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Article ; Online: S. Engebretsen responds.

Engebretsen, Solveig

Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke

2021 Volume 141, Issue 2

Title translation	S. Engebretsen svarer.
MeSH term(s)	Humans ; Models, Theoretical ; Pandemics
Language	Norwegian
Publishing date	2021-02-01
Publishing country	Norway
Document type	Journal Article ; Comment
ZDB-ID	603504-8
ISSN	0807-7096 ; 0029-2001
ISSN (online)	0807-7096
ISSN	0029-2001
DOI	10.4045/tidsskr.21.0021
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Article ; Online: Mathematical models during a pandemic.

Engebretsen, Solveig / Osnes, Andreas Nygård

Tidsskrift for den Norske laegeforening : tidsskrift for praktisk medicin, ny raekke

2020 Volume 140, Issue 18

Title translation	Matematiske modeller under en pandemi.
MeSH term(s)	Humans ; Influenza, Human/epidemiology ; Models, Theoretical ; Pandemics ; Quarantine
Language	Norwegian
Publishing date	2020-12-11
Publishing country	Norway
Document type	Journal Article
ZDB-ID	603504-8
ISSN	0807-7096 ; 0029-2001
ISSN (online)	0807-7096
ISSN	0029-2001
DOI	10.4045/tidsskr.20.0876
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Article ; Online: A compelling demonstration of why traditional statistical regression models cannot be used to identify risk factors from case data on infectious diseases: a simulation study.

Engebretsen, Solveig / Rø, Gunnar / de Blasio, Birgitte Freiesleben

BMC medical research methodology

2022 Volume 22, Issue 1, Page(s) 146

Abstract: Background: Regression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply regression ... ...

Abstract	Background: Regression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply regression models to investigate whether different risk factors can explain this overrepresentation among immigrants without considering dependence between the cases. Methods: We study the appropriateness of traditional statistical regression methods for identifying risk factors for infectious diseases, by a simulation study. We model infectious disease spread by a simple, population-structured version of an SIR (susceptible-infected-recovered)-model, which is one of the most famous and well-established models for infectious disease spread. The population is thus divided into different sub-groups. We vary the contact structure between the sub-groups of the population. We analyse the relation between individual-level risk of infection and group-level relative risk. We analyse whether Poisson regression estimators can capture the true, underlying parameters of transmission. We assess both the quantitative and qualitative accuracy of the estimated regression coefficients. Results: We illustrate that there is no clear relationship between differences in individual characteristics and group-level overrepresentation -small differences on the individual level can result in arbitrarily high overrepresentation. We demonstrate that individual risk of infection cannot be properly defined without simultaneous specification of the infection level of the population. We argue that the estimated regression coefficients are not interpretable and show that it is not possible to adjust for other variables by standard regression methods. Finally, we illustrate that regression models can result in the significance of variables unrelated to infection risk in the constructed simulation example (e.g. ethnicity), particularly when a large proportion of contacts is within the same group. Conclusions: Traditional regression models which are valid for modelling risk between groups for non-communicable diseases are not valid for infectious diseases. By applying such methods to identify risk factors of infectious diseases, one risks ending up with wrong conclusions. Output from such analyses should therefore be treated with great caution.
MeSH term(s)	COVID-19/epidemiology ; Communicable Diseases/epidemiology ; Humans ; Models, Statistical ; Regression Analysis ; Risk Factors
Language	English
Publishing date	2022-05-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-022-01565-1
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Article ; Online: Partially linear monotone methods with automatic variable selection and monotonicity direction discovery.

Engebretsen, Solveig / Glad, Ingrid K

Statistics in medicine

2020 Volume 39, Issue 25, Page(s) 3549–3568

Abstract: In many statistical regression and prediction problems, it is reasonable to assume monotone relationships between certain predictor variables and the outcome. Genomic effects on phenotypes are, for instance, often assumed to be monotone. However, in some ...

