BACKGROUND: Community infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have increased rapidly since the emergence of the Omicron strain. During the eighth and ninth pandemic waves-when movement restrictions in the community were eased-the all-case registration system was changed, and the actual status of infection became uncertain.
METHODS: We conducted regular rapid antigen tests (R-RATs) once or twice a week as self-testing to examine the actual state of coronavirus disease (COVID-19) diagnosis among healthcare employees.
RESULTS: Overall, 320 (1.42/day) and 299 (1.76/day) employees were infected in the eighth and ninth pandemic waves. During both periods, 59/263 doctors (22.4%), 335/806 nurses (41.6%), 92/194 administrative employees (47.4%), and 129/218 clinical laboratory technicians (59.2%) were infected. In the eighth wave, 56 of 195 employees were infected through close contact; in the ninth wave, 26 of 62 employees were infected. No significant difference was observed in the number of vaccinations between infected and non-infected employees. The positivity rate of R-RATs was 0.41% and 0.45% in the eighth and ninth waves. R-RATs detected infection in 212 and 229 employees during the eighth and ninth waves, respectively; the ratio of R-RAT-detected positive employees to those who reported infection was significantly higher during the ninth wave (odds ratio: 1.67, 95% confidence interval: 1.17-2.37, p < 0.001).
CONCLUSIONS: The number of infected healthcare employees remained high during the eighth and ninth pandemic waves in Japan. The R-RAT is considered effective for detecting mild or asymptomatic COVID-19 at an early stage and at a high rate in healthcare employees.

OBJECTIVE: To determine risk factors of medical adherence and describe strategies to increase adherence in patients with chronic rhinologic disease.
METHODS: PubMED, SCOPUS, CINAHL, and Cochrane.
METHODS: Systematic review of 4 databases (PubMED, SCOPUS, CINAHL, Cochrane) from inception of databases to September 1, 2022 to identify studies that evaluated factors related to and affected by medical adherence in patients with chronic rhinologic disease.
RESULTS: Of 1491 studies screened, 25 studies met inclusion criteria. Of these, 7 studies described how sensory attributes of intranasal sprays affect adherence, including odor, taste, aftertaste, and side effects. Five studies described record keeping diaries/notification systems to improve adherence, with demonstration of web-based platforms to send reminders as well as keep record of medication usage to improve adherence. Eight studies described patient-specific risk factors to nonadherence, with demonstration of increased age and conscientious personalities correlating with medical adherence. Five studies looked at pediatric patients specifically, with adherence rates in children parallelling that of adults. Additionally, nonadherence in children may have greater implications for school performance.
CONCLUSIONS: Overall, adherence to topical medical therapy in patients with chronic rhinologic disease is affected by patient-related and medication-specific factors which should be considered when counseling patients. Web-based diary or notification systems may help increase adherence. Additionally, children are equally adherent to topical medical therapy as adults and nonadherence may have negative implications for school performance.

BACKGROUND: In 2022, the Surveillance Department of the Ministry of Public Health in Qatar adopted an integrated project called the Notification Enhancement Project (NEP) to enhance the infectious disease notification system. Efficient surveillance and notification promote early alerts and allow immediate interference in reducing morbidity and mortality from outbreaks. The project was designed to improve the knowledge, attitudes, practices, and notification processes of healthcare workers in Qatar by increasing their reporting rates.
METHODS: The strategy for comprehensively enhancing notifications was based on the observation and evaluation of the current notification system, the implementation of interventions, and post-evaluation follow-up. To implement the project, we relied on three aspects: effective methods used in previous relevant studies through a literature review, feedback received from healthcare workers, and suggestions from public health surveillance experts from the Ministry of Public Health, Qatar. A preassessment was conducted through an online survey by the Ministry of Public Health. The effectiveness of the different interventions was assessed by analyzing the data of notified patients reported through the Disease Surveillance and Reporting Electronic System. Pre- and postintervention assessments were performed by comparing the percentage of patients notified by healthcare providers with that of patients confirmed by healthcare providers in the laboratory to compare the notification rates over three time periods between January and December 2022.
RESULTS: There was significant improvement in the infectious disease notification process. A comparison before and after the implementation of the interventions revealed an increase in the communicable disease notification rate among healthcare workers. Pre- and postintervention data were compared. Infectious disease notification activities by healthcare workers increased from 2.5% between January and May 2022 to 41.4% between November and December 2022.
CONCLUSIONS: This study highlights the efficiency of different interventions in correcting the underreporting of infectious diseases. Our findings suggest that implementing the Notification Enhancement Project significantly improves notification rates. We recommend continuing interventions through constant education and training, maintaining solid communication with HCWs through regular reminder emails and feedback, periodic assessment of the electronic notification system, and engagement of healthcare workers and other stakeholders to sustain and expand progress achieved through continuous evaluation.

