BackgroundThe Google Flu Trends service was launched in 2008 to track changes in the volume of online search queries related to flu-like symptoms. Over the last few years, the trend data produced by this service has shown a consistent relationship with the actual number of flu reports collected by the US Centers for Disease Control and Prevention (CDC), often identifying increases in flu cases weeks in advance of CDC records. However, contrary to popular belief, Google Flu Trends is not an early epidemic detection system. Instead, it is designed as a baseline indicator of the trend, or changes, in the number of disease cases.ObjectiveTo evaluate whether these trends can be used as a basis for an early warning system for epidemics.MethodsWe present the first detailed algorithmic analysis of how Google Flu Trends can be used as a basis for building a fully automated system for early warning of epidemics in advance of methods used by the CDC. Based on our work, we present a novel early epidemic detection system, called FluBreaks (dritte.org/flubreaks), based on Google Flu Trends data. We compared the accuracy and practicality of three types of algorithms: normal distribution algorithms, Poisson distribution algorithms, and negative binomial distribution algorithms. We explored the relative merits of these methods, and related our findings to changes in Internet penetration and population size for the regions in Google Flu Trends providing data.ResultsAcross our performance metrics of percentage true-positives (RTP), percentage false-positives (RFP), percentage overlap (OT), and percentage early alarms (EA), Poisson- and negative binomial-based algorithms performed better in all except RFP. Poisson-based algorithms had average values of 99%, 28%, 71%, and 76% for RTP, RFP, OT, and EA, respectively, whereas negative binomial-based algorithms had average values of 97.8%, 17.8%, 60%, and 55% for RTP, RFP, OT, and EA, respectively. Moreover, the EA was also affected by the region’s population size. Regions with larger populations (regions 4 and 6) had higher values of EA than region 10 (which had the smallest population) for negative binomial- and Poisson-based algorithms. The difference was 12.5% and 13.5% on average in negative binomial- and Poisson-based algorithms, respectively.ConclusionsWe present the first detailed comparative analysis of popular early epidemic detection algorithms on Google Flu Trends data. We note that realizing this opportunity requires moving beyond the cumulative sum and historical limits method-based normal distribution approaches, traditionally employed by the CDC, to negative binomial- and Poisson-based algorithms to deal with potentially noisy search query data from regions with varying population and Internet penetrations. Based on our work, we have developed FluBreaks, an early warning system for flu epidemics using Google Flu Trends.

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Highlights Bi-phasic illness observed in COVID 19 leading to acute respiratory distress syndrome after week two of illness. Haemochromatosis compromises host defence mechanisms and predisposes to infection. Excess intracellular and circulating iron may increase COVID-19 viral replication leading to a hyperinflammatory state. Hepatotoxicity may have been exacerbated by IL-1 receptor antagonist use in combination with haemochromatosis, acute COVID-19 infection and haemophagocytic lymphohistiocytosis.

A 45-year-old Caucasian woman of Northern European ancestry presents with new symptomatic normocytic anaemia. Admission bloodwork revealed a haemoglobin of 58 g/L (reference range 118-148 g/L) with reticulocytopenia of 8 Â 10 9 /L (reference range 20-110 Â 10 9 /L). Serum erythropoietin exceeded 750 IU/L (reference range 2.6-18.5 IU/L). A weak pan-reactive antibody was identified during antibody screen by indirect antigen testing (IAT), being mildly enhanced by low ionising strength saline (LISS) and in papain-treated cells. It was not eliminated by Knop's inhibition reagent (IGBRL reagents, Filton, UK) or in dithiothreitol-treated cells. Direct antigen testing (DAT) was negative.Our patient's sole co-morbidity was alcohol excess (>200 United Kingdom (UK) units of alcohol/week). Comprehensive preliminary assessments were otherwise unrevealing, bar weak positive antinuclear antibodies with homogenous (AC-1) pattern and positive pANCA (anti-MPO negative). A bone marrow biopsy was undertaken.Pending diagnosis, a supportive red cell transfusion regimen was initiated. As the detected pan-reactive antibody was extremely weak, it