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Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. We used wearable sensors to detect close-range interactions (\"contacts\") between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.

Little quantitative information is available on the mixing patterns of children in school environments. Describing and understanding contacts between children at school would help quantify the transmission opportunities of respiratory infections and identify situations within schools where the risk of transmission is higher. We report on measurements carried out in a French school (6-12 years children), where we collected data on the time-resolved face-to-face proximity of children and teachers using a proximity-sensing infrastructure based on radio frequency identification devices. Data on face-to-face interactions were collected on Thursday, October 1(st) and Friday, October 2(nd) 2009. We recorded 77,602 contact events between 242 individuals (232 children and 10 teachers). In this setting, each child has on average 323 contacts per day with 47 other children, leading to an average daily interaction time of 176 minutes. Most contacts are brief, but long contacts are also observed. Contacts occur mostly within each class, and each child spends on average three times more time in contact with classmates than with children of other classes. We describe the temporal evolution of the contact network and the trajectories followed by the children in the school, which constrain the contact patterns. We determine an exposure matrix aimed at informing mathematical models. This matrix exhibits a class and age structure which is very different from the homogeneous mixing hypothesis. We report on important properties of the contact patterns between school children that are relevant for modeling the propagation of diseases and for evaluating control measures. We discuss public health implications related to the management of schools in case of epidemics and pandemics. Our results can help define a prioritization of control measures based on preventive measures, case isolation, classes and school closures, that could reduce the disruption to education during epidemics.

Detailed information are lacking on influenza transmissibility in hospital although clusters are regularly reported. In this pilot study, our goal was to estimate the transmission rate of H3N2 2012-influenza, among patients and health care professionals in a short-term Acute Care for the Elderly Unit by using a stochastic approach and a simple susceptible-exposed-infectious-removed model. Transmission parameters were derived from documented individual contact data collected by Radio Frequency IDentification technology at the epidemic peak. From our model, nurses appeared to transmit infection to a patient more frequently with a transmission rate of 1.04 per day on average compared to 0.38 from medical doctors. This transmission rate was 0.34 between nurses. These results, even obtained in this specific context, might give a relevant insight of the influenza dynamics in hospitals and will help to improve and to target control measures for preventing nosocomial transmission of influenza. The investigation of nosocomial transmission of SARS-COV-2 might gain from similar approaches.

Background The spread of infectious diseases crucially depends on the pattern of contacts between individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. However, there are few empirical studies available that provide estimates of the number and duration of contacts between social groups. Moreover, their space and time resolutions are limited, so that data are not explicit at the person-to-person level, and the dynamic nature of the contacts is disregarded. In this study, we aimed to assess the role of data-driven dynamic contact patterns between individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population. Methods We considered high-resolution data about face-to-face interactions between the attendees at a conference, obtained from the deployment of an infrastructure based on radiofrequency identification (RFID) devices that assessed mutual face-to-face proximity. The spread of epidemics along these interactions was simulated using an SEIR (Susceptible, Exposed, Infectious, Recovered) model, using both the dynamic network of contacts defined by the collected data, and two aggregated versions of such networks, to assess the role of the data temporal aspects. Results We show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation that retains only the topology of the contact network fails to reproduce the size of the epidemic. Conclusions These results have important implications for understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics. Please see related article BMC Medicine, 2011, 9:88

Arboviruses represent a major cause of illness in Africa and have the potential to trigger widespread epidemics. We present data on arbovirus epidemics in Africa in 2023 and demonstrate the need for global public health authorities to intensify efforts in the surveillance and control of arbovirus diseases. Data were collected from the World Health Organization Weekly Bulletin on Outbreaks and Other Emergencies, Africa Centers for Disease Control and Prevention Weekly Event Based Surveillance Report, and other online sources. In 2023, a total of 7 arboviruses were responsible for 29 outbreaks across 25 countries in Africa, 22 of which occurred in West Africa; the outbreaks resulted in 19,569 confirmed cases and 820 deaths. Arbovirus epidemics in Africa pose a threat not only to public health within the continent but also globally, underscoring the urgent need for substantial investment in arbovirus surveillance, research, and preparedness capacities in Africa to prevent and respond to health crises effectively.

