Modelling the Lodi, 2023 and Fano 2024, Italy dengue outbreaks: the effects of control strategies and environmental extremes

White, Steven M. ORCID: https://orcid.org/0000-0002-3192-9969; Tegar, Sandeep ORCID: https://orcid.org/0009-0003-2445-9860; Purse, Bethan V. ORCID: https://orcid.org/0000-0001-5140-2710; Cobbold, Christina A. ORCID: https://orcid.org/0000-0001-8814-7688; Brass, Dominic P. ORCID: https://orcid.org/0000-0002-4900-9124. 2025 Modelling the Lodi, 2023 and Fano 2024, Italy dengue outbreaks: the effects of control strategies and environmental extremes. Transboundary and Emerging Diseases, 2025 (1), 5542740. 10, pp. 10.1155/tbed/5542740

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Abstract/Summary

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 ( R 2 = 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 ( R 2 = 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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1155/tbed/5542740
UKCEH and CEH Sections/Science Areas:	Biodiversity and Land Use (2025-)
ISSN:	1865-1674
Additional Information:	Open Access paper - full text available via Official URL link.
Additional Keywords:	Aedes albopictus, arbovirus, control, dengue, mosquito-borne disease, outbreak, public health
NORA Subject Terms:	Health Meteorology and Climatology
Date made live:	02 Oct 2025 15:49 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/540335

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