Estimates of the prevalence of pandemic (H1N1) 2009, United States, April-July 2009

Reconstructing the age-structured case count of COVID-19 from sentinel surveillance data in Japan: A modeling study

OBJECTIVES

To reconstruct age-structured case counts of COVID-19 using sentinel reporting, which replaced universal reporting of COVID-19 from May 2023 in Japan.

METHODS

Using COVID-19 sentinel data stratified by discrete age groups in selected prefectures and referring to universal case count data up to May 8, 2023, we fitted a statistical model to handle weekly growth rates as a function of age and time so as to convert sentinel data to case counts after cessation of universal reporting.

RESULTS

The age distribution of cases in sentinel reporting was significantly biased toward younger age groups compared to universal reporting. When comparing the epidemic size of the 9th wave (May 8 to September 18, 2023) to the 8th wave (October 3, 2022 to April 10, 2023), using the wave-on-wave ratio of total cumulative sentinel cases led to a significant underestimation of the wave-on-wave in Tokyo (0.975, vs 1.461 by universal reporting) and Okinawa (1.299, vs 1.472). The estimates of growth rates, scaling factors between universal and sentinel cases, and expected universal case count showed robustness to changes in the ending week of the data period.

CONCLUSION

Our model quantified COVID-19 dynamics, comparably to universal reporting that ended in May 2023, enabling detailed and up-to-date health burden analysis using sentinel reports. The cumulative incidence was greater than that suggested from sentinel data in Tokyo, Nara, and Okinawa. Per-population burdens among children were particularly high in Osaka and Nara, indicating a strong bias in sentinel reporting toward pediatric cases.

Estimated number and incidence of influenza-associated acute respiratory infection cases in winter 2021/22 in Wanzhou District, China

OBJECTIVES

Understanding the burden of influenza-associated acute respiratory infection (ARI) and severe ARI (SARI) is crucial for public health decision-making. A population-based study with multiple data sources was conducted to estimate the burden of influenza-associated ARI in Wanzhou District, Chongqing, southern China.

STUDY DESIGN

Population-based surveillance study.

METHODS

Active surveillance of ARI was conducted in different levels of health facilities in the Wanzhou District between October 2021 and March 2022. Nasal or throat swabs were collected and tested for influenza viruses in hospital-based surveillance. A health utilisation survey was used to estimate health-seeking behaviour, and all electronic medical records were collected. An epidemiological model was used to estimate the disease burden.

RESULTS

There were an estimated 52,960 influenza-associated ARI (95 % confidence interval [CI]: 39,213-84,891), including 2,529 SARI cases (95 % CI: 1,385-21,712) during winter 2021/22 in the Wanzhou District. The incidence rate for all influenza-associated ARI and SARI was 3,385/100,000 and 162/100,000, respectively. A higher incidence rate of influenza-associated ARI was observed among individuals aged <50 years, while a higher influenza-associated SARI rate was observed in those aged ≥50 years.

CONCLUSIONS

Using an epidemiological model with data from multiple sources, this study documented a substantial burden of influenza-associated ARI in the Wanzhou District, highlighting the need for influenza vaccination and providing a possible foundation for public health decision-making.

Incidence of symptomatic Borrelia burgdorferi sensu lato infection in Romania, 2018-2023

BACKGROUND

Lyme borreliosis (LB), caused by Borrelia burgdorferi sensu lato (Bbsl), is the most common tick-borne disease in Europe. Although public health surveillance for LB has been conducted in Romania since 2007, the extent of under-detection of Bbsl infections by LB surveillance has not been estimated. We therefore estimated the under-detection of symptomatic Bbsl infections by LB surveillance to better understand the LB burden in Romania.

METHODS

The number of incident symptomatic Bbsl infections were estimated from a seroprevalence study conducted in six counties (population 2.3 M) and estimates of the symptomatic proportion and duration of persistence of anti-Bbsl immunoglobulin G (IgG) antibodies. The number of incident symptomatic Bbsl infections were compared with the number of surveillance-reported LB cases to derive an under-detection multiplier, and then the under-detection multiplier was applied to LB surveillance data to estimate the incidence of symptomatic Bbsl infection from 2018 to 2023.

RESULTS

We estimate that there were 1968 individuals with incident symptomatic Bbsl infection in the six counties where the seroprevalence study was conducted in 2020, compared with the 187 surveillance-reported LB cases, resulting in an under-detection multiplier of 10.5 (i.e., for every surveillance-reported LB case, there were 10.5 symptomatic incident Bbsl infections). The incidence of symptomatic Bbsl infection in the six counties was 86.9/100,000 population in 2023, similar to the incidence in 2018-2020 (86.0) and higher than in 2021-2022 (40.3).

