Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jul 20;40(16):3843-3864.
doi: 10.1002/sim.9004. Epub 2021 May 6.

Nowcasting COVID-19 incidence indicators during the Italian first outbreak

Pierfrancesco Alaimo Di Loro  1 , Fabio Divino  2 , Alessio Farcomeni  3 , Giovanna Jona Lasinio  1 , Gianfranco Lovison  4   5 , Antonello Maruotti  6   7 , Marco Mingione  1   8
Affiliations

Nowcasting COVID-19 incidence indicators during the Italian first outbreak

Pierfrancesco Alaimo Di Loro et al. Stat Med. .

Abstract

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external variables are provided. This ensures to accurately predict important characteristics of the epidemic (e.g., peak time and height), allowing for a better allocation of health resources over time. Parameter estimation is carried out in a maximum likelihood framework. All computational details required to reproduce the approach and replicate the results are provided.

Keywords: COVID-19; Richards' equation; SARS-CoV-2; growth curves.

PubMed Disclaimer

Figures

FIGURE 1
FIGURE 1
Time series of the Italian daily incidence indicators: daily positives, A and daily deceased, B
FIGURE 2
FIGURE 2
Time series of the Italian cumulative incidence indicators: cumulative positives, A and cumulative deceased, B
FIGURE 3
FIGURE 3
Time series of Italian daily prevalence indicators: current positive, A and ICU occupancy, B
FIGURE 4
FIGURE 4
Example of Richards' curve, A and derivative of the Richards' curve, B
FIGURE 5
FIGURE 5
Bootstrapped trajectories corresponding to the Huber Sandwich covariance matrix in the point of maximum for the model with baseline on daily positives
FIGURE 6
FIGURE 6
Observed (black dots) and fitted values (gray solid lines) with 95% confidence intervals (gray dashed lines) for the model with baseline on daily positives
FIGURE 7
FIGURE 7
Deviance residuals for the model with baseline on daily positives
FIGURE 8
FIGURE 8
Deviance residuals distribution aggregated by day of the week for daily positives
FIGURE 9
FIGURE 9
Observed (black dots) and fitted values (gray solid lines) with 95% confidence intervals (gray dashed lines) for the model with baseline and week‐day additive effect, estimated on the daily positives
FIGURE 10
FIGURE 10
Deviance residuals for the model with baseline and week‐day additive effect estimated on daily positives
FIGURE 11
FIGURE 11
Root mean squared prediction error for daily positives at different steps‐ahead
FIGURE 12
FIGURE 12
Estimation of the date of the peak for daily positives at different steps‐before
See this image and copyright information in PMC

References

    1. Flaxman S, Mishra S, Gandy A, et al. Estimating the number of infections and the impact of non‐pharmaceutical interventions on COVID‐19 in European countries: technical description update; 2020. arXiv:200411342.
    1. Peeri NC, Shrestha N, Rahman S, et al. The SARS, MERS and novel coronavirus (COVID‐19) epidemics, the newest and biggest global health threats: what lessons have we learned? Int J Epidemiol. 2020. - PMC - PubMed
    1. Hu B, Guo H, Zhou P, Shi ZL. Characteristics of SARS‐CoV‐2 and COVID‐19. Nat Rev Microbiol. 2020;1‐14. - PMC - PubMed
    1. Liang K. Mathematical model of infection kinetics and its analysis for COVID‐19, SARS and MERS. Infect Genet Evolut. 2020;82:104306. http://www.sciencedirect.com/science/article/pii/S15671348203%01374. - PMC - PubMed
    1. Diekmann O, Heesterbeek H, Britton T. Mathematical Tools for Understanding Infectious Disease Dynamics. Princeton University Press: Princeton, NJ; 2013.

LinkOut - more resources