Temporal trends in microbial detection during the COVID-19 pandemic: Analysis of the Japan surveillance for Infection Prevention and Healthcare Epidemiology (J-SIPHE) database

The impact of the COVID-19 pandemic on the incidence of microbial infections and other metrics related to antimicrobial resistance (AMR) has not yet been fully described. Using data from Japan Surveillance for Infection Prevention and Healthcare Epidemiology (J-SIPHE), a national surveillance database system that routinely collects clinical and epidemiological data on microbial infections, infection control practices, antimicrobial use, and AMR emergence from participating institutions in Japan, we assessed the temporal changes in AMR-related metrics before and after the start of the COVID-19 pandemic. We found that an apparent decrease in the incidence of microbial infections in 2020 compared with 2019 may have been driven primarily by a reduction in bed occupancy, although the incidence showed a constant or even slightly increasing trend after adjusting for bed occupancy. Meanwhile, we found that the incidence of Streptococcus pneumoniae dramatically decreased from April 2020 onward, probably due to stringent non-pharmaceutical interventions against COVID-19. Antimicrobial use showed a weak increasing trend, while the use of hand sanitiser at the included medical institutions increased by about 50% in 2020 compared with 2019. © 2022 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases

Visualization and Visual Knowledge Discovery from Big Uncertain Data

In the current uncertain world, data are kept growing bigger. Big data refer to the data flow of huge volume, high velocity, wide variety, and different levels of veracity (e.g., precise data, imprecise/uncertain data). Embedded in these big data are implicit, previously unknown, but valuable information and knowledge. With huge volumes of information and knowledge that can be discovered by techniques like data mining, a challenge is to validate and visualize the data mining results. To validate data for better data aggregation in estimation and prediction and for establishing trustworthy artificial intelligence, the synergy of visualization models and data mining strategies are needed. Hence, in this paper, we present a solution for visualization and visual knowledge discovery from big uncertain data. Our solution aims to discover knowledge in the form of frequently co-occurring patterns from big uncertain data and visualize the discovered knowledge. In particular, the solution shows the upper and lower bounds on frequency of these patterns. Evaluation with real-life Coronavirus disease 2019 (COVID-19) data demonstrates the effectiveness and practicality of our solution in visualization and visual knowledge discovery from big health informatics data collected from the current uncertain world. © 2022 IEEE.

Insights into the Association of Chronic Kidney Disease (CKD) and Cardiovascular Risk Among Diabetic vs. Non-Diabetic Patients.

Diabetic patients with chronic kidney disease have a high risk of developing cardiovascular disease-related mortality and morbidity compared to non-diabetic chronic kidney disease patients. Majority of chronic kidney disease patients with diabetes remain undiagnosed for and have a higher incidence of cardiovascular comorbidities even when they do not progress to end-stage renal failure. Both traditional cardiovascular risk factors and nontraditional cardiovascular risk factors are known to be present in higher magnitude in diabetic patients with chronic kidney disease and are known to partially account for the increased incidence of cardiovascular disease compared to non-diabetic chronic kidney disease patients. Moreover, there is no definitive evidence for potential therapeutic treatment options for cardiovascular disease among diabetic patients with chronic kidney disease as these patients have been often not included in major cardiovascular trials. Therefore, there is a need for recognizing diabetic patients with chronic kidney disease patients having high cardiovascular disease risk for definite and immediate medical attention at an individual patient level. Increased awareness, timely diagnosis, and intervention with respect to the control of these plays a pivotal role in avoiding undesirable cardiovascular disease events and leads to improved treatment outcomes among these patients. Further research is warranted to understand the risk factors for cardiovascular disease and to develop and implement preventive and treatment strategies to decrease the high morbidity and mortality among diabetic patients with chronic kidney disease. This review summarizes the available epidemiological data, risk factors, discusses clinical presentations, and suggests prevention and management strategies of cardiovascular disease risk among diabetic patients with chronic kidney disease.

Emergence of SARS-CoV-2 Alpha lineage and its correlation with quantitative wastewater-based epidemiology data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) gave rise to an international public health emergency in 3 months after its emergence in Wuhan, China. Typically for an RNA virus, random mutations occur constantly leading to new lineages, incidental with a higher transmissibility. The highly infective alpha lineage, firstly discovered in the UK, led to elevated mortality and morbidity rates as a consequence of Covid-19, worldwide. Wastewater surveillance proved to be a powerful tool for early detection and subsequent monitoring of the dynamics of SARS-CoV-2 and its variants in a defined catchment. Using a combination of sequencing and RT-qPCR approaches, we investigated the total SARS-CoV-2 concentration and the emergence of the alpha lineage in wastewater samples in Vienna, Austria linking it to clinical data. Based on a non-linear regression model and occurrence of signature mutations, we conclude that the alpha variant was present in Vienna sewage samples already in December 2020, even one month before the first clinical case was officially confirmed and reported by the health authorities. This provides evidence that a well-designed wastewater monitoring approach can provide a fast snapshot and may detect the circulating lineages in wastewater weeks before they are detectable in the clinical samples. Furthermore, declining 14 days prevalence data with simultaneously increasing SARS-CoV-2 total concentration in wastewater indicate a different shedding behavior for the alpha variant. Overall, our results support wastewater surveillance to be a suitable approach to spot early circulating SARS-CoV-2 lineages based on whole genome sequencing and signature mutations analysis.

Real-time pandemic surveillance using hospital admissions and mobility data.

