Just a bad flu? Tackling the "infodemic" in Ireland through a comparative analysis of hospitalised cases of COVID-19 and influenza

Epidemiological trends of respiratory tract pathogens detected via mPCR in Australian adult patients before COVID-19

BACKGROUND

Respiratory tract infections (RTIs) are a major global health burden due to their high morbidity and mortality. This retrospective study described the epidemiology of respiratory pathogens in adults over a 5-year period at an Australian tertiary healthcare network.

METHODS

All multiplex reverse transcription polymerase chain reaction respiratory samples taken between the 1st of November 2014 and the 31st of October 2019 were included in this study. Overall prevalence and variations according to seasons, age groups and sex were analysed, as well as factors associated with prolonged hospital and intensive care length of stay.

RESULTS

There were 12,453 pathogens detected amongst the 12,185 positive samples, with coinfection rates of 3.7%. Picornavirus (Rhinovirus), Influenza A and respiratory syncytial virus were the most commonly detected pathogens. Mycoplasma pneumoniae was the most commonly detected atypical bacteria. Significant differences in the prevalence of Chlamydia pneumoniae and Human metapneumovirus infections were found between sexes. Longest median length of intensive care and hospital stay was for Legionella species. Seasonal variations were evident for certain pathogens.

CONCLUSIONS

The high rates of pathogen detection and hospitalisation in this real-world study highlights the significant burden of RTIs, and the urgent need for an improved understanding of the pathogenicity as well as preventative and treatment options of RTIs.

Diabetes Research and Resource Sharing During the COVID-19 Pandemic: A Systematic Review and Experience from an Academic/Non-Profit Resource Website

BACKGROUND

The magnitude of the response of the diabetes professional community to the COVID-19 pandemic is not known. We aimed to examine diabetes technology research trends and resources offered by professional organizations during this period.

METHODS

We explored patterns of the response from the professional diabetes community to the pandemic by (1) systematically searching for articles related to diabetes, COVID-19, and diabetes technologies; (2) examining publication trends of research protocols (clinicaltrials.gov) and preprints (medRxiv); and (3) reviewing online resources from professional organizations including our website (COVIDinDiabetes.org; an Emory University-Diabetes Technology Society collaboration).

RESULTS

We identified 492 articles published between December 2019 and December 2022 meeting our inclusion criteria. Telemedicine and continuous glucose monitoring were the most common reported technologies from most parts of the world. The largest number of preprint articles was published in 2020, with a decline in 2021 and 2022. The number of research protocols related to COVID-19 was the highest in 2020 and declined in 2021 and 2022. Resources from organizations included protocols adapted to treat patients with diabetes and COVID-19, training programs, emergency preparedness, and literature on diabetes and COVID-19. On our website (COVIDinDiabetes.org), there were 12 236 visits and 18 149 pageviews, with 1.6 actions per visits, with most visits coming from North America (N = 7233, 54.2%), South America (N = 2663, 21.8%), and Europe (N = 1219).

CONCLUSIONS

We conclude that the COVID-19 pandemic promoted unprecedented global research productivity related to diabetes and COVID-19 and that the transition to the use of technology resources has been evident during this period.

Real-time forecasting of COVID-19 spread according to protective behavior and vaccination: autoregressive integrated moving average models

BACKGROUND

Mathematical and statistical models are used to predict trends in epidemic spread and determine the effectiveness of control measures. Automatic regressive integrated moving average (ARIMA) models are used for time-series forecasting, but only few models of the 2019 coronavirus disease (COVID-19) pandemic have incorporated protective behaviors or vaccination, known to be effective for pandemic control.

METHODS

To improve the accuracy of prediction, we applied newly developed ARIMA models with predictors (mask wearing, avoiding going out, and vaccination) to forecast weekly COVID-19 case growth rates in Canada, France, Italy, and Israel between January 2021 and March 2022. The open-source data was sourced from the YouGov survey and Our World in Data. Prediction performance was evaluated using the root mean square error (RMSE) and the corrected Akaike information criterion (AICc).

