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Biology subjects

Vairo, F.

Publications and source records attributed to Vairo, F..

2 recordsLinked to original sources

End-of-outbreak determination under seasonal transmission

At the apparent end of an infectious disease outbreak, policymakers must decide when to relax interventions, balancing epidemiological safety and economic efficiency. Here, we extend a renewal equation framework for estimating the risk of additional cases to scenarios with seasonal transmission, such as Aedes-borne virus transmission in temperate settings. In simulations, we demonstrate that the time of year is key to when it is safe to relax interventions under seasonal transmission: measures can be relaxed sooner if the last observed case occurs towards the end of the transmission season. We apply our framework to the 2017 chikungunya outbreak in Italy, integrating incidence data with estimates of temperature suitability for transmission, and further extend our approach to account for case under-reporting. Our model incorporating an end-of-season decline in transmission indicates a lower risk of additional cases late in the outbreak than a model based on incidence alone: assuming 60% case reporting, the seasonal model reaches a 1% risk threshold 22 days after the last observed case, compared with 39 days for the incidence-only model. Incorporating seasonality in model-based assessments of transmission risks at the end of an outbreak can therefore support more timely and reliable decision making.

ecology↗

COVID-19: Viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection.

BackgroundEpidemiological, virological and pathogenetic characteristics of SARS-CoV-2 infection are under evaluation. A better understanding of the pathophysiology associated with COVID-19 is crucial to improve treatment modalities and to develop effective prevention strategies. Transcriptomic and proteomic data on the host response against SARS-CoV-2 still have anecdotic character; currently available data from other coronavirus infections are therefore a key source of information. MethodsWe investigated selected molecular aspects of three human coronavirus (HCoV) infections, namely SARS-CoV, MERS-CoV and HCoV-229E, through a network based-approach. A functional analysis of HCoV-host interactome was carried out in order to provide a theoretic host-pathogen interaction model for HCoV infections and in order to translate the results in prediction for SARS-CoV-2 pathogenesis. The 3D model of S-glycoprotein of SARS-CoV-2 was compared to the structure of the corresponding SARS-CoV, HCoV-229E and MERS-CoV S-glycoprotein. SARS-CoV, MERS-CoV, HCoV-229E and the host interactome were inferred through published protein-protein interactions (PPI) as well as gene co-expression, triggered by HCoV S-glycoprotein in host cells. ResultsAlthough the amino acid sequences of the S-glycoprotein were found to be different between the various HCoV, the structures showed high similarity, but the best 3D structural overlap shared by SARS-CoV and SARS-CoV-2, consistent with the shared ACE2 predicted receptor. The host interactome, linked to the S-glycoprotein of SARS-CoV and MERS-CoV, mainly highlighted innate immunity pathway components, such as Toll Like receptors, cytokines and chemokines. ConclusionsIn this paper, we developed a network-based model with the aim to define molecular aspects of pathogenic phenotypes in HCoV infections. The resulting pattern may facilitate the process of structure-guided pharmaceutical and diagnostic research with the prospect to identify potential new biological targets.

bioinformatics↗