CONCLUSIONS

There is a higher incidence of symptomatic Bbsl infection than is reported through public health surveillance for LB in Romania. Additional efforts are needed to strengthen disease prevention and address the important public health problem of LB.

Mathematical modeling for estimating influenza vaccine efficacy: A case study of the Valencian Community, Spain

Vaccine efficacy and its quantification is a crucial concept for the proper design of public health vaccination policies. In this work we proposed a mathematical model to estimate the efficacy of the influenza vaccine in a real-word scenario. In particular, our model is a SEIR-type epidemiological model, which distinguishes vaccinated and unvaccinated populations. Mathematically, its dynamics is governed by a nonlinear system of ordinary differential equations, where the non-linearity arises from the effective contacts between susceptible and infected individuals. Two key aspects of this study is that we use a vaccine distribution over time that is based on real data specific to the elderly people in the Valencian Community and the calibration process takes into account that over one influenza season a specific proportion of the population becomes infected with influenza. To consider the effectiveness of the vaccine, the model incorporates a parameter, the vaccine attenuation factor, which is related with the vaccine efficacy against the influenza virus. With this framework, in order to calibrate the model parameters and to obtain an influenza vaccine efficacy estimation, we considered the 2016-2017 influenza season in the Valencian Community, Spain, using the influenza reported cases of vaccinated and unvaccinated. In order to ensure the model identifiability, we choose to deterministically calibrate the parameters for different scenarios and we find the one with the minimum error in order to determine the vaccine efficacy. The calibration results suggest that the influenza vaccine developed for 2016-2017 influenza season has an efficacy of approximately 76.7%, and that the risk of becoming infected is five times higher for an unvaccinated individual in comparison with a vaccinated one. This estimation partially agrees with some previous studies related to the influenza vaccine. This study presents a new integrated mathematical approach to study the influenza vaccine efficacy and gives further insight into this important public health topic.

A proactive/reactive mass screening approach with uncertain symptomatic cases

We study the problem of mass screening of heterogeneous populations under limited testing budget. Mass screening is an essential tool that arises in various settings, e.g., the COVID-19 pandemic. The objective of mass screening is to classify the entire population as positive or negative for a disease as efficiently and accurately as possible. Under limited budget, testing facilities need to allocate a portion of the budget to target sub-populations (i.e., proactive screening) while reserving the remaining budget to screen for symptomatic cases (i.e., reactive screening). This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations for proactive/reactive screening. The framework also incorporates two widely used testing schemes: Individual and Dorfman group testing. By leveraging the special structure of the resulting bilinear optimization problem, we identify key structural properties, which in turn enable us to develop efficient solution schemes. Furthermore, we extend the model to accommodate customized testing schemes across different sub-populations and introduce a highly efficient heuristic solution algorithm for the generalized model. We conduct a comprehensive case study on COVID-19 in the US, utilizing geographically-based data. Numerical results demonstrate a significant improvement of up to 52% in total misclassifications compared to conventional screening strategies. In addition, our case study offers valuable managerial insights regarding the allocation of proactive/reactive measures and budget across diverse geographic regions.

Assessing the Underestimation of Adult Pertussis Disease in Five Latin American Countries

INTRODUCTION

Pertussis, a contagious respiratory disease, is underreported in adults. The study objective was to quantify underestimation of pertussis cases in adults aged ≥ 50 years in five Latin American countries (Argentina, Brazil, Chile, Mexico, Peru).

METHODS

A previously published probabilistic model was adapted to adjust the number of pertussis cases reported to national surveillance systems by successive multiplication steps (proportion of pertussis cases seeking healthcare; proportion with a specimen collected; proportion sent for confirmatory testing; proportion positive for pertussis; proportion reported to passive surveillance). The proportions at each step were added in a random effects model to produce a pooled overall proportion, and a final multiplier was calculated as the simple inverse of this proportion. This multiplier was applied to the number of cases reported to surveillance to estimate the number of pertussis cases. Monte Carlo simulation with 10,000 iterations estimated median as well as upper and lower 90% values. Input data were obtained from surveillance systems and published sources.

RESULTS

The estimated median underestimation factor for pertussis cases in adults ranged from 104 (90% limits 40, 451) in Chile to 114 (90% limits 39, 419) in Argentina. In all five countries, the largest estimated number of cases was in the group aged 50-59 years. The highest number per 100,000 population was in the group aged ≥ 90 years in most countries. The estimated median underestimation factor for pertussis hospitalizations was 2.3 (90% limits 1.8, 3.3) in Brazil and 2.4 (90% limits 1.8, 3.2) in Chile (data not available for other countries).

