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Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study

Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study
Comparing lagged impacts of mobility changes and environmental factors on COVID-19 waves in rural and urban India: a Bayesian spatiotemporal modelling study

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

medRxiv
Cleary, Eimear
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Atuhaire, Fatumah
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Sorcihetta, Alessandro
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Ruktanonchai, Nick
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Ruktanonchai, Cori
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Cunningham, Alexander
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Pasqui, Massimiliano
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Schiavina, Marcello
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Melchiorri, Michele
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Bondarenko, Maksym
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Shepherd, Harry E.R.
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Padmadas, Sabu S.
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Wesolowski, Amy
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Cummings, Derek A.T.
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Tatem, Andrew J.
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Lai, Shengjie
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Cleary, Eimear
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Atuhaire, Fatumah
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Sorcihetta, Alessandro
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Ruktanonchai, Nick
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Ruktanonchai, Cori
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Cunningham, Alexander
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Pasqui, Massimiliano
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Schiavina, Marcello
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Melchiorri, Michele
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Bondarenko, Maksym
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Shepherd, Harry E.R.
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Padmadas, Sabu S.
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Wesolowski, Amy
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Cummings, Derek A.T.
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Tatem, Andrew J.
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Lai, Shengjie
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[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

Previous research in India has identified urbanisation, human mobility and population demographics as key variables associated with higher district level COVID-19 incidence. However, the spatiotemporal dynamics of mobility patterns in rural and urban areas in India, in conjunction with other drivers of COVID-19 transmission, have not been fully investigated. We explored travel networks within India during two pandemic waves using aggregated and anonymized weekly human movement datasets obtained from Google, and quantified changes in mobility before and during the pandemic compared with the mean baseline mobility for the 8-week time period at the beginning of 2020. We fit Bayesian spatiotemporal hierarchical models coupled with distributed lag non-linear models (DLNM) within the integrated nested Laplace approximate (INLA) package in R to examine the lag-response associations of drivers of COVID-19 transmission in urban, suburban, and rural districts in India during two pandemic waves in 2020-2021. Model results demonstrate that recovery of mobility to 99% that of pre-pandemic levels was associated with an increase in relative risk of COVID-19 transmission during the Delta wave of transmission. This increased mobility, coupled with reduced stringency in public intervention policy and the emergence of the Delta variant, were the main contributors to the high COVID-19 transmission peak in India in April 2021. During both pandemic waves in India, reduction in human mobility, higher stringency of interventions, and climate factors (temperature and precipitation) had 2-week lag-response impacts on the R t of COVID-19 transmission, with variations in drivers of COVID-19 transmission observed across urban, rural and suburban areas. With the increased likelihood of emergent novel infections and disease outbreaks under a changing global climate, providing a framework for understanding the lagged impact of spatiotemporal drivers of infection transmission will be crucial for informing interventions.

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2024.06.12.24308871v1.full - Author's Original
Available under License Creative Commons Attribution.
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Published date: 14 June 2024

Identifiers

Local EPrints ID: 492286
URI: http://eprints.soton.ac.uk/id/eprint/492286
PURE UUID: e199e40d-9ba4-4b87-b348-a84197aee092
ORCID for Eimear Cleary: ORCID iD orcid.org/0000-0003-2549-8565
ORCID for Harry E.R. Shepherd: ORCID iD orcid.org/0000-0002-7077-3581
ORCID for Sabu S. Padmadas: ORCID iD orcid.org/0000-0002-6538-9374
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X
ORCID for Shengjie Lai: ORCID iD orcid.org/0000-0001-9781-8148

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Date deposited: 23 Jul 2024 17:04
Last modified: 24 Jul 2024 02:02

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Contributors

Author: Eimear Cleary ORCID iD
Author: Fatumah Atuhaire
Author: Alessandro Sorcihetta
Author: Nick Ruktanonchai
Author: Cori Ruktanonchai
Author: Massimiliano Pasqui
Author: Marcello Schiavina
Author: Michele Melchiorri
Author: Maksym Bondarenko
Author: Amy Wesolowski
Author: Derek A.T. Cummings
Author: Andrew J. Tatem ORCID iD
Author: Shengjie Lai ORCID iD

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