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Article ; Online: COVID-19 non-pharmaceutical intervention portfolio effectiveness and risk communication predominance

Louis Yat Hin Chan / Baoyin Yuan / Matteo Convertino

Scientific Reports, Vol 11, Iss 1, Pp 1-

2021 Volume 17

Abstract: Abstract Non-pharmaceutical interventions (NPIs) including resource allocation, risk communication, social distancing and travel restriction, are mainstream actions to control the spreading of Coronavirus disease 2019 (COVID-19) worldwide. Different ... ...

Abstract	Abstract Non-pharmaceutical interventions (NPIs) including resource allocation, risk communication, social distancing and travel restriction, are mainstream actions to control the spreading of Coronavirus disease 2019 (COVID-19) worldwide. Different countries implemented their own combinations of NPIs to prevent local epidemics and healthcare system overloaded. Portfolios, as temporal sets of NPIs have various systemic impacts on preventing cases in populations. Here, we developed a probabilistic modeling framework to evaluate the effectiveness of NPI portfolios at the macroscale. We employed a deconvolution method to back-calculate incidence of infections and estimate the effective reproduction number by using the package EpiEstim. We then evaluated the effectiveness of NPIs using ratios of the reproduction numbers and considered them individually and as a portfolio systemically. Based on estimates from Japan, we estimated time delays of symptomatic-to-confirmation and infection-to-confirmation as 7.4 and 11.4 days, respectively. These were used to correct surveillance data of other countries. Considering 50 countries, risk communication and returning to normal life were the most and least effective yielding the aggregated effectiveness of 0.11 and − 0.05 that correspond to a 22.4% and 12.2% reduction and increase in case growth. The latter is quantified by the change in reproduction number before and after intervention implementation. Countries with the optimal NPI portfolio are along an empirical Pareto frontier where mean and variance of effectiveness are maximized and minimized independently of incidence levels. Results indicate that implemented interventions, regardless of NPI portfolios, had distinct incidence reductions and a clear timing effect on infection dynamics measured by sequences of reproduction numbers. Overall, the successful suppression of the epidemic cannot work without the non-linear effect of NPI portfolios whose effectiveness optimality may relate to country-specific socio-environmental ...
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: COVID-19 non-pharmaceutical intervention portfolio effectiveness and risk communication predominance.

Chan, Louis Yat Hin / Yuan, Baoyin / Convertino, Matteo

Scientific reports

2021 Volume 11, Issue 1, Page(s) 10605

Abstract: Non-pharmaceutical interventions (NPIs) including resource allocation, risk communication, social distancing and travel restriction, are mainstream actions to control the spreading of Coronavirus disease 2019 (COVID-19) worldwide. Different countries ... ...

Abstract	Non-pharmaceutical interventions (NPIs) including resource allocation, risk communication, social distancing and travel restriction, are mainstream actions to control the spreading of Coronavirus disease 2019 (COVID-19) worldwide. Different countries implemented their own combinations of NPIs to prevent local epidemics and healthcare system overloaded. Portfolios, as temporal sets of NPIs have various systemic impacts on preventing cases in populations. Here, we developed a probabilistic modeling framework to evaluate the effectiveness of NPI portfolios at the macroscale. We employed a deconvolution method to back-calculate incidence of infections and estimate the effective reproduction number by using the package EpiEstim. We then evaluated the effectiveness of NPIs using ratios of the reproduction numbers and considered them individually and as a portfolio systemically. Based on estimates from Japan, we estimated time delays of symptomatic-to-confirmation and infection-to-confirmation as 7.4 and 11.4 days, respectively. These were used to correct surveillance data of other countries. Considering 50 countries, risk communication and returning to normal life were the most and least effective yielding the aggregated effectiveness of 0.11 and - 0.05 that correspond to a 22.4% and 12.2% reduction and increase in case growth. The latter is quantified by the change in reproduction number before and after intervention implementation. Countries with the optimal NPI portfolio are along an empirical Pareto frontier where mean and variance of effectiveness are maximized and minimized independently of incidence levels. Results indicate that implemented interventions, regardless of NPI portfolios, had distinct incidence reductions and a clear timing effect on infection dynamics measured by sequences of reproduction numbers. Overall, the successful suppression of the epidemic cannot work without the non-linear effect of NPI portfolios whose effectiveness optimality may relate to country-specific socio-environmental factors.
MeSH term(s)	Algorithms ; Basic Reproduction Number ; COVID-19/economics ; COVID-19/epidemiology ; COVID-19/prevention & control ; COVID-19/transmission ; Clinical Laboratory Techniques/methods ; Communicable Disease Control/methods ; Communication ; Computer Simulation ; Disease Transmission, Infectious/prevention & control ; Disease Transmission, Infectious/statistics & numerical data ; Humans ; Japan/epidemiology ; Models, Statistical ; SARS-CoV-2/isolation & purification
Language	English
Publishing date	2021-05-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-88309-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The influence of cross-border mobility on the COVID-19 epidemic in Nordic countries

