Improved prediction of new COVID-19 cases using a simple vector autoregressive model: Evidence from seven New York State counties

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and SARS. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback-they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission.

Relative Effectiveness of Cell-Cultured versus Egg-Based Seasonal Influenza Vaccines in Preventing Influenza-Related Outcomes in Subjects 18 Years Old or Older: A Systematic Review and Meta-Analysis

Avian mutations in vaccine strains obtained from embryonated eggs could impair vaccine effectiveness. We performed a systematic review and meta-analysis of the adjusted relative vaccine effectiveness (arVE) of seed cell-cultured influenza vaccines (ccIV) compared to egg-based influenza vaccines (eIV) in preventing laboratory-confirmed influenza related outcomes (IRO) or IRO by clinical codes, in subjects 18 and over. We completed the literature search in January 2021; applied exclusion criteria, evaluated risk of bias of the evidence, and performed heterogeneity, publication bias, qualitative, quantitative and sensitivity analyses. All estimates were computed using a random approach. International Prospective Register of Systematic Reviews, CRD42021228290. We identified 12 publications that reported 26 adjusted arVE results. Five publications reported 13 laboratory confirmed arVE and seven reported 13 code-ascertained arVE. Nine publications with 22 results were at low risk of bias. Heterogeneity was explained by season. We found a significant 11% (8 to 14%) adjusted arVE favoring ccIV in preventing any IRO in the 2017–2018 influenza season. The arVE was 3% (−2% to 7%) in the 2018–2019 influenza season. We found moderate evidence of a significant advantage of the ccIV in preventing IRO, compared to eIV, in a well-matched A(H3N2) predominant season.

Does Seasonal Influenza Related Hospital Occupancy Surge Impact Hospital Staff Sickness Presenteeism and Productivity Costs?

Background—The workload of public hospital staff is heightened during seasonal influenza surges in hospitals serving densely populated cities. Such work environments may subject staff to increased risk of sickness presenteeism. Presenteeism is detrimental to nurses’ health and may lead to downstream productivity loss, resulting in financial costs for hospital organizations. Aims—This study aims to quantify how seasonal influenza hospital occupancy surge impacts nurses’ sickness presenteeism and related productivity costs in high-intensity inpatient metropolitan hospitals. Methods—Full-time nurses in three Hong Kong acute-care hospitals were surveyed. Generalized estimating equations (GEE) was applied to account for clustering in small number of hospitals. Results—A total of 71.3% of nurses reported two or more presenteeism events last year. A 6.8% increase in hospital inpatient occupancy rate was associated with an increase of 19% (1.19, 95% CI: 1.06–1.34) in nurse presenteeism. Presenteeism productivity loss costs between nurses working healthy (USD1983) and worked sick (USD 2008) were not significantly different, while sick leave costs were highest (USD 2703). Conclusion—Presenteeism prevalence is high amongst acute-care hospital nurses and workload increase during influenza flu surge significantly heightened nurse sickness presenteeism. Annual presenteeism productivity loss costs in this study of USD 24,096 were one of the highest reported worldwide. Productivity loss was also considerably high regardless of nurses’ health states, pointing towards other potential risk factors at play. When scheduling nurses to tackle flu surge, managers may want to consider impaired productivity due to staff presenteeism. Further longitudinal research is essential in identifying management modifiable risk factors that impact nurse presenteeism and impairing downstream productivity loss.

Development of specific immunity in laboratory animals after co-immunization against seasonal influenza and COVID-19