CONCLUSION

This analysis indicates that the number of pertussis cases in adults aged ≥ 50 years in five Latin American countries is approximately 100 times higher than the number captured in surveillance data. These results could support decision-making in the diagnosis, management, and prevention of pertussis disease in adults.

Estimated Incidence of Symptomatic Lyme Borreliosis Cases in Lublin, Poland in 2021

Lyme borreliosis (LB), the most common tick-borne disease in Europe, is endemic to Poland. Despite public health surveillance with mandatory reporting of LB cases by physicians and laboratories, many symptomatic LB cases are not included in surveillance in Poland. We estimated the extent of the under-ascertainment of symptomatic LB cases via surveillance in the Polish province of Lublin to better understand Poland's LB burden. The number of incident symptomatic LB cases in Lublin in 2010 was estimated from two seroprevalence studies conducted among adults in Lublin, as well as estimates of the proportion of asymptomatic LB cases and the duration of LB antibody persistence. The estimated number of incident symptomatic LB cases was compared to the number of surveillance-reported cases in Lublin to derive an under-ascertainment multiplier. This multiplier was applied to the number of surveillance-reported cases in 2021 to estimate the number and population-based incidence of symptomatic LB cases in Lublin in 2021. We estimate that there are 5.9 symptomatic LB cases for every surveillance-reported LB case in Lublin. Adjusting for under-ascertainment, the estimated number of symptomatic LB cases in Lublin in 2021 was 6204 (population-based incidence: 467.6/100,000). After adjustment for under-ascertainment, the incidence of symptomatic LB in Lublin, Poland, is high.

Estimated number of symptomatic Lyme borreliosis cases in Germany in 2021 after adjusting for under-ascertainment

BACKGROUND

Although nine of 16 federal states in Germany conduct public health surveillance for Lyme borreliosis (LB), the extent of under-ascertainment is unknown.

OBJECTIVE

As a model for European countries that conduct LB surveillance, we sought to estimate the population-based incidence of symptomatic LB after adjusting for under-ascertainment.

METHODS

Estimating seroprevalence-derived under-ascertainment relies on data from seroprevalence studies, public health surveillance, and published literature. The number of symptomatic LB cases in states that conduct LB surveillance was estimated from studies reporting the seroprevalence of antibodies against Borrelia burgdorferi sensu lato, the proportion of LB cases that are asymptomatic, and the duration of antibody detection. The number of estimated incident symptomatic LB cases was compared with the number of surveillance-reported LB cases to derive under-ascertainment multipliers. The multipliers were applied to the number of 2021 surveillance-reported LB cases to estimate the population-based incidence of symptomatic LB in Germany.

RESULTS

Adjusting for seroprevalence-based under-ascertainment multipliers, the estimated number of symptomatic LB cases in states that conducted surveillance was 129,870 (408 per 100,000 population) in 2021. As there were 11,051 surveillance-reported cases in 2021 in these states, these data indicate there were 12 symptomatic LB cases for every surveillance-reported LB case.

CONCLUSIONS

We demonstrate that symptomatic LB is underdetected in Germany and that this seroprevalence-based approach can be applied elsewhere in Europe where requisite data are available. Nationwide expansion of LB surveillance would further elucidate the true LB disease burden in Germany and could support targeted disease prevention efforts to address the high LB disease burden.

Estimating the Under-ascertainment of COVID-19 cases in Toronto, Ontario, March to May 2020

Background

Public health surveillance data do not always capture all cases, due in part to test availability and health care seeking behaviour. Our study aimed to estimate under-ascertainment multipliers for each step in the reporting chain for COVID-19 in Toronto, Canada.

Design and methods

We applied stochastic modeling to estimate these proportions for the period from March 2020 (the beginning of the pandemic) through to May 23, 2020, and for three distinct windows with different laboratory testing criteria within this period.

Results

For each laboratory-confirmed symptomatic case reported to Toronto Public Health during the entire period, the estimated number of COVID-19 infections in the community was 18 (5th and 95th percentile: 12, 29). The factor most associated with under-reporting was the proportion of those who sought care that received a test.

Conclusions

Public health officials should use improved estimates to better understand the burden of COVID-19 and other similar infections.

