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Globally local: Hyper-local modeling for accurate forecast of COVID-19

IMPORTANCE: Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable c...

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Autores principales: Gopalakrishnan, Vishrawas, Pethe, Sayali, Kefayati, Sarah, Srinivasan, Raman, Hake, Paul, Deshpande, Ajay, Liu, Xuan, Hoang, Etter, Davila, Marbelly, Bianco, Simone, Kaufman, James H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Author(s). Published by Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518202/
https://www.ncbi.nlm.nih.gov/pubmed/34688165
http://dx.doi.org/10.1016/j.epidem.2021.100510
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author Gopalakrishnan, Vishrawas
Pethe, Sayali
Kefayati, Sarah
Srinivasan, Raman
Hake, Paul
Deshpande, Ajay
Liu, Xuan
Hoang, Etter
Davila, Marbelly
Bianco, Simone
Kaufman, James H.
author_facet Gopalakrishnan, Vishrawas
Pethe, Sayali
Kefayati, Sarah
Srinivasan, Raman
Hake, Paul
Deshpande, Ajay
Liu, Xuan
Hoang, Etter
Davila, Marbelly
Bianco, Simone
Kaufman, James H.
author_sort Gopalakrishnan, Vishrawas
collection PubMed
description IMPORTANCE: Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it. OBJECTIVE: This paper’s primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels. DESIGN: We use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. To understand the impact of this hypothesis, the output of this study was implemented in Tampa General Hospital (TGH) to provide resource demand forecast. RESULTS: The Mean Absolute Percentage Error (MAPE) in the forecast confirmed cases can be 30% higher for modeling at the state-level than aggregating model results at the scale of counties or clusters of counties. Similarly, modeling at a state-level and crafting policy decisions based on them may not be effective — county-level forecasts made by partitioning state-level forecasts are 3x worse for confirmed cases and 20x worse for deaths relative to the same model at the county level. By leveraging these results, TGH was able to accurately allocate clinical resources to tackle COVID-19 cases, continue elective surgical procedures largely uninterrupted and avoid costly construction of overflow capacity in the first two epidemic waves. CONCLUSIONS AND RELEVANCE: Accurate forecasting at the county level requires hyper-local modeling with county resolution. State-level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. Actionable decisions such as deciding whether to cancel planned surgeries or construct overflow capacity require models with local specificity.
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spelling pubmed-85182022021-10-15 Globally local: Hyper-local modeling for accurate forecast of COVID-19 Gopalakrishnan, Vishrawas Pethe, Sayali Kefayati, Sarah Srinivasan, Raman Hake, Paul Deshpande, Ajay Liu, Xuan Hoang, Etter Davila, Marbelly Bianco, Simone Kaufman, James H. Epidemics Article IMPORTANCE: Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it. OBJECTIVE: This paper’s primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels. DESIGN: We use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. To understand the impact of this hypothesis, the output of this study was implemented in Tampa General Hospital (TGH) to provide resource demand forecast. RESULTS: The Mean Absolute Percentage Error (MAPE) in the forecast confirmed cases can be 30% higher for modeling at the state-level than aggregating model results at the scale of counties or clusters of counties. Similarly, modeling at a state-level and crafting policy decisions based on them may not be effective — county-level forecasts made by partitioning state-level forecasts are 3x worse for confirmed cases and 20x worse for deaths relative to the same model at the county level. By leveraging these results, TGH was able to accurately allocate clinical resources to tackle COVID-19 cases, continue elective surgical procedures largely uninterrupted and avoid costly construction of overflow capacity in the first two epidemic waves. CONCLUSIONS AND RELEVANCE: Accurate forecasting at the county level requires hyper-local modeling with county resolution. State-level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. Actionable decisions such as deciding whether to cancel planned surgeries or construct overflow capacity require models with local specificity. The Author(s). Published by Elsevier B.V. 2021-12 2021-10-15 /pmc/articles/PMC8518202/ /pubmed/34688165 http://dx.doi.org/10.1016/j.epidem.2021.100510 Text en © 2021 The Author(s) Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Gopalakrishnan, Vishrawas
Pethe, Sayali
Kefayati, Sarah
Srinivasan, Raman
Hake, Paul
Deshpande, Ajay
Liu, Xuan
Hoang, Etter
Davila, Marbelly
Bianco, Simone
Kaufman, James H.
Globally local: Hyper-local modeling for accurate forecast of COVID-19
title Globally local: Hyper-local modeling for accurate forecast of COVID-19
title_full Globally local: Hyper-local modeling for accurate forecast of COVID-19
title_fullStr Globally local: Hyper-local modeling for accurate forecast of COVID-19
title_full_unstemmed Globally local: Hyper-local modeling for accurate forecast of COVID-19
title_short Globally local: Hyper-local modeling for accurate forecast of COVID-19
title_sort globally local: hyper-local modeling for accurate forecast of covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8518202/
https://www.ncbi.nlm.nih.gov/pubmed/34688165
http://dx.doi.org/10.1016/j.epidem.2021.100510
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