Introduction. In clinical practice, the differential diagnosis of COVID-19 can be challenging during the flu season, entailing serious consequences such as delays in appropriate control measures against the SARS-CoV-2 pandemic. Another problem is posed by co-infection of SARS-CoV-2 and influenza virus (IV), which significantly contributes to the severity of the COVID-19 disease. This study was aimed to explore the cross-impact of co-administration of Russian influenza and COVID-19 vaccines on development of specific immunity in laboratory animals.Materials and methods. The study was conducted on BALB/c mice. The animals were inoculated intramuscularly with the vaccine for COVID-19 prevention (CoviVac) and the vaccine for influenza prevention (Flu-M). The sera from the immunized animals were examined separately. Three IV strains were used in the hemagglutination inhibition assay. Antibodies (Abs) against SARS-CoV-2 were detected by an enzyme-linked immunosorbent assay (ELISA). The neutralization test was performed to detect virus neutralizing antibodies against SARS-CoV-2 and IV.Results. Relatively high titers of specific Abs were found in the groups of animals inoculated with one vaccine and with two vaccines concurrently. In the groups of animals inoculated with CoviVac and with two vaccines concurrently, both in the ELISA test and in the neutralization test, the average titers of specific Abs against SARSCoV- 2 did not demonstrate any statistical difference. The group of animals inoculated concurrently with two vaccines demonstrated statistically higher titers of Abs against IV after the second immunization compared to the group of animals inoculated with Flu-M.Discussion. The study has shown that post-vaccination immunity both to IV and to SARS-CoV-2 develops after co-vaccination with two vaccines. The observed enhanced post-vaccination immune response to IV in the coimmunized laboratory animals needs further research.Conclusion. The performed studies suggest the possibility of co-administration of two vaccines to prevent influenza and COVID-19.

Hospital outcomes of community-acquired COVID-19 versus influenza: Insights from the Swiss hospital-based surveillance of influenza and COVID-19

Background Since the onset of the COVID-19 pandemic, the disease has frequently been compared with seasonal influenza, but this comparison is based on little empirical data. Aim This study compares in-hospital outcomes for patients with community-acquired COVID-19 and patients with community-acquired influenza in Switzerland. Methods This retrospective multi-centre cohort study includes patients > 18 years admitted for COVID-19 or influenza A/B infection determined by RT-PCR. Primary and secondary outcomes were in-hospital mortality and intensive care unit (ICU) admission for patients with COVID-19 or influenza. We used Cox regression (cause-specific and Fine-Gray subdistribution hazard models) to account for time-dependency and competing events with inverse probability weighting to adjust for confounders. Results In 2020, 2,843 patients with COVID-19 from 14 centres were included. Between 2018 and 2020, 1,381 patients with influenza from seven centres were included; 1,722 (61%) of the patients with COVID-19 and 666 (48%) of the patients with influenza were male (p < 0.001). The patients with COVID-19 were younger (median 67 years; interquartile range (IQR): 54–78) than the patients with influenza (median 74 years; IQR: 61–84) (p < 0.001). A larger percentage of patients with COVID-19 (12.8%) than patients with influenza (4.4%) died in hospital (p < 0.001). The final adjusted subdistribution hazard ratio for mortality was 3.01 (95% CI: 2.22–4.09; p < 0.001) for COVID-19 compared with influenza and 2.44 (95% CI: 2.00–3.00, p < 0.001) for ICU admission. Conclusion Community-acquired COVID-19 was associated with worse outcomes compared with community-acquired influenza, as the hazards of ICU admission and in-hospital death were about two-fold to three-fold higher.

Characterizing the transmission patterns of seasonal influenza in Italy: lessons from the last decade

Background Despite thousands of influenza cases annually recorded by surveillance systems around the globe, estimating the transmission patterns of seasonal influenza is challenging. Methods We develop an age-structured mathematical model to influenza transmission to analyze ten consecutive seasons (from 2010 to 2011 to 2019–2020) of influenza epidemiological and virological data reported to the Italian surveillance system. Results We estimate that 18.4–29.3% of influenza infections are detected by the surveillance system. Influenza infection attack rate varied between 12.7 and 30.5% and is generally larger for seasons characterized by the circulation of A/H3N2 and/or B types/subtypes. Individuals aged 14 years or less are the most affected age-segment of the population, with A viruses especially affecting children aged 0–4 years. For all influenza types/subtypes, the mean effective reproduction number is estimated to be generally in the range 1.09–1.33 (9 out of 10 seasons) and never exceeding 1.41. The age-specific susceptibility to infection appears to be a type/subtype-specific feature. Conclusions The results presented in this study provide insights on type/subtype-specific transmission patterns of seasonal influenza that could be instrumental to fine-tune immunization strategies and non-pharmaceutical interventions aimed at limiting seasonal influenza spread and burden.

