Tags:
Bayesianinference,
spatio-temporal,
epidemiology
Publications
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Fernandez, Liliana Vazquez; Palomares, Alfonso Diz-Lois; Rao-Skirbekk, Shilpa & Vicedo-Cabrera, Ana María
(2026).
Temperature-attributable mortality projections under scenarios of climate change for Oslo, Norway.
BMC Public Health.
26.
doi:
10.1186/s12889-025-25980-3.
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Background & Aim
Climate change and evolving of population dynamics, including ageing and changes in population size, are reshaping temperature-attributable mortality patterns. However, there is limited evidence on the prospective trajectory of heat- and cold-attributable mortality in Oslo, particularly under combined scenarios of global warming and population development. This study aims to project heat- and cold-attributable mortality in Oslo and assess the distinct contributions of each of these drivers, utilising high-resolution data.
Methods
We conducted a two-step approach with time series analysis with distributed lag non-linear models to estimate heat- and cold-attributable mortality relationship based on mean daily ambient temperature. Then, we performed a health impact assessment to compute the attributable mortality to heat and cold in the baseline period (2010–2019) and by the end of the century using regional population projections, mortality rates and projected daily temperature under two climate scenarios: RCP4.5 and RCP8.5.
Results
For the RCP4.5/Medium Road scenario, the attributable mortality fractions for heat and cold are projected to increase over time, with values ranging from 9.05% (95%CI: 1.55–15.90) in 2010–2019 to 9.78% (95% CI: 2.96–15.86) in 2090–2099. Cold mortality consistently dominates the total, while heat mortality remains relatively low, starting at 1.80% (95%CI: 0.10–3.68) at baseline and increasing slightly to 3.12% (95%CI: 0.34–5.94) by the end of the century. In contrast, the RCP8.5/Strong Ageing scenario shows a more pronounced rise, with temperature-attributable mortality increasing from 9.07% (95%CI: 1.53–15.89) in 2010–2019 to 11.86% (95%CI: 4.29–18.53) in 2090–2099. In this scenario, heat mortality contributes significantly more, rising from 1.83% (95%CI: 0.12–3.85) in 2010–2019 to 5.99% (95%CI: 1.23–10.35) by 2090–2099, reflecting the greater climate and population impact under RCP8.5 and the Strong Ageing pathway.
Conclusions
Our findings highlight the need for climate and population dynamics to be considered in public health policies. Tailored interventions are crucial to mitigate heat and cold-attributable mortality, particularly for vulnerable populations. Future research should integrate socio-economic factors and explore adaptation strategies to refine mortality projections and inform policy.
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Palomares, Alfonso Diz-Lois; Blasio, Birgitte Freiesleben De; Chan, Louis Yat Hin; Ruscio, Francesco Di & Midtbø, Jørgen E.
(2026).
Combining an agent-based model with Gaussian process emulation to model the emergence of the SARS-CoV-2 Omicron variant in Norway.
Epidemics.
ISSN 1755-4365.
54.
doi:
10.1016/j.epidem.2026.100895.
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Chan, Yat Hin; Rø, Gunnar; Midtbø, Jørgen E.; Ruscio, Francesco Di; Watle, Sara Sofie Viksmoen & Juvet, Lene Kristine
[Show all 19 contributors for this article]
(2024).
Modeling geographic vaccination strategies for COVID-19 in Norway.
PLoS Computational Biology.
ISSN 1553-734X.
20(1).
doi:
10.1371/journal.pcbi.1011426.
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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.
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Zhang, Siqi; Breitner, Susanne; Stafoggia, Massimo; Donato, Francesca de'; Samoli, Evangelia & Zafeiratou, Sofia
[Show all 15 contributors for this article]
(2024).
Effect modification of air pollution on the association between heat and mortality in five European countries.
Environmental Research.
ISSN 0013-9351.
263(1).
doi:
10.1016/j.envres.2024.120023.
