Subnational analysis of the initial phase of the COVID-19 epidemic in Brazil

TA Mellan; Henrique Hoeltgebaum; Swapnil Mishra; Charles Whittaker; Iwona Hawryluk; Axel Gandy; H Juliette T Unwin; Michaela AC Vollmer; Helen Coupland; Nuno Faria; Juan Vesga; Neil M Ferguson; Ricardo P Schnekenberg; Christl A Donelly; Harrison Zhu; Michael John Hutchinson; Oliver Ratmann; Melodie Monod; Seth Flaxman; Samir Bhatt

Subnational analysis of the initial phase of the COVID-19 epidemic in Brazil

TA Mellan, Henrique Hoeltgebaum, Swapnil Mishra, Charles Whittaker, Iwona Hawryluk, Axel Gandy, H Juliette T Unwin, Michaela AC Vollmer, Helen Coupland, Nuno Faria, Juan Vesga, Neil M Ferguson, Ricardo P Schnekenberg, Christl A Donelly, Harrison Zhu, Michael John Hutchinson, Oliver Ratmann, Melodie Monod, Seth Flaxman, Samir Bhatt

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

Since the beginning of the pandemic, Brazil has reported the second highest number of COVID-19 deaths in the world. Here we characterise the early transmission that seeded the country-wide spread of the disease, and assess attempts to attenuate the spread through implementing non-pharmaceutical interventions (NPIs) at subnational level. The analysis presented uses a Bayesian hierarchical approach to model transmission based on mortality data. The statistical model encodes a causal inferential bias for generic infectious disease transmission --- deaths are generated by infections which arise from earlier infections. As transmission is heterogeneous at subnational level, from differences such as the timing of seeding and hospital capacities, this is modelled by partially pooling parameters across geographic regions, using state-level mobility covariates for the reproduction number (
), and through inference of region-specific epidemiological parameters. We report extensive heterogeneity in the initial epidemic trajectory across Brazil underscoring the importance of sub-national analyses in understanding asynchronous state-level epidemics underlying the national spread and burden of COVID-19.

Original language	English
Title of host publication	2023 ICLR First Workshop on Machine Learning Global Health
Publisher	OpenReview
Publication status	Published - 2 Mar 2023

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Mellan, TA., Hoeltgebaum, H., Mishra, S., Whittaker, C., Hawryluk, I., Gandy, A., Unwin, H. J. T., Vollmer, M. AC., Coupland, H., Faria, N., Vesga, J., Ferguson, N. M., Schnekenberg, R. P., Donelly, C. A., Zhu, H., Hutchinson, M. J., Ratmann, O., Monod, M., Flaxman, S., & Bhatt, S. (2023). Subnational analysis of the initial phase of the COVID-19 epidemic in Brazil. In 2023 ICLR First Workshop on Machine Learning Global Health OpenReview. https://openreview.net/forum?id=EW0eeykOIrR

@inproceedings{e95ba920fb254542964d29c59607a4e5,

title = "Subnational analysis of the initial phase of the COVID-19 epidemic in Brazil",

abstract = "Since the beginning of the pandemic, Brazil has reported the second highest number of COVID-19 deaths in the world. Here we characterise the early transmission that seeded the country-wide spread of the disease, and assess attempts to attenuate the spread through implementing non-pharmaceutical interventions (NPIs) at subnational level. The analysis presented uses a Bayesian hierarchical approach to model transmission based on mortality data. The statistical model encodes a causal inferential bias for generic infectious disease transmission --- deaths are generated by infections which arise from earlier infections. As transmission is heterogeneous at subnational level, from differences such as the timing of seeding and hospital capacities, this is modelled by partially pooling parameters across geographic regions, using state-level mobility covariates for the reproduction number (), and through inference of region-specific epidemiological parameters. We report extensive heterogeneity in the initial epidemic trajectory across Brazil underscoring the importance of sub-national analyses in understanding asynchronous state-level epidemics underlying the national spread and burden of COVID-19.",