Shubin, Mikhail / Brustad, Hilde Kjelgaard / Midtbo, Jorgen Eriksson / Gunther, Felix / Alessandretti, Laura / Ala-Nissila, Tapio / Scalia Tomba, Gianpaolo / Kivela, Mikko / Yat Hin Chan, Louis / Leskela, Lasse

medRxiv

Abstract: Restrictions of cross-border mobility are typically used to prevent an emerging disease from entering a country in order to slow down its spread. However, such interventions can come with a significant societal cost and should thus be based on careful ... ...

Abstract	Restrictions of cross-border mobility are typically used to prevent an emerging disease from entering a country in order to slow down its spread. However, such interventions can come with a significant societal cost and should thus be based on careful analysis and quantitative understanding on their effects. To this end, we model the influence of cross-border mobility on the spread of COVID-19 during 2020 in the neighbouring Nordic countries of Denmark, Finland, Norway and Sweden. We investigate the immediate impact of cross-border travel on disease spread and employ counterfactual scenarios to explore the cumulative effects of introducing additional infected individuals into a population during the ongoing epidemic. Our results indicate that border restrictions can significantly influence the course of an epidemic, but this impact is highly contingent on the prevailing epidemic status of the involved countries. In particular, there are several instances in which the contribution of cross-border movement was found to be negligible. Our findings underscore the critical importance of accurate data and models on both epidemic progression and travel patterns in informing decisions related to inter-country mobility restrictions.
Keywords	covid19
Language	English
Publishing date	2023-11-16
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.11.15.23297934
Database	COVID19

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Article ; Online: A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting.

Engebretsen, Solveig / Diz-Lois Palomares, Alfonso / Rø, Gunnar / Kristoffersen, Anja Bråthen / Lindstrøm, Jonas Christoffer / Engø-Monsen, Kenth / Kamineni, Meghana / Hin Chan, Louis Yat / Dale, Ørjan / Midtbø, Jørgen Eriksson / Stenerud, Kristian Lindalen / Di Ruscio, Francesco / White, Richard / Frigessi, Arnoldo / de Blasio, Birgitte Freiesleben

PLoS computational biology

2023 Volume 19, Issue 1, Page(s) e1010860

Abstract: The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal ... ...

Abstract	The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.
MeSH term(s)	Humans ; COVID-19/epidemiology ; COVID-19/prevention & control ; Bayes Theorem ; Pandemics ; Awareness ; Communicable Disease Control ; Forecasting
Language	English
Publishing date	2023-01-23
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1010860
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The influence of cross-border mobility on the COVID-19 epidemic in Nordic countries

Shubin, Mikhail / Brustad, Hilde Kjelgaard / Midtbø, Jørgen Eriksson / Günther, Felix / Alessandretti, Laura / Ala-Nissila, Tapio / Tomba, Gianpaolo Scalia / Kivelä, Mikko / Hin Chan, Louis Yat / Leskelä, Lasse

medRxiv

Abstract	Restrictions of cross-border mobility are typically used to prevent an emerging disease from entering a country in order to slow down its spread. However, such interventions can come with a significant societal cost and should thus be based on careful analysis and quantitative understanding on their effects. To this end, we model the influence of cross-border mobility on the spread of COVID-19 during 2020 in the neighbouring Nordic countries of Denmark, Finland, Norway and Sweden. We investigate the immediate impact of cross-border travel on disease spread and employ counterfactual scenarios to explore the cumulative effects of introducing additional infected individuals into a population during the ongoing epidemic. Our results indicate that border restrictions can significantly influence the course of an epidemic, but this impact is highly contingent on the prevailing epidemic status of the involved countries. In particular, there are several instances in which the contribution of cross-border movement was found to be negligible. Our findings underscore the critical importance of accurate data and models on both epidemic progression and travel patterns in informing decisions related to inter-country mobility restrictions.
Keywords	covid19
Language	English
Publishing date	2023-11-16
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.11.15.23297934
Database	COVID19

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Article ; Online: Modeling geographic vaccination strategies for COVID-19 in Norway.