In this paper, we propose the notification optimization method by providing multiple alternative times as a reminder for a forecasted activity with and without probabilistic considerations for the activity that needs to be completed and needs notification. It is important to consider various factors when sending notifications to people after obtaining the results of the forecasted activity. We should not send notifications only when we have forecasted results because future daily activities are unpredictable. Therefore, it is important to strike a balance between providing useful reminders and avoiding excessive interruptions, especially for low probabilities of forecasted activity. Our study investigates the impact of the low probability of forecasted activity and optimizes the notification time with reinforcement learning. We also show the gaps between forecasted activities that are useful for self-improvement by people for the balance of important tasks, such as tasks completed as planned and additional tasks to be completed. For evaluation, we utilize two datasets: the existing dataset and data we collected in the field with the technology we have developed. In the data collection, we have 23 activities from six participants. To evaluate the effectiveness of these approaches, we assess the percentage of positive responses, user response rate, and response duration as performance criteria. Our proposed method provides a more effective way to optimize notifications. By incorporating the probability level of activity that needs to be done and needs notification into the state, we achieve a better response rate than the baseline, with the advantage of reaching 27.15%, as well as than the other criteria, which are also improved by using probability.

The World Organization for Animal Health (OIE) has recently developed a Wildlife Health Framework to respond to the need of members to manage the risk from emerging diseases at the animal-human-ecosystem interface. One of its objectives is to improve surveillance systems, early detection and notification of wildlife diseases. Members share information on disease occurrence by reporting through the OIE World Animal Health Information System (OIE-WAHIS-formerly known as \'WAHIS\'). To evaluate the capacity of a surveillance system to detect disease events, it is important to quantify the gap between all known events and those officially notified to the OIE. This study used capture-recapture analysis to estimate the sensitivity of the OIE-WAHIS system for a OIE-listed wildlife disease by comparing information from publicly available sources to identify undetected events. This article presents a case study of the occurrence of tularemia in lagomorphs among selected North American and European countries during the period 2014-2019. First, an analysis using three data sources (OIE-WAHIS, ProMED, WHO-EIOS [Epidemic Intelligence from Open Sources]) was conducted. Subsequent analysis then explored the model integrating information from a fourth source (scientific literature collected in PubMed). Two models were built to evaluate both the sensitivity of the OIE-WAHIS using media reports (ProMED and WHO-EIOS), which is likely to represent current closer to real-time events, and published scientific data, which is more useful for retrospective analysis. Using the three-source approach, the predicted number of tularemia events was 93 (95% CI: 75-114), with an OIE-WAHIS sensitivity of 90%. In the four-source approach, the number of predicted events increased to 120 (95% CI: 99-143), dropping the sensitivity of the OIE-WAHIS to 70%. The results indicate a good sensitivity of the OIE-WAHIS system using the three-source approach, but lower sensitivity when including information from the scientific literature. Further analysis should be undertaken to identify diseases and regions for which international reporting presents a low sensitivity. This will enable evaluation and prioritization of underreported OIE-listed wildlife diseases and identify areas of focus as part of the Wildlife Health Framework. This study also highlights the need for stronger collaborations between academia and National Veterinary Services to enhance surveillance systems for notifiable diseases.

