Learning analytic implementations are increasingly being included in learning management systems in higher education. We lay out some concerns with the way learning analytics – both data and algorithms – are often presented within an unproblematized Big Data discourse. We describe some potential problems with the often implicit assumptions about learning and learners – and indeed the tendency not to theorize learning explicitly – that underpin such implementations. Finally, we describe an attempt to devise our own analytics, grounded in a sociomaterial conception of learning. We use the data obtained to suggest that the relationships between learning and the digital traces left by participants in online learning are far from trivial, and that any analytics that relies on these as proxies for learning tends towards a behaviorist evaluation of learning processes

Objectives To inform public health practitioners who are designing, adapting and implementing testing and tracing strategies for COVID-19 control. Study design Monitoring and evaluation of a national public health protection programme. Methods All close contacts of laboratory confirmed cases of COVID-19 identified between the 19th May and 2nd August were included; secondary attack rates, and numbers needed to test were estimated. Results 4,586 of 7,272 (63%) close contacts of cases were tested with at least one test. The secondary attack rate in close contacts who were tested was 7% (95%CI 6.3 - 7.8%). At the ‘Day 0’ test, 14.6% (95%CI 11.6 – 17.6%) of symptomatic close contacts tested positive compared with 5.2% (95%CI 4.4 – 5.9%) of asymptomatic close contacts. Conclusions The application of additional symptom based criteria for testing in this high incidence population (close contacts) is of limited utility because of the low negative predictive value of absence of symptoms.

Recent infection testing algorithms (RITA) for HIV combine serological assays with epidemiological data to determine likely recent infections, indicators of ongoing transmission. In 2016, we integrated RITA into national HIV surveillance in Ireland to better inform HIV prevention interventions. We determined the avidity index (AI) of new HIV diagnoses and linked the results with data captured in the national infectious disease reporting system. RITA classified a diagnosis as recent based on an AI < 1.5, unless epidemiological criteria (CD4 count <200 cells/mm3; viral load <400 copies/ml; the presence of AIDS-defining illness; prior antiretroviral therapy use) indicated a potential false-recent result. Of 508 diagnoses in 2016, we linked 448 (88.1%) to an avidity test result. RITA classified 12.5% of diagnoses as recent, with the highest proportion (26.3%) amongst people who inject drugs. On multivariable logistic regression recent infection was more likely with a concurrent sexually transmitted infection (aOR 2.59; 95% CI 1.04–6.45). Data were incomplete for at least one RITA criterion in 48% of cases. The study demonstrated the feasibility of integrating RITA into routine surveillance and showed some ongoing HIV transmission. To improve the interpretation of RITA, further efforts are required to improve completeness of the required epidemiological data.

Objectives There is limited evidence on the risk of in-flight transmission of SARS-CoV-2. This study estimated the extent of in-flight SARS-CoV-2 transmission on international flights arriving into Ireland during December 2020. Study design Cross-sectional analysis. Methods National surveillance data identified all notified cases of COVID-19 who were infectious while travelling on international flights to Ireland during December 2020. Close contacts of cases were tested for SARS-CoV-2 and results were collated to estimate the pooled secondary attack rate across all flights. Laboratory and epidemiological data were obtained from the Health Service Executive Covid Care Tracker, a national database of COVID-19 cases in Ireland. Results 165 infectious cases of COVID-19 were identified on 134 incoming flights; 40.0% were symptomatic on board. There were 2,099 flight close contacts identified, of whom 40.9% had results of a SARS-CoV-2 PCR test within 14 days of arrival. The pooled secondary attack rate for these contacts was 7.0%, and was higher among those on flights of ≥5 hours duration (p=0.008). Over half (59.1%) of close contacts had no SARS-CoV-2 test result recorded; reasons included incorrect or absent contact details (26.5%), and no response when contacted (17.8%). Conclusions In this national study investigating transmission of SARS-CoV-2 from international flights arriving into Ireland, the pooled secondary attack rate was 7.0%. International travel is likely to have contributed to the third wave of SARS-CoV-2 infections in Ireland in early 2021. Application of non-pharmaceutical interventions remains central to mitigating the risk of in-flight transmission.

Background The serial interval is the period of time between the onset of symptoms in an infector and an infectee and is an important parameter which can impact on the estimation of the reproduction number. Whilst several parameters influencing infection transmission are expected to be consistent across populations, the serial interval can vary across and within populations over time. Therefore, local estimates are preferable for use in epidemiological models developed at a regional level. We used data collected as part of the national contact tracing process in Ireland to estimate the serial interval of SARS-CoV-2 infection in the Irish population, and to estimate the proportion of transmission events that occurred prior to the onset of symptoms. Results After data cleaning, the final dataset consisted of 471 infected close contacts from 471 primary cases. The median serial interval was 4 days, mean serial interval was 4.0 (95% confidence intervals 3.7, 4.3) days, whilst the 25th and 75th percentiles were 2 and 6 days respectively. We found that intervals were lower when the primary or secondary case were in the older age cohort (greater than 64 years). Simulating from an incubation period distribution from international literature, we estimated that 67% of transmission events had greater than 50% probability of occurring prior to the onset of symptoms in the infector. Conclusions Whilst our analysis was based on a large sample size, data were collected for the primary purpose of interrupting transmission chains. Similar to other studies estimating the serial interval, our analysis is restricted to transmission pairs where the infector is known with some degree of certainty. Such pairs may represent more intense contacts with infected individuals than might occur in the overall population. It is therefore possible that our analysis is biased towards shorter serial intervals than the overall population.