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Evidence suggests that air pollution modifies the association between heat and mortality. However, most studies have been conducted in cities without rural data. This time-series study examined potential effect modification of particulate matter (PM) and ozone (O3) on heat-related mortality using small-area data from five European countries, and explored the influence of area characteristics.
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Fernandez, Liliana Vazquez; Palomares, Alfonso Diz-Lois; Vicedo-Cabrera, Ana Maria; Gasparrini, Antonio; Blasio, Birgitte Freiesleben De & Ruscio, Francesco Di
[Show all 9 contributors for this article]
(2024).
Municipality assessment of temperature-related mortality risks in Norway.
Environmental Research.
ISSN 0013-9351.
266.
doi:
10.1016/j.envres.2024.120614.
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Background & aim Understanding local vulnerability to heat and cold is crucial for public health planning, yet few studies have provided a nationwide analysis of temperature-related mortality across diverse communities. This study analyses the association between ambient air temperature and non-accidental mortality across mainland Norway, using a constrained hierarchical clustering algorithm to group municipalities with similar geographic, environmental, socioeconomic, and demographic patterns. Methods This study analysed the association between ambient air temperature and non-accidental mortality across 356 Norwegian municipalities, using daily data from 1996 to 2018. We applied a case time series design with distributed lag non-linear models. A downscaling procedure assessed the effect of 21 vulnerability factors on temperature-related mortality risks, using Principal Components Analysis to explore heterogeneity across clusters. Findings Cold temperatures contributed to an estimated 3879 deaths per year (95% CI 3718-4130), while heat was associated with 44 deaths annually (95%CI: 29–58). The highest heat-related mortality risk occurred in the South-East, and the highest cold-related risk in the Central-East. Greater heat-related mortality correlated with medium-to sparsely-populated areas, while higher education levels were linked to reduced vulnerability to both heat and cold. Interpretation By providing the first comprehensive assessment of temperature-related excess mortality and associated risk factors in Norway, our findings underscore the need for targeted, equitable health policies that integrate environmental and socioeconomic factors. These insights are essential to guide climate adaptation strategies, prioritising vulnerable rural communities and socioeconomically disadvantaged groups to mitigate future climate-related health impacts.
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Storvik, Geir Olve; Palomares, Alfonso Diz-Lois; Engebretsen, Solveig; Rø, Gunnar Øyvind Isaksson; Engø-Monsen, Kenth & Kristoffersen, Anja Bråthen
[Show all 8 contributors for this article]
(2023).
A sequential Monte Carlo approach to estimate a time-varying reproduction number in infectious disease models: the Covid-19 case.
Journal of the Royal Statistical Society, Series A (Statistics in Society).
ISSN 0964-1998.
186(4),
p. 616–632.
doi:
10.1093/jrsssa/qnad043.
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Abstract The Covid-19 pandemic has required most countries to implement complex sequences of non-pharmaceutical interventions, with the aim of controlling the transmission of the virus in the population. To be able to take rapid decisions, a detailed understanding of the current situation is necessary. Estimates of time-varying, instantaneous reproduction numbers represent a way to quantify the viral transmission in real time. They are often defined through a mathematical compartmental model of the epidemic, like a stochastic SEIR model, whose parameters must be estimated from multiple time series of epidemiological data. Because of very high dimensional parameter spaces (partly due to the stochasticity in the spread models) and incomplete and delayed data, inference is very challenging. We propose a state-space formalization of the model and a sequential Monte Carlo approach which allow to estimate a daily-varying reproduction number for the Covid-19 epidemic in Norway with sufficient precision, on the basis of daily hospitalization and positive test incidences. The method was in regular use in Norway during the pandemics and appears to be a powerful instrument for epidemic monitoring and management.
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Zhang, Siqi; Breitner, Susanne; Rai, Masna; Nikolaou, Nikolaos; Stafoggia, Massimo & Donato, Francesca de'
[Show all 16 contributors for this article]
(2023).
Assessment of short-term heat effects on cardiovascular mortality and vulnerability factors using small area data in Europe.
Environment International.
ISSN 0160-4120.
