Most studies of epidemic detection focus on their start and rarely on the whole signal or the end of the epidemic. In some cases, it may be necessary to retrospectively identify outbreak signals from surveillance data. Our study aims at evaluating the ability of change point analysis (CPA) methods to locate the whole disease outbreak signal. We will compare our approach with the results coming from experts’ signal inspections, considered as the gold standard method. We simulated 840 time series, each of which includes an epidemic-free baseline (7 options) and a type of epidemic (4 options). We tested the ability of 4 CPA methods (Max-likelihood, Kruskall-Wallis, Kernel, Bayesian) methods and expert inspection to identify the simulated outbreaks. We evaluated the performances using metrics including delay, accuracy, bias, sensitivity, specificity and Bayesian probability of correct classification (PCC). A minimum of 15 h was required for experts for analyzing the 840 curves and a maximum of 25 min for a CPA algorithm. The Kernel algorithm was the most effective overall in terms of accuracy, bias and global decision (PCC = 0.904), compared to PCC of 0.848 for human expert review. For the aim of retrospectively identifying the start and end of a disease outbreak, in the absence of human resources available to do this work, we recommend using the Kernel change point model. And in case of experts’ availability, we also suggest to supplement the Human expertise with a CPA, especially when the signal noise difference is below 0.

When outbreak detection algorithms (ODAs) are considered individually, the task of outbreak detection can be seen as a classification problem and the ODA as a sensor providing a binary decision (outbreak yes or no) for each day of surveillance. When they are considered jointly (in cases where several ODAs analyze the same surveillance signal), the outbreak detection problem should be treated as a decision fusion (DF) problem of multiple sensors. This study evaluated the benefit for a decisions support system of using DF methods (fusing multiple ODA decisions) compared to using a single method of outbreak detection. For each day, we merged the decisions of six ODAs using 5 DF methods (two voting methods, logistic regression, CART and Bayesian network - BN). Classical metrics of accuracy, prediction and timelines were used during the evaluation steps. In our results, we observed the greatest gain (77%) in positive predictive value compared to the best ODA if we used DF methods with a learning step (BN, logistic regression, and CART). To identify disease outbreaks in systems using several ODAs to analyze surveillance data, we recommend using a DF method based on a Bayesian network. This method is at least equivalent to the best of the algorithms considered, regardless of the situation faced by the system. For those less familiar with this kind of technique, we propose that logistic regression be used when a training dataset is available.

An increased incidence of invasive fungal infection was reported in SARS-CoV-2-infected patients hospitalised in the intensive care unit. However, the impact of COVID-19 on Candida airway colonisation has not yet been assessed. This study aimed to test the impact of several factors on Candida airway colonisation, including SARS-CoV-2 infection. We conducted a two-pronged monocentric retrospective study. First, we analysed the prevalence of positive yeast culture in respiratory samples obtained from 23 departments of the University Hospital of Marseille between 1 January 2018 and 31 March 2022. We then conducted a case-control study, comparing patients with documented Candida airway colonisation to two control groups. We observed an increase in the prevalence of yeast isolation over the study period. The case-control study included 300 patients. In the multivariate logistic regression, diabetes, mechanical ventilation, length of stay in the hospital, invasive fungal disease, and the use of antibacterials were independently associated with Candida airway colonisation. The association of SARS-CoV-2 infection with an increased risk of Candida airway colonisation is likely to be a consequence of confounding factors. Nevertheless, we found the length of stay in the hospital, mechanical ventilation, diabetes, and the use of antibacterials to be statistically significant independent risk factors of Candida airway colonisation.

(1) Background: We collected COVID-19 mortality data and the age distribution of the deceased in France and other European countries, as well as specifically in the cities of Paris and Marseille, and compared them. (2) Methods: Data on mortality related to COVID-19 and the associated age distribution were collected from government institutions in various European countries. In France, data were obtained from INSEE and Santé Publique France. All-cause mortality was also examined in order to study potential excess mortality using EuroMOMO. The Marseille data came from the epidemiological surveillance system. (3) Results: France is one of the European countries most impacted by COVID-19. Its proportion of deaths in people under 60 years of age is higher (6.5%) than that of Italy (4.6%) or Spain (4.7%). Excess mortality (5% more deaths) was also observed. Ile-de-France and the Grand-Est are the two French regions with the highest mortality. The proportion of deaths in the under-60 age group was considerable in Ile-de-France (9.9% vs. 4.5% in the Southern region). There are significantly higher numbers of patients hospitalized, in intensive care and deceased in Paris than in Marseille. (4) Conclusions: No patient management, i.e., from screening to diagnosis, including biological assessment and clinical examination, likely explains the high mortality associated with COVID-19.