Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series.

BACKGROUND: The problem of dealing with misreported data is very common in a wide range of contexts for different reasons. The current situation caused by the Covid-19 worldwide pandemic is a clear example, where the data provided by official sources were not always reliable due to data collection issues and to the high proportion of asymptomatic cases. In this work, a flexible framework is proposed, with the objective of quantifying the severity of misreporting in a time series and reconstructing the most likely evolution of the process. METHODS: The performance of Bayesian Synthetic Likelihood to estimate the parameters of a model based on AutoRegressive Conditional Heteroskedastic time series capable of dealing with misreported information and to reconstruct the most likely evolution of the phenomenon is assessed through a comprehensive simulation study and illustrated by reconstructing the weekly Covid-19 incidence in each Spanish Autonomous Community. RESULTS: Only around 51% of the Covid-19 cases in the period 2020/02/23-2022/02/27 were reported in Spain, showing relevant differences in the severity of underreporting across the regions. CONCLUSIONS: The proposed methodology provides public health decision-makers with a valuable tool in order to improve the assessment of a disease evolution under different scenarios.

Spatiotemporal Bayesian estimation of the number of under-reported COVID-19 cases in Victoria Australia.

Having an estimate of the number of under-reported cases is crucial in determining the true burden of a disease. In the COVID-19 pandemic, there is a great need to quantify the true disease burden by capturing the true incidence rate to establish appropriate measures and strategies to combat the disease. This study investigates the under-reporting of COVID-19 cases in Victoria, Australia, during the third wave of the pandemic as a result of variation in geographic area and time. It is aimed to determine potential under-reported areas and generate the true picture of the disease in terms of the number of cases. A two-tiered Bayesian hierarchical model approach is employed to estimate the true incidence and detection rates through Bayesian model averaging. The proposed model goes beyond testing inequality across areas by looking into other covariates such as weather, vaccination rates, and access to vaccination and testing centres, including interactions and variations between space and time. This model aims for parsimony yet allows a broader range of scope to capture the underlying dynamic of the reported COVID-19 cases. Moreover, it is a data-driven, flexible, and generalisable model to a global context such as cross-country estimation and across time points under strict pandemic conditions.

Lessons learned from an under-reported mumps epidemic among Rohingya refugees in Cox's Bazar District, Bangladesh.

BACKGROUND: In 2018, a large mumps epidemic coincided with an outbreak of diphtheria in refugee camps established in Bangladesh for the Rohingya people. These refugees did not receive a mumps-containing vaccine. METHODS: Cases of mumps were reported to the WHO's Early Warning, Alert and Response System (EWARS) during the Rohingya refugee crisis. The authors present amalgamated epidemiological data of a major, previously under-reported, mumps epidemic. RESULTS: In total, 19 215 mumps cases across a total of 218 facilities were reported to EWARS during 2018. The attack rate was 2.1% of the whole population. Of these cases, 7687 (40%) were in children aged <5 y. Mumps was more commonly seen among males than females. CONCLUSION: Detailed reporting of outbreaks of all vaccine-preventable diseases is essential to ensure appropriate vaccination decisions can be made in future humanitarian crises.

Campylobacter bacteremia: a rare and under-reported event?

Bacteria belonging to the species Campylobacter are the most common cause of bacterial diarrhoea in humans. The clinical phenotype associated with Campylobacter infections ranges from asymptomatic conditions to severe colitis and bacteremia. In susceptible patients, Campylobacter infections are associated with significant morbidity and mortality, with both host factors and bacterial factors being involved in the pathogenesis of bacteremia. In the host, age, gender and immune-compromising conditions may predispose for Campylobacter infections, whilst the most important bacterial determinants mentioned in the literature are cytotoxin production and flagellar motility. The role of sialylated lipo-oligosaccharide (LOS) and serum resistance in bacteremia is inconclusive at this time, and the clinical significance of Campylobacter bacteremia is not yet fully understood. More emphasis on the detection of Campylobacter species from blood cultures in susceptible patients at risk for Campylobacter infections will increase our understanding of the pathogenesis and the relevance of Campylobacter bacteremia.