Abstract	In many statistical regression and prediction problems, it is reasonable to assume monotone relationships between certain predictor variables and the outcome. Genomic effects on phenotypes are, for instance, often assumed to be monotone. However, in some settings, it may be reasonable to assume a partially linear model, where some of the covariates can be assumed to have a linear effect. One example is a prediction model using both high-dimensional gene expression data, and low-dimensional clinical data, or when combining continuous and categorical covariates. We study methods for fitting the partially linear monotone model, where some covariates are assumed to have a linear effect on the response, and some are assumed to have a monotone (potentially nonlinear) effect. Most existing methods in the literature for fitting such models are subject to the limitation that they have to be provided the monotonicity directions a priori for the different monotone effects. We here present methods for fitting partially linear monotone models which perform both automatic variable selection, and monotonicity direction discovery. The proposed methods perform comparably to, or better than, existing methods, in terms of estimation, prediction, and variable selection performance, in simulation experiments in both classical and high-dimensional data settings.
MeSH term(s)	Algorithms ; Computer Simulation ; Linear Models ; Regression Analysis
Language	English
Publishing date	2020-08-26
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.8680
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Article ; Online: A compelling demonstration of why traditional statistical regression models cannot be used to identify risk factors from case data on infectious diseases

Solveig Engebretsen / Gunnar Rø / Birgitte Freiesleben de Blasio

BMC Medical Research Methodology, Vol 22, Iss 1, Pp 1-

a simulation study

2022 Volume 13

Abstract: Abstract Background Regression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply ... ...

Abstract	Abstract Background Regression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply regression models to investigate whether different risk factors can explain this overrepresentation among immigrants without considering dependence between the cases. Methods We study the appropriateness of traditional statistical regression methods for identifying risk factors for infectious diseases, by a simulation study. We model infectious disease spread by a simple, population-structured version of an SIR (susceptible-infected-recovered)-model, which is one of the most famous and well-established models for infectious disease spread. The population is thus divided into different sub-groups. We vary the contact structure between the sub-groups of the population. We analyse the relation between individual-level risk of infection and group-level relative risk. We analyse whether Poisson regression estimators can capture the true, underlying parameters of transmission. We assess both the quantitative and qualitative accuracy of the estimated regression coefficients. Results We illustrate that there is no clear relationship between differences in individual characteristics and group-level overrepresentation —small differences on the individual level can result in arbitrarily high overrepresentation. We demonstrate that individual risk of infection cannot be properly defined without simultaneous specification of the infection level of the population. We argue that the estimated regression coefficients are not interpretable and show that it is not possible to adjust for other variables by standard regression methods. Finally, we illustrate that regression models can result in the significance of variables unrelated to infection risk in the constructed simulation example (e.g. ethnicity), particularly when a large proportion of contacts is within the same group. ...
Keywords	Relative risk ; Communicable diseases ; Infectious diseases ; Regression models ; Overrepresentation ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Statistical predictions with glmnet.

Engebretsen, Solveig / Bohlin, Jon

Clinical epigenetics

2019 Volume 11, Issue 1, Page(s) 123

Abstract: Elastic net type regression methods have become very popular for prediction of certain outcomes in epigenome-wide association studies (EWAS). The methods considered accept biased coefficient estimates in return for lower variance thus obtaining improved ... ...

Abstract	Elastic net type regression methods have become very popular for prediction of certain outcomes in epigenome-wide association studies (EWAS). The methods considered accept biased coefficient estimates in return for lower variance thus obtaining improved prediction accuracy. We provide guidelines on how to obtain parsimonious models with low mean squared error and include easy to follow walk-through examples for each step in R.
MeSH term(s)	Algorithms ; Computational Biology ; DNA Methylation ; Epigenomics/methods ; Genome-Wide Association Study ; Humans ; Models, Statistical
Language	English
Publishing date	2019-08-23
Publishing country	Germany
Document type	Letter ; Research Support, Non-U.S. Gov't
ZDB-ID	2553921-8
ISSN	1868-7083 ; 1868-7075
ISSN (online)	1868-7083
ISSN	1868-7075
DOI	10.1186/s13148-019-0730-1
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Article ; Online: Effects of non-compulsory and mandatory COVID-19 interventions on travel distance and time away from home, Norway, 2021.

Kamineni, Meghana / Engø-Monsen, Kenth / Midtbø, Jørgen E / Forland, Frode / de Blasio, Birgitte Freiesleben / Frigessi, Arnoldo / Engebretsen, Solveig

Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin

2023 Volume 28, Issue 17

Abstract: BackgroundGiven the societal, economic and health costs of COVID-19 non-pharmaceutical interventions (NPI), it is important to assess their effects. Human mobility serves as a surrogate measure for human contacts and compliance with NPI. In Nordic ... ...

