Quantifying the annual incidence and underestimation of seasonal influenza: A modelling approach

A simple modification to the classical SIR model to estimate the proportion of under-reported infections using case studies in flu and COVID-19

Background

Under-reporting and, thus, uncertainty around the true incidence of health events is common in all public health reporting systems. While the problem of under-reporting is acknowledged in epidemiology, the guidance and methods available for assessing and correcting the resulting bias are obscure.

Objective

We aim to design a simple modification to the Susceptible - Infected - Removed (SIR) model for estimating the fraction or proportion of reported infection cases.

Methods

The suggested modification involves rescaling of the classical SIR model producing its mathematically equivalent version with explicit dependence on the reporting parameter (true proportion of cases reported). We justify the rescaling using the phase plane analysis of the SIR model system and show how this rescaling parameter can be estimated from the data along with the other model parameters.

Results

We demonstrate how the proposed method is cross-validated using simulated data with known disease cases and then apply it to two empirical reported data sets to estimate the fraction of reported cases in Missoula County, Montana, USA, using: (1) flu data for 2016-2017 and (2) COVID-19 data for fall of 2020.

Conclusions

We establish with the simulated and COVID-19 data that when most of the disease cases are presumed reported, the value of the additional reporting parameter in the modified SIR model is close or equal to one, so that the original SIR model is appropriate for data analysis. Conversely, the flu example shows that when the reporting parameter is close to zero, the original SIR model is not accurately estimating the usual rate parameters, and the re-scaled SIR model should be used. This research demonstrates the role of under-reporting of disease data and the importance of accounting for under-reporting when modeling simulated, endemic, and pandemic disease data. Correctly reporting the "true" number of disease cases will have downstream impacts on predictions of disease dynamics. A simple parameter adjustment to the SIR modeling framework can help alleviate bias and uncertainty around crucial epidemiological metrics (e.g.: basic disease reproduction number) and public health decision making.

Evaluating equity-promoting interventions to prevent race-based inequities in influenza outcomes

Importance

Seasonal influenza hospitalizations pose a considerable burden in the United States, with BIPOC (Black, Indigenous, and other People of Color) communities being disproportionately affected.

Objective

To determine and quantify the effects of different types of mitigation strategies on inequities in influenza outcomes (symptomatic infections and hospitalizations).

Design

In this simulation study, we fit a race-stratified agent-based model of influenza transmission to demographic and hospitalization data of the United States.

Participants

We consider five racial-ethnic groups: non-Hispanic White persons, non- Hispanic Black persons, non-Hispanic Asian persons, non-Hispanic American Indian or Alaska Native persons, and Hispanic or Latino persons.

Setting

We tested five idealized equity-promoting interventions to determine their effectiveness in reducing inequity in influenza outcomes. The interventions assumed (i) equalized vaccination rates, (ii) equalized comorbidities, (iii) work-risk distribution proportional to the distribution of the population, (iv) reduced work contacts for all, or (v) a combination of equalizing vaccination rates and comorbidities and reducing work contacts.

Main Outcomes and Measures

Reduction in symptomatic or hospitalization risk ratios, defined as the ratio of the number of symptomatic infections (hospitalizations respectively) in each age- and racial-ethnic group and their corresponding white counterpart. We also evaluated the reduction in the absolute mean number of symptomatic infections or hospitalizations in each age- and racial-ethnic group compared to the fitted scenario (baseline).

Results

Our analysis suggests that symptomatic infections were equalized and reduced (by up to 17% in BIPOC adults aged 18-49) by strategies reducing work contacts or equalizing vaccination rates. Reducing comorbidities resulted in significant decreases in hospitalizations, with a reduction of over 40% in BIPOC groups. All tested interventions reduced the inequity in influenza hospitalizations in all racial-ethnic groups, but interventions reducing comorbidities in marginalized populations were the most effective. Notably, these interventions resulted in better outcomes across all racial-ethnic groups, not only those prioritized by the interventions.

Conclusions and Relevance

In this simulation modeling study, equalizing vaccination rates and reducing number of work contacts (which are relatively simple strategies to implement) reduced the both the inequity in hospitalizations and the absolute number of symptomatic infections and hospitalizations in all age and racial-ethnic groups.

