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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Health Serv Res . 2026 Mar 11;26:546. doi: 10.1186/s12913-026-14279-7 Search in PMC Search in PubMed View in NLM Catalog Add to search Show available content in en pa The role of hospitals in monitoring the emergency: the experience of “Sentinel network” of the Italian Federation of Health Trusts Chiara Noviello Chiara Noviello 1 Dipartimento Interdisciplinare di Medicina, Università degli Studi di Bari Aldo Moro, Piazza Giulio Cesare 11, Bari, 70124 Italy Find articles by Chiara Noviello 1 , Francesco Paolo Bianchi Francesco Paolo Bianchi 2 Brindisi Health Trust, Brindisi, Italy Find articles by Francesco Paolo Bianchi 2 , Annamaria Lobifaro Annamaria Lobifaro 1 Dipartimento Interdisciplinare di Medicina, Università degli Studi di Bari Aldo Moro, Piazza Giulio Cesare 11, Bari, 70124 Italy Find articles by Annamaria Lobifaro 1 , Nicola Pinelli Nicola Pinelli 3 Federazione Italiana delle Aziende Sanitarie e Ospedaliere, Roma, Italy Find articles by Nicola Pinelli 3 , Giacomo Riformato Giacomo Riformato 1 Dipartimento Interdisciplinare di Medicina, Università degli Studi di Bari Aldo Moro, Piazza Giulio Cesare 11, Bari, 70124 Italy Find articles by Giacomo Riformato 1 , Silvio Tafuri Silvio Tafuri 1 Dipartimento Interdisciplinare di Medicina, Università degli Studi di Bari Aldo Moro, Piazza Giulio Cesare 11, Bari, 70124 Italy Find articles by Silvio Tafuri 1 ; FIASO Working Group** , Giovanni Migliore Giovanni Migliore 3 Federazione Italiana delle Aziende Sanitarie e Ospedaliere, Roma, Italy Find articles by Giovanni Migliore 3 , Pasquale Stefanizzi Pasquale Stefanizzi 1 Dipartimento Interdisciplinare di Medicina, Università degli Studi di Bari Aldo Moro, Piazza Giulio Cesare 11, Bari, 70124 Italy Find articles by Pasquale Stefanizzi 1, ✉ Author information Article notes Copyright and License information 1 Dipartimento Interdisciplinare di Medicina, Università degli Studi di Bari Aldo Moro, Piazza Giulio Cesare 11, Bari, 70124 Italy 2 Brindisi Health Trust, Brindisi, Italy 3 Federazione Italiana delle Aziende Sanitarie e Ospedaliere, Roma, Italy ✉ Corresponding author. Received 2025 May 23; Accepted 2026 Feb 25; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13094122 PMID: 41814333 Abstract The COVID-19 pandemic showed few weak points in health-system resilience and highlighted the need to strengthen surveillance capacities. In Italy, the Federation of Health Trusts (FIASO) established the Sentinel Hospital Network (SHNet) to monitor COVID-19 hospitalizations and support operational decision-making. A multicentre ecological study was conducted across 21 hospitals (November 2021–March 2023), collecting weekly data on admissions, vaccination status, and classification of cases as for or with COVID-19. Trends were compared with national surveillance from the Istituto Superiore di Sanità (ISS). SHNet recorded 48,117 admissions in 2022. Basically vaccination reduced the severe disease, with an 87% decrease in ICU admissions for COVID-19. Hospitalizations with COVID-19 showed limited variation. Strong correlation with ISS trends confirmed SHNet reliability. SHNet provided timely, clinically detailed data complementing national surveillance and improved understanding of hospital burden. Sentinel surveillance represents a preacious tool for preparedness, enabling rapid response, resource optimization, and resilience in future health emergencies. Keywords: Preparedness, Epidemic-monitoring, Sentinel surveillance systems, Hospital network, Healthcare Clinical trial number Not applicable. Background The SARS-CoV-2 pandemic has represented an extraordinary global challenge to healthcare systems [ 1 ] Essential healthcare services were disrupted across countries with different income levels and in all geographical regions. [ 2 ] In the early pandemic stages, the level and time of healthcare service disruption were underestimated by policymakers and healthcare leaders. The long-term impacts on non-COVID-19 health outcomes suggest a limited resilience within healthcare systems. [ 3 ]. Consequently, the COVID-19 pandemic time put in evidence limitations in learning lessons from previous events—such as the SARS outbreak in 2002, MERS in 2012, and Ebola in 2014–15—into effective preparedness measures [ 4 ]. Although the World Health Organization (WHO) declared the pandemic age over in March 2023 [ 5 ], more challenges persisted due to new virus mutations, unequal vaccine distribution, and, in some cases, vaccine hesitancy [ 6 ]. Following the end of the emergency phase, an opportunity arises to improve the assessment of a resilient healthcare system. A resilient healthcare system is based on capacity to prepare, respond and adapt to public health crises, continuing delivering high-quality essential services at any levels [ 7 ]. Governments and institutions must prioritize the creation of a resilient healthcare systems able to absorb shocks, learning from previous experiences, and consequently transforming its characteristics. Furthermore it needs to maintain good levels of the essential health services [ 8 ]. Resilient healthcare systems depend on strong surveillance, evidence-based decision-making, sustainable financing mechanism and a well-trained workforce to address healthcare challenges efficiently. Considering the emerging variants and future pathogens, it is essential to improve microbiological surveillance with better universal diagnostics, pan-vaccines, and broad-spectrum antimicrobials, in order to mitigate epidemic threats. Effective surveillance infrastructures, including early warning systems, are necessary for timely epidemic containment [ 9 – 11 ]. Proactive planning, helps to minimize service disruptions reducing morbidity and mortality during crises, therefore facilitating the integration of health service continuity and emergency response [ 12 ]. A common conclusion from studies is that the COVID-19 pandemic has highlighted the urgent need to establish healthcare facilities and ability to manage large numbers of patients requiring intensive medical care during emergencies [ 13 ]. The issue of epidemic monitoring has been extensively studied in scientific literature, with most research focusing on emergency department management or COVID-specific hospitals. Studies have explored hospital capacity challenges, addressing short-term adaptations to decrease strain and long-term strategies to improve healthcare infrastructure [ 13 , 14 ]. A sentinel surveillance system (SSS) can be useful as coordination system for emergency events, and it is a combination of passive and active monitoring system. SSS uses a selected centers network focusing on representative samples to promptly identify trends, to assess the impact of health interventions, and to detect emerging situations. The concept of sentinel surveillance systems is longstanding. In 1997, the Agency for Cooperation in International Health (ACIH) engaged scientists to establish sentinel sites for diseases surveillance [ 15 ]. In 1998, the Alumni for Global Surveillance Network (AGSnet) was founded, aiming to enhance global epidemiological monitoring under the World Health Organization (WHO) guidance. AGSnet’s findings demonstrated the system’s effectiveness in generating valuable insights into infectious diseases of international concern [ 16 ]. The SSS has been applied in numerous contexts, both before and after the pandemic, as empowerment of the assessment of disease behavior within populations. Based on this, the magnitude of the public health issue can be evaluated, and an effective strategy can be developed [ 17 ]. The COVID-19 pandemic underscored the importance to monitor hospitalized severe acute