author = "TA Mellan and Henrique Hoeltgebaum and Swapnil Mishra and Charles Whittaker and Iwona Hawryluk and Axel Gandy and Unwin, {H Juliette T} and Vollmer, {Michaela AC} and Helen Coupland and Nuno Faria and Juan Vesga and Ferguson, {Neil M} and Schnekenberg, {Ricardo P} and Donelly, {Christl A} and Harrison Zhu and Hutchinson, {Michael John} and Oliver Ratmann and Melodie Monod and Seth Flaxman and Samir Bhatt",

year = "2023",

month = mar,

day = "2",

language = "English",

booktitle = "2023 ICLR First Workshop on Machine Learning Global Health",

publisher = "OpenReview",

}

Mellan, TA, Hoeltgebaum, H, Mishra, S, Whittaker, C, Hawryluk, I, Gandy, A, Unwin, HJT, Vollmer, MAC, Coupland, H, Faria, N, Vesga, J, Ferguson, NM, Schnekenberg, RP, Donelly, CA, Zhu, H, Hutchinson, MJ, Ratmann, O, Monod, M, Flaxman, S & Bhatt, S 2023, Subnational analysis of the initial phase of the COVID-19 epidemic in Brazil. in 2023 ICLR First Workshop on Machine Learning Global Health. OpenReview. <https://openreview.net/forum?id=EW0eeykOIrR>

TY - GEN

T1 - Subnational analysis of the initial phase of the COVID-19 epidemic in Brazil

AU - Mellan, TA

AU - Hoeltgebaum, Henrique

AU - Mishra, Swapnil

AU - Whittaker, Charles

AU - Hawryluk, Iwona

AU - Gandy, Axel

AU - Unwin, H Juliette T

AU - Vollmer, Michaela AC

AU - Coupland, Helen

AU - Faria, Nuno

AU - Vesga, Juan

AU - Ferguson, Neil M

AU - Schnekenberg, Ricardo P

AU - Donelly, Christl A

AU - Zhu, Harrison

AU - Hutchinson, Michael John

AU - Ratmann, Oliver

AU - Monod, Melodie

AU - Flaxman, Seth

AU - Bhatt, Samir

PY - 2023/3/2

Y1 - 2023/3/2

N2 - Since the beginning of the pandemic, Brazil has reported the second highest number of COVID-19 deaths in the world. Here we characterise the early transmission that seeded the country-wide spread of the disease, and assess attempts to attenuate the spread through implementing non-pharmaceutical interventions (NPIs) at subnational level. The analysis presented uses a Bayesian hierarchical approach to model transmission based on mortality data. The statistical model encodes a causal inferential bias for generic infectious disease transmission --- deaths are generated by infections which arise from earlier infections. As transmission is heterogeneous at subnational level, from differences such as the timing of seeding and hospital capacities, this is modelled by partially pooling parameters across geographic regions, using state-level mobility covariates for the reproduction number (), and through inference of region-specific epidemiological parameters. We report extensive heterogeneity in the initial epidemic trajectory across Brazil underscoring the importance of sub-national analyses in understanding asynchronous state-level epidemics underlying the national spread and burden of COVID-19.

AB - Since the beginning of the pandemic, Brazil has reported the second highest number of COVID-19 deaths in the world. Here we characterise the early transmission that seeded the country-wide spread of the disease, and assess attempts to attenuate the spread through implementing non-pharmaceutical interventions (NPIs) at subnational level. The analysis presented uses a Bayesian hierarchical approach to model transmission based on mortality data. The statistical model encodes a causal inferential bias for generic infectious disease transmission --- deaths are generated by infections which arise from earlier infections. As transmission is heterogeneous at subnational level, from differences such as the timing of seeding and hospital capacities, this is modelled by partially pooling parameters across geographic regions, using state-level mobility covariates for the reproduction number (), and through inference of region-specific epidemiological parameters. We report extensive heterogeneity in the initial epidemic trajectory across Brazil underscoring the importance of sub-national analyses in understanding asynchronous state-level epidemics underlying the national spread and burden of COVID-19.

M3 - Conference Contribution (Conference Proceeding)

BT - 2023 ICLR First Workshop on Machine Learning Global Health

PB - OpenReview

ER -

Subnational analysis of the initial phase of the COVID-19 epidemic in Brazil

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