Increasing situational awareness through nowcasting of the reproduction number
The time varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks, but delays between infection and reporting hinder its accurate estimation in real time. We propose a nowcasting method for improving the timeliness and accuracy of R estimates, based on comparisons of successive versions of surveillance databases. The method was validated against COVID-19 surveillance data collected in Italy over an 18-month period. Compared to traditional methods, the nowcasted reproduction number reduced the estimation delay from 13 to 8 days, while maintaining a better accuracy. Moreover, it allowed anticipating the detection of periods of epidemic growth by between 6 and 23 days. The method offers a simple and generally applicable tool to improve situational awareness during an epidemic outbreak, allowing for informed public health response planning..
Medienart: |
Preprint |
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Erscheinungsjahr: |
2023 |
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Erschienen: |
2023 |
Enthalten in: |
arXiv.org - (2023) vom: 26. Sept. Zur Gesamtaufnahme - year:2023 |
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Sprache: |
Englisch |
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Beteiligte Personen: |
Bizzotto, Andrea [VerfasserIn] |
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Links: |
Volltext [kostenfrei] |
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Themen: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
XAR040955419 |
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245 | 1 | 0 | |a Increasing situational awareness through nowcasting of the reproduction number |
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520 | |a The time varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks, but delays between infection and reporting hinder its accurate estimation in real time. We propose a nowcasting method for improving the timeliness and accuracy of R estimates, based on comparisons of successive versions of surveillance databases. The method was validated against COVID-19 surveillance data collected in Italy over an 18-month period. Compared to traditional methods, the nowcasted reproduction number reduced the estimation delay from 13 to 8 days, while maintaining a better accuracy. Moreover, it allowed anticipating the detection of periods of epidemic growth by between 6 and 23 days. The method offers a simple and generally applicable tool to improve situational awareness during an epidemic outbreak, allowing for informed public health response planning. | ||
650 | 4 | |a Quantitative Biology - Populations and Evolution |7 (dpeaa)DE-84 | |
650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
700 | 1 | |a Guzzetta, Giorgio |4 aut | |
700 | 1 | |a Marziano, Valentina |4 aut | |
700 | 1 | |a del Manso, Martina |4 aut | |
700 | 1 | |a Urdiales, Alberto Mateo |4 aut | |
700 | 1 | |a Petrone, Daniele |4 aut | |
700 | 1 | |a Cannone, Andrea |4 aut | |
700 | 1 | |a Sacco, Chiara |4 aut | |
700 | 1 | |a Poletti, Piero |4 aut | |
700 | 1 | |a Manica, Mattia |4 aut | |
700 | 1 | |a Zardini, Agnese |4 aut | |
700 | 1 | |a Trentini, Filippo |4 aut | |
700 | 1 | |a Fabiani, Massimo |4 aut | |
700 | 1 | |a Bella, Antonino |4 aut | |
700 | 1 | |a Riccardo, Flavia |4 aut | |
700 | 1 | |a Pezzotti, Patrizio |4 aut | |
700 | 1 | |a Ajelli, Marco |4 aut | |
700 | 1 | |a Merler, Stefano |4 aut | |
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