Real-time surveillance systems: Applicability for the control of influenza in acute care

Decision-making algorithm and predictive model to assess the impact of infectious disease epidemics on the healthcare system: the COVID-19 case study in Italy

Until SARS-CoV-2 vaccination consolidates immunity worldwide, infectious strains will continue to cause infection, causing critical pressures on the healthcare systems and lockdowns. To improve decision-making strategies and prediction based on epidemiological data, so far biased by highly-variable testing criteria, an algorithm using unbiased morbidity parameters, i.e. Intensive Care Units (ICU) and Ordinary Hospitalization (HO), is proposed. ICU/HO acceleration and velocities are mathematically modeled using official available data to yield two thresholds, alerting on 30% ICU and 40% HO of COVID-19 daily occupancy settled by the Italian Minister of Health, as a case of study. A predictive model is also proposed to estimate the daily occupancy of ICU and HO in hospitals for each region, using a Susceptible-Infected-Recovered-Death (SIRD) epidemic model to further extend occupancy prediction in each regional district. Computed data validated the proposed models in Italy after more than one-year of pandemic obtaining agreements with the Italian Presidential Decree, regardless of the different regional trends of epidemic waves. Therefore, the decision-making algorithm and prediction model resulted valuable tools, retrospectively, to be tested prospectively in sustainable strategies to curb the impact of COVID-19, or of any other pandemic threats with any aggregate of data, on the local healthcare systems.

New insights and antimicrobial stewardship opportunities in viral pneumonia: five lung ultrasound cases

Background

Antimicrobial stewardship is crucial to avoid antimicrobial resistance in microbes and adverse drug effects in patients. In respiratory infections, however, viral pneumonia is difficult to distinguish from bacterial pneumonia, which explains the overuse of antibiotic therapy in this indication.

Cases

Five cases of lung consolidation are presented. Lung ultrasound, in conjunction with procalcitonin levels, were used to exclude or corroborate bacterial pneumonia.

Conclusion

Lung ultrasound is easy to learn and perform and is helpful in guiding diagnosis in unclear cases of pneumonia and may also offer new insights into the spectrum of certain virus diseases. The use of lung ultrasound can raise awareness in clinicians of the need for antimicrobial stewardship and may help to avoid the unnecessary use of antibiotics.

Supplementary Information

The online version of this article (10.1007/s00508-021-01946-4) contains supplementary material, which is available to authorized users.

Multi-agent system collision model to predict the transmission of seasonal influenza in Tokyo from 2014-2015 to 2018-2019 seasons

The objective of this study was to apply the multi-agent system (MAS) collision model to predict seasonal influenza epidemic in Tokyo for 5 seasons (2014-2015 to 2018-2019 seasons). The MAS collision model assumes each individual as a particle inside a square domain. The particles move within the domain and disease transmission occurs in a certain probability when an infected particle collides a susceptible particle. The probability was determined based on the basic reproduction number calculated using the actual data. The simulation started with 1 infected particle and 999 susceptible particles to correspond to the onset of an influenza epidemic. We performed the simulation for 150 days and the calculation was repeated 500 times for each season. To improve the accuracy of the prediction, we selected simulations which have similar incidence number to the actual data in specific weeks. Analysis including all simulations corresponded good to the actual data in 2014-2015 and 2015-2016 seasons. However, the model failed to predict the sharp peak incidence after the New Year Holidays in 2016-2017, 2017-2018, and 2018-2019 seasons. A model which included simulations selected by the week of peak incidence predicted the week and number of peak incidence better than a model including all simulations in all seasons. The reproduction number was also similar to the actual data in this model. In conclusion, the MAS collision model predicted the epidemic curve with good accuracy by selecting the simulations using the actual data without changing the initial parameters such as the basic reproduction number and infection time.

Epidemiological and clinical characteristics of community-acquired and nosocomial influenza cases and risk factors associated with complications: A four season analysis of all adult patients admitted in a tertiary hospital

Background

Information on the characteristics of patients with nosocomial influenza and associated complications is scarce. This study compared epidemiological and clinical characteristics of patients admitted with hospital‐acquired influenza (HAI) to those with community‐acquired influenza (CAI) and analyzed risk factors associated with complications.

Methods

This retrospective, observational study included all adult patients with confirmed influenza virus infection admitted to Son Espases University Hospital during the influenza season in Spain (October to May) from 2012‐2013 to 2015‐2016. Symptom onset before admission was included as CAI, and 2 days after admission or within 48 hours after previous discharge were considered as HAI.

Results

Overall, 666 patients with laboratory‐confirmed influenza were included; 590 (88.6%) and 76 (11.4%) had CAI and HAI, respectively. Baseline characteristics and vaccination rates were similar in both groups. Patients with HAI had significantly fewer symptoms, less radiological alterations, and earlier microbiological diagnosis than those with CAI. Eighty‐five (14.4%) and 20 (27.6%) CAI and HAI patients, respectively, experienced at least one complication, including septic shock, admission to the intensive care unit, mechanical ventilation or evolution to death (any one, P = .003). Univariate and multivariate binary logistic regression was performed to assess independent risk factors associated with the occurrence of complications: nosocomial infection, diabetes, oseltamivir treatment, having received no vaccination, microbiological delay, dyspnea, and the state of confusion were the most important significant factors.

Conclusions

Our study shows the need to implement microbiological diagnostic measures in the first 48 hours to reduce HAI frequency and associated complications.

Real-time surveillance systems: Applicability for the control of influenza in acute care

Background

The high morbidity and mortality caused by influenza viruses translate into a great impact on specialized health care. Apart from the annual vaccination, the relevance of other measures to prevent and control this infection is unknown. The objective of our research was to determine the importance of a real‐time surveillance system to establish early extended transmission precautions.

Methods

Quasi‐experimental before‐and‐after study comparing the influenza cases detected in hospitalized adults during the 2016/2017 season (264 patients) with those detected after the implementation of a real‐time surveillance system in the 2017/2018 season (519 patients). The improvements included early microbiological diagnosis, immediate communication of results, constant updating of patient information, coordination among professionals, periodic surveillance of the adequacy of preventive measures, and greater control of roommates. The effectiveness of the intervention was determined from the nosocomial infection rate in each season.

Results

After the real‐time surveillance system for influenza was implemented, patients with early microbiological diagnosis and immediate isolation increased significantly (13.7% vs 68.2%; P < .001). In addition, nosocomial infections decreased from 17% to 9.2% (P = .001) and overall hospital stay was significantly reduced. Assuming that the entire effect was due to the intervention, the absolute risk reduction was 7.8% and number needed to treat was 12.8.

Conclusion

The results in our study reveal the impact of nosocomial transmission of influenza virus in a tertiary hospital and highlight the need to supplement traditional strategies with novel methodologies such as modern surveillance systems based on early diagnosis, close case monitoring, and coordination among professionals.