Safety profile and SARS-CoV-2 breakthrough infections among HCWs receiving anti-SARS-CoV-2 and influenza vaccines simultaneously: an Italian observational study

In October/December 2021, World Health Organization and other international agencies recommended the offer of the third dose of anti-SARS-CoV-2 vaccine. In this period, the routine offer of seasonal influenza vaccination was also guaranteed and simultaneous administration of the two vaccines was encouraged. This study aims to evaluate the safety profile and to estimate the incidence of SARS-CoV-2 breakthrough infections in subjects receiving the anti-SARS-CoV-2 and influenza vaccines simultaneously. The study population was represented by healthcare workers (HCWs) of Bari Policlinico General Hospital who received the influenza (Flucelvax Tetra®) and/or anti-SARS-CoV-2 vaccination (BNT162b2 mRNA COVID-19 vaccine, Comirnaty®) either in coadministration or separately in October 2021. Reports of adverse events following immunization (AEFIs) were investigated to study the safety of both vaccines in coadministration and in separate-instance administration. Post-vaccination SARS-CoV-2 breakthrough infection was also studied. 942 HCWs accepted to join our study. 610/942 received both vaccines simultaneously. 25.26 % subjects (238/942) were only vaccinated against SARS-CoV-2, while the remaining 94 HCWs received the influenza vaccination first and subsequently received the anti-SARS-CoV2 booster dose. 717 HCWs reported AEFIs (Reporting Rate 76.1 per 100 subjects). Simultaneous administration of the two vaccines was not related with an increase of the rate of AEFIs compared to the single administration of SARS-CoV-2 vaccine, but the AEFIs' rate was lower among subjects who received only influenza vaccine. Post-vaccination SARS-CoV-2 infections were notified for 41.5 % of enrolled subjects (391/942). Incidence of breakthrough infection and symptomatic disease was not significantly different between the simultaneous administration group and other subjects. Our data suggests that simultaneous administration of a quadrivalent influenza vaccine and an mRNA anti-SARS-CoV-2 vaccine neither affected the safety of said products nor was associated with a higher risk of SARS-CoV-2 breakthrough infection.

Wastewater surveillance of SARS-CoV-2 and influenza in preK-12 schools shows school, community, and citywide infections

Wastewater surveillance is a passive and efficient way to monitor the spread of infectious diseases in large populations and high transmission areas such as preK-12 schools. Infections caused by respiratory viruses in school-aged children are likely underreported, particularly because many children may be asymptomatic or mildly symptomatic. Wastewater monitoring of SARS-CoV-2 has been studied extensively and primarily by sampling at centralized wastewater treatment plants, and there are limited studies on SARS-CoV-2 in preK-12 school wastewater. Similarly, wastewater detections of influenza have only been reported in wastewater treatment plant and university manhole samples. Here, we present the results of a 17-month wastewater monitoring program for SARS-CoV-2 (n = 2176 samples) and influenza A and B (n = 1217 samples) in 51 preK-12 schools. We show that school wastewater concentrations of SARS-CoV-2 RNA were strongly associated with COVID-19 cases in schools and community positivity rates, and that influenza detections in school wastewater were significantly associated with citywide influenza diagnosis rates. Results were communicated back to schools and local communities to enable mitigation strategies to stop the spread, and direct resources such as testing and vaccination clinics. This study demonstrates that school wastewater surveillance is reflective of local infections at several population levels and plays a crucial role in the detection and mitigation of outbreaks.

Incidence, Severity, and Mortality of Influenza During 2010-2020 in Korea: A Nationwide Study Based on the Population-Based National Health Insurance Service Database

BACKGROUND

The epidemiology of influenza is commonly used to understand and establish relevant health policies for emerging respiratory infections, including coronavirus disease 2019 (COVID-19). However, Korea has no confirmed nationwide data on influenza incidence, severity, and mortality rate.

METHODS

We conducted a cross-sectional study to obtain epidemic data on influenza at the national level using National Health Insurance claims data during 2010 to 2020. Influenza cases were defined as 90-day timeframe episodes based on all inpatient and outpatient claims data with disease code J09, J10, and J11. Influenza incidence, severity, and mortality rate were calculated, and logistic regressions were performed to assess the associations of demographic characteristics and comorbidity with influenza-related hospitalization, severe illness, and death.