Abstract	BackgroundGiven the societal, economic and health costs of COVID-19 non-pharmaceutical interventions (NPI), it is important to assess their effects. Human mobility serves as a surrogate measure for human contacts and compliance with NPI. In Nordic countries, NPI have mostly been advised and sometimes made mandatory. It is unclear if making NPI mandatory further reduced mobility.AimWe investigated the effect of non-compulsory and follow-up mandatory measures in major cities and rural regions on human mobility in Norway. We identified NPI categories that most affected mobility.MethodsWe used mobile phone mobility data from the largest Norwegian operator. We analysed non-compulsory and mandatory measures with before-after and synthetic difference-in-differences approaches. By regression, we investigated the impact of different NPI on mobility.ResultsNationally and in less populated regions, time travelled, but not distance, decreased after follow-up mandatory measures. In urban areas, however, distance decreased after follow-up mandates, and the reduction exceeded the decrease after initial non-compulsory measures. Stricter metre rules, gyms reopening, and restaurants and shops reopening were significantly associated with changes in mobility.ConclusionOverall, distance travelled from home decreased after non-compulsory measures, and in urban areas, distance further decreased after follow-up mandates. Time travelled reduced more after mandates than after non-compulsory measures for all regions and interventions. Stricter distancing and reopening of gyms, restaurants and shops were associated with changes in mobility.
MeSH term(s)	Humans ; COVID-19/epidemiology ; COVID-19/prevention & control ; SARS-CoV-2 ; Travel ; Norway/epidemiology ; Scandinavian and Nordic Countries
Language	English
Publishing date	2023-04-27
Publishing country	Sweden
Document type	Journal Article
ZDB-ID	1338803-4
ISSN	1560-7917 ; 1025-496X
ISSN (online)	1560-7917
ISSN	1025-496X
DOI	10.2807/1560-7917.ES.2023.28.17.2200382
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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
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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
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Article ; Online: Whole exome sequencing of high-risk neuroblastoma identifies novel non-synonymous variants.

Przybyła, Weronika / Gjersvoll Paulsen, Kirsti Marie / Mishra, Charitra Kumar / Nygård, Ståle / Engebretsen, Solveig / Ruud, Ellen / Trøen, Gunhild / Beiske, Klaus / Baumbusch, Lars Oliver

PloS one

2022 Volume 17, Issue 8, Page(s) e0273280

Abstract: Neuroblastoma (NBL), one of the main death-causing cancers in children, is known for its remarkable genetic heterogeneity and varied patient outcome spanning from spontaneous regression to widespread disease. Specific copy number variations and single ... ...

Abstract	Neuroblastoma (NBL), one of the main death-causing cancers in children, is known for its remarkable genetic heterogeneity and varied patient outcome spanning from spontaneous regression to widespread disease. Specific copy number variations and single gene rearrangements have been proven to be associated with biological behavior and prognosis; however, there is still an unmet need to enlarge the existing armamentarium of prognostic and therapeutic targets. We performed whole exome sequencing (WES) of samples from 18 primary tumors and six relapse samples originating from 18 NBL patients. Our cohort consists of 16 high-risk, one intermediate, and one very low risk patient. The obtained results confirmed known mutational hotspots in ALK and revealed other non-synonymous variants of NBL-related genes (TP53, DMD, ROS, LMO3, PRUNE2, ERBB3, and PHOX2B) and of genes cardinal for other cancers (KRAS, PIK3CA, and FLT3). Beyond, GOSeq analysis determined genes involved in biological adhesion, neurological cell-cell adhesion, JNK cascade, and immune response of cell surface signaling pathways. We were able to identify novel coding variants present in more than one patient in nine biologically relevant genes for NBL, including TMEM14B, TTN, FLG, RHBG, SHROOM3, UTRN, HLA-DRB1, OR6C68, and XIRP2. Our results may provide novel information about genes and signaling pathways relevant for the pathogenesis and clinical course in high-risk NBL.
MeSH term(s)	Child ; DNA Copy Number Variations ; Humans ; Membrane Transport Proteins/genetics ; Mutation ; Neoplasm Recurrence, Local ; Neuroblastoma/genetics ; Neuroblastoma/metabolism ; Whole Exome Sequencing/methods
Chemical Substances	Membrane Transport Proteins ; RHBG protein, human
Language	English
Publishing date	2022-08-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0273280
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