A nonparametric estimation for infectious diseases with latent period
Wensheng Wang,
Hui Zhou and
Anwei Zhu
Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 19, 6701-6718
Abstract:
Predicting the future contagion of infectious diseases depends on the ability to estimate the current number of cases of infection. In this paper, a full smoothing method is proposed to evaluate the number of daily new cases of infection during the epidemic period. Under mild regularity assumptions, we obtain the consistency and asymptotic normality of the resulting estimator. Both simulated examples and a real data example are used for illustration.
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1865402 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:51:y:2022:i:19:p:6701-6718
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2020.1865402
Access Statistics for this article
Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe
More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().