Global variation in early epidemic growth rates and reproduction number of seasonal influenza

Using the near real-time effective reproduction number Rt as an early-warning tool for seasonal bronchiolitis and influenza-like illness epidemics

Yearly bronchiolitis and influenza-like illness epidemics in France often involve high morbidity and mortality, which severely impact health care. Epidemics are declared by the French National Institute of Public Health based on syndromic surveillance of primary care and emergency departments (EDs), using statistics-based alarms. Although the effective reproduction number (Rt) is used to monitor the dynamics of epidemics, it has never been used as an early-warning tool for bronchiolitis or influenza-like illness epidemics in France. We assessed whether Rt is useful for detecting seasonal epidemics by comparing it to the tool currently used (MASS) by epidemiologists to declare epidemic phases. We used anonymized ED syndromic data from the Île-de-France region in France from 2010 to 2022. We estimated Rt and compared the indication of accelerated transmission (Rt > 1) to the MASS epidemic alarm time points. We computed the difference between those 2 time points, time to epidemic peak, and the daily cases documented at first indication and peak. Rt provided alarms for influenza-like illness and bronchiolitis epidemics that were, respectively, a median of 6 days (IQR, 4, 8) and 64 days (IQR, 52, 80) earlier than the alarms provided by MASS. Rt detected earlier signals of bronchiolitis and influenza-like illness epidemics. Using this early-warning indicator in combination with others to declare an annual epidemic could provide opportunities to improve health care system readiness.

Wastewater-based effective reproduction number and prediction under the absence of shedding information

Estimating effective reproduction number (Re) and predicting disease incidences are essential to formulate effective strategies for disease control. Although recent studies developed models for inferring Re from wastewater-based data, they require information on shedding dynamics. Here, we proposed a framework of Re estimation and prediction without shedding information. The framework consists of a space-state model for smoothing wastewater-based data and a renewal equation modified for wastewater-based data. The applicability of the framework was tested with simulated data and real-world data on Influenza A virus (IAV) and SARS-CoV-2 concentration in wastewater in 2022/2023 season in the USA. We confirmed the state-space model effectively fits various simulated epidemic curves and real-world data. In simulations, we found wastewater-based Re (Reww) closely aligns with instantaneous clinical Re when shedding dynamics are rapid. For more prolonged shedding, Reww approximates a smoothed Re over time. We also observed the necessary sampling frequency to trace dynamics of wastewater concentration and Reww accurately in the framework varies depending on the precision of detection methods, the epidemic status, the transmissibility of infectious diseases, and shedding dynamics. By applying our framework to real-world data, we found Reww for SARS-CoV-2 showed similar trend and values to clinically-based Re. Reww for IAV ranged from 0.66 to 1.52 with a clear peak in the winter season, which agrees with previously reported Re. We also succeeded in predicting wastewater concentration in a few weeks from available wastewater-based data. These results indicate that our framework potentially enables near real-time monitoring of approximated Re and prediction of infectious disease dynamics through wastewater surveillance, which limits the delay between infection and reporting. Our framework is useful especially for regions where reliable clinical surveillance is not available and notifiable surveillance is abolished, and can be expanded to multiple infectious diseases that have been detected from wastewater.

Model-based analysis of the incidence trends and transmission dynamics of COVID-19 associated with the Omicron variant in representative cities in China

BACKGROUND

In 2022, Omicron outbreaks occurred at multiple sites in China. It is of great importance to track the incidence trends and transmission dynamics of coronavirus disease 2019 (COVID-19) to guide further interventions.

METHODS

Given the population size, economic level and transport level similarities, two groups of outbreaks (Shanghai vs. Chengdu and Sanya vs. Beihai) were selected for analysis. We developed the SEAIQRD, ARIMA, and LSTM models to seek optimal modeling techniques for waves associated with the Omicron variant regarding data predictive performance and mechanism transmission dynamics, respectively. In addition, we quantitatively modeled the impacts of different combinations of more stringent interventions on the course of the epidemic through scenario analyses.

RESULTS

The best-performing LSTM model showed better prediction accuracy than the best-performing SEAIQRD and ARIMA models in most cases studied. The SEAIQRD model had an absolute advantage in exploring the transmission dynamics of the outbreaks. Regardless of the time to inflection point or the time to Rt curve below 1.0, Shanghai was later than Chengdu (day 46 vs. day 12/day 54 vs. day 14), and Sanya was later than Beihai (day 16 vs. day 12/day 20 vs. day 16). Regardless of the number of peak cases or the cumulative number of infections, Shanghai was higher than Chengdu (34,350 vs. 188/623,870 vs. 2,181), and Sanya was higher than Beihai (1,105 vs. 203/16,289 vs. 3,184). Scenario analyses suggested that upgrading control level in advance, while increasing the index decline rate and quarantine rate, were of great significance for shortening the time to peak and Rt below 1.0, as well as reducing the number of peak cases and final affected population.

CONCLUSIONS

The LSTM model has great potential for predicting the prevalence of Omicron outbreaks, whereas the SEAIQRD model is highly effective in revealing their internal transmission mechanisms. We recommended the use of joint interventions to contain the spread of the virus.

Seleno-Metabolites and Their Precursors: A New Dawn for Several Illnesses?

Selenium (Se) is an essential element for human health as it is involved in different physiological functions. Moreover, a great number of Se compounds can be considered potential agents in the prevention and treatment of some diseases. It is widely recognized that Se activity is related to multiple factors, such as its chemical form, dose, and its metabolism. The understanding of its complex biochemistry is necessary as it has been demonstrated that the metabolites of the Se molecules used to be the ones that exert the biological activity. Therefore, the aim of this review is to summarize the recent information about its most remarkable metabolites of acknowledged biological effects: hydrogen selenide (HSe-/H2Se) and methylselenol (CH3SeH). In addition, special attention is paid to the main seleno-containing precursors of these derivatives and their role in different pathologies.