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

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.

Enhanced SARS-CoV-2 case prediction using public health data and machine learning models

“…However, the underlying assumptions can sometimes be overly simplified and may not reflect or make full use of the extensive and changing public health data that is available, ultimately resulting in unreliable estimates and forecasts. 10 , 11 …”

Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models

“…Even in the transition of the COVID-19 pandemic to an endemic state, the by now well-known routine of alternating infection peaks and troughs will demand close observation for the foreseeable future (Telenti et al 2021). However, analyzing and monitoring the state and development of the pandemic is complicated by the nature of the data on COVID-19 case numbers: a glimpse at Figure 1 reveals key characteristics include the strong persistence and nonstationarity of case numbers (Dolton 2021), as well as alternating regimes of increasing and decreasing infections caused by policy interventions, medical innovations, seasonal climate conditions, and the evolution of the virus itself (Doornik et al 2022;Fiscon et al 2021). In addition, infection dynamics are overlapped by a seasonal pattern of increasing volatility, generated by a varying number of tests over the days of the week (Bergman et al 2020), as well as by measurement errors (Hortaçsu et al 2021).…”

Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework

“…In the current context of increasing interest and resources toward computational research, the COVID-19 pandemic has generated great interest in mathematical modeling of infectious diseases aimed at projecting case counts and mortality, vaccination efficacy, efficacy of non-pharmaceutical interventions, and economical impacts 7 – 11 . Common approaches reported in the literature for modeling the spread of infectious disease include multi-compartmental models, contact network models, and agent-based models (also referred to as individual-based models) 12 – 14 . While multi-compartmental models have had great success in forecasting progression of contagion, they are based on the assumption of full and homogenous mixing among the population.…”