Challenges for modelling interventions for future pandemics

Epidemics, Год журнала: 2022, Номер 38, С. 100546 - 100546

Опубликована: Фев. 11, 2022

Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical pharmaceutical interventions for the control of epidemics that has been widely used during COVID-19 pandemic. In this paper, lessons learned from previous are highlight challenges future pandemic control. We consider availability use data, as well need correct parameterisation calibration model frameworks. discuss arise in describing distinguishing between interventions, within structures, allowing both host dynamics. also health economic political aspects interventions. Given diversity these challenges, broad variety interdisciplinary expertise is needed address them, combining mathematical knowledge with biological social insights, including economics communication skills. Addressing requires strong cross-disciplinary collaboration together close scientists policy makers.

Язык: Английский

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Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling

Epidemics, Год журнала: 2022, Номер 38, С. 100547 - 100547

Опубликована: Фев. 10, 2022

The estimation of parameters and model structure for informing infectious disease response has become a focal point the recent pandemic. However, it also highlighted plethora challenges remaining in fast robust extraction information using data models to help inform policy. In this paper, we identify discuss four broad paradigm relating modelling, namely Uncertainty Quantification framework, estimation, model-based inference prediction, expert judgement. We postulate priorities methodology facilitate preparation future pandemics.

Язык: Английский

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The challenges of data in future pandemics

Epidemics, Год журнала: 2022, Номер 40, С. 100612 - 100612

Опубликована: Июль 20, 2022

The use of data has been essential throughout the unfolding COVID-19 pandemic. We have needed it to populate our models, inform understanding, and shape responses disease. However, not always easy find access, varied in quality coverage, difficult reuse or repurpose. This paper reviews these other challenges recommends steps develop a ecosystem better able deal with future pandemics by supporting preparedness, prevention, detection response.

Язык: Английский

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Ten simple rules for good model-sharing practices

PLoS Computational Biology, Год журнала: 2025, Номер 21(1), С. e1012702 - e1012702

Опубликована: Янв. 10, 2025

Computational models are complex scientific constructs that have become essential for us to better understand the world. Many valuable peers within and beyond disciplinary boundaries. However, there no widely agreed-upon standards sharing models. This paper suggests 10 simple rules you both (i) ensure share in a way is at least “good enough,” (ii) enable others lead change towards model-sharing practices.

Язык: Английский

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Visual preferences for communicating modelling: a global analysis of COVID-19 policy and decision makers

Infectious Disease Modelling, Год журнала: 2025, Номер 10(3), С. 924 - 934

Опубликована: Апрель 23, 2025

Effective communication of modelling results to policy and decision makers has been a longstanding challenge in times crises. This takes many forms - visualisations, reports, presentations requires careful consideration ensure accurate maintenance the key scientific messages. Science-to-policy is further exacerbated when presenting fundamentally uncertain science such as infectious disease other types modelled evidence, something which understudied. Here we assess visualisation national COVID-19 13 different countries. We present synthesis recommendations on what aspects visuals, graphs, plots policymakers found be most helpful their response work. work serves first evidence base for developing guidelines translation into policy.

Язык: Английский

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Framework to guide the use of mathematical modelling in evidence-based policy decision-making