The Smart Bed Notification System is a system that alerts the movement of objects (patients with stroke) on the care mattress when patients are at risk of being dropped or falling. The notification device consists of two subsystems, namely hardware and software. In the hardware part, it consists of a camera to track patient movements, Raspberry Pi as a system controller that is equipped with a WiFi interface to transmit data wirelessly to smartphones that are equipped with Android-based applications. In the software part, however, openCV installed on Raspy is designed to picture the surveillance results of the webcam. Furthermore, the smartphone has an Android-based feature, which displays alerts of patient movement. Stroke patients may be tracked remotely over the Internet by nurses or their relatives.

In response to the COVID-19 pandemic the routine surveillance system for infectious diseases had to be adapted. The disease was unknown before the first cases were reported under a catch-all notification requirement for new and threatening pathogens and diseases, but specific notification requirements for SARS-CoV‑2 detection by laboratories as well as for suspect cases of COVID-19 diagnosed by physicians were soon integrated in the infectious diseases protection act. This article describes how the notification system for infectious diseases was adapted in 2020 to meet the requirements of the COVID-19 pandemic.In addition to the notification requirements, the list of data that is collected through the notification system was also amended. To facilitate the work of laboratories and local health authorities we have established the possibility for electronic reporting.Additionally, the software used for case and contact management within the local health authorities had to be adapted accordingly.COVID-19 notification data is important for the assessment of the current epidemiological situation and daily updated data was published by the Robert Koch Institute. To ensure timely data and good data quality, the IT infrastructure within the public health system has to be further modernized and the electronic notification system should be further strengthened.
UNASSIGNED: Die COVID-19-Pandemie hat Anpassungen des Meldesystems gemäß Infektionsschutzgesetz (IfSG) erforderlich gemacht. Da COVID-19 bis dahin unbekannt war, gab es noch keine spezifische Meldepflicht für diese Infektionskrankheit. Sie fiel jedoch unter die Meldepflicht für bedrohliche übertragbare Krankheiten nach § 6 Abs. 1 Nr. 5 IfSG. Sobald absehbar war, dass es sich nicht nur um Einzelfälle handeln würde, wurde die Meldepflicht zunächst per Verordnung angepasst und später in das IfSG aufgenommen. In diesem Beitrag werden die Anpassungen des Meldesystems beschrieben, die im Rahmen der COVID-19-Pandemie im Verlauf des Jahres 2020 erfolgten.Neben der Einführung der Meldepflicht für COVID-19 wurden auch die Meldeinhalte erweitert, um Informationen, die speziell für COVID-19 relevant sind, erfassen zu können. Um den Arbeitsaufwand in Laboren und im Öffentlichen Gesundheitsdienst (ÖGD) beim Absetzen bzw. Verarbeiten der Meldungen zu reduzieren, wurde ein elektronisches Verfahren für Labormeldungen von SARS-CoV-2-Nachweisen eingeführt. Dies geschah im Rahmen der Entwicklung des Deutschen Elektronischen Melde- und Informationssystems für den Infektionsschutz (DEMIS). Zudem wurden umfangreiche Anpassungen in der Software für das Fall- und Kontaktpersonenmanagement vorgenommen.Die an das Robert Koch-Institut (RKI) übermittelten Meldedaten bilden eine wichtige Grundlage für die Bewertung der epidemiologischen Situation und werden während der COVID-19-Pandemie tagesaktuell über diverse Wege zur Verfügung gestellt. Damit diese Daten immer zeitnah und in guter Qualität vorliegen, sollte die IT-Infrastruktur im ÖGD noch weiter modernisiert werden. Insbesondere sollte DEMIS wie geplant weiter ausgebaut werden.

