Predicting clinical needs derived from the COVID-19 pandemic: the case of Spain

  1. Luis Angel Hierro
  2. Antonio José Garzón
  3. Pedro Atienza
  4. José Luis Márquez

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Abstract

Background

The evolution of the pandemic caused by COVID-19, its high reproductive number and the associated clinical needs, is overwhelming national health systems. We propose a method for predicting both ICU requirements and the number of deaths, and which will enable the health authorities of the countries involved to plan the resources needed to face the pandemic as many days in advance as possible.

Methods

We use official Spanish data to predict ICU admissions and deaths based on the number of infections. We employ OLS to perform the econometric estimation, and through RMSE, MSE, MAPE, and SMAPE forecast performance measures we select the best lagged predictor of both dependent variables.

Findings

For Spain, our prediction shows that the best predictor of ICU admissions is the number of people infected eight days before, and that the best predictor of deaths is the number of people infected five days before. In the first case, we obtain a 98% coefficient of determination, and in the second a 97% coefficient. The estimated delayed elasticities find that a 1% increase in the number of cases today will imply a 0.72% increase in ICU patients eight days later and a 1.09% increase in the number of deaths five days later.

Interpretation

The model is not intended to analyse the epidemiology of COVID-19. Our objective is rather to estimate a leading indicator of clinical needs. Having a forecast model available several days in advance can enable governments to more effectively face the gap between needs and resources triggered by the outbreak and thus reduce the deaths caused by COVID-19.

Research in context

Evidence before this study

Daily news regarding the exponential growth of those affected by COVID-19 shows that healthcare resources are being overwhelmed by clinical needs in many countries. In particular, serious problems are arising in the most affected countries due to the shortage of ICU beds and the large number of deaths that the authorities are unable to deal with. National health authorities do not have adequate prediction mechanisms to facilitate clinical crisis management. We have performed bibliographic searches of the usual terms used to designate COVID-19, together with those of “prediction”, “estimation”, “ICU”, “mortality” and the like, both in Pubmed and in Google Scholar. The predictive literature related to COVID-19 remains very sparse and the few models that do exist are based on exponential adjustments for forecasting the population affected. However, these models lose their predictive accuracy when the growth rate of infections decreases, added to which such models fail to determine the most statistically efficient maximum prediction time.

Added value of this study

We apply a previously unused method based on predictions through delayed logarithmic estimates of ICU admissions and deaths based on the number of infections. For Spain, we estimate that the best predictor of ICU admissions is the number of people infected eight days before and that the best predictor of deaths is the number of those infected five days before. The findings herald a step forward that improves the possibility of managing the health crisis.

Implications of all the available evidence

We provide a method to estimate a leading indicator of needs, which thus far has been unavailable to health authorities and which should allow them to plan for the resources required. Furthermore, it is a versatile and simple method that is applicable to any country, state, region, city or hospital area as well as to any type of health care need associated with the COVID-19 pandemic and similar future ones.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.03.20051821: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.04.03.20051821v1 on medRxiv