2021
DOI: 10.48550/arxiv.2102.11249
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting

Iwona Hawryluk,
Henrique Hoeltgebaum,
Swapnil Mishra
et al.

Abstract: Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing, with prominent examples in a wide range of fields. An important example of this problem is the nowcasting of COVID-19 mortality: given a stream of reported counts of daily deaths, can we correct for the delays in reporting to paint an accurate picture of the present, with uncertainty? Without this correction, raw data will often mislead by suggesting an improving situation. We present a flex… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2021
2021
2021
2021

Publication Types

Select...
1

Relationship

1
0

Authors

Journals

citations
Cited by 1 publication
(1 citation statement)
references
References 9 publications
0
1
0
Order By: Relevance
“…Epidemiological model details and sensitivity analyses (tables S5 to S10) can be found in the supplementary materials. The model is fitted to both COVID-19 mortality data [with a correction for systematic reporting delays (44,45)] and the estimated increase through time in the proportion of infections due to P.1 derived from genomic data (table S4). We assumed that within-category immunity wanes over time (50% wane within a year, although sensitivity analyses varying the rapidity of waning are presented in table S7) and that cross-immunity (the degree to which previous infection with a virus belonging to one category protects against subsequent infection with the other) is symmetric between categories.…”
Section: Mathematical Modeling Of Lineage P1 Epidemiological Characte...mentioning
confidence: 99%
“…Epidemiological model details and sensitivity analyses (tables S5 to S10) can be found in the supplementary materials. The model is fitted to both COVID-19 mortality data [with a correction for systematic reporting delays (44,45)] and the estimated increase through time in the proportion of infections due to P.1 derived from genomic data (table S4). We assumed that within-category immunity wanes over time (50% wane within a year, although sensitivity analyses varying the rapidity of waning are presented in table S7) and that cross-immunity (the degree to which previous infection with a virus belonging to one category protects against subsequent infection with the other) is symmetric between categories.…”
Section: Mathematical Modeling Of Lineage P1 Epidemiological Characte...mentioning
confidence: 99%