179.
doi:
10.1016/j.envint.2023.108154.
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Short-term associations between heat and cardiovascular disease (CVD) mortality have been examined mostly in large cities. However, different vulnerability and exposure levels may contribute to spatial heterogeneity. This study assessed heat effects on CVD mortality and potential vulnerability factors using data from three European countries, including urban and rural settings.
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Engebretsen, Solveig; Palomares, Alfonso Diz-Lois; Rø, Gunnar Øyvind Isaksson; Kristoffersen, Anja Bråthen; Lindstrøm, Jonas Christoffer & Engø-Monsen, Kenth
[Show all 15 contributors for this article]
(2023).
A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting.
PLoS Computational Biology.
ISSN 1553-734X.
19(1).
doi:
10.1371/journal.pcbi.1010860.
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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.
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Lindstrøm, Jonas Christoffer; Engebretsen, Solveig; Kristoffersen, Anja Bråthen; Rø, Gunnar Øyvind Isaksson; Palomares, Alfonso Diz-Lois & Engø-Monsen, Kenth
[Show all 14 contributors for this article]
(2021).
Increased transmissibility of the alpha SARS-CoV-2 variant: evidence from contact tracing data in Oslo, January to February 2021.
Infectious Diseases.
ISSN 2374-4235.
54(1),
p. 72–77.
doi:
10.1080/23744235.2021.1977382.
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Background: Information about the contagiousness of new SARS-CoV-2 variants, including the alpha lineage, and how they spread in various locations is essential. Country-specific estimates are needed because local interventions influence transmissibility.
Methods: We analysed contact tracing data from Oslo municipality, reported from January through February 2021, when the alpha lineage became predominant in Norway and estimated the relative transmissibility of the alpha lineage with the use of Poisson regression.
Results: Within households, we found an increase in the secondary attack rate by 60% (95% CI 20–114%) among cases infected with the alpha lineage compared to other variants; including all close contacts, the relative increase in the secondary attack rate was 24% (95% CI −6%−43%). There was a significantly higher risk of infecting household members in index cases aged 40–59 years who were infected with the alpha lineage; we found no association between transmission and household size. Overall, including all close contacts, we found that the reproduction number among cases with the alpha lineage was increased by 24% (95% CI 0%−52%), corresponding to an absolute increase of 0.19, compared to the group of index cases infected with other variants.
Conclusion: Our study suggests that households are the primary locations for rapid transmission of the new lineage alpha.
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Palomares, Alfonso Diz-Lois & Storvik, Geir Olve
(2026).
Parameter estimation in Conditional Sequential Monte Carlo algorithms through Particle Learning.
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In this work, we explore particle learning strategies for the joint estimation of static parameters and latent states within conditional sequential Monte Carlo (CSMC) algorithms. Building on this idea, we propose the p(parameter)-CSMC algorithm, which incorporates both parameter learning and ancestor sampling, leading to much better mixing properties compared to Gibbs sampling in settings where strong internal correlations may challenge effective exploration. We include an application to the estimation of weights in a branching process model against synthetic data and show that, in this setting, performance is dramatically enhanced, with substantially faster mixing and markedly reduced autocorrelation compared with standard particle Gibbs implementations.
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Engebretsen, Solveig; Thorarinsdottir, Thordis Linda; Palomares, Alfonso Diz-Lois; Storvik, Geir Olve; Frigessi, Arnoldo & Blasio, Birgitte Freiesleben De
(2025).
Contribution to the Discussion of 'Some statistical aspects of the Covid-19 response' by Wood et al.
Journal of the Royal Statistical Society, Series A (Statistics in Society).
ISSN 0964-1998.
189(1),
p. 71–72.
doi:
10.1093/jrsssa/qnaf080.
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Storvik, Geir Olve & Palomares, Alfonso Diz-Lois
(2023).
Sequential Monte Carlo for infectious disease models - the Covid-19 case.
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Published
Oct. 10, 2023 10:55 AM
- Last modified
Oct. 10, 2023 10:55 AM
