Correction to: Quality & Quantity https://doi.org/10.1007/s11135-024-01913-x

In this article few references was missing and should have been as below:

  • *Ansell, L., Dalla Valle, L.: A new data integration framework for Covid-19 social media information. Sci. Rep. 13(1), 6170 (2023)

  • *Barone, S., Chakhunashvili, A.: Pandemetrics: systematically assessing, monitoring, and controlling the evolution of a pandemic. Qual. Quant. 57, 1701–1723 (2022)

  • *Dowd, J., Andriano, L., Brazel, D. M., Rotondi, V., Block, P., Ding, X., Liu, Y., Mills, M.: Demographic science aids in understanding the spread and fatality rates of COVID-19. Proc. Natl. Acad. Sci. 117(18), 9696–9698 (2020)

  • *De Nicola, G., Schneble, M., Kauermann, G., Berger, U.: Regional now- and forecasting for data reported with delay: toward surveillance of Covid-19 infections. Adv. Stat. Anal. 106, 407–426 (2022)

  • *Pinheiro, C.A.R., Galati, M., Summerville, N., Lambrecht, M.: Using network analysis and machine learning to identify virus spread trends in COVID-19. Big Data Res. 25, 100242 (2021)

  • *Rahimi, I., Gandomi, A.H., Asteris, P.G., Chen, F.: Analysis and prediction of COVID-19 using SIR, SEIQR, and machine learning models: Australia, Italy, and UK cases. Information 12(3), 109 (2021)

  • *Rue, H., Riebler, A., Sørbye, S.H., Illian, J.B., Simpson, D.P., Lindgren, F.K.: Bayesian computing with INLA: a review. Annu. Rev. Stat. 4, 395–421 (2017)

  • *Yao, L., Dong, W., Wan, J., Howard, S., Li, M., Graff, J.: Graphical trajectory comparison to identify errors in data of COVID-19: a cross-country analysis. J. Pers. Med. 11(10), 955 (2021)

In this article, the reference citation in the paragraph “In the context of what ? have defined as”pandemetrics”, correctly reporting and monitoring the evolution of pandemics is indeed a hard task, especially when pandemics spread suddenly and little is known about the origin, mode of transmission, triggering factors, etc. as was the case with the COVID-19 pandemic. Regarding the years in which this pandemic developed (2020–2022, mainly), there was enormous scientific production on many aspects of it, especially in finding ways to slow down its spread. One of the aims of this production also concerned errors in the detection and timing of the main elements that characterize the evolution of a pandemic. For example, ? provide a stable tool for monitoring current infection levels in situations involving compulsory registration of sensitive data and when cases are reported with delay to a central register, with this delay deferring an up-to-date view of the state of things. ? explored the powerful interaction of demography and current age-specific mortality for COVID-19, which is also something we used in our analysis. The analyses on evaluating errors in monitoring and reporting cases, recoveries and deaths are important as a criterion to decide which containment measures are appropriate (see, for example, ? to understand how a cross-country comparison about errors in collecting and analysing data can help in improving pandemic forecasting)” was incorrectly displayed, The corrected paragraph with citations should read as below:

“In the context of what ? Barone and Chakhunashvili (2022) have defined as”pandemetrics”, correctly reporting and monitoring the evolution of pandemics is indeed a hard task, especially when pandemics spread suddenly and little is known about the origin, mode of transmission, triggering factors, etc. as was the case with the COVID-19 pandemic. Regarding the years in which this pandemic developed (2020–2022, mainly), there was enormous scientific production on many aspects of it, especially in finding ways to slow down its spread. One of the aims of this production also concerned errors in the detection and timing of the main elements that characterize the evolution of a pandemic. For example, ? De Nicola et al. (2022) provide a stable tool for monitoring current infection levels in situations involving compulsory registration of sensitive data and when cases are reported with delay to a central register, with this delay deferring an up-to-date view of the state of things. Dowd et al. (2020) explored the powerful interaction of demography and current age-specific mortality for COVID-19, which is also something we used in our analysis. The analyses on evaluating errors in monitoring and reporting cases, recoveries and deaths are important as a criterion to decide which containment measures are appropriate (see, for example, ? De Nicola et al. (2022) to understand how a cross-country comparison about errors in collecting and analysing data can help in improving pandemic forecasting).

The reference citations in the paragraph “The existing literature on model components is wide and articulated, especially in pandemic monitoring and prediction. For example, ? considered data on confirmed and recovered cases and deaths, the growth rate and the trend of COVID-19 infections in Australia, Italy and the UK. ? employed machine learning models to understand the correlation between population movements and virus spread and to predict possible new outbreaks. ? relaxed the Normality assumption for modelling COVID-19 data, but focused exclusively on temporal effects.” should have been read as below

“The existing literature on model components is wide and articulated, especially in pandemic monitoring and prediction. For example, Rahimi et al. (2021) considered data on confirmed and recovered cases and deaths, the growth rate and the trend of COVID-19 infections in Australia, Italy and the UK. Pinheiro et al. (2021) employed machine learning models to understand the correlation between population movements and virus spread and to predict possible new outbreaks. Ansell and Dalla Valle (2023) relaxed the Normality assumption for modelling COVID-19 data, but focused exclusively on temporal effects.”

The sentence starting “The INLA approach implements Laplace’s method approximation to solve nasty integrals by Taylor expansion around the mode with a nested version of it to get the posterior of interest in a computationally feasible way. Details are, for example, in (?).” should have been mentioned as “The INLA approach implements Laplace’s method approximation to solve nasty integrals by Taylor expansion around the mode with a nested version of it to get the sterior of interest in a computationally feasible way. Details are, for example, in Rue et al. (2017)”.

The original article has been corrected.