Inherent heterogeneity of influenza A virus stability following aerosolization

Efficient human-to-human transmission represents a necessary adaptation for a zoonotic influenza A virus (IAV) to cause a pandemic. As such, many emerging IAVs are characterized for transmissibility phenotypes in mammalian models, with an emphasis on elucidating viral determinants of transmission and the role host immune responses contribute to mammalian adaptation. Investigations of virus infectivity and stability in aerosols concurrent with transmission assessments have increased in recent years, enhancing our understanding of this dynamic process. Here, we employ a diverse panel of 17 human and zoonotic IAVs, inclusive of seasonally circulating H1N1 and H3N2 viruses, and avian and swine viruses associated with human infection, to evaluate differences in spray factor (a value that assesses efficiency of the aerosolization process), stability, and infectivity following aerosolization. While most seasonal influenza viruses did not exhibit substantial variability within these parameters, there was more heterogeneity among zoonotic influenza viruses, which possess a diverse range of transmission phenotypes. Aging of aerosols at different relative humidities identified strain-specific levels of stability with different profiles identified between zoonotic H3, H5, and H7 subtype viruses associated with human infection. As studies continue to elucidate the complex components governing virus transmissibility, notably aerosol matrices and environmental parameters, considering the relative role of subtype- and strain-specific factors to modulate these parameters will improve our understanding of the pandemic potential of zoonotic influenza A viruses. Importance Transmission of respiratory pathogens through the air can facilitate the rapid and expansive spread of infection and disease through a susceptible population. While seasonal influenza viruses are quite capable of airborne spread, there is a lack of knowledge regarding how well influenza viruses remain viable after aerosolization, and if influenza viruses capable of jumping species barriers to cause human infection differ in this property from seasonal strains. We evaluated a diverse panel of influenza viruses associated with human infection (originating from human, avian, and swine reservoirs) for their ability to remain viable after aerosolization in the laboratory under a range of conditions. We found greater diversity among avian and swine-origin viruses compared with seasonal influenza viruses; strain-specific stability was also noted. Although influenza virus stability in aerosols is an underreported property, if molecular markers associated with enhanced stability are identified, we will be able to quickly recognize emerging strains of influenza that present the greatest pandemic threat.

Global variation in event-based surveillance for disease outbreak detection: A time series analysis (Preprint)

BACKGROUND Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. OBJECTIVE To assess the variation in timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability, using the example of seasonal influenza epidemics in 24 countries. METHODS We obtained influenza-related reports from two EBS systems, HealthMap and the WHO Epidemic Intelligence from Open Sources (EIOS), and weekly virologic influenza counts from FluNet as a gold standard. Epidemic influenza periods were detected based on report frequency using Bayesian change point analysis. Timely sensitivity, i.e., outbreak detection within the first two weeks before or after an outbreak onset, was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. RESULTS Overall, monitoring the frequency of EBS reports detected 73.5% of outbreaks, but only 9.2% within two weeks of onset; in the best case, monitoring the frequency of health-related reports identified 29% of outbreaks within two weeks of onset. We observed a large degree of variability in all evaluation metrics across countries. The number of EBS reports available within a country, the human development index, and the country’s geographical location partially explained this variability. CONCLUSIONS Monitoring the frequency of EBS reports allowed just under 10% of seasonal influenza outbreaks to be detected in a timely manner in a worldwide analysis, with a large variability in detection capabilities. This article documents the global variation of EBS performance and demonstrates that monitoring report frequency alone in EBS may be insufficient for timely detection of outbreaks. Moreover, factors such as human development index and geographical location of a country may influence the performance of EBS and should be considered in future evaluations.