ABSTRACT
The still ongoing pandemic of SARS-CoV-2 virus and COVID-19 disease, affecting the population worldwide, has demonstrated the need of more accurate methodologies for assessing, monitoring, and controlling an outbreak of such devastating proportions. Authoritative attempts have been made in traditional fields of medicine (epidemiology, virology, infectiology) to address these shortcomings, mainly by relying on mathematical and statistical modeling. However, here, we propose approaching the methodological work from a different, and to some extent alternative, standpoint. Applied systematically, the concepts and tools of statistical engineering and quality management, developed not only in healthcare settings, but also in other scientific contexts, can be very useful in assessing, monitoring, and controlling pandemic events. We propose a methodology based on a set of tools and techniques, formulas, graphs, and tables to support the decision-making concerning the management of a pandemic like COVID-19. This methodological body is hereby named Pandemetrics. This name intends to emphasize the peculiarity of our approach to measuring, and graphically presenting the unique context of the COVID-19 pandemic.
ABSTRACT
Infection with the tick-borne encephalitis virus (TBEV) can cause meningitis, meningoencephalitis and myelitis in humans. TBEV is an enveloped RNA virus of the family Flaviviridae, which is mostly transmitted via tick bites. However, transmission by consumption of virus-contaminated goat raw milk and goat raw milk products has also been described. Only a few methods have been reported for the detection of TBEV in food so far. Here, we compare different virus extraction methods for goat raw milk and goat raw milk cream cheese and subsequent detection of TBEV-RNA by RT-qPCR. Langat virus (LGTV), a naturally attenuated TBEV strain, was used for artificial contamination experiments. Mengovirus and the human coronavirus 229E were compared to assess their suitability to serve as internal process controls. Out of three tested extraction protocols for raw milk, sample centrifugation followed by direct RNA extraction from the aqueous interphase yielded the best results, with a recovery rate (RR) of 31.8 ± 4.9% for LGTV and a detection limit of 6.7 × 103 LGTV genome copies/ml. Out of two methods for cream cheese, treatment of the samples with TRI Reagent® and chloroform prior to RNA extraction showed the best RR of 4.7 ± 1.6% for LGTV and a detection limit of 9.4 × 104 LGTV genome copies/g. RRs of Mengovirus and LGTV were similar for both methods; therefore, Mengovirus is suggested as internal process control virus. The developed methods may be useful for screening or surveillance studies, as well as in outbreak investigations.
ABSTRACT
OBJECTIVE: Outbreaks of influenza-like diseases often cause spikes in the demand for hospital beds. Early detection of these outbreaks can enable improved management of hospital resources. The objective of this study was to test whether surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between emergency department (ED) presentations with influenza-like illnesses provide efficient early detection of these outbreaks. METHODS: Our study used data on ED presentations to major public hospitals in Queensland, Australia across 2017-2020. We developed surveillance algorithms for each hospital that flag potential outbreaks when the average time between successive ED presentations with influenza-like illnesses becomes anomalously small. We designed one set of algorithms to be responsive to a wide range of anomalous decreases in the time between presentations. These algorithms concurrently monitor three exponentially weighted moving averages (EWMAs) of the time between presentations and flag an outbreak when at least one EWMA falls below its control limit. We designed another set of algorithms to be highly responsive to narrower ranges of anomalous decreases in the time between presentations. These algorithms monitor one EWMA of the time between presentations and flag an outbreak when the EWMA falls below its control limit. Our algorithms use dynamic control limits to reflect that the average time between presentations depends on the time of year, time of day, and day of the week. RESULTS: We compared the performance of the algorithms in detecting the start of two epidemic events at the hospital-level: the 2019 seasonal influenza outbreak and the early-2020 COVID-19 outbreak. The algorithm that concurrently monitors three EWMAs provided significantly earlier detection of these outbreaks than the algorithms that monitor one EWMA. CONCLUSION: Surveillance algorithms designed to be responsive to a wide range of anomalous decreases in the time between ED presentations are highly efficient at detecting outbreaks of influenza-like diseases at the hospital level.