Background Description of the epidemiology of influenza is needed to inform influenza vaccination policy. Here we examined influenza virus circulation in countries in the Asia-Pacific region and compared the timing of seasonal epidemics with the timing of influenza vaccination. Methods Data were obtained from the World Health Organization (WHO) FluNet database for 2010–2017 for countries in the WHO Asia-Pacific region. Data from countries covering ≥5 consecutive seasons and ≥ 100 influenza positive cases per year were included. Median proportions of cases for each influenza virus type were calculated by country and season. The timing and amplitude of the epidemic peaks were determined by Fourier decomposition. Vaccination timing was considered appropriate for each country if it was recommended ≤4 months before the primary peak of influenza circulation. Results Seven hundred eleven thousand seven hundred thirty-four influenza cases were included from 19 countries. Peak circulation coincided with the winter seasons in most countries, although patterns were less clear in some countries in the inter-tropical area due to substantial secondary peaks. Influenza A/H3N2 dominated overall, but proportions of A and B strains varied by year and by country. Influenza B represented 31.4% of all cases. The WHO-recommended timing for influenza vaccination was appropriate in 12 countries. Vaccination timing recommendations were considered inappropriate in Laos, Cambodia, and Thailand, and were inconclusive for India, Sri Lanka, Singapore, and Vietnam due to unclear seasonality of influenza virus circulation. Conclusions Influenza virus circulation varied considerably across the Asia-Pacific region with an unusually high burden of influenza B. The recommended timing for vaccination was appropriate in most countries, except for several countries with unclear seasonality, mainly located in the inter-tropical area.

Delay between symptom onset and access to care is essential to prevent clinical worsening for different infectious diseases. For COVID-19, this delay might be associated with the clinical prognosis, but also with the different characteristics of patients. The objective was to describe characteristics and symptoms of community-acquired (CA) COVID-19 patients at hospital admission according to the delay between symptom onset and hospital admission, and to identify determinants associated with delay of admission. The present work was based on prospective NOSO-COR cohort data, and restricted to patients with laboratory confirmed CA SARS-CoV-2 infection admitted to Lyon hospitals between February 8 and June 30, 2020. Long delay of hospital admission was defined as ≥6 days between symptom onset and hospital admission. Determinants of the delay between symptom onset and hospital admission were identified by univariate and multiple logistic regression analysis. Data from 827 patients were analysed. Patients with a long delay between symptom onset and hospital admission were younger (p<0.01), had higher body mass index (p<0.01), and were more frequently admitted to intensive care unit (p<0.01). Their plasma levels of C-reactive protein were also significantly higher (p<0.01). The crude in-hospital fatality rate was lower in this group (13.3% versus 27.6%), p<0.01. Multiple analysis with correction for multiple testing showed that age ≥75 years was associated with a short delay between symptom onset and hospital admission (≤5 days) (aOR: 0.47 95% CI (0.34-0.66)) and CRP>100 mg/L at admission was associated with a long delay (aOR: 1.84 95% CI (1.32-2.55)). Delay between symptom onset and hospital admission is a major issue regarding prognosis of COVID-19 but can be related to multiple factors such as individual characteristics, organization of care and severe pathogenic processes. Age seems to play a key role in the delay of access to care and the disease prognosis.

Worldwide, COVID-19 outbreaks in nursing homes have often been sudden and massive. The study investigated the role SARS-CoV-2 virus spread in nearby population plays in introducing the disease in nursing homes. This was carried out through modelling the occurrences of first cases in each of 943 nursing homes of Auvergne-Rhône-Alpes French Region over the first epidemic wave (March-July, 2020). The cumulative probabilities of COVID-19 outbreak in the nursing homes and those of hospitalization for the disease in the population were modelled in each of the twelve Départements of the Region over period March-July 2020. This allowed estimating the duration of the active outbreak period, the dates and heights of the peaks of outbreak probabilities in nursing homes, and the dates and heights of the peaks of hospitalization probabilities in the population. Spearman coefficient estimated the correlation between the two peak series. The cumulative proportion of nursing homes with COVID-19 outbreaks was 52% (490/943; range: 22-70% acc. Département). The active outbreak period in the nursing homes lasted 11 to 21 days (acc. Département) and ended before lockdown end. Spearman correlation between outbreak probability peaks in nursing homes and hospitalization probability peaks in the population (surrogate of the incidence peaks) was estimated at 0.71 (95% CI: [0.66; 0.78]). The modelling highlighted a strong correlation between the outbreak in nursing homes and the external pressure of the disease. It indicated that avoiding disease outbreaks in nursing homes requires a tight control of virus spread in the surrounding populations.

