Riccardo Ievoli

INTRODUCTIONInteger Autoregressive Models (INAR) have demonstrated their versatility in applications involving epidemiological count data. Among the possible model specifications, the equidispersed first-order Poisson INAR (1) represents a straightforward and parsimonious solution for practitioners due to its simplicity of use and availability of bootstrap-based inference. However, two preliminary checks are relevant: testing for the presence of an INAR effect and assessing whether the arrival process can reasonably be modeled as equidispersed Poisson, which simplifies both estimation and forecasting.OBJECTIVESThis work proposes a novel bootstrap procedure for testing the presence of an INAR(1) effect in count time series. The procedure adapts the fast-double bootstrap algorithm proposed in Davidson, et al by introducing an additional loop within each bootstrap replication. In addition, it provides diagnostic information on the equidispersion of the arrival process by extending the approach of Palazzo et al.METHODSTo test the presence of the INAR effect, both the semi-parametric and the parametric bootstrap-based score statistics can be redesigned for the fast-double bootstrap algorithm with and without specifying a given distribution for the disturbances. Then, by exploiting the different behavior of the parametric and semi-parametric bootstrap distribution in the specific case of the Poisson score statistic under model misspecification, a procedure is implemented to diagnose the non-equidispersion of the disturbances. To show the applicability of the procedure, a small-scale Monte Carlo simulation is implemented by employing four data-generating processes for the parametric arrivals to capture under-/over-dispersion and zero-inflation. Then, freely available surveillance low-count data regarding infectious diseases have been considered for an empirical assessment of the procedure. One of the data sources regards the weekly number of syphilis cases (dataset: syph) in the United States (51 states, T=209).RESULTSSimulation analysis confirms that the fast-double bootstrap algorithm can provide non-negligible improvements in terms of empirical size (under the null hypothesis of no INAR effect) and power (presence of INAR effect) due to the pivotality of both the bootstrapped and the asymptotic score statistics in many cases, especially when the time series are moderately small (T=50, 75). Moreover, the procedure fails when the parametric bootstrapped Poisson score statistic is used under model misspecification (presence of under-/over-dispersion or zero-inflation) due to the non-pivotality issue. In this sense, a further comparison between bootstrapped parametric and semi-parametric score statistics can represent a diagnostic tool to empirically verify equi-dispersion in the data. In terms of empirical applications, the syph data do not always show evidence of an INAR effect, whereas the diagnostic tool suggests avoiding the use of Poisson INAR for the majority of the series investigated. After the pretests, a summary of the applications can be provided through ex-post bootstrap-based confidence intervals.CONCLUSIONSThe proposed fast-double bootstrap procedure represents a computationally feasible method to pretest count data for the presence of a short-term mechanism. The diagnostic tool should be formalized in further research and also compared with the existing proposals in terms of performance achieved under different simulation scenarios.

Performance management and risk management in the public sector have undergone significant improvements; however, few empirical studies have conceptualised the integration of performance management and risk management. This study aimed to understand why it fails in practice and turns into disintegration. To do this, we analyse the role of different actors involved in the integration between performance management and risk management. We have conducted our analysis at the organisational level in a regional context, adopting two different research methods, documentary analysis and collections of interviews. This paper contributes to the theoretical debate with interesting new insights about organisational practices in the public sector. In this research, we adopted the disintegration framework, broadly used as a research methodology in the ontological and social paradigm as reported (Täubig in Totale Institution Asyl, Juventa Verlag, Munich, 2009) for analysing the integration and disintegration between performance management and risk management. This approach presumes the collective engagement of researchers and practitioners, which can help bring to the surface the knowledge embedded in practice and transform it into actionable knowledge to produce practice changes. This study contributes to the public accounting literature by providing empirical evidence about organisational practices in the public sector. It offers a practical and general understanding of performance management and risk management practices functioning in public government. It shows the fundamental role played by key actors when performance management and risk management practices are implemented. This empirical research also has practical implications, creating the basis for the implementation of an integrated system of performance management and risk management in regional governments.

Summary statistics of football matches such as final score, possession and percentage of completed passes are not satisfyingly informative about style of play seen on the pitch. In this sense, networks and graphs are able to quantify how teams play differently from each others. We study the distribution of triad census, i.e., the distribution of local structures in networks and we show how it is possible to characterize passing networks of football teams. We describe the triadic structure and analyse its distribution under some specific probabilistic assumptions, introducing, in this context, some tests to verify the presence of specific triadic patterns in football data. We firstly run an omnibus test against random structure to asses whether observed triadic distribution deviates from randomness. Then, we redesign the Dirichlet-Multinomial test to recognize different triadic behaviours after choosing some reference patterns. The proposed tests are applied to a real dataset regarding 288 matches in the Group Stage of UEFA Champions League among three consecutive seasons.

During the waves of the COVID-19 pandemic, both national and/or territorial healthcare systems have been severely stressed in many countries. The availability (and complexity) of data requires proper comparisons for understanding differences in the performance of health services. With this aim, we propose a methodological approach to compare the performance of the Italian healthcare system at the territorial level, i.e., considering NUTS 2 regions. Our approach consists of three steps: the choice of a distance measure between available time series, the application of weighted multidimensional scaling (wMDS) based on this distance, and, finally, a cluster analysis on the MDS coordinates. We separately consider daily time series regarding the deceased, intensive care units, and ordinary hospitalizations of patients affected by COVID-19. The proposed procedure identifies four clusters apart from two outlier regions. Changes between the waves at a regional level emerge from the main results, allowing the pressure on territorial health services to be mapped between 2020 and 2022.

Persistent alveolar air leak (PAAL) after major lung resection remains a common complication in thoracic surgery. The aim of this study was to identify a subset of patients with high risk of developing PAAL after pulmonary lobectomy. Another objective was to evaluate the influence of PAAL on postoperative complications and length of hospital stay. A retrospective analysis on 895 patients undergoing pulmonary lobectomy from January 2014 to December 2019 was performed. PAAL was defined as air leak lasting more than 5 days after lung surgery. Univariate analyses and logistic regressions were performed to identify the predictors of PAAL. A backward selection algorithm was used to identify the optimal set of predictors. The incidence of PAAL was 8.2% (74/895). Male gender (p=0.017), BMI (p<0.001), transient ischemic attack (p=0.031), FEV1 (p=0.018), lobectomy combined with adjacent subsegmentectomy (p=0.018), partial and extended pleural adhesions (p=0.033 and p=0.038, respectively) were identified as independent risk factors for PAAL through logistic regression. A weak positive correlation was found between video-assisted thoracic surgery (VATS) and PAAL following pulmonary lobectomy (p=0.100). PAAL was found to be associated with higher risk of postoperative morbidity (p=0.002) and with longer hospital stay (p<0.001). Both preoperative and intraoperative risk factors may be responsible for PAAL after pulmonary lobectomy. VATS does not appear to prevent this postoperative complication. An alveolar air leak lasting beyond 5 days after pulmonary lobectomy is associated with worse postoperative outcomes.