respiratory infections (SARI) to assess public health threats, epidemic trends, and healthcare system capacity. International organizations have also emphasized the importance of surveillance, establishing and sustaining an effective SARI surveillance system requiring substantial resources and a multidisciplinary approach to integrate different data sources [ 18 – 21 ]. For this purpose, Italian Ministry of Health established, since March 2020, a constant hospitalized patients surveillance affected by SARS-CoV-2 [ 22 ]. This system showed some limits such as missing or incomplete informations affecting the ability to distinguish hospitalized patients for COVID-19 associated SARI and those admitted for other conditions with concomitant SARS-CoV-2 infection. To enhance pandemic trend monitoring, the Italian Federation of Health Trusts (FIASO) organized a Sentinel Surveillance Network (SHNet), relying on voluntary participation from selected Italian hospitals. The SHNet constitution, designed to monitor COVID-19 admissions and to organize solutions for improved pandemic management, provides additional information for the streamlined hospital administrations, allowing an extraordinary events integration into standard operations without causing disruption. Sentinel hospitals, therefore, align with the framework outlined in the WHO’s 2007 report on health system resilience [ 23 ]. A hospital network, rather than isolated facilities, enhances resilience and emergency preparedness. This study examines the role of the FIASO SHNet in monitoring COVID-19 trends, the level of hospital resilience, and the ability to be back to routine care. The aim is to emphasize how continuous monitoring, the production of weekly reports and the classification of hospital admissions have effectively implemented the data given by the ISS. This classification had significant clinical and organizational implications and added analytical depth that is often missing from other surveillance reports. Methods Study design and setting This was a multicentre ecological observational study coordinated by the FIASO through the SHNet. The network was established in November 2021 and led by the National Reference Hospital for Infectious Diseases “L. Spallanzani” in Rome. Initially involving 11 hospitals, SHNet expanded to 21 institutions, including four paediatric centres, representing different geographical regions and hospital types: ASST Ospedali Civili di Brescia ASL Città di Torino Fondazione IRCCS Policlinico San Matteo Azienda Sanitaria Friuli Occidentale IRCCS Ospedale Policlinico San Martino in Genoa IRCCS University Hospital in Bologna ASL Area Vasta Sud Est Toscana University Hospital of Pisa Azienda Ospedaliera Santa Maria in Terni University Hospital Ospedali Riuniti in Ancona ASL Roma 6 Policlinico Tor Vergata ASL Teramo Azienda Ospedaliera Specialistica dei Colli Monaldi-Cotugno-CTO ASL Taranto University Hospital Consorziale Policlinico di Bari Ospedali Riuniti Azienda Ospedaliera Universitaria in Foggia ASM Matera, IRCCS Burlo Garofolo IRCCS Gaslini Ospedale Pediatrico Bambino Gesù Paediatric Hospital Santobono Pausilipon. Across participating hospitals, 220 ICU beds and 1963 medical ward beds were monitored. Data collection covered the period from November 2021 to March 2023. Data collection Seventy consecutive weekly data collection were performed. Each Tuesday at 08:00, all centers reported COVID-19 hospitalizations from general wards and ICUs. Data were systematically entered into a centralized database hosted by FIASO. The form had three sections: (1) for general ward patients, 2) ICU patients, and (3) pediatric patients. For each patient, hospitals recorded demographic characteristics, vaccination status (primary cycle, booster, second booster - for vulnerable, elderly, or immunocompromised individuals, as per Decree-Law No. 172/2021 and subsequent circulars [ 24 ]-), date of last vaccination, and comorbidities. For ICU cases, the underlying cause of admission (e.g., trauma, obstetric complications, cardiovascular, metabolic, respiratory, oncological, or neurological conditions) was also documented. Upon presentation to the emergency department, all patients underwent molecular screening tests. The use of a standardised testing method ensured consistency of results across all hospitals participating in the project. Case definitions The collected data aimed to distinguish between hospital admissions for and with COVID-19 . Case definitions were established by the coordinating centre following a literature review and expert consensus. To improve the operational clarity and reliability of this classification, explicit, standardized diagnostic criteria were applied. A predefined checklist guided case assignment: Admission for COVID-19 required: Positive SARS-CoV-2 molecular test Presence of respiratory symptoms compatible with viral pneumonia Laboratory and/or radiological evidence of lower respiratory tract involvement (e.g., interstitial infiltrates on chest imaging, hypoxemia, elevated inflammatory markers). Admission with COVID-19 was defined as: Positive SARS-CoV-2 molecular test No respiratory involvement or symptoms attributable to COVID-19, Hospitalization for related medical, surgical, obstetric, or orthopedic conditions. Borderline or complex cases (e.g., patients with chronic pulmonary disease, heart failure, or mixed symptomatology) were reviewed jointly by the clinical and infection control teams, applying the checklist to minimize subjective interpretation across centers. To ensure methodological transparency and reliability, standardized quality-control procedures were implemented across the 21 participating hospitals. Each site designated a local data manager responsible for verifying data completeness and consistency before weekly submission, including cross-checks against data from the previous week. The coordinating centre (INMI “L. Spallanzani”) conducted random audits, comparing reported data with source documentation. Any discrepancies, missing data, or inconsistencies were resolved collaboratively between local data managers and the central coordination team. Adherence to the study guidelines and data definitions was monitored through regular virtual coordination meetings and periodic re-training sessions for hospital staff involved in data entry. Measures and outcomes The primary measure was the number of hospital admissions, categorized by admission type (general vs. intensive care) and by reason for admission ( for vs. with COVID-19). Secondary measures included vaccination status, comorbidity profiles, and trends in mean age among admitted patients, stratified by vaccination status. Hospitalization rates were calculated using the number of admissions as the numerator and the population within the corresponding Local Health Authority (ASL) for each sentinel hospital, multiplied by 100,000, as the denominator. The vaccinated and unvaccinated populations were estimated using weekly national proportions - as provided by the vaccine counter of the Italian Ministry of Health [ 25 ]. Statistical analysis Field data from SHNet were compared with national data from National Institute of Health (ISS). Since the onset of the SARS-CoV-2 pandemic in Italy in February 2020, the ISS established a dedicated integrated surveillance system (microbiological and epidemiological) based on the registration of each positive SARS-CoV-2 test results and each hospitalization related to the SARS-CoV-2 infection. After SARS-CoV-2 vaccination campaign started, data about vaccination status of each patient were also recorded as mandated by Circular N° 1997 dated 22 January 2020, issued by Ministry of Health [ 25 ]. Data are available as open access [ 26 ]. Spearman’s correlation test was used to