Assessing the impact of data-driven limitations on tracing and forecasting the outbreak dynamics of COVID-19

Giulia Fiscon, Francesco Salvadore, Valerio Guarrasi, Anna Rosa Garbuglia, Paola Paci

Research output: Contribution to journalArticlepeer-review

Abstract

The availability of the epidemiological data strongly affects the reliability of several mathematical models in tracing and forecasting COVID-19 pandemic, hampering a fair assessment of their relative performance. The marked difference between the lethality of the virus when comparing the first and second waves is an evident sign of the poor reliability of the data, also related to the variability over time in the number of performed swabs. During the early epidemic stage, swabs were made only to patients with severe symptoms taken to hospital or intensive care unit. Thus, asymptomatic people, not seeking medical assistance, remained undetected. Conversely, during the second wave of infection, total infectives included also a percentage of detected asymptomatic infectives, being tested due to close contacts with swab positives and thus registered by the health system. Here, we compared the outcomes of two SIR-type models (the standard SIR model and the A-SIR model that explicitly considers asymptomatic infectives) in reproducing the COVID-19 epidemic dynamic in Italy, Spain, Germany, and France during the first two infection waves, simulated separately. We found that the A-SIR model overcame the SIR model in simulating the first wave, whereas these discrepancies are reduced in simulating the second wave, when the accuracy of the epidemiological data is considerably higher. These results indicate that increasing the complexity of the model is useless and unnecessarily wasteful if not supported by an increased quality of the available data.

Original languageEnglish
Article number104657
Pages (from-to)1-14
Number of pages14
JournalComputers in Biology and Medicine
Volume135
DOIs
Publication statusPublished - Aug 2021

Keywords

  • COVID-19
  • Disease wave modelling
  • Epidemiology
  • SARS-CoV-2
  • SIR-Type models
  • Symptomatic and asymptomatic transmission

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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