Authors' reply to the discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11-Jun-21: 11-Jun-21

Yee Whye Teh; Bryn Elesedy; Bobby He; Michael Hutchinson; Sheheryar Zaidi; Avishkar Bhoopchand; Ulrich Paquet; Ne nad Tomasev; Jonathan Read; Peter Diggle

doi:10.1111/rssa.12976

Authors' reply to the discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11-Jun-21: 11-Jun-21

Yee Whye Teh
, Bryn Elesedy
, Bobby He
, Michael Hutchinson
, Sheheryar Zaidi
, Avishkar Bhoopchand
, Ulrich Paquet
, Ne nad Tomasev
, Jonathan Read
, Peter Diggle

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	S107-S109
Journal	Journal of the Royal Statistical Society Series a-Statistics in Society
Volume	185
Issue number	S1
DOIs	https://doi.org/10.1111/rssa.12976
Publication status	Published - 1 Nov 2022
Externally published	Yes

Access to Document

10.1111/rssa.12976Licence: CC BY

Cite this

Teh, Y. W., Elesedy, B., He, B., Hutchinson, M., Zaidi, S., Bhoopchand, A., Paquet, U., Tomasev, N. N., Read, J., & Diggle, P. (2022). Authors' reply to the discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11-Jun-21: 11-Jun-21. Journal of the Royal Statistical Society Series a-Statistics in Society, 185(S1), S107-S109. https://doi.org/10.1111/rssa.12976

Teh, Yee Whye ; Elesedy, Bryn ; He, Bobby et al. / Authors' reply to the discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11-Jun-21: 11-Jun-21. In: Journal of the Royal Statistical Society Series a-Statistics in Society. 2022 ; Vol. 185, No. S1. pp. S107-S109.

@article{33e67ca6cbd54b0f8dfb41a39f8af510,

title = "Authors' reply to the discussion of {\textquoteleft}Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities{\textquoteright} by Teh et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11-Jun-21: 11-Jun-21",

author = "Teh, \{Yee Whye\} and Bryn Elesedy and Bobby He and Michael Hutchinson and Sheheryar Zaidi and Avishkar Bhoopchand and Ulrich Paquet and Tomasev, \{Ne nad\} and Jonathan Read and Peter Diggle",

year = "2022",

month = nov,

day = "1",

doi = "10.1111/rssa.12976",

language = "English",

volume = "185",

pages = "S107--S109",

journal = "Journal of the Royal Statistical Society Series a-Statistics in Society",

number = "S1",

}

Teh, YW, Elesedy, B, He, B, Hutchinson, M, Zaidi, S, Bhoopchand, A, Paquet, U, Tomasev, NN, Read, J & Diggle, P 2022, 'Authors' reply to the discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11-Jun-21: 11-Jun-21', Journal of the Royal Statistical Society Series a-Statistics in Society, vol. 185, no. S1, pp. S107-S109. https://doi.org/10.1111/rssa.12976

Authors' reply to the discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11-Jun-21: 11-Jun-21. / Teh, Yee Whye; Elesedy, Bryn; He, Bobby et al.
In: Journal of the Royal Statistical Society Series a-Statistics in Society, Vol. 185, No. S1, 01.11.2022, p. S107-S109.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Authors' reply to the discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11-Jun-21: 11-Jun-21

AU - Teh, Yee Whye

AU - Elesedy, Bryn

AU - He, Bobby

AU - Hutchinson, Michael

AU - Zaidi, Sheheryar

AU - Bhoopchand, Avishkar

AU - Paquet, Ulrich

AU - Tomasev, Ne nad

AU - Read, Jonathan

AU - Diggle, Peter

PY - 2022/11/1

Y1 - 2022/11/1

U2 - 10.1111/rssa.12976

DO - 10.1111/rssa.12976

M3 - Article

VL - 185

SP - S107-S109

JO - Journal of the Royal Statistical Society Series a-Statistics in Society

JF - Journal of the Royal Statistical Society Series a-Statistics in Society

IS - S1

ER -

Teh YW, Elesedy B, He B, Hutchinson M, Zaidi S, Bhoopchand A et al. Authors' reply to the discussion of ‘Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities’ by Teh et al. in Session 2 of the Royal Statistical Society's Special Topic Meeting on COVID-19 transmission: 11-Jun-21: 11-Jun-21. Journal of the Royal Statistical Society Series a-Statistics in Society. 2022 Nov 1;185(S1):S107-S109. doi: 10.1111/rssa.12976