Water quality depends on many factors. Some of them are essential for maintaining the minimum sustainability of water. Because of the great dependence of fishes on the condition of the aquatic environment, the water quality can directly affect their activity. Therefore monitoring water quality is a very important issue to consider, especially in the fish farming industry. In this paper a digital fish farm monitoring system is introduced and a collection of experimental data of water quality monitoring was presented, which were directly collected from a fish pond. As the quality factor of water affects its aquatic life form sustainability, therefore the quality factors of the water were measured using digital sensors. Temperature, pH factor and Turbidity were selected as the basic quality factors to measure. The dataset contains data recorded from two different water levels to analyze the aquatic environment more efficiently. Each level has 9623 sets of data of the selected parameters. Collection was continued all day long for several days. Later collected sensor data were analyzed as short period time series to find its properties. Machine Learning regression method was used to predict near future conditions. Moreover data were processed to find any repetitive patterns in its properties. This dataset represents the exact condition of the environment of the fish pond. Therefore it can be used to develop a system to monitor fish farms digitally. Using these data in machine learning, predicting the future is possible for advance monitoring of a fish farm. The dataset is available in Mendeley Data[1].

BackgroundLaboratory-confirmed cases of Shiga toxin-producing Escherichia coli (STEC) have been notifiable to the National Notification System for Infectious Diseases in Switzerland since 1999. Since 2015, a large increase in case numbers has been observed. Around the same time, syndromic multiplex PCR started to replace other diagnostic methods in standard laboratory practice for gastrointestinal pathogen testing, suggesting that the increase in notified cases is due to a change in test practices and numbers.AimThis study examined the impact of changes in diagnostic methods, in particular the introduction of multiplex PCR panels, on routine STEC surveillance data in Switzerland.MethodsWe analysed routine laboratory data from 11 laboratories, which reported 61.9% of all STEC cases from 2007 to 2016 to calculate the positivity, i.e. the rate of the number of positive STEC tests divided by the total number of tests performed.ResultsThe introduction of multiplex PCR had a strong impact on STEC test frequency and identified cases, with the number of tests performed increasing sevenfold from 2007 to 2016. Still, age- and sex-standardised positivity increased from 0.8% in 2007 to 1.7% in 2016.ConclusionIncreasing positivity suggests that the increase in case notifications cannot be attributed to an increase in test numbers alone. Therefore, we cannot exclude a real epidemiological trend for the observed increase. Modernising the notification system to address current gaps in information availability, e.g. diagnostic methods, and improved triangulation of clinical presentation, diagnostic and serotype information are needed to deal with emerging disease and technological advances.

The outbreak of the coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, spread worldwide after its emergence in China. Whether rich or poor, all nations are struggling to cope with this new global health crisis. The speed of the threat\'s emergence and the quick response required from public health authorities and the public itself makes evident the need for a major reform in pandemic surveillance and notification systems. The development and implementation of a graded, individual-level pandemic notification system could be an effective tool to combat future threats of epidemics. This paper describes a prototype model of such a notification system and its potential advantages and challenges for implementation. Similar to other emergency alerts, this system would include a number of threat levels (level 1-5) with a higher level indicating increasing severity and intensity of safety measures (eg, level 1: general hygiene, level 2: enhanced hygiene, level 3: physical distancing, level 4: shelter in place, and level 5: lockdown). The notifications would be transmitted to cellular devices via text message (for lower threat levels) or push notification (for higher threat levels). The notification system would allow the public to be informed about the threat level in real time and act accordingly in an organized manner. New Zealand and the United Kingdom have recently launched similar alert systems designed to coordinate the ongoing COVID-19 pandemic response more efficiently. Implementing such a system, however, faces multiple challenges. Extensive preparation and coordination among all levels of government and relevant sectors are required. Additionally, such systems may be effective primarily in countries where there exists at least moderate trust in government. Advance and ongoing public education about the nature of the system and its steps would be an essential part of the system, such that all members of the public understand the meaning of each step in advance, similar to what has been established in systems for other emergency responses. This educational component is of utmost importance to minimize adverse public reaction and unintended consequences. The use of mass media and local communities could be considered where mobile phone penetration is low. The implementation of such a notification system would be more challenging in developing countries for several reasons, including inadequate technology, limited use of data plans, high population density, poverty, mistrust in government, and tendency to ignore or failure to understand the warning messages. Despite the challenges, an individual-level pandemic notification system could provide added benefits by giving an additional route for notification that would be complementary to existing platforms.