assess correlation between FIASO and ISS data. This non-parametric measure was selected to quantify the alignment of weekly trends without presuming linearity or equivalence of absolute values. Statistical significance ( p < 0.05) indicating data reliability. All analyses were conducted in Stata MP17. A sensitivity analysis was conducted to assess the robustness of hospitalization-rate estimates to variations in vaccination coverage. For each epidemiological week, vaccination coverage was perturbed by ±5% of its observed value, and adjusted denominators for vaccinated and unvaccinated populations were reconstructed accordingly. Weekly hospitalization rates and rate ratios (RR) were recalculated under each scenario, and the percentage deviation from the original RR was quantified. This procedure evaluated whether plausible fluctuations in coverage materially influenced RR stability or interpretation. Weekly hospitalization rates (per 100,000) and 95% confidence intervals (CIs) were calculated assuming a Poisson distribution: Rate ratios (RR) comparing unvaccinated to vaccinated individuals were computed with log-normal CIs: p -values were derived from a z-test on log-RR. Three representative weeks were selected: early wave peak (18 Jan), absolute peak (25 Jan), and decline phase (15 Feb 2022). Ethics approval and consent to participate This study was based exclusively on aggregated and anonymized data collected as part of routine public health surveillance activities within the SHNet network. No identifiable personal data were collected, accessed, or processed for the purposes of this analysis. Data were entered into and stored in a secure, password-protected centralized database with restricted access limited to authorized personnel only. According to Italian legislation governing public health surveillance activities and the processing of anonymized data (Legislative Decree 196/2003, as amended by Legislative Decree 101/2018, implementing Regulation (EU) 2016/679 – General Data Protection Regulation), formal approval from a Research Ethics Committee was not required for this ecological study. The study was conducted in accordance with the Declaration of Helsinki and relevant national regulations. The requirement for informed consent was waived as the study did not involve identifiable human participants. Results Data from sentinel surveillance system In 2022, the SHNet documented 48,117 hospitalizations with a mean patient age of 72 years. 34,770 (72.3%) patients were vaccinated (mean age 74) while 13,347 (27.7%) were unvaccinated (mean age 70). Hospitalization trends followed a cyclical pattern, peaking on 1980 on 25 January, then declining to 198 by 7June. A summer resurgence peaked at 1134 hospitalizations 19 July, after which numbers stabilized at 189–516 weekly admissions through year-end of all admissions, 3202 (6.6%) required intensive care while 44,915 (93.3%) were treated in medical ward. 1,821 (56.9%) vaccinated and 1381 (43.1%) unvaccinated patients were represented in ICU, 32,949 (73.3%) vaccinated and 11,966 (26.7%) unvaccinated patients were represented in medical ward. When stratifying hospitalizations by type, 21,315 cases of patients admitted to medical wards with COVID-19 were recorded, of whom 17,250 (80.9%) were vaccinated and 4065 (19.1%) unvaccinated. In ICUs, 879 patients were hospitalized with COVID , with 692 (78.7%) vaccinated and 187 (21.3%) unvaccinated. For patients admitted for COVID , 19,992 were hospitalized in medical wards, of whom 13,614 (68.1%) vaccinated and 6378 (31.9%) unvaccinated. In ICUs, 1867 patients were hospitalized for COVID-19 , including 976 (52.3%) vaccinated and 891 (47.7%) unvaccinated. Data from ISS During the same period, the ISS reported 992,490 hospitalizations. Among these 25,433 (2.6%) patients required ICU care while 470,812 (97.4%) were admitted to medical wards. On the 4 January 2022, 78.5% of the population had completed the primary vaccination cycle (46,521,181 primary series doses), while 47.3% had received a booster dose (21,544,385 doses). By 21December 2022, vaccination coverage had risen to 84.4% primary (50,002,822 primary series doses), and 68.2% booster (40,420,576 doses). Comparison from FIASO and ISS database To account for the different scales of the two datasets, ISS and FIASO hospitalization counts were displayed using dual vertical axes, allowing clear visualization of weekly trends across both sources. The comparison of hospitalization data from ISS and FIASO (Fig. 1 ) provides a comprehensive analysis. The comparison revealed a strong correlation in hospitalization trends (Spearman’s rho = 0.95; p < 0.05) confirming the robustness of sentinel hospital data. Fig. 1. Open in a new tab Trends in total hospitalizations, ordinary care, and ICU admissions: a comparison between ISS and FIASO data Both medical ward and ICU hospitalizations demonstrated highly significant correlations (Spearman’s rho = 0.95 and 0.93, respectively - p < 0.001). Hospitalization rates were consistently higher among unvaccinated individuals across all categories. During the early wave peak on 18 January 2022, overall rates were 31.3 per 100,000 in unvaccinated versus 7.6 per 100,000 in vaccinated individuals (RR = 4.13; 95% CI: 3.79–4.49; p < 0.0001). Similar patterns were observed for general ward (RR = 3.83) and ICU admissions (RR = 7.94). At the absolute peak on 25 January 2022, overall rates remained higher in unvaccinated individuals (31.7 vs 7.7 per 100,000; RR = 4.12), with comparable differences for general ward (RR = 3.92) and ICU admissions (RR = 6.66). During the decline phase on 15 February 2022, hospitalization rates decreased in both groups, but unvaccinated individuals still had substantially higher rates (overall RR = 3.51; general ward RR = 3.35; ICU RR = 4.38). The high correlation of trends (rho = 0.95) reflects temporal alignment but does not confirm absolute rate accuracy, and complementary validation metrics could be considered. Hospitalization rates revealed a higher risk of admission for unvaccinated individuals during the initial weeks of the survey, with these rates subsequently converging with those of vaccinated individuals in the later weeks. Additionally, the data indicated a significantly increased risk of admission to medical units among unvaccinated individuals compared to their vaccinated counterparts. The trends in hospitalization rates further confirmed that the risk of admission to intensive care was markedly higher for unvaccinated individuals. On the other side, vaccinated individuals showed a declining trend in intensive care admission rates, which remained more stable over the time (Fig. 2 ). Fig. 2. Open in a new tab Hospitalization rates for COVID-19, including total admissions, admissions to medical units, and intensive care units (FIASO survey), stratified by vaccination status, alongside trends in vaccination coverage for the primary series and first booster dose Analysis of hospitalization types ( for COVID-19 vs. with COVID-19 ) showed that increased vaccination coverage significantly reduced weekly admissions for COVID-19 , from 1504 to 303 cases (−79.8%). Hospitalizations with COVID-19 declined gradually from 663 to 497 cases (−25.0%). Medical unit admissions showed a similar trend: for COVID -19 cases decreasing from 1308 to 276 (−78.8%) while with COVID-19 hospitalizations fell from 641 to 481 patients (−24.9%). Intensive care hospitalizations for COVID-19 dropped from 198 to 25 cases (−87.3%) whereas with COVID-19 cases remained stable from 22 to 16 cases (−2.7%) (Fig. 3 ). These findings highlight the role of vaccination in reducing severe cases and its limited impact on incidence of hospitalizations. Fig. 3. Open in a new tab Trends in total COVID+ hospitalizations, including medical units and intensive care units (FIASO survey), stratified by hospitalization type ( for COVID-19 vs. with COVID-19 ), presented alongside vaccination coverage trends for the primary series and first booster dose Sensitivity analyses varying vaccination coverage by ±5% produced proportional changes in RR magnitude but did not alter the direction of the association, with hospitalization risk remaining consistently higher among unvaccinated individuals. Discussion The COVID-19 situation in Italy in January 2023 reflects significant epidemiological trends, with over 25 million cases and 184,642 deaths, ranking 8th globally and 3rd in Europe for total cases and fatal events [ 27 ]. During this period, the Omicron B.1.1.529 variant was dominant, showing high transmissibility and immune elusion due to 50 mutations. While Omicron increased reinfection risk, it was associated with a 35–80% lower likelihood of severe outcomes, including hospitalization, ICU admission and death, compared to the Delta variant. These findings provide insights into pandemic response in the Country [ 28 ]. The correlation between total hospitalizations reported by ISS and admissions from the SHNet how strong consistency with FIASO data, validating our study in capturing hospitalization trends. Individual-level covariates were not available, and multivariable adjustment was therefore not feasible. This limitation is acknowledged, and the correlation analysis should be interpreted solely as a comparison of temporal trajectories rather than an adjusted association measure. This concordance highlights confidence in the strength of findings and determines their applicability to the analysis of COVID-19 dynamics within hospital settings. In both analysis, ICU burden is disproportionately higher in unvaccinated individuals, especially in for COVID-19 admissions. Initial hospitalization rates, significantly higher among unvaccinated individuals, highlights the protective effect of vaccination in reducing severe disease. Differentiating between patients hospitalized for COVID-19 and those hospitalized with COVID-19 clarifies the impact of vaccination on hospital admissions, beds occupancy offering useful information for hospital planning. Higher vaccine coverage among patients and healthcare workers significantly reduced for COVID-19 hospitalizations (−79.8%) supporting the role of vaccination in preventing severe illness. This analysis underlines the contribution of vaccination to mitigating pressures on healthcare services [ 29 – 33 ]. However, the observed reductions in hospitalization and ICU burden are likely shaped by multiple concurrent factors—such as increasing hybrid immunity, immunogenicity, waning immunity and changes in testing policies, evolving variant virulence, and differences in healthcare-seeking behaviour—. These elements align with international evidence, which explains multifactorial protective mechanisms. Emerging variants induce strain-specific antibody responses in addition to T cell-mediated immunity. Vaccination, however, mainly elicits cross-reactive immune responses, consistent with a backboosting effect. As immunological memory expands, the severity of COVID-19 infections tend to decrease, resulting from repeated exposure to both infection and vaccination. This contributes to a growing disconnection between the number of cases, the rates of hospitalization and death [ 34 , 35 ]. Trends in ICU admission by vaccination status show that unvaccinated patients, initially outnumbered the vaccinated individuals. This pattern reflects early pandemic stages, when lower vaccination rates left unvaccinated individuals more vulnerable to severe outcomes [ 36 – 40 ]. Higher vaccination rates significantly reduced ICU admissions for COVID-19 (−87.3%), underscoring vaccine effectiveness in preventing critical illness. This evidence was absent from ISS reports and came out by stratified analysis of FIASO by hospital admission type. While vaccination has reduced severe COVID-19 cases, persistence of ICU admissions with COVID-19 underscores the importance of strict infection control measures- as well as the influence of testing-policy changes that may affect case detection and admission thresholds, alongside continued surveillance and improved management strategies to mitigate healthcare burdens and sustain routine services. [ 41 , 42 ]. The need to allocate patients affected by SARS-CoV-2 infections to single use rooms instead of multiple use - reduces overall bed capacity. However reallocating patients who were negative contributed to more organized care pathways. These factors affect clinical outcomes and patient satisfaction, highlighting the necessity for effective strategies to optimize resource management and maintain high standards of care. There are limited informations on emergency management networks even if other countries established ad hoc sentinel networks for COVID-19. General practitioners collected data in the early stage of the pandemic (2021) but these networks reported only symptomatic cases excluding those identified via screening as seen in Italian sentinel hospitals [ 43 ]. In resource-limited settings like Bangladesh, hospital-based sentinel surveillance similarly included only symptomatic patients, omitting asymptomatic cases. Additionally, while associations between age, comorbidities, and infection were assessed, vaccination data were unavailable. Only severe influenza and COVID-19 surveillance system in Australia, provided comprehensive data, though it excluded with COVID-19 admissions [ 44 , 45 ]. In Italy, the management of with COVID-19 cases supported the gradual return to standard clinical practice. Our analysis presents several strengths. The FIASO dataset aligned with ISS data demonstrates significant correlations in hospitalization trends across 19 sentinel hospitals reflecting the national scenario. This enabled severity assessment (general wards vs ICU) and tracking severe cases over time. The FIASO study also offers detailed insights into hospitalization trends by vaccination status and type ( for vs. with COVID -19), complementing ISS data and underscoring the importance of parallel, field-based data collection for pandemic analysis. The absence of detailed clinical outcome measures—such as length of hospital stay, in-hospital mortality or post-discharge status—represents a significant limit. Although the regional portal for swab registration included clinical outcomes at the time of control testing or hospital discharge (e.g., death, transfer to another ward, clinical resolution or discharge home), these data were not integrated into the dataset used for this analysis. As a result, outcome-based evaluations were not feasible and remained available only through ISS weekly reports. This limitation reflects the original scope of the FIASO project, which was designed as a rapid hospital-based surveillance system focusing on burden and severity rather than on clinical trajectories. Although the study includes 22 hospitals across Italy, encompassing general and specialized centers, university and IRCCS hospitals, and diverse geographic areas, a detailed comparison with the entire national hospital network (e.g., hospital size, bed count, and specialization) is not available. Therefore, the generalizability of our findings to all Italian hospitals may be limited. Nonetheless, the diversity of participating centers supports the relevance of the study results.Another limitation is the potential variability in data collection practices across hospitals; molecular screening tests were used in all the hospital. Screening test done in all patients. Weekly referents organized virtual meetings to evaluate the process and how homogeneous was the