Assessing the quality of data for drivers of disease emergence.

Drivers are factors that have the potential to directly or indirectly influence the likelihood of infectious diseases emerging or re-emerging. It is likely that an emerging infectious disease (EID) rarely occurs as the result of only one driver; rather, a network of sub-drivers (factors that can influence a driver) are likely to provide conditions that allow a pathogen to (re-)emerge and become established. Data on sub-drivers have therefore been used by modellers to identify hotspots where EIDs may next occur, or to estimate which sub-drivers have the greatest influence on the likelihood of their occurrence. To minimise error and bias when modelling how sub-drivers interact, and thus aid in predicting the likelihood of infectious disease emergence, researchers need good-quality data to describe these sub-drivers. This study assesses the quality of the available data on sub-drivers of West Nile virus against various criteria as a case study. The data were found to be of varying quality with regard to fulfilling the criteria. The characteristic with the lowest score was completeness, i.e. where sufficient data are available to fulfil all the requirements for the model. This is an important characteristic as an incomplete data set could lead to erroneous conclusions being drawn from modelling studies. Thus, the availability of good-quality data is essential to reduce uncertainty when estimating the likelihood of where EID outbreaks may occur and identifying the points on the risk pathway where preventive measures may be taken.

Les facteurs d'émergence sont des éléments ayant le potentiel direct ou indirect d'influencer la probabilité d'émergence ou de réémergence d'une maladie infectieuse. Il est probablement rare qu'une maladie infectieuse émergente apparaisse en raison d'un seul facteur ; c'est plutôt un faisceau de sous-facteurs (éléments pouvant avoir une influence sur un même facteur) qui contribue à ce que les conditions soient réunies pour qu'un agent pathogène puisse (ré)émerger et s'établir. Les concepteurs de modèles ont donc utilisé les données relatives aux sous-facteurs pour identifier les zones sensibles où les prochaines maladies infectieuses émergentes pourraient survenir, ou pour faire une estimation des sous-facteurs ayant la plus grande influence sur la probabilité de leur occurrence. Les chercheurs ont besoin de données de qualité pour décrire ces sous-facteurs, afin de minimiser le risque d'erreur et de biais lors de la modélisation de l'interaction entre les différents sous-facteurs, et de contribuer ainsi à mieux prédire la probabilité d'apparition d'une maladie infectieuse émergente. Les auteurs présentent une étude de cas qui a consisté à évaluer la qualité des données disponibles relatives aux sous-facteurs d'émergence du virus de la fièvre de West Nile au regard de différents critères. Il est apparu que la qualité des données était variable au regard des critères examinés. Le paramètre dont le score était le plus bas est celui de la complétude - le fait que suffisamment de données soient disponibles pour répondre à toutes les exigences du modèle. Il s'agit pourtant d'un paramètre important car des données incomplètes peuvent inciter à tirer des conclusions erronées des études de modélisation. La disponibilité de données de bonne qualité est essentielle pour réduire l'incertitude lors de l'estimation de la probabilité d'apparition de maladies infectieuses émergentes dans des zones déterminées, ainsi que pour identifier les points critiques de concrétisation du risque où des mesures préventives pourraient être mises en place.

Los inductores o factores de inducción [drivers] son aquellos que, directa o indirectamente, pueden influir en la probabilidad de que surjan o resurjan enfermedades infecciosas. Todo indica que rara vez una enfermedad infecciosa emergente aparece por efecto de un solo factor de inducción, sino que es probable que haya más bien una combinación de "subfactores de influencia" [sub-drivers] (factores que pueden influir en un inductor) que cree condiciones propicias para que un patógeno (re)surja y logre asentarse. Los creadores de modelos, por consiguiente, se han servido de datos sobre estos subfactores de influencia para localizar aquellas zonas donde con mayor probabilidad puedan aparecer próximamente enfermedades infecciosas emergentes o para determinar cuáles son los subfactores que más influyen en la probabilidad de que ello ocurra. Para reducir al mínimo los errores y sesgos al modelizar la interacción entre los subfactores y ayudar así a calcular la probabilidad de que surja una enfermedad infecciosa emergente, los investigadores necesitan datos de buena calidad para caracterizar estos subfactores. En el análisis expuesto por los autores se utilizó el virus del Nilo Occidental como ejemplo de estudio para evaluar, con arreglo a diversos criterios, la calidad de los datos existentes sobre los subfactores que inciden en la aparición de este virus. Lo que se constató, en relación con el grado de cumplimiento de los criterios, es que esos datos eran de calidad variable. La característica o parámetro que deparó la puntuación más baja fue la completud, es decir, la existencia de datos suficientes para aportar al modelo toda la información requerida para que este funcione bien. Se trata de una característica importante, pues un conjunto incompleto de datos podría llevar a extraer conclusiones erróneas de los estudios de modelización. Por ello, para reducir la incertidumbre a la hora de calcular la probabilidad de que en cierto lugar surjan brotes de enfermedades infecciosas emergentes y de determinar, dentro de la cadena de materialización del riesgo, aquellos eslabones en los que cabe adoptar medidas preventivas, es indispensable disponer de datos de buena calidad.

