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Background: Emerging arboviral diseases in Europe pose a challenge due to difficulties in detecting and diagnosing cases during the initial circulation of the pathogen. Early outbreak detection enables public health authorities to take effective actions to reduce disease transmission. Quantification of the reporting delays of cases is vital to plan and assess surveillance and control strategies. Here, we provide estimates of reporting delays during an emerging arboviral outbreak and indications on how delays may have impacted onward transmission., Methodology/principal Findings: Using descriptive statistics and Kaplan-Meyer curves we analyzed case reporting delays (the period between the date of symptom onset and the date of notification to the public health authorities) during the 2017 Italian chikungunya outbreak. We further investigated the effect of outbreak detection on reporting delays by means of a Cox proportional hazard model. We estimated that the overall median reporting delay was 15.5 days, but this was reduced to 8 days after the notification of the first case. Cases with symptom onset after outbreak detection had about a 3.5 times higher reporting rate, however only 3.6% were notified within 24h from symptom onset. Remarkably, we found that 45.9% of identified cases developed symptoms before the detection of the outbreak., Conclusions/significance: These results suggest that efforts should be undertaken to improve the early detection and identification of arboviral cases, as well as the management of vector species to mitigate the impact of long reporting delays., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Manica et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Outbreaks of Aedes-borne diseases in temperate areas are not frequent, and limited in number of cases. We investigate the associations between habitat factors and temperature on individuals' risk of chikungunya (CHIKV) in a non-endemic area by spatially analyzing the data from the 2017 Italian outbreak., Methodology/principal Findings: We adopted a case-control study design to analyze the association between land-cover variables, temperature, and human population density with CHIKV cases. The observational unit was the area, at different scales, surrounding the residence of each CHIKV notified case. The statistical analysis was conducted considering the whole dataset and separately for the resort town of Anzio and the metropolitan city of Rome, which were the two main foci of the outbreak. In Rome, a higher probability for the occurrence of CHIKV cases is associated with lower temperature (OR = 0.72; 95% CI: 0.61-0.85) and with cells with higher vegetation coverage and human population density (OR = 1.03; 95% CI: 1.00-1.05). In Anzio, CHIKV case occurrence was positively associated with human population density (OR = 1.03; 95% CI: 1.00-1.06) but not with habitat factors or temperature., Conclusion/significance: Using temperature, human population density and vegetation coverage data as drives for CHIKV transmission, our estimates could be instrumental in assessing spatial heterogeneity in the risk of experiencing arboviral diseases in non-endemic temperate areas., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Solimini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)