data collection expressing doubts or correcting them when necessary. A check list including with and for would have powered the data however the objective clinical parameters (pulmonary infections/respiratory symptoms vs primary access diagnosis) keep a good level of power if associated to the instrumental parameters. With the end of the pandemic, maintaining the hospital care paradigm developed is essential to move beyond the traditional division into COVID and Non-COVID wards. This model, proposed by FIASO, aligns with the objective of a gradual return to normality, as outlined in Legislative Decree 24 of 24 March 2022. Under this framework, SARS-CoV-2-positive patients without significant symptoms should receive specialist care based on their primary medical needs. For instance, patients requiring surgical, oncological, or other specialized care should be managed in appropriate settings without unnecessary delays due to infection status [ 46 – 48 ]. A fundamental priority for policymakers in this post-pandemic phase is ensuring that all healthcare workers, based on lesson learned during pandemic period, are trained in managing infectious patients and in adopting strategies to reduce hospital risk [ 49 ]. Rather than limiting expertise to specific groups, protocols should be universally understood across healthcare professions. Furthermore, a binary approach to infectiousness—where a positive test equates to an infectious patient—should be replaced by a multifactorial risk assessment. A precautionary approach should consider how patients could be potentially infectious until the opposite is proven, integrating clinical and laboratory criteria to be more accurately in determination of how infectious they are. In the new organizational model, patients must be differentiated: for COVID-19 patient care should be prioritized within specialized units, primarily infectious disease wards equipped with isolation and negative pressure rooms, or within respiratory ICU. Bed allocation in these units should be adaptable based on epidemiological trends and vaccination coverage, avoiding major structural or organizational disruptions. Hospital stays should be determined by the resolution of COVID-19-related pathology rather than by the duration of infectiousness. Conversely, with COVID-19 patients should be treated in general wards suited to their primary condition. During the contagious period, functional isolation should minimize transmission risk without the need of a prolonged hospitalization. Assessing the effective infectious period is crucial, given the complexities of positive and negative test results at different infection stages, as discussed by Sethuraman et al. [ 50 ]. This reinforces the need for a nuanced, evidence-based approach to determining infectiousness, as recognized in updated international recovery protocols [ 51 ]. In the post-emergency phase, the new hospital structure aims for more efficient patient management, proposing settings organized by patient care needs rather than by specialist departments. This allows for the deployment of fewer personnel, thereby optimizing human resource management [ 52 ]. Lessons from the SHNet highlight three key principles: preparedness, adaptability and data infrastructure enhancement. Preparedness requires dedicated public health teams and surveillance coordination units ready to manage high workloads. Adaptability is essential to keep operational efficiency as demonstrated by the efficient ability of SHNet during the pandemic. Rapid communication between departments and continuous data updates remains critical to facilitate an agile healthcare response. Data infrastructure enhancement is necessary for a complete and information enabling informed decision-making and effective response coordination [ 49 – 53 ]. The SHNet model thus provides a valuable example of a surveillance method that policymakers should consider for managing future pandemicsImportantly, the SHNet model is not limited to SARS-CoV-2 surveillance. Its structure, based on sentinel hospitals, standardized case definitions, and differentiation between admissions for and with infection, is directly applicable to other respiratory pathogens such as seasonal and pandemic influenza, respiratory syncytial virus (RSV), and emerging viral threats. Similar to COVID-19, these infections contribute substantially to hospital and intensive care unit burden, particularly among older adults and individuals with comorbidities. Integrating SHNet into existing severe acute respiratory infection (SARI) surveillance frameworks could improve early detection of epidemic waves, enable more accurate estimation of disease severity, and support timely healthcare system preparedness beyond COVID-19.This approach would complement the data collected by institutions such as the ISS, with the responsibility of contextualizing the data within an emerging pandemic scenario. Stratification of data collected through sentinel hospitals enables the identification of patients most at risk of infection in a pandemic context. Consequently this empowers policymakers to dedicate more efficient economic resources informing and educating stakeholders about the appropriate exercise of their functions and minimizing energy waste where efficiency is set. Future studies on this topic could aim to evaluate whether this new model offers long-term advantages for healthcare management and also to assess its impact on overall costs. Conclusions Building on lessons from the COVID-19 pandemic, healthcare facilities should modernize high-care units to address staff shortages and reduce costs. The FIASO proposed this approach, successfully implemented at Bari General University Hospital, the largest in Apulia, improving resource management for COVID-19 patients. COVID-19 severely disrupted national healthcare systems necessitating infection containment strategies while maintaining essential services. Initially many services were cut diverting resources to COVID-19 care. In the pandemic’s second phase, hospital activities normalized adapting to SARS-CoV-2 threats within clinical settings. The main results coming out shows how useful is to integrate passive monitoring of epidemic event with active monitoring as considered by the division in with and for COVID-19 . A second line of monitoring (SHNet), alongside the official ISS system, interacts with real healthcare system needs and can help identify potential solutions. Organizing patients according to their care requirements provided clearer insight into patient allocation, without implying improvements in resource management or turnover. To set up situation in regular meetings with integration of informations coming from different type of hospitals speeded the decision making processes. A key challenge for public healthcare institutions is developing protocols for future pandemics while maintaining SHNet. These hospitals equipped with advanced epidemiological surveillance enable early outbreak detection, continuous disease monitoring and rapid containment measures. Systematic real-time data collection supports public health interventions and decision-making at regional and national levels. Powering SHNet is essential to minimize healthcare disruptions enhancing preparedness for future infectious threats. Beyond COVID-19, the SHNet experience suggests that hospital-based sentinel surveillance could represent a scalable and sustainable model for monitoring influenza, RSV, and future respiratory pathogens, supporting preparedness and resilience of healthcare systems in the post-pandemic era. Acknowledgements **FIASO Working Group: Antonio Piscitelli 5 , Cristina Rosati 6 , Mariella Baroni 6 , Sara Cutti 7 , Giulia Giovinazzi 8 , Francesca Valent 8 , Roberto Vito