RESULTS

A model with mask wearing and vaccination variables performed best for the pandemic period in which the Alpha and Delta viral variants were predominant (before November 2021). A model using only past case growth rates as autoregressive predictors performed best for the Omicron period (after December 2021). The models suggested that protective behaviors and vaccination are associated with the reduction of COVID-19 case growth rates, with booster vaccine coverage playing a particularly vital role during the Omicron period. For example, each unit increase in mask wearing and avoiding going out significantly reduced the case growth rate during the Alpha/Delta period in Canada (-0.81 and -0.54, respectively; both p < 0.05). In the Omicron period, each unit increase in the number of booster doses resulted in a significant reduction of the case growth rate in Canada (-0.03), Israel (-0.12), Italy (-0.02), and France (-0.03); all p < 0.05.

CONCLUSIONS

The key findings of this study are incorporating behavior and vaccination as predictors led to accurate predictions and highlighted their significant role in controlling the pandemic. These models are easily interpretable and can be embedded in a "real-time" schedule with weekly data updates. They can support timely decision making about policies to control dynamically changing epidemics.

Evaluation of Non-Pharmacological Measures Implemented in the Management of the COVID-19 Pandemic in Romania

The management of the COVID-19 pandemic in Romania has included the involvement of not only the medical system, but also that of the administrative and social services. All these organizations are working together to lower the impact on the health of the general population, to increase the health system’s response capabilities and even to diminish the negative effects upon the economy due to the epidemic’s length. Therefore, non-pharmacological measures (NPMs) imposed through restrictive measures (administrative, economic and individual) have influenced the evolution of morbidity and mortality. Even from the first months of the pandemic’s progression, researchers have shown the impact of the NPMs’ existence, as there were many studies on all NPMs in conjunction, as well as those targeting specific measures such as school closures. Our study started by establishing a temporal relationship between the non-pharmacological measures found in most countries (wearing a mask, washing hands and physical distancing, limiting economic activities, closing schools, limiting internal and international movement, banning public and private events in closed spaces) and the evolution of the pandemic in Romania. The degree of novelty brought by this study consists of extending the analysis to the pre-existing state of the health system and to the measures meant to increase the resilience of the population, as well as to the measures aimed at reducing the type of risk, and factors that can equally influence the evolution of the number of cases. The results of the statistical analysis show the important effects of certain NPMs (mask mandates, online schooling, decisions regarding imposing or lifting local restrictions) as well as the reduced impact of other measures (hand disinfection, social distancing or the restriction of public and private events). Hence, it can be concluded that during such pandemics, implementing quick, simple measures can prevent the spread of the disease and help fight the contagion in a better manner.

Hospital length of stay for COVID-19 patients: A systematic review and meta-analysis

The length of stay in the hospital for COVID-19 can aid in understanding the disease's prognosis. Thus, the goal of this study was to collectively estimate the hospital length of stay (LoS) in COVID-19 hospitalized individuals. To locate related studies, international databases (including Google Scholar, Science Direct, PubMed, and Scopus) were searched. The I² index, the Cochran Q test, and T² were used to analyze study heterogeneity. The mean LoS in COVID- 19 hospitalized patients was estimated using a random-effects model. COVID-19's total pooled estimated hospital LoS was 15.35, 95%CI:13.47-17.23; p<0.001, I² = 80.0). South America had the highest pooled estimated hospital LoS of COVID-19 among the continents, at 20.85 (95%CI: 14.80-26.91; p<0.001, I² = 0.01), whereas Africa had the lowest at 8.56 8 (95%CI: 1.00-22.76). The >60 age group had the highest pooled estimated COVID-19 hospital LoS of 16.60 (95%CI: 12.94-20.25; p<0.001, I² = 82.6), while the 40 age group had the lowest hospital LoS of 10.15 (95% CI: 4.90-15.39, p<0.001, I² = 22.1). The metanalysis revealed that COVID-19's hospital LoS was more than 10 days. However, it appears that this duration varies depending on a number of factors, including the patient's age and the availability of resources.