Forecasting the burden of COVID-19 has been impeded by limitations in data, with case reporting biased by testing practices, death counts lagging far behind infections, and hospital census reflecting time-varying patient access, admission criteria, and demographics. Here, we show that hospital admissions coupled with mobility data can reliably predict severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission rates and healthcare demand. Using a forecasting model that has guided mitigation policies in Austin, TX, we estimate that the local reproduction number had an initial 7-d average of 5.8 (95% credible interval [CrI]: 3.6 to 7.9) and reached a low of 0.65 (95% CrI: 0.52 to 0.77) after the summer 2020 surge. Estimated case detection rates ranged from 17.2% (95% CrI: 11.8 to 22.1%) at the outset to a high of 70% (95% CrI: 64 to 80%) in January 2021, and infection prevalence remained above 0.1% between April 2020 and March 1, 2021, peaking at 0.8% (0.7-0.9%) in early January 2021. As precautionary behaviors increased safety in public spaces, the relationship between mobility and transmission weakened. We estimate that mobility-associated transmission was 62% (95% CrI: 52 to 68%) lower in February 2021 compared to March 2020. In a retrospective comparison, the 95% CrIs of our 1, 2, and 3 wk ahead forecasts contained 93.6%, 89.9%, and 87.7% of reported data, respectively. Developed by a task force including scientists, public health officials, policy makers, and hospital executives, this model can reliably project COVID-19 healthcare needs in US cities.

Open Data Resources on COVID-19 in Six European Countries: Issues and Opportunities.

Since the beginning of the COVID-19 pandemic in March 2020, national and international authorities started to develop and update datasets to provide data to researchers, journalists and health care providers as well as public opinion. These data became one of the most important sources of information, which are updated daily and analysed by scientists in order to investigate and predict the spread of this epidemic. Despite this positive reaction from both national and international authorities in providing aggregated information on the diffusion of COVID-19, different challenges have been underlined in previously published studies. Different papers have discussed strengths and weaknesses of these types of datasets by focusing on different quality perspectives, which include the statistical methods adopted to analyse them; the lack of standards and models in the adoption of data for their management and distribution; and the analysis of different data quality characteristics. These studies have analysed datasets at the general level or by focusing the attention on specific indicators such as the number of cases or deaths. This paper further investigates issues and opportunities in the diffusion of these datasets under two main perspectives. At the general level, it analyses how data are organized and distributed to scientific and non-scientific communities. Moreover, it further explores the indicators adopted to describe the spread of the COVID-19 epidemic while also highlighting the level of detail used to describe them in terms of gender, age ranges and territorial units. The paper focuses on six European countries: Belgium, France, Germany, Italy, Spain and UK.

Is the press properly presenting the epidemiological data on COVID-19? An analysis of newspapers from 25 countries.

We conducted a cross-sectional study to assess how the top 3 highest circulation newspapers from 25 countries are comparing and presenting COVID-19 epidemiological data to their readers. Of 75 newspapers evaluated, 51(68%) presented at their websites at least one comparison of cases and/or deaths between regions of their country and/or between countries. Quality assessment of the comparisons showed that only a minority of newspapers adjusted the data for population size in case comparisons between regions (37.2%) and between countries (25.6%), and the same was true for death comparisons between regions (27.3%) and between countries (27%). Of those making comparisons, only 13.7% explained the difference in the interpretation of cases and deaths. Of 17 that presented a logarithmic curve, only 29.4% explained its meaning. Although the press plays a key role in conveying correct medical information to the general public, we identified inconsistencies in the reporting of COVID-19 epidemiological data.

Data Sharing in Southeast Asia During the First Wave of the COVID-19 Pandemic.

Background: When a new pathogen emerges, consistent case reporting is critical for public health surveillance. Tracking cases geographically and over time is key for understanding the spread of an infectious disease and effectively designing interventions to contain and mitigate an epidemic. In this paper we describe the reporting systems on COVID-19 in Southeast Asia during the first wave in 2020, and highlight the impact of specific reporting methods. Methods: We reviewed key epidemiological variables from various sources including a regionally comprehensive dataset, national trackers, dashboards, and case bulletins for 11 countries during the first wave of the epidemic in Southeast Asia. We recorded timelines of shifts in epidemiological reporting systems and described the differences in how epidemiological data are reported across countries and timepoints. Results: Our findings suggest that countries in Southeast Asia generally reported precise and detailed epidemiological data during the first wave of the pandemic. Changes in reporting rarely occurred for demographic data, while reporting shifts for geographic and temporal data were frequent. Most countries provided COVID-19 individual-level data daily using HTML and PDF, necessitating scraping and extraction before data could be used in analyses. Conclusion: Our study highlights the importance of more nuanced analyses of COVID-19 epidemiological data within and across countries because of the frequent shifts in reporting. As governments continue to respond to impacts on health and the economy, data sharing also needs to be prioritised given its foundational role in policymaking, and in the implementation and evaluation of interventions.

Understanding the cycles of COVID-19 incidence: Principal Component Analysis and interaction of biological and socio-economic factors.

The incidence curve of coronavirus disease 19 (COVID-19) shows cyclical patterns over time. We examine the cyclical properties of the incidence curves in various countries and use principal components analysis to shed light on the underlying dynamics that are common to all countries. We find that the cyclical series of 37 countries can be summarized in four principal components which explain over 90% of the variation. We also discuss the influence of complex interactions between biological viral natural history and socio-political reactions and measures adopted by different countries on the cyclical patterns exhibited by COVID-19 around the globe.