Assessing the impact of data-driven limitations on tracing and forecasting the outbreak dynamics of COVID-19. (August 2021)

• Record Type: Journal Article
• Authors: Fiscon, Giulia; Salvadore, Francesco; Guarrasi, Valerio; Garbuglia, Anna Rosa; Paci, Paola
• Is Part Of: Computers in biology and medicine. Volume 135(2021)
• Publication Date: 2021-08
• Subjects: COVID-19 -- SARS-CoV-2 -- Epidemiology -- SIR-Type models -- Symptomatic and asymptomatic transmission -- Disease wave modelling; SIR (Susceptible-Infected-Recovered) A-SIR (Asymptomatic Susceptible-Infected-Recovered); Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104657
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: Mathematical modelling can provide valuable insights about COVID-19 dynamics proviso reliable epidemiological data. Poor quality of epidemiological data, especially due to the presence of asymptomatic people, affect the model accuracy. SIR-type model is able to explain the infection dynamic when the data quality is fairly good for a well model fitting. Increasing the model complexity is unnecessarily wasteful if not supported by an increased quality of the available data.