Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data

Susswein, Zachary; Rest, Eva C; Bansal, Shweta

doi:10.7554/elife.80466

Cited by 14 publications

(13 citation statements)

References 47 publications

(56 reference statements)

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“…Since the onset of the COVID-19 pandemic, mobile phone data has played a crucial role in addressing the public health crisis [27, 38, 35, 32, 20, 26, 34, 22, 43, 40, 29, 21, 19, 18]. During this period, numerous network operators and private enterprises have made considerable efforts to swiftly share their data within the confines of legal regulations.…”

Section: Discussionmentioning

confidence: 99%

Characterizing US spatial connectivity: implications for geographical disease dynamics and metapopulation modeling

Pullano,

Alvarez-Zuzek,

Colizza

et al. 2023

Preprint

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SummaryBackgroundHuman mobility is expected to be a critical factor in the geographic diffusion of infectious diseases, and this assumption led to the implementation of social distancing policies during the early fight against the COVID-19 emergency in the United States. Yet, because of substantial data gaps in the past, what still eludes our understanding are the following questions: 1) How does mobility contribute to the spread of infection within the United States at local, regional, and national scales? 2) How do seasonality and shifts in behavior affect mobility over time? 3) At what geographic level is mobility homogeneous across the United States? Addressing these questions is critical to developing accurate transmission models, predicting the spatial propagation of disease across scales, and understanding the optimal geographical and temporal scale for the implementation of control policies.MethodsWe address this problem using high-resolution human mobility data measured via mobile app usage. We compute the daily coupling network between US counties, and we integrate our mobility data into a spatially explicit transmission model to reproduce the national invasion of the first wave of SARS-CoV-2 in the US.FindingsTemporally, we observe that intercounty connectivity is largely seasonal and was unperturbed by mobility restrictions during the early phase of the COVID-19 pandemic. Spatially, we identify 104 geographic clusters of US counties that are highly connected by mobility within the cluster and more sparsely connected to counties outside the cluster. These clusters are stable across time and highly overlap with US state boundaries. Together, these results suggest that intercounty connectivity in the US is relatively static across time and is homogeneous at the sub-state level. We also find that while having access to county-level, daily mobility data best captures the spatial invasion of disease, static mobility data aggregated to the scale of our mobility data-based clusters also performs well in capturing spatial diffusion of infection.InterpretationOur work demonstrates that intercounty mobility was negligibly affected outside the lockdown period of Spring 2020, explaining the broad spatial distribution of COVID-19 outbreaks in the US during the early phase of the pandemic. Such geographically dispersed outbreaks place a significant strain on national public health resources and necessitate complex metapopulation modeling approaches for predicting disease dynamics and control design. We thus inform the design of such metapopulation models to balance high disease predictability with low data requirements.

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Section: Discussionmentioning

confidence: 99%

Characterizing US spatial connectivity: implications for geographical disease dynamics and metapopulation modeling

Pullano,

Alvarez-Zuzek,

Colizza

et al. 2023

Preprint

Self Cite

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“…A number of studies have suggested that SARS-CoV-2 transmission is seasonally varying, modulated by environmental variables and environmentally-mediated social behavior [ 24 – 27 ]. We integrated seasonality in the regional transmissibility (see Model subsection) based on estimates provided in Ref.…”

Section: Methodsmentioning

confidence: 99%

The impact of spatial connectivity on NPIs effectiveness

Sabbatini,

Pullano,

Di Domenico

et al. 2024

BMC Infect Dis

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Background France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 – the second since the start of the pandemic – to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness. Methods Focusing on September 2020–June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions. Results The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions. Conclusions Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness.

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“…A number of studies have suggested that SARS-CoV-2 transmission is seasonally varying, modulated by environmental variables and environmentally-mediated social behavior [25][26][27][28] . We integrated seasonality in the regional transmissibility (see Model subsection) based on estimates provided in Ref.…”

Section: Seasonalitymentioning

confidence: 99%

The impact of spatial connectivity on NPIs effectiveness

Sabbatini,

Pullano,

Di Domenico

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Background. France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 - the second since the start of the pandemic - to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness. Methods. Focusing on September 2020 - June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions. Results. The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions. Conclusions. Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness. Keywords. COVID-19, NPIs, modeling, curfew, restrictions.

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Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data

Cited by 14 publications

References 47 publications

Characterizing US spatial connectivity: implications for geographical disease dynamics and metapopulation modeling

Characterizing US spatial connectivity: implications for geographical disease dynamics and metapopulation modeling

The impact of spatial connectivity on NPIs effectiveness

The impact of spatial connectivity on NPIs effectiveness

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