Rizzello 8 , Michele Chittaro 9 , L. Little D’Anna 9 , Alberto Ferrazzano 9 , Francesca Antinolfi 9 , Francesco Copello 10 , Riccardo Papalia 10 , Gianni Orengo 10 , Valentino Tisa 10 , Ilaria Barberis 10 , Annamaria Longanesi 11 , Ilaria Nonni 11 , Roberto Turillazzi 12 , Gloria Bocci 12 , Matteo Filippi 13 , Nathascia Spoto 13 , Massimo Rizzo 14 , Valerio Mattia Scandali 15 , Marco Lombardi 15 , Arturo Pasqualucci 15 , Maria Iolanda Spitaleri 16 , Filomena Pietrantonio 16 , Giulia Laurelli 16 , Fabio Vinci 16 , Roberto Iosacco 16 , Rita Toti 16 , Elena Ialleni 16 , Stefano Villani 16 , Annalisa Sciannamea 16 , Letizia Gargano 16 , Andrea Magrini 17 , Francesca Ignesti 17 , Alberto D’Annunzio 18 , Odile Grieco 18 , Manuela Di Virgilio 19 , Valentina Rodomonti 19 , Maria Cristina Boccia 20 , Vittoria Maria Vinci 20 , Vittoria Maria Vinci 21 , Desirée Caselli ([email protected]) 22 ***, Sara Pennelli 23 , Giuliano Fanelli 23 , Francesco Di Mona 24 , Margherita Maragno 24 , Marianela Urriza 25 , Alessandro Manfredi 25 , Marcello Mariani 26 , Antonio Grieco 26 , Gianni Macrina 26 , Giuseppe Spiga 26 , Antonella Ciucci 26 , Marta Ciofi Degli Atti 27 , Caterina Rizzo 27 , Lara Ricotta 27 , Alfonso Bernardo 28 , Pasquale De Rosa 28 , Marilena Avanzato 29 , Michele Morandi 29 , Luca Maina 29 , Antonella Carcieri 29 , Stefano Taraglio 29 , ***Desirèe Caselli: nominated Consortia representative - [email protected] 5. AO S. Croce e Carle di Cuneo 6. ASST Spedali Civili di Brescia 7. Fondazione IRCCS Policlinico San Matteo 8. APSS Trento 9. Azienda Sanitaria Friuli Occidentale 10. IRCCS Policlinico San Martino di Genova 11. IRCCS Policlinico S. Orsola-Malpighi 12. Azienda USL Toscana Sud Est 13. AOU Pisana 14. AO Terni 15. AOU Ospedali Riuniti di Ancona 16. ASL Roma 6 17. Policlinico Tor Vergata 18. IRCCS Spallanzani 19. ASL Teramo 20. Azienda Ospedaliera dei Colli Monaldi-Cotugno-CTO 21. ASL Taranto 22. AOU Consorziale Policlinico di Bari 23. AOU Riuniti di Foggia 24. ASM Matera 25. IRCCS Burlo Garofolo 26. IRCCS Gaslini 27. Ospedale Pediatrico Bambino Gesù 28. AORN Santobono Pausilipon 29. ASL Città di Torino Abbreviations WHO World Health Organization ACIH Agency for Cooperation in International Health AGSnet Alumni for Global Surveillance Network SHNet Sentinel Hospital Network SARI Severe acute respiratory infections FIASO Italian Federation of Health Trust ICU Intensive Care Unit COPD Chronic obstructive pulmonary disease ASL Local Health Authority ISS National Institute of Health SSS Sentinel Surveillance System Author contributions CN, PS and ST have given substantial contributions to the conception or the design of the manuscript, AL and NP to acquisition of the data, FPB and GR to analysis and interpretation of the data, PS and GM made the supervision activity of working group, FIASO Working Group Consortium Members have performed data collection and have implemented FIASO database. All authors have participated to drafting the manuscript, PS and ST revised it critically. All authors read and approved the final version of the manuscript. Funding The authors report no involvement in the research by the sponsor that could have influenced the outcome of this work. Data availability Dataset is available, if requested to corresponding author Pasquale Stefanizzi [[email protected]]. Declarations Ethics approval and consent to participate The study was conducted in conformity with the European regulations on data management (General Data Protection Regulation GDPR - UE 2016/679) and previous Italian law on privacy (Art. 20–21 DL 196/2003 named “Personal data protection code” published in the Official Gazette n. 190 on 14 August 2004) in accordance with Declaration of Helsinki. Data were encrypted prior to analyses and each patient was assigned a unique identification code. This code removes the possibility of tracing the patient’s identity. According to Italian law, the use of administrative data does not require any written informed consent by patients [ 54 – 56 ]. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. European Centre for Disease Prevention and Control. Rapid Risk Assessment: Increased transmission of COVID-19 in the EU/EEA and the UK - thirteenth update [Internet]. 2020 [cited 12 September 2024]. Available from: https://www.ecdc.europa.eu/en/publications-data/covid-19-risk-assessment-increased-transmission-thirteenth-update . 2. Woolf SH, Chapman DA, Sabo RT, Weinberger DM, Hill L, Taylor DDH. Excess deaths from COVID-19 and other causes, March-July 2020. JAMA. 2020, Oct, 20;324(15):1562–64. 10.1001/jama.2020.19545. PMID: 33044483; PMCID: PMC7576405. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Sundararaman T, Muraleedharan VR, Ranjan A. Pandemic resilience and health systems preparedness: lessons from COVID-19 for the twenty-first century. J Soc Econ Dev. 2021;23(Suppl 2):290–300. 10.1007/s40847-020-00133-x. Epub 2021 Jan 6. PMID: 34720480; PMCID: PMC7786882. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Smith ED. Sustaining preparedness in hospitals. Adv Health Care Manag. 2024, Feb, 7;22. 10.1108/S1474-823120240000022008. PMID: 38262015. [ DOI ] [ PubMed ] 5. United Nations. WHO chief declares end to COVID-19 as a global health emergency [Internet]. 5 May 2023 [cited 4 December 2024]. Available from: https://news.un.org/en/story/2023/05/1136367 . 6. Freudenberg LS, Pomykala KL, Herrmann K. COVID-19 pandemic: what have we learned and what to expect in the future? Semin Nucl Med. 2022, Jan;52(1):86–89. 10.1053/j.semnuclmed.2021.07.001. Epub 2021 Jul 30. PMID: 34389160; PMCID: PMC8321776. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. Kruk ME, Myers M, Varpilah ST, Dahn BT. What is a resilient health system? Lessons from Ebola. Lancet. 2015, May, 9;385(9980):1910–12. 10.1016/S0140-6736(15)60755-3. PMID: 25987159. [ DOI ] [ PubMed ] [ Google Scholar ] 8. Blanchet K. Thinking shift on health systems: from blueprint health programmes towards resilience of health systems comment on “constraints to applying systems thinking concepts in health systems: a regional perspective from surveying stakeholders in Eastern Mediterranean countries”. Int J Health Policy Manag. 2015, Mar, 3;4(5):307–09. 10.15171/ijhpm.2015.49. PMID: 25905481; PMCID: PMC4417634. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Hanefeld J, Mayhew S, Legido-Quigley H, Martineau F, Karanikolos M, Blanchet K, et al. Towards an understanding of resilience: responding to health systems shocks. Health Policy Plan. 33(10):1144. 10.1093/heapol/czy087. 2018 Dec 1. Erratum for:Health Policy Plan.2018 Apr 1; 33(3):355-367.doi 10.1093/heapol/czx183. PMID: 30517618; PMCID: PMC6415725. [ DOI ] [ PMC free article ] [ PubMed ] 10. Hu B, Guo H, Si H, Shi Z. Emergence of SARS and COVID-19 and preparedness for the next emerging disease X. Front. Med. 2024, Feb;18(1):1–18. 10.1007/s11684-024-1066-6. Epub 2024 Apr 2. PMID: 38561562. [ DOI ] [ PubMed ] [ Google Scholar ] 11. Kruk ME. Emergency preparedness and public health systems lessons for developing countries. Am J Prev Med. 2008, Jun;34(6):529–34. 10.1016/j.amepre.2008.02.012. PMID: 18471591; PMCID: PMC7135738. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2018;112(10):423–35. 10.1093/trstmh/try078. Corrigendum to:Neglected tropical diseases and vitamin B12:a review of the current evidence. Trans R Soc Trop Med Hyg. 2019 May 1; 113(5):292. 10.1093/trstmh/trz013. Erratum for:Trans R Soc Trop Med Hyg.2018 Oct 1; 112(10):423-435. 10.1093/trstmh/try078. PMID: 30927006; PMCID: PMC8055068. [ DOI ] [ PMC free article ] [ PubMed ] 13. Ndayishimiye C, Sowada C, Dyjach P, Stasiak A, Middleton J, Lopes H, et al. Associations between the COVID-19 pandemic and hospital infrastructure adaptation and planning-A scoping review. Int J Environ Res Public Health. 