Hafnia alvei Pneumonia: A Rare Cause of Infection in a Patient with COVID-19

Herein, we describe a case report of a critically ill patient, a 48-year-old man without comorbidities admitted to the hospital with a serious type 1 (hypoxemic) respiratory insufficiency and confirmed diagnosis of COVID-19. After 5 days with invasive mechanical ventilation, the patient developed a bacterial co-infection, namely a pneumonia by Hafnia alvei, requiring the last line of respiratory support: extracorporeal membrane oxygenation (ECMO). Subsequently, his clinical situation gradually stabilized, until he was discharged from the hospital on day 61, being accompanied in ambulatory consultation by the physical medicine and pulmonology department during the post-COVID-19 recovery. H. alvei is a Gram-negative bacterium that is rarely isolated from human specimens and is rarely considered to be pathogenic. However, COVID-19 disease can cause substantial organ dysfunction and can be associated with bacterial secondary infections which can favor the emergence of rare infectious diseases by uncommon microorganisms.

Using machine learning to predict COVID-19 infection and severity risk among 4,510 aged adults: a UK Biobank cohort study

BackgroundMany risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization).MethodsAmong aged adults (69.3 ± 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4,510 participants with 7,539 test cases. We downloaded baseline data from 10-14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean.ResultsThe “best-fit” model for predicting COVID-19 risk achieved excellent discrimination (AUC=0.969, 95% CI=0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization “best-fit” model was more modest (AUC=0.803, 95% CI=0.663-0.943) and included only serology titers.ConclusionsAccurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.

Bayesian hierarchical models for disease mapping applied to contagious pathologies

Disease mapping aims to determine the underlying disease risk from scattered epidemiological data and to represent it on a smoothed colored map. This methodology is based on Bayesian inference and is classically dedicated to non-infectious diseases whose incidence is low and whose cases distribution is spatially (and eventually temporally) structured. Over the last decades, disease mapping has received many major improvements to extend its scope of application: integrating the temporal dimension, dealing with missing data, taking into account various a prioris (environmental and population covariates, assumptions concerning the repartition and the evolution of the risk), dealing with overdispersion, etc. We aim to adapt this approach to rare infectious diseases. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood; this can result in local overdispersion and in higher spatial and temporal dependencies due to direct and/or indirect transmission. We have proposed and tested 60 Bayesian hierarchical models on 400 simulated datasets and bovine tuberculosis real data. This analysis shows the relevance of the CAR (Conditional AutoRegressive) processes to deal with the structure of the risk. We can also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. In addition our study provided relevant maps which are congruent with the real risk (simulated data) and with the knowledge concerning bovine tuberculosis (real data).Author summaryDisease mapping is dedicated to non-infectious diseases whose incidence is low and whose distribution is spatially (and eventually temporally) structured. In this paper, we aim to adapt this approach to rare infectious pathologies. In the context of a contagious disease, the outcome of a primary case can in addition generate secondary occurrences of the pathology in a close spatial and temporal neighborhood, resulting in local overdispersion and in high spatial and temporal dependencies. We thus explored different adapted spatial, temporal and spatiotemporal links and highlight the most adapted to likely risk structures for infectious diseases. We also conclude that the negative binomial models outperform the Poisson models with a Gaussian noise to handle overdispersion. Our study also provided relevant maps which are congruent with the real risk (in case of simulated data) and with the knowledge concerning bovine tuberculosis (when applying to real data). Thus disease mapping appears as a promising way to investigate rare infectious diseases.

On a Vaccination Model for Non-fatal Rare Infectious Diseases; with Emphasis on Rubella in Kuwait

An immunization model for non-fatal rare contagious diseases in non-fixed size communities (developing countries) is proposed. It reveals the demographic and epidemiological data needed in a country to decide upon the best strategy of vaccination. The approach is illustrated with reference to rubella in Kuwait population. Comparisons between rubella vaccination strategies of the U.S.A., U.K. and a recently suggested one for Kuwait are carried out.