Transmission of SARS-CoV-2 among healthcare workers and patients in a teaching hospital in the Netherlands confirmed by whole-genome sequencing.

AIM: To investigate the sources of infection among healthcare workers (HCWs) and patients in a teaching hospital in the Netherlands during the early stages of the coronavirus disease 2019 (COVID-19) pandemic using epidemiological and whole-genome sequencing data. METHODS: From 3rd April to 11th May 2020, 88 HCWs and 215 patients were diagnosed with COVID-19. Whole-genome sequences were obtained for 30 HCWs and 20 patients. RESULTS: Seven and 11 sequence types were identified in HCWs and patients, respectively. Cluster A was the most common sequence type, detected in 23 (77%) HCWs; of these, 14 (61%) had direct patient contact and nine (39%) had indirect patient contact. In addition, seven patients who were not hospitalized in the COVID-19 cohort isolation ward who became positive during their admission were infected with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) cluster A. Following universal masking of all HCWs and emphasis on physical distancing during meals and breaks, no further evidence was found for patient-to-HCW or HCW-to-HCW transmission or vice versa. CONCLUSION: The finding that patients and HCWs were infected with SARS-CoV-2 cluster A suggests both HCW-to-HCW and HCW-to-patient transmission.

Global status of Middle East respiratory syndrome coronavirus in dromedary camels: a systematic review.

Dromedary camels have been shown to be the main reservoir for human Middle East respiratory syndrome (MERS) infections. This systematic review aims to compile and analyse all published data on MERS-coronavirus (CoV) in the global camel population to provide an overview of current knowledge on the distribution, spread and risk factors of infections in dromedary camels. We included original research articles containing laboratory evidence of MERS-CoV infections in dromedary camels in the field from 2013 to April 2018. In general, camels only show minor clinical signs of disease after being infected with MERS-CoV. Serological evidence of MERS-CoV in camels has been found in 20 countries, with molecular evidence for virus circulation in 13 countries. The seroprevalence of MERS-CoV antibodies increases with age in camels, while the prevalence of viral shedding as determined by MERS-CoV RNA detection in nasal swabs decreases. In several studies, camels that were sampled at animal markets or quarantine facilities were seropositive more often than camels at farms as well as imported camels vs. locally bred camels. Some studies show a relatively higher seroprevalence and viral detection during the cooler winter months. Knowledge of the animal reservoir of MERS-CoV is essential to develop intervention and control measures to prevent human infections.

Serological evidence for exposure to avian influenza viruses within poultry workers in southern China.

The risk of infection with avian influenza viruses for poultry workers is relatively unknown in China, and study results are often biased by the notification of only the severe human cases. Protein microarray was used to detect binding antibodies to 13 different haemagglutinin (HA1-part) antigens of avian influenza A(H5N1), A(H7N7), A(H7N9) and A(H9N2) viruses, in serum samples from poultry workers and healthy blood donors collected in the course of 3 years in Guangdong Province, China. Significantly higher antibody titre levels were detected in poultry workers when compared to blood donors for the most recent H5 and H9 strains tested. These differences were most pronounced in younger age groups for antigens from older strains, but were observed in all age groups for the recent H5 and H9 antigens. For the H7 strains tested, only poultry workers from two retail live poultry markets had significantly higher antibody titres compared to blood donors.