In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 7 ( 2021-7-21), p. e0254826-
Abstract:
Mexico has experienced one of the highest COVID-19 mortality rates in the world. A delayed implementation of social distancing interventions in late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. In this study we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on the methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatiotemporal transmission patterns of SARS-CoV-2. The early estimates of the reproduction number for Mexico were estimated between R t ~1.1–1.3 from the genomic and case incidence data. Moreover, the mean estimate of R t has fluctuated around ~1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories based on epidemic growth rates. Our results show that the sequential mortality forecasts from the GLM and Richards model predict a downward trend in the number of deaths for all thirteen forecast periods for Mexico and Mexico City. However, the sub-epidemic and IHME models perform better predicting a more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21, 09/28-10/27, 09/28-10/27) for Mexico and Mexico City. Our findings indicate that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0254826
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10.1371/journal.pone.0254826.g001
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10.1371/journal.pone.0254826.g002
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10.1371/journal.pone.0254826.g003
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10.1371/journal.pone.0254826.g006
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10.1371/journal.pone.0254826.g007
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10.1371/journal.pone.0254826.g008
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10.1371/journal.pone.0254826.g009
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10.1371/journal.pone.0254826.g012
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10.1371/journal.pone.0254826.t001
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10.1371/journal.pone.0254826.t003
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10.1371/journal.pone.0254826.s001
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10.1371/journal.pone.0254826.s021
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10.1371/journal.pone.0254826.s022
DOI:
10.1371/journal.pone.0254826.s023
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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