Standard-dose quadrivalent influenza vaccines (QIV) are designed to provide protection against all four influenza strains. Adjuvanted QIV (aQIV), indicated for individuals aged 65+ years, combines MF59^® adjuvant (an oil-in-water emulsion of squalene oil) with a standard dose of antigen, and is designed to produce stronger and longer immune response, especially in the elderly where immunosenescence reduces vaccine effectiveness. This study evaluated the cost-effectiveness of aQIV vs. egg-based standard-dose QIV (QIVe) in the elderly population, from the payer and societal perspective in Spain. A dynamic transmission model, which accounts for herd protection, was used to predict the number of medically attended infections in Spain. A decision tree structure was used to forecast influenza-related costs and benefits. Influenza-related probabilities of outpatient visit, hospitalization, work absenteeism, mortality, and associated utilities and costs were extracted from Spanish and European published literature. Relative vaccine effectiveness (rVE) was sourced from two different meta-analyses: the first meta-analysis was informed by laboratory-confirmed influenza studies only, resulting in a rVE = 34.6% (CI95% 2–66%) in favor of aQIV; the second meta-analysis included real world evidence influenza-related medical encounters outcomes, resulting in a rVE = 13.9% (CI95% 4.2–23.5%) in benefit of aQIV. All costs were expressed in 2021 euros. Results indicate that replacing QIVe with aQIV in the Spanish elderly population would prevent on average 43,664 influenza complicated cases, 1111 hospitalizations, and 569 deaths (with a rVE = 34.6%) or 19,104 influenza complicated cases, 486 hospitalizations, and 252 deaths (with a rVE = 13.9%). When the rVE of aQIV vs. QIVe is 34.6%, the incremental cost per quality adjusted life years (QALY) gained was €2240 from the payer; from the societal perspective, aQIV was cost saving compared with QIVe. If the rVE was 13.9%, the incremental cost per QALY was €6694 and €3936 from the payer and societal perspective, respectively. Sensitivity analyses validated the robustness of these findings. Results indicate that replacing QIVe with aQIV in the Spanish elderly population is a cost-effective strategy for the Spanish healthcare system.

As of July 14 ^th , COVID-19 has caused in Italy 34.984 deaths and 243.344 infection cases. Strict lockdown policies were necessary to contain the first outbreak wave and prevent the Italian healthcare system from being overwhelmed by patients requiring intensive care. After the progressive reopening, predicting how the epidemic situation will evolve is urgent and fundamental to control any future outbreak and prevent a second wave. We defined a time-varying optimization procedure to repeatedly calibrate the SIDARTHE model ¹ with data up to June 24 ^th . The computed parameter distributions allow us to robustly analyse how the epidemic situation evolved and outline possible future scenarios. Assuming a seasonal regime for COVID-19, we tested different lockdown policies. Our results suggest that an intermittent lockdown where six “open days” are allowed every other week may prevent a resurgent exponential outbreak and, at the same time, ease the societal burden of an extensive lockdown.

Recent technological and computational advances have enabled the collection of data at an unprecedented rate. On the one hand, the large amount of data suddenly available has opened up new opportunities for new data-driven research but, on the other hand, it has brought into light new obstacles and challenges related to storage and analysis limits. Here, we strengthen an upscaling approach borrowed from theoretical ecology that allows us to infer with small errors relevant patterns of a dataset in its entirety, although only a limited fraction of it has been analysed. In particular we show that, after reducing the input amount of information on the system under study, by applying our framework it is still possible to recover two statistical patterns of interest of the entire dataset. Tested against big ecological, human activity and genomics data, our framework was successful in the reconstruction of global statistics related to both the number of types and their abundances while starting from limited presence/absence information on small random samples of the datasets. These results pave the way for future applications of our procedure in different life science contexts, from social activities to natural ecosystems.