BMJ Open, Год журнала: 2025, Номер 15(4), С. e093645 - e093645

Опубликована: Апрель 1, 2025

Introduction The COVID-19 pandemic highlighted the significance of mathematical modelling in decision-making and limited capacity many low-income middle-income countries (LMICs). Thus, we studied how supported policy processes LMICs during (details a separate paper). We found that strong researcher–policymaker relationships co-creation facilitated knowledge translation, while scepticism, political pressures demand for quick outputs were barriers. also noted routine use modelled evidence requires sustained funding, building policy-facing modelling, robust data infrastructure dedicated translation mechanisms. These lessons helped us co-create framework roadmap improving public health decision-making. This communication paper describes components provides an implementation approach recommendations. include (1) (2) building, (3) infrastructure, (4) platforms (5) culture use. Key arguments Our integrates supply (modellers) (policymakers) sides contextual factors enable change. It is designed to be generic disease-agnostic any could support. not tool but guiding help build evidence-based target audience modellers policymakers, it other partners implementers Conclusion was created through engagements with policymakers researchers reflects their real-life experiences pandemic. Its purpose guide stakeholders, especially lower-resourced settings, capacity, prioritising efforts creating enabling environment using models as part base inform To validate its robustness impact, further work needed implement evaluate this diverse settings.

Язык: Английский

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Reflections On Epidemiological Modeling To Inform Policy During The COVID-19 Pandemic In Western Europe, 2020–23

Health Affairs, Год журнала: 2023, Номер 42(12), С. 1630 - 1636

Опубликована: Дек. 1, 2023

We reflect on epidemiological modeling conducted throughout the COVID-19 pandemic in Western Europe, specifically Belgium, France, Italy, Netherlands, Portugal, Switzerland, and United Kingdom. Europe was initially one of worst-hit regions during pandemic. European countries deployed a range policy responses to pandemic, which were often informed by mathematical, computational, statistical models. Models differed terms temporal scope, stage, interventions modeled, analytical form. This diversity modulated differences data availability quality, government interventions, societal responses, technical capacity. Many these models decisive making at key junctures, such as introduction vaccination emergence Alpha, Delta, Omicron variants. However, also faced intense criticism from press, other scientists, politicians around their accuracy appropriateness for decision making. Hence, evaluating success influence is an essential task. Modeling needs be supported infrastructure systems collect share data, model development, collaboration between groups, well two-way engagement modelers both makers public.

Язык: Английский

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Modelling: Understanding pandemics and how to control them

Epidemics, Год журнала: 2022, Номер 39, С. 100588 - 100588

Опубликована: Май 31, 2022

New disease challenges, societal demands and better or novel types of data, drive innovations in the structure, formulation analysis epidemic models. Innovations modelling can lead to new insights into processes use available yielding improved control stimulating collection data types. Here we identify key challenges for formulation, mathematical models pathogen transmission relevant current future pandemics.

Язык: Английский

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The trade‐off between deaths by infection and socio‐economic costs in the emerging infectious disease

Population Ecology, Год журнала: 2024, Номер 66(3), С. 158 - 170

Опубликована: Янв. 15, 2024

Abstract COVID‐19, caused by the novel coronavirus (SARS‐CoV‐2), is an emerging infectious disease (EID) with a relatively high infectivity and mortality rate. During state of emergency announced Japanese government in spring 2020, citizens were requested to stay home, number infected people was drastically reduced without legally‐binding lockdown. It well‐acknowledged that there trade‐off between maintaining economic activity preventing spread diseases. We aimed reduce total loss epidemic EID like COVID‐19 present study. focused on early late stages proposed framework resulted from damage infection cost for countermeasure. Mathematical models used estimate effect interventions deaths infection. The converted into monetary base different policies compared. In stage, we calculated when behavioral restrictions implemented. favorable intensity intervention depended basic reproduction number, fatality rate, impact. indicators showed it ratio maintain hospitalization system per determine which strategy should be adopted.

Язык: Английский

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How does policy modelling work in practice? A global analysis on the use of modelling in Covid-19 decision-making

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Авг. 13, 2024

Abstract This study examines the use and utility of infectious disease modelling in national international COVID-19 outbreak response. We investigate modelling-policy practices 13 countries, by carrying out expert interviews with a range modellers, decision makers, scientific advisors. The included countries span all six UN geographic regions. document experiences collate lessons learned during pandemic across four key themes: structures pathways to policy, communication, collaboration knowledge transfer, evaluation reflection. Full analysis interpretation breadth interview responses is presented, providing evidence for best practice on translation policy.

Язык: Английский

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