Modelling the Lodi, 2023 and Fano 2024, Italy Dengue Outbreaks: The Effects of Control Strategies and Environmental Extremes

IF 3 2区农林科学 Q2 INFECTIOUS DISEASES

Transboundary and Emerging Diseases Pub Date : 2025-09-24 DOI:10.1155/tbed/5542740

Steven M. White, Sandeep Tegar, Bethan V. Purse, Christina A. Cobbold, Dominic P. Brass

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Abstract

Autochthonous cases of dengue in Europe are increasing. In 2023 (Lodi province) and 2024 (Fano, Pesaro and Urbino province), Italy saw the largest modern dengue outbreaks to date. Public health measures were adopted to mitigate transmission. The efficacy of these measures is unknown. We model the 2023 and 2024 dengue outbreaks to estimate the likely date of introduction of the primary case and efficacy of control measures, exploring explanations for the patterns of dengue cases for the two outbreaks. We apply a climate-driven mathematical model for Aedes albopictus and dengue virus transmission to the 2023 and 2024 outbreaks, comparing outputs to case data. The model accurately predicts the initial timeline of the Lodi dengue outbreak (R² = 0.83), with a peak in cases in late August 2023, after which the control efforts reduced transmission. The model also accurately predicts the Fano dengue outbreak (R² = 0.65), showing an increase in cases, peaking in mid-September 2024, after which there was an abrupt fall in cases. Our results suggest this can be attributed to substantial rainfall, and that public health measures may have latterly prevented a second peak in cases. The high predictive and explanatory ability of the model when applied to the Lodi and Fano outbreaks indicates that this framework may be of high value for public health decision-making for predicting the frequency and magnitude of future dengue outbreaks when coupled with real-time case data.

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模拟Lodi， 2023和Fano 2024，意大利登革热暴发：控制策略和极端环境的影响

欧洲的登革热本地病例正在增加。意大利在2023年（洛迪省）和2024年（法诺、佩萨罗和乌尔比诺省）发生了迄今为止规模最大的现代登革热疫情。采取了公共卫生措施以减轻传播。这些措施的效果尚不清楚。我们建立了2023年和2024年登革热暴发的模型，以估计主要病例引入的可能日期和控制措施的效果，探索两次暴发的登革热病例模式的解释。我们将白纹伊蚊和登革热病毒传播的气候驱动数学模型应用于2023年和2024年的疫情，并将产出与病例数据进行比较。该模型准确预测了洛迪登革热暴发的初始时间线（R2 = 0.83），病例高峰出现在2023年8月下旬，此后控制工作减少了传播。该模型还准确预测了法诺登革热暴发（R2 = 0.65），显示病例增加，在2024年9月中旬达到高峰，之后病例急剧下降。我们的研究结果表明，这可归因于大量降雨，公共卫生措施可能最近阻止了病例的第二次高峰。该模型应用于Lodi和Fano疫情时具有很高的预测和解释能力，这表明该框架与实时病例数据相结合，可能对公共卫生决策预测未来登革热疫情的频率和规模具有很高的价值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Transboundary and Emerging Diseases 农林科学-传染病学

CiteScore

8.90

自引率

9.30%

发文量

350

审稿时长

1 months

期刊介绍： Transboundary and Emerging Diseases brings together in one place the latest research on infectious diseases considered to hold the greatest economic threat to animals and humans worldwide. The journal provides a venue for global research on their diagnosis, prevention and management, and for papers on public health, pathogenesis, epidemiology, statistical modeling, diagnostics, biosecurity issues, genomics, vaccine development and rapid communication of new outbreaks. Papers should include timely research approaches using state-of-the-art technologies. The editors encourage papers adopting a science-based approach on socio-economic and environmental factors influencing the management of the bio-security threat posed by these diseases, including risk analysis and disease spread modeling. Preference will be given to communications focusing on novel science-based approaches to controlling transboundary and emerging diseases. The following topics are generally considered out-of-scope, but decisions are made on a case-by-case basis (for example, studies on cryptic wildlife populations, and those on potential species extinctions): Pathogen discovery: a common pathogen newly recognised in a specific country, or a new pathogen or genetic sequence for which there is little context about — or insights regarding — its emergence or spread. Prevalence estimation surveys and risk factor studies based on survey (rather than longitudinal) methodology, except when such studies are unique. Surveys of knowledge, attitudes and practices are within scope. Diagnostic test development if not accompanied by robust sensitivity and specificity estimation from field studies. Studies focused only on laboratory methods in which relevance to disease emergence and spread is not obvious or can not be inferred (“pure research” type studies). Narrative literature reviews which do not generate new knowledge. Systematic and scoping reviews, and meta-analyses are within scope.