RESULTS

There were 0.4-5.9% influenza cases in the population from 2010 to 2020, with 9.7-18.9%, 0.2-0.9%, and 0.03-0.08% hospitalized, used in the intensive care unit, and dead, respectively. Age-standardized incidence and mortality rates were 424.3-6847.4 and 0.2-1.9 per 100,000 population, respectively. While more than half of the influenza cases occurred in populations aged younger than 20 years, deaths in older than 60 years accounted for more than two-thirds of all deaths.

CONCLUSION

This study provided the simplest but most important statistics regarding Korean influenza epidemics as a reference. These can be used to understand and manage other new acute respiratory diseases, including COVID-19, and establish influenza-related policies.

Mitigating co-circulation of seasonal influenza and COVID-19 pandemic in the presence of vaccination: A mathematical modeling approach

The co-circulation of two respiratory infections with similar symptoms in a population can significantly overburden a healthcare system by slowing the testing and treatment. The persistent emergence of contagious variants of SARS-CoV-2, along with imperfect vaccines and their waning protections, have increased the likelihood of new COVID-19 outbreaks taking place during a typical flu season. Here, we developed a mathematical model for the co-circulation dynamics of COVID-19 and influenza, under different scenarios of influenza vaccine coverage, COVID-19 vaccine booster coverage and efficacy, and testing capacity. We investigated the required minimal and optimal coverage of COVID-19 booster (third) and fourth doses, in conjunction with the influenza vaccine, to avoid the coincidence of infection peaks for both diseases in a single season. We show that the testing delay brought on by the high number of influenza cases impacts the dynamics of influenza and COVID-19 transmission. The earlier the peak of the flu season and the greater the number of infections with flu-like symptoms, the greater the risk of flu transmission, which slows down COVID-19 testing, resulting in the delay of complete isolation of patients with COVID-19 who have not been isolated before the clinical presentation of symptoms and have been continuing their normal daily activities. Furthermore, our simulations stress the importance of vaccine uptake for preventing infection, severe illness, and hospitalization at the individual level and for disease outbreak control at the population level to avoid putting strain on already weak and overwhelmed healthcare systems. As such, ensuring optimal vaccine coverage for COVID-19 and influenza to reduce the burden of these infections is paramount. We showed that by keeping the influenza vaccine coverage about 35% and increasing the coverage of booster or fourth dose of COVID-19 not only reduces the infections with COVID-19 but also can delay its peak time. If the influenza vaccine coverage is increased to 55%, unexpectedly, it increases the peak size of influenza infections slightly, while it reduces the peak size of COVID-19 as well as significantly delays the peaks of both of these diseases. Mask-wearing coupled with a moderate increase in the vaccine uptake may mitigate COVID-19 and prevent an influenza outbreak.

Soft Computing Paradigms to Find the Numerical Solutions of a Nonlinear Influenza Disease Model

The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e., GA-ASM, are implemented as global and local search schemes. The mathematical nonlinear influenza disease system is dependent of four classes, susceptible S(u), infected I(u), recovered R(u) and cross-immune individuals C(u). For the solutions of these classes based on influenza disease system, the design of an objective function is presented using these differential system equations and its corresponding initial conditions. The optimization of this objective function is using the hybrid computing combination of GA-ASM for solving all classes of the influenza disease nonlinear system. The obtained numerical results will be compared by the Adams numerical results to check the authenticity of the designed ANN-GA-ASM. In addition, the designed approach through statistical based operators shows the consistency and stability for solving the influenza disease nonlinear system.

Influenza-associated in-hospital mortality during the 2017/2018 influenza season: a retrospective multicentre cohort study in central Germany

The aim of this retrospective cohort study at eight hospitals in Germany was to specify influenza-associated in-hospital mortality during the 2017/2018 flu season, which was the strongest in Germany in the past 30 years. A total of 1560 patients were included in the study. Overall, in-hospital mortality was 6.7% (n = 103), in patients treated in the intensive care unit (n = 161) mortality was 22.4%. The proportion of deceased patients per hospital was between 0% and 7.0%. Influenza was the immediate cause of death in 82.8% (n = 82) of the decedents.