempts to estimate the risk rate of each municipality in Spain, taking into account: (1) The SARS-COV-2 transmission dynamics, (2) the patterns of recurrent mobility in Spain, and (3) the demography of the Spanish population.

With regard to the transmission of the virus, we use a compartmental model, viz., it segments the population according to its epidemiological condition into compartments, that are:

• Susceptible: An individual that has not contracted the illness, but may contract it.

• Exposed: An individual that is infected but that is in the incubation phase and is therefore not yet infectious.

• Asymptomatic: An individual that is already infected and is infectious, but shows no notable symptoms.

• Infected: An individual that shows symptoms easily attributable to a COVID-19 infection.

• Hospitalized: An individual that is infected, but has been detected and needs hospitalisation. This individual no longer propagates the illness because it is to be supposed that he or she is confined inside the hospital.

• Recovered: An individual that is no longer infectious and cannot contract the same virus again, either because he or she has recovered from the infection and has developed immunity, or because he or she has deceased.

The transitions between compartments are regulated by the probabilities of transmission, recovery, etc., derived from the sCOVID-19 studies published to date.

As regards mobility, we have used data provided by the National Institute of Statistics (INE) that quantify work-related journeys between municipalities and inside municipalities. This set of data reports the flux between municipalities (but only fluxes that have more than 10 trips), for all the municipalities in Spain that have more than 100 inhabitants. We incorporate mobility to the model because we consider that it is fundamental to understand how an infection propagates across the territory. Our model allows us to simulate what risk results would be obtained should complete or local mobility restrictions be imposed.

With regard to demography, we have considered it essential to divide the population of the country into three compartments: youngsters (aged 0 to 25), adults (26 to 65) and elderly (>66). The most recent evidence on the COVID-19 is that it affects each one of these groups in different way. In our model, the differences that we establish between these three groups are essentially that:

• Youngsters and the elderly do not move round the territory with the same probability as adults.

• Youngsters have, with more probability that the rest, infections in which only an asymptomatic phase (or one with slight symptoms) is observed, and these cases are therefore harder to detect.

• There is a much greater probability of elderly people requiring hospitalisation than youngsters or adults.

Limitations

• The model does not predict the importation of international cases.

• The model takes as valid the epidemiological parameters reported up to now, but that they might vary as a function of epidemiological studies.

• The model assumes that the data on mobility reported by the Spanish Institute of Statistics (INE) will not vary, that is to say, the estimate would change substantially if mobility were to be restricted.

Advantages

• The model allows us to alter the epidemiological parameters as they are reported in upcoming epidemiological studies.

 • The model allows us to study the influence of the asymptomatic period and its associated infectuosness.

• In function of these parameters can estimate the map of risk of new cases, anticipating us to the propagation of the virus by asymptomatic individuals.

• The restrictions of massive mobility (quarantine) can be easily entered in the model, allowing the obtain new values of risk under these measures. This could be useful to the sanitary authorities, that could use this model to test the efficiency of mobility restrictions as regards the spread of the virus.