Artificial intelligence for modelling infectious disease epidemics

Nature, Год журнала: 2025, Номер 638(8051), С. 623 - 635

Опубликована: Фев. 19, 2025

Язык: Английский

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Mechanistic models for West Nile virus transmission: a systematic review of features, aims and parametrization

Proceedings of the Royal Society B Biological Sciences, Год журнала: 2024, Номер 291(2018)

Опубликована: Март 13, 2024

Mathematical models within the Ross–Macdonald framework increasingly play a role in our understanding of vector-borne disease dynamics and as tools for assessing scenarios to respond emerging threats. These threats are typically characterized by high degree heterogeneity, introducing range possible complexities challenges maintain link with empirical evidence. We systematically identified analysed total 77 published papers presenting compartmental West Nile virus (WNV) that use parameter values derived from studies. Using set 15 criteria, we measured dissimilarity compared framework. also retrieved purpose type traced sources their parameters. Our review highlights increasing refinements WNV models. Models prediction included highest number refinements. found uneven distributions evidence values. several parametrizing such complex For parameters common most models, synthesize ranges. The study potential improve quality applicability policy establishing closer collaboration between mathematical modelling work.

Язык: Английский

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Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report

Infectious Disease Modelling, Год журнала: 2024, Номер 9(2), С. 501 - 518

Опубликована: Фев. 23, 2024

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and lessons learnt from Covid-19 pandemic. This report summarizes rich discussions that occurred during workshop. The participants discussed multisource data integration highlighted benefits combining traditional surveillance with more novel sources like mobility data, social media, wastewater monitoring. Significant advancements were noted development predictive models, examples various countries showcasing use machine learning artificial intelligence detecting monitoring disease trends. role open collaboration between stakeholders was stressed, advocating for continuation such partnerships beyond A major gap identified absence common international framework sharing, which is crucial global pandemic preparedness. Overall, underscored need robust, adaptable frameworks different across sectors, as key elements enhancing future response

Язык: Английский

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Inference of epidemic dynamics in the COVID-19 era and beyond

Epidemics, Год журнала: 2024, Номер 48, С. 100784 - 100784

Опубликована: Июль 31, 2024

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats supporting decision making real-time. Motivated by unprecedented volume breadth of data generated during pandemic, we review modern opportunities for analysis to address questions emerge a major epidemic. Following broad chronology insights required - from understanding initial dynamics retrospective evaluation interventions, describe theoretical foundations each approach underlying intuition. Through series case studies, illustrate real life applications, discuss implications future work.

Язык: Английский

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The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy

Epidemics, Год журнала: 2023, Номер 46, С. 100738 - 100738

Опубликована: Дек. 29, 2023

Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of burden in US to guide pandemic planning decision-making context high uncertainty. This effort was born out an attempt coordinate, synthesize effectively use unprecedented amount predictive modeling that emerged throughout pandemic. Here we describe history this massive collective research effort, process convening maintaining open hub active over multiple years, provide a blueprint for future efforts. We detail generating 17 rounds scenarios at different stages pandemic, disseminating results public health community lay public. also highlight how SMH expanded generate influenza during 2022-23 season. identify key impacts on draw lessons improve collaborative efforts, scenario projections, interface between models policy.

Язык: Английский

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COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina

Epidemics, Год журнала: 2024, Номер 46, С. 100752 - 100752

Опубликована: Фев. 23, 2024

We document the evolution and use of stochastic agent-based COVID-19 SIMu-lation model (COVSIM) to study impact population behaviors public health policy on disease spread within age, race/ethnicity, urbanicity subpopulations in North Carolina. detail methodologies used complexities COVID-19, including multiple agent attributes (i.e., high-risk medical status), census tract-level interaction network, state behavior masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), variants. describe its uses outside Scenario Modeling Hub (CSMH), which has focused interplay nonpharmaceutical interventions, equitability vaccine distribution, supporting local county decision-makers This work led publications meetings with a variety stakeholders. When COVSIM joined CSMH January 2022, we found it was sustainable way support new challenges allowed group focus broader scientific questions. The informed adaptions our modeling approach, redesigning high-performance computing implementation.

Язык: Английский

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Best practices for estimating and reporting epidemiological delay distributions of infectious diseases

PLoS Computational Biology, Год журнала: 2024, Номер 20(10), С. e1012520 - e1012520

Опубликована: Окт. 28, 2024

Epidemiological delays are key quantities that inform public health policy and clinical practice. They used as inputs for mathematical statistical models, which in turn can guide control strategies. In recent work, we found censoring, right truncation, dynamical bias were rarely addressed correctly when estimating these biases large enough to have knock-on impacts across a number of use cases. Here, formulate checklist best practices reporting epidemiological delays. We also provide flowchart practitioners based on their data. Our examples focused the incubation period serial interval due importance outbreak response modeling, but our recommendations applicable other The recommendations, literature experience delay distributions during responses, help improve robustness utility reported estimates guidance evaluation downstream transmission models or analyses.

Язык: Английский

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flepiMoP: The evolution of a flexible infectious disease modeling pipeline during the COVID-19 pandemic

Epidemics, Год журнала: 2024, Номер 47, С. 100753 - 100753

Опубликована: Март 2, 2024

The COVID-19 pandemic led to an unprecedented demand for projections of disease burden and healthcare utilization under scenarios ranging from unmitigated spread strict social distancing policies. In response, members the Johns Hopkins Infectious Disease Dynamics Group developed flepiMoP (formerly called COVID Scenario Modeling Pipeline), a comprehensive open-source software pipeline designed creating simulating compartmental models infectious transmission inferring parameters through these models. framework has been used extensively produce short-term forecasts longer-term scenario at state county level in US, other countries various geographic scales, more recently seasonal influenza. this paper, we highlight how evolved throughout address changing epidemiological dynamics, new interventions, shifts policy-relevant model outputs. As reached mature state, provide detailed overview flepiMoP's key features remaining limitations, thereby distributing its documentation as flexible powerful tool researchers public health professionals rapidly build deploy large-scale complex any pathogen demographic setup.

Язык: Английский

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Machine learning for data-centric epidemic forecasting

Nature Machine Intelligence, Год журнала: 2024, Номер unknown

Опубликована: Сен. 27, 2024

Язык: Английский

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Projecting the future impact of emerging SARS-CoV-2 variants under uncertainty: Modeling the initial Omicron outbreak

Epidemics, Год журнала: 2024, Номер 47, С. 100759 - 100759

Опубликована: Март 2, 2024

Over the past several years, emergence of novel SARS-CoV-2 variants has led to multiple waves increased COVID-19 incidence. When Omicron variant emerged, there was considerable concern about its potential impact in winter 2021-2022 due fitness. However, also uncertainty regarding likely questions relative transmissibility, severity, and degree immune escape. We sought evaluate ability an agent-based model forecast incidence context this emerging pathogen variant. To project cases deaths Indiana, we calibrated our hospitalizations, deaths, test-positivity rates through November 2021, then projected April 2022 under four different scenarios that covered plausible ranges Omicron's Our initial projections from December 2021 March indicated a pessimistic scenario with high disease peak weekly Indiana would be larger than previous 2020. retrospective analyses indicate severity closer optimistic scenario, even though hospitalizations reached new peak, fewer occurred during peak. According results, rapid spread consistent combination higher transmissibility escape earlier variants. updated starting January accurately predicted mid-January decline rapidly over next months. The performance shows following variant, models can help quantify range outbreak magnitudes trajectories. Agent-based are particularly useful these because they efficiently track individual vaccination infection histories varying degrees cross-protection.

Язык: Английский

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