Chan, Louis Yat Hin / Rø, Gunnar / Midtbø, Jørgen Eriksson / Di Ruscio, Francesco / Watle, Sara Sofie Viksmoen / Juvet, Lene Kristine / Littmann, Jasper / Aavitsland, Preben / Nygård, Karin Maria / Berg, Are Stuwitz / Bukholm, Geir / Kristoffersen, Anja Bråthen / Engø-Monsen, Kenth / Engebretsen, Solveig / Swanson, David / Palomares, Alfonso Diz-Lois / Lindstrøm, Jonas Christoffer / Frigessi, Arnoldo / de Blasio, Birgitte Freiesleben

PLoS computational biology

2024 Volume 20, Issue 1, Page(s) e1011426

Abstract: Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient ... ...

Abstract	Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.
MeSH term(s)	Humans ; Aged ; Pandemics/prevention & control ; COVID-19/epidemiology ; COVID-19/prevention & control ; Vaccination ; Norway/epidemiology ; Vaccines
Chemical Substances	Vaccines
Language	English
Publishing date	2024-01-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011426
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A real-time regional model for COVID-19

Solveig Engebretsen / Alfonso Diz-Lois Palomares / Gunnar Rø / Anja Bråthen Kristoffersen / Jonas Christoffer Lindstrøm / Kenth Engø-Monsen / Meghana Kamineni / Louis Yat Hin Chan / Ørjan Dale / Jørgen Eriksson Midtbø / Kristian Lindalen Stenerud / Francesco Di Ruscio / Richard White / Arnoldo Frigessi / Birgitte Freiesleben de Blasio

PLoS Computational Biology, Vol 19, Iss 1, p e

Probabilistic situational awareness and forecasting.

2023 Volume 1010860

Abstract	The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.
Keywords	Biology (General) ; QH301-705.5
Subject code	910
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Modeling geographic vaccination strategies for COVID-19 in Norway

Chan, Louis Yat Hin / Rø, Gunnar / Midtbø, Jørgen Eriksson / Di Ruscio, Francesco / Watle, Sara Sofie Viksmoen / Juvet, Lene Kristine / Littmann, Jasper / Aavitsland, Preben / Nygard, Karin Maria / Berg, Are Stuwitz / Bukholm, Geir / Kristoffersen, Anja Brathen / Engø-Monsen, Kenth / Engebretsen, Solveig / Swanson, David / Palomares, Alfonso Diz-Lois / Lindstrøm, Jonas Christoffer / Frigessi, Arnoldo / de Blasio, Birgitte Freiesleben

medRxiv

Abstract	Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.
Keywords	covid19
Language	English
Publishing date	2023-08-21
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.08.16.23294112
Database	COVID19

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Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Modeling geographic vaccination strategies for COVID-19 in Norway

medRxiv

Abstract	Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.
Keywords	covid19
Language	English
Publishing date	2023-08-21
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2023.08.16.23294112
Database	COVID19

Full text online

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Regional probabilistic situational awareness and forecasting of COVID-19

Engebretsen, Solveig / Palomares, Alfonso Diz-Lois / Rø, Gunnar Øvind Isaksson / Kristoffersen, Anja Bråthen / Lindstrøm, Jonas Christoffer / Engø-Monsen, Kenth / Chan, Louis Yat Hin / Dale, Ørjan / Midtbø, Jørgen Eriksson / Lindalen-Stenerud, Kristian / Di Ruscio, Francesco / White, Richard Aubrey / Frigessi, Arnoldo / de Blasio, Birgitte Freiesleben

medRxiv

Abstract: Mathematical models and statistical inference are fundamental for surveillance and control of the COVID-19 pandemic. Several aspects cause regional heterogeneity in disease spread. Individual behaviour, mobility, viral variants and transmission vary ... ...

Abstract	Mathematical models and statistical inference are fundamental for surveillance and control of the COVID-19 pandemic. Several aspects cause regional heterogeneity in disease spread. Individual behaviour, mobility, viral variants and transmission vary locally, temporally and with the season, and interventions and vaccination are often implemented regionally. Therefore, we developed a new regional changepoint stochastic SEIR metapopulation model. The model is informed by real-time mobility estimates from mobile phone data, laboratory-confirmed cases, and hospitalisation incidence. To estimate locally and time-varying transmissibility, case detection probabilities, and missed imported cases, we present a new sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, suitable for real-time surveillance. By comparing the reproduction number before and after lockdown, we find a national transmissibility reduction of 85% (95% CI 78%-89%). The estimated effect varied regionally and was larger for the most populated regions than in the national average.
Keywords	covid19
Language	English
Publishing date	2021-10-27
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2021.10.25.21265166
Database	COVID19

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