Background Since 2011, the Global Influenza Hospital Surveillance Network (GIHSN) has used active surveillance to prospectively collect epidemiological and virological data on patients hospitalized with influenza virus infection. Here, we describe influenza virus strain circulation in the GIHSN participant countries during 2017–2018 season and examine factors associated with complicated hospitalization among patients admitted with laboratory-confirmed influenza illness. Methods The study enrolled patients who were hospitalized in a GIHSN hospital in the previous 48 h with acute respiratory symptoms and who had symptoms consistent with influenza within the 7 days before admission. Enrolled patients were tested by reverse transcription-polymerase chain reaction to confirm influenza virus infection. “Complicated hospitalization” was defined as a need for mechanical ventilation, admission to an intensive care unit, or in-hospital death. In each of four age strata (< 15, 15–< 50, 50–< 65, and ≥ 65 years), factors associated with complicated hospitalization in influenza-positive patients were identified by mixed effects logistic regression and those associated with length of hospital stay using a linear mixed-effects regression model. Results The study included 12,803 hospitalized patients at 14 coordinating sites in 13 countries, of which 4306 (34%) tested positive for influenza. Influenza viruses B/Yamagata, A/H3N2, and A/H1N1pdm09 strains dominated and cocirculated, although the dominant strains varied between sites. Complicated hospitalization occurred in 10.6% of influenza-positive patients. Factors associated with complicated hospitalization in influenza-positive patients included chronic obstructive pulmonary disease (15–< 50 years and ≥ 65 years), diabetes (15–< 50 years), male sex (50–< 65 years), hospitalization during the last 12 months (50–< 65 years), and current smoking (≥65 years). Chronic obstructive pulmonary disease (50–< 65 years), other chronic conditions (15–< 50 years), influenza A (50–< 65 years), and hospitalization during the last 12 months (< 15 years) were associated with a longer hospital stay. The proportion of patients with complicated influenza did not differ between influenza A and B. Conclusions Complicated hospitalizations occurred in over 10% of patients hospitalized with influenza virus infection. Factors commonly associated with complicated or longer hospitalization differed by age group but commonly included chronic obstructive pulmonary disease, diabetes, and hospitalization during the last 12 months.

Background Acute respiratory infections (ARIs) are a great public health challenge globally. The prevalence of respiratory viruses in patients with ARIs attending at different hospital settings is fully undetermined. Methods Laboratory-based surveillance for ARIs was conducted at inpatient and outpatient settings of 11 hospitals in North China. The first 2–5 patients with ARIs were recruited in each hospital weekly from 2012 through 2015. The presence of respiratory viruses was screened by PCR assays. The prevalence of respiratory viruses was determined and compared between patients at different hospital settings. Results A total of 3487 hospitalized cases and 6437 outpatients/Emergency Department (ED) patients were enrolled. The most commonly detected viruses in the hospitalized cases were respiratory syncytial virus (RSV, 33.3%) in children less than two years old, adenoviruses (13.0%) in patients 15–34 years old, and influenza viruses (IFVs, 9.6%) in patients ≥65 years. IFVs were the most common virus in outpatient/ED patients across all age groups (22.7%). After controlling for the confounders caused by other viruses and covariates, adenoviruses (adjusted odds ratio [aOR]: 3.97, 99% confidence interval [99% CI]: 2.19–7.20) and RSV (aOR: 2.04, 99% CI: 1.34–3.11) were independently associated with increased hospitalization in children, as well as adenoviruses in adults (aOR: 2.14, 99% CI: 1.19–3.85). Additionally, co-infection of RSV with IFVs was associated with increased hospitalization in children (aOR: 12.20, 99% CI: 2.65–56.18). Conclusions A substantial proportion of ARIs was associated with respiratory viruses in North China. RSV, adenoviruses, and co-infection of RSV and IFVs were more frequent in hospitalized children (or adenoviruses in adults), which might predict the severity of ARIs. Attending clinicians should be more vigilant of these infections.