2022 Jul 4;19(13):8195. 10.3390/ijerph19138195. PMID: 35805855; PMCID: PMC9266736. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Kayipmaz AE, Demircan A. Pandemic hospitals and reorganizing emergency departments. Turk J Med Sci. 2021, Dec;17(51(SI-1)):3221–28. 10.3906/sag-2106-169. PMID: 34284534; PMCID: PMC8771009. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Global surveillance of emerging and re-emerging diseases. Wkly Epidemiol Rec. 2001, Jul, 20;76(29):218–24. English, French. PMID: 11488092. [ PubMed ] 16. Arita I, Nakane M, Kojima K, Yoshihara N, Nakano T, El-Gohary A. Role of a sentinel surveillance system in the context of global surveillance of infectious diseases. The Lancet Infect Dis. 2004, Mar;4(3):171–77. 10.1016/S1473-30990400942-9. Erratum in:Lancet Infect Dis. 2004 Aug; 4(8):533. PMID: 14998504; PMCID: PMC7129469. [ DOI ] [ PMC free article ] [ PubMed ] 17. Brady M, Duffy R, Domegan L, Salmon A, Maharjan B, O’Broin C, et al. Establishing severe acute respiratory infection (SARI) surveillance in a sentinel hospital, Ireland & comma; 2021 to 2022. Euro Surveill. 2023, Jun;28(23):2200740. 10.2807/1560-7917.ES.2023.28.23.2200740. PMID: 37289427; PMCID: PMC10318943. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. World Health Organization (WHO). International health regulations [Internet]. Geneva: WHO; 2005 [cited 4 December 2024]. Available from: https://www.who.int/publications/i/item/9789241580410 . 19. World Health Organization Regional Office for Europe (WHO/Europe). WHO Regional Office for Europe guidance for sentinel influenza surveillance in humans [Internet]. Copenhagen: WHO/Europe; 2011 [cited 4 December 2024]. Available from: https://apps.who.int/iris/handle/10665/349780 . 20. Mandl KD, Overhage JM, Wagner MM, Lober WB, Sebastiani P, Mostashari F, et al. Implementing syndromic surveillance: a practical guide informed by the early experience. J Am Med Inf Assoc. 2004, Mar-Apr;11(2):141–50. 10.1197/jamia.M1356. Epub 2003 Nov 21. PMID: 14633933; PMCID: PMC353021. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Marbus SD, van der Hoek W, van Dissel JT, van Gageldonk-Lafeber AB. Experience of establishing severe acute respiratory surveillance in the Netherlands: evaluation and challenges. Public Health Pract (Oxf). 2020, Nov;1:100014. 10.1016/j.puhip.2020.100014. Epub 2020 Dec 22. PMID: 34171043; PMCID: PMC7260511. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Riccardo F, Guzzetta G, Urdiales AM, Del Manso M, Andrianou XD, Bella A, et al. Italian COVID-19 monitoring group. COVID-19 response: effectiveness of weekly rapid risk assessments, Italy. Bull World Health Organ. 2022, Feb, 1;100(2):161–67. 10.2471/BLT.21.286317. Epub 2021 Nov 25. PMID: 35125541; PMCID: PMC8795855. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. World Health Organization, et al. Everybody’s business-strengthening health systems to improve health outcomes: WHO’s framework for action [Internet]. 2007. [cited 10 January 2025]. Available from: https://www.who.int/publications/i/item/everybody-s-business--strengthening-health-systems-to-improve-health-outcomes . 24. Decree-Law No. 172 of November 26, 2021: Urgent Measures for the Containment of the COVID-19 Epidemic and the Safe Conduct of Economic and Social Activities. Published in the Official Gazette No. 282, November 26, 2021. 25. Gazzetta Ufficiale, Ministry of Health. Preventive measures against the new Coronavirus (2019-nCoV). (20A00738) GU Serie Generale no. 26, 01 February 2020 ORDINANCE, 30 January 2020. 2020. 26. Ministry of Health. Italy/covid19 open data vaccines [Internet]. 2025 [cited 13 January 2025]. Available from: https://github.com/italia/covid19-opendata-vaccini/tree/master . 27. Ortenzi F, Albanese E, Fadda M. A transdisciplinary analysis of COVID-19 in Italy: the most affected country in Europe. Int. J. Environ. Res. Public Health. 2020, Dec, 18;17(24):9488. 10.3390/ijerph17249488. PMID: 33352883; PMCID: PMC7767319. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Tian D, Sun Y, Xu H, Ye Q. The emergence and epidemic characteristics of the highly mutated SARS-CoV-2 Omicron variant. J Med Virol. 2022, Jun;94(6):2376–83. 10.1002/jmv.27643. Epub 2022 Feb 11. PMID: 35118687; PMCID: PMC9015498. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Nyberg T, Ferguson NM, Nash SG, Webster HH, Flaxman S, Andrews N, et al. Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study. Lancet. 2022, Apr, 2;399(10332):1303–12. 10.1016/S0140-6736(22)00462-7. Epub 2022 Mar 16. PMID: 35305296; PMCID: PMC8926413. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. De Maria L, Sponselli S, Caputi A, Pipoli A, Giannelli G, Delvecchio G, Zagaria S, Cavone D, Stefanizzi P, Bianchi FP, Tafuri S, Vimercati L. Comparison of three different waves in healthcare workers during the COVID-19 pandemic: a Retrospective observational study in an Italian University hospital. J Clin Med. 2022, May, 29;11(11):3074. 10.3390/jcm11113074. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Moscara L, Venerito V, Martinelli A, Di Lorenzo A, Toro F, Violante F, Tafuri S, Stefanizzi P. Safety profile and SARS-CoV-2 breakthrough infections among HCWs receiving anti-SARS-CoV-2 and influenza vaccines simultaneously: an Italian observational study. Vaccine. 2023, Aug, 31;41(38):5655–61. 10.1016/j.vaccine.2023.07.043. [ DOI ] [ PubMed ] [ Google Scholar ] 32. Stefanizzi P, Bianchi FP, Moscara L, Martinelli A, Di Lorenzo A, Gesualdo L, Simone S, Rendina M, Tafuri S. Determinants of compliance to influenza and COVID-19 vaccination in a cohort of solid organ transplant patients in Puglia, southern Italy (2017-2022). Hum Vaccin Immunother. 19(3):2266932. 10.1080/21645515.2023.2266932. 2023 Dec 15. [ DOI ] [ PMC free article ] [ PubMed ] 33. Stefanizzi P, Moscara L, Palmieri C, Martinelli A, Fontanelli S, Bellomo C, Spinelli G, Tafuri S. Safety of SARS-CoV-2 XBB.1.5 and seasonal influenza vaccines co-administration: data from a perspective observational active surveillance study. Puglia (Italy), season 2023/2024. Virology. 2025, Sep;610:110613. 10.1016/j.virol.2025.110613. Epub 2025 Jun 20. PMID: 40561867. [ DOI ] [ PubMed ] [ Google Scholar ] 34. Chemaitelly H, Ayoub HH, Coyle P, Tang P, Hasan MR, Yassine HM, Al Thani AA, Al-Kanaani Z, Al-Kuwari E, Jeremijenko A, Kaleeckal AH, Latif AN, Shaik RM, Abdul-Rahim HF, Nasrallah GK, Al-Kuwari MG, Butt AA, Al-Romaihi HE, Al-Thani MH, Al-Khal A, Bertollini R, Abu-Raddad LJ. Differential protection against SARS-CoV-2 reinfection pre- and post-Omicron. Nature. 2025, Mar;639(8056):1024–31. 10.1038/s41586-024-08511-9. Epub 2025 Feb 5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Vimercati L, Stefanizzi P, De Maria L, Caputi A, Cavone D, Quarato M, Gesualdo L, Lopalco PL, Migliore G, Sponselli S, Graziano G, Larocca AMV, Tafuri S. Large-scale IgM and IgG SARS-CoV-2 serological screening among healthcare workers with a low infection prevalence based on nasopharyngeal swab tests in an Italian university hospital: perspectives for public health. Environ Res. 2021, Apr;195:110793. 10.1016/j.envres.2021.110793. Epub 2021 Jan 27. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Collatuzzo G, Visci G, Violante FS, Porru S, Spiteri G, Monaco MGL, Larese Fillon F, Negro C, Janke C, Castelletti N, De Palma G, Sansone E, Mates D, Teodorescu S, Fabiánová E, Bérešová J, Vimercati L, Tafuri S, Abedini M, Ditano G, Asafo SS, Boffetta P. Orchestra WP5 Working group. Determinants of anti-S immune response at 6 months after COVID-19 vaccination in a multicentric European cohort of healthcare workers - ORCHESTRA project. Front. Immunol. 2022, Sep;29(13):986085. 10.3389/fimmu.2022.986085. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 37. Porru S, Monaco MGL, Spiteri G, Carta A, Caliskan G, Violán C, Torán-Monserrat P, Vimercati L, Tafuri S, Boffetta P, Violante FS, Sala E, Sansone E, Gobba F, Casolari L, Wieser A, Janke C, Tardon A, Rodriguez-Suarez MM, Liviero F, Scapellato ML, dell'Omo M, Murgia N, Mates D, Calota VC, Strhársky J, Mrázová M, Pira E, Godono A, Magnano GC, Negro C, Verlato G. Orchestra WP5 Working group. Incidence and Determinants of symptomatic and asymptomatic SARS-CoV-2 breakthrough infections after booster dose in a Large European Multicentric cohort of Health workers-ORCHESTRA project. J Epidemiol Glob Health. 2023, Sep;13(3):577–88. 10.1007/s44197-023-00139-8. Epub 2023 Jul 22. PMID: 37480426; PMCID: PMC10468456. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Bianchi FP, Tafuri S, Migliore G, Vimercati L, Martinelli A, Lobifaro A, Diella G, Stefanizzi P. Control room Working group. BNT162b2 mRNA COVID-19 vaccine effectiveness in the Prevention of SARS-CoV-2 infection and symptomatic disease in five-month follow-up: a Retrospective cohort study. Vaccines (Basel). 2021, Oct, 7;9(10):1143. 10.3390/vaccines9101143. PMID: 34696252; PMCID: PMC8538139. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 39. Srivastava K, Carreño JM, Gleason C, Monahan B, Singh G, Abbad A, et al. SARS-CoV-2-infection- and vaccine-induced antibody responses are long lasting with an initial waning phase followed by a stabilization phase. Immunity. 2024, Mar, 12;57(3):587–99.e4. 10.1016/j.immuni.2024.01.017. Epub 2024 Feb 22. PMID: 38395697; PMCID: PMC11066813. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Fonville JM, Wilks SH, James SL, Fox A, Ventresca M, Aban M, et al. Antibody landscapes after influenza virus infection or vaccination. Science. 2014, Nov, 21;346(6212):996–1000. 10.1126/science.1256427. PMID: 25414313; PMCID: PMC4246172. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Mahrokhian SH, Tostanoski LH, Vidal SJ, Barouch DH. COVID-19 vaccines: immune correlates and clinical outcomes. Hum Vaccin Immunother. 2024, Dec, 31;20(1):2324549. 10.1080/21645515.2024.2324549. Epub 2024 Mar 22. PMID: 38517241; PMCID: PMC10962618. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Moynihan R, Sanders S, Michaleff ZA, Scott AM, Clark J, To EJ, et al. Impact of COVID-19 pandemic on utilisation of healthcare services: a systematic review. BMJ Open. 2021, Mar, 16;11(3):e045343. 10.1136/bmjopen-2020-045343. PMID: 33727273; PMCID: PMC7969768. [ DOI ] [ PMC free article ] [ PubMed ] 43. Porru S, Monaco MGL, Spiteri G, Carta A, Pezzani MD, Lippi G, Gibellini D, Tacconelli E, Dalla Vecchia I, Sala E, Sansone E, De Palma G, Bonfanti C, Lombardo M, Terlenghi L, Pira E, Mansour I, Coggiola M, Ciocan C, Godono A, Tardon A, Rodriguez-Suarez MM, Fernandez-Tardon G, Jimeno-Demuth FJ, Castro-Delgado RV, Iglesias Cabo T, Scapellato ML, Liviero F, Moretto A, Mason P, Pavanello S, Volpin A, Vimercati L, Tafuri S, De Maria L, Sponselli S, Stefanizzi P, Caputi A, Gobba F, Modenese A, Casolari L, Garavini D, D'Elia C, Mariani S, Filon FL, Cegolon L, Negro C, Ronchese F, Rui F, De Michieli P, Murgia N, Dell'Omo M, Muzi G, Fiordi T, Gambelunghe A, Folletti I, Mates D, Calota VC, Neamtu A, Perseca O, Staicu CA, Voinoiu A, Fabiánová E, Bérešová J, Adamčáková ZK, Nedela R, Lesňáková A, Holčíková J, Boffetta P, Abedini M, Ditano G, Asafo SS, Visci G, Violante FS, Zunarelli C, Verlato G. SARS-CoV-2 breakthrough infections. Incidence Risk Factors a Large Eur Multicentric Cohort Health Workers. Vaccines (Basel). 2022, Jul, 27;10(8):1193. 10.3390/vaccines10081193. [ Google Scholar ] 44. Maeder M, Auderset D, Borel B, Masserey E, Schwarz J, Mueller Y. Trends in COVID-Related activity in sentinel family Medicine practices: an observational study. Int J Public Health. 2023;67:1605361. 10.3389/ijph.2022.1605361. Published 2023 Jan 16. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 45. Das P, Akhtar Z, Mah-E-Muneer S, et al. Establishing a sentinel surveillance system for the novel COVID-19 in a resource-limited country: methods, system attributes and early findings. BMJ Open. 2021;11(12):e055169. 10.1136/bmjopen-2021-055169. Published 2021 Dec 2. [ DOI ] [ PMC free article ] [ PubMed ] 46. Lokuge K, D’Onise K, Banks E, Street T, Jantos S, Baptista M, Glass K. Opening up safely: public health system requirements for ongoing COVID-19 management based on evaluation of Australia’s surveillance system performance. BMC Med. 2022, Apr, 15;20(1):157. 10.1186/s12916-022-02344-x. PMID: 35421989; PMCID: PMC9010199. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Gazzetta Ufficiale. DECREE-LAW 24 March 2022, no. 24. Urgent provisions for overcoming measures to counter the spread of the COVID-19 epidemic, following the end of the state of emergency. (22G00034). GU Ser Generale no. 70, 24 March 2022 [Internet]. 2022 [cited 15 January 2025]. Available from: https://www.gazzettaufficiale.it/eli/id/2022/03/24/22G00034/s . 48. Spolverato G, Capelli G, Restivo A, Bao QR, Pucciarelli S, Pawlik TM, et al. The management of surgical patients during the coronavirus disease 2019 (COVID-19) pandemic. Surgery. 2020, Jul;168(1):4–10. 10.1016/j.surg.2020.04.036. Epub 2020 May 1. PMID: 32451162; PMCID: PMC7252049. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Gallé F, Quaranta A, Napoli C, Diella G, De Giglio O, Caggiano G, Di Muzio M, Stefanizzi P, Orsi GB, Liguori G, Montagna MT. How do vaccinators experience the pandemic? Lifestyle behaviors in a sample of Italian public Health workers during the COVID-19 Era. Vaccines (Basel). 2022, Feb, 6;10(2):247. 10.3390/vaccines10020247. PMID: 35214705; PMCID: PMC8879067. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 50. Sethuraman N, Jeremiah SS, Ryo A. Interpreting diagnostic tests for SARS-CoV-2. JAMA. 2020, Jun, 9;323(22):2249–51. 10.1001/jama.2020.8259. PMID: 32374370. [ DOI ] [ PubMed ] [ Google Scholar ] 51. World Health Organization (WHO). Criteria for releasing COVID-19 patients from isolation [Internet]. [cited 15 January 2025]. Available from: https://www.who.int/news-room/commentaries/detail/criteria-for-releasing-covid-19-patients-from-isolation . 52. Bianchi FP, Cuscianna E, Di Lorenzo A, Daleno A, Mele F, Marra M, et al. A new paradigm of hospital care for SARS-COV-2 patients in the post-emergency phase in Italy. Ann Ig. 2023, Mar-Apr;35(2):250–53. [ DOI ] [ PubMed ] [ Google Scholar ] 53. Bagaria J, Jansen T, Marques DF, et al. Rapidly adapting primary care sentinel surveillance across seven countries in Europe for COVID-19 in the first half of 2020: strengths, challenges, and lessons learned. Euro Surveill. 2022;27(26):2100864. 10.2807/1560-7917.ES.2022.27.26.2100864. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 54. European Union (EU). General Data Protection Regulation GDPR “REGOLAMENTO (UE) 2016/679 DEL PARLAMENTO EUROPEO E DEL CONSIGLIO del 27 aprile 2016 relativo alla protezione delle persone fisiche con riguardo al trattamento dei dati personali, nonché alla libera circolazione di tali dati e che abroga la direttiva 95/46/CE (regolamento generale sulla protezione dei dati)”. Available from: https://eur-lex.europa.eu/legal-content/IT/TXT/PDF/?uri=CELEX:32016R0679 . 55. della Repubblica P. DECRETO LEGISLATIVO 30 giugno 2003, n. 196- Codice in materia di protezione dei dati personali. Available from: https://presidenza.governo.it/USRI/confessioni/doc/dlgs196.pdf . 56. World Medical Association. Declaration of Helsinki - medical Research involving human participants. 2024. Available from: October. https://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/ . Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Data Availability Statement Dataset is available, if requested to corresponding author Pasquale Stefanizzi [[email protected]]. 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