Using an influenza surveillance system to estimate the number of SARS-CoV-2 infections in Beijing, China, weeks 2 to 6 2023

With COVID-19 public health control measures downgraded in China in January 2023, reported COVID-19 case numbers may underestimate the true numbers after the SARS-CoV-2 Omicron wave. Using a multiplier model based on our influenza surveillance system, we estimated that the overall incidence of SARS-CoV-2 infections was 392/100,000 population in Beijing during the 5 weeks following policy adjustment. No notable change occurred after the Spring Festival in early February. The multiplier model provides an opportunity for assessing the actual COVID-19 situation.

Using real-time data to guide decision-making during an influenza pandemic: A modelling analysis

Influenza pandemics typically occur in multiple waves of infection, often associated with initial emergence of a novel virus, followed (in temperate regions) by a resurgence accompanying the onset of the annual influenza season. Here, we examined whether data collected from an initial pandemic wave could be informative, for the need to implement non-pharmaceutical measures in any resurgent wave. Drawing from the 2009 H1N1 pandemic in 10 states in the USA, we calibrated simple mathematical models of influenza transmission dynamics to data for laboratory confirmed hospitalisations during the initial 'spring' wave. We then projected pandemic outcomes (cumulative hospitalisations) during the fall wave, and compared these projections with data. Model results showed reasonable agreement for all states that reported a substantial number of cases in the spring wave. Using this model we propose a probabilistic decision framework that can be used to determine the need for preemptive measures such as postponing school openings, in advance of a fall wave. This work illustrates how model-based evidence synthesis, in real-time during an early pandemic wave, could be used to inform timely decisions for pandemic response.

Hospital admissions with influenza and impact of age and comorbidities on severe clinical outcomes in Brazil and Mexico

Background

The risk of hospitalization or death after influenza infection is higher at the extremes of age and in individuals with comorbidities. We estimated the number of hospitalizations with influenza and characterized the cumulative risk of comorbidities and age on severe outcomes in Mexico and Brazil.

Methods

We used national hospital discharge data from Brazil (SIH/SUS) from 2010–2018 and Mexico (SAEH) from 2010–2017 to estimate the number of influenza admissions using ICD-10 discharge codes, stratified by age (0–4, 5–17, 18–49, 50–64, and ≥65 years). Duration of hospital stay, admission to the intensive care unit (ICU), and in-hospital case fatality rates (CFRs) defined the severe outcomes. Rates were compared between patients with or without pre-specified comorbidities and by age.

Results

A total of 327,572 admissions with influenza were recorded in Brazil and 20,613 in Mexico, with peaks period most years. In Brazil, the median hospital stay duration was 3.0 days (interquartile range, 2.0–5.0), ICU admission rate was 3.3% (95% CI, 3.2–3.3%), and in-hospital CFR was 4.6% (95% CI, 4.5–4.7). In Mexico, the median duration of stay was 5.0 days (interquartile range, 3.0–7.0), ICU admission rate was 1.8% (95% CI, 1.6–2.0%), and in-hospital CFR was 6.9% (95% CI, 6.5–7.2). In Brazil, ICU admission and in-hospital CFR were higher in adults aged ≥50 years and increased in the presence of comorbidities, especially cardiovascular disease. In Mexico, comorbidities increased the risk of ICU admission by 1.9 (95% CI, 1.0–3.5) and in-hospital CFR by 13.9 (95% CI, 8.4–22.9) in children 0–4 years.

Conclusion

The SIH/SUS and SAEH databases can be used to estimate hospital admissions with influenza, and the disease severity. Age and comorbidities, especially cardiovascular disease, are cumulatively associated with more severe outcomes, with differences between countries. This association should be further analyzed in prospective surveillance studies designed to support influenza vaccination strategy decisions.

Comparative epidemiology of five waves of COVID-19 in Mexico, March 2020–August 2022

Background

The Mexican Institute of Social Security (IMSS) is the largest health care provider in Mexico, covering about 48% of the Mexican population. In this report, we describe the epidemiological patterns related to confirmed cases, hospitalizations, intubations, and in-hospital mortality due to COVID-19 and associated factors, during five epidemic waves recorded in the IMSS surveillance system.

Methods

We analyzed COVID-19 laboratory-confirmed cases from the Online Epidemiological Surveillance System (SINOLAVE) from March 29th, 2020, to August 27th, 2022. We constructed weekly epidemic curves describing temporal patterns of confirmed cases and hospitalizations by age, gender, and wave. We also estimated hospitalization, intubation, and hospital case fatality rates. The mean days of in-hospital stay and hospital admission delay were calculated across five pandemic waves. Logistic regression models were employed to assess the association between demographic factors, comorbidities, wave, and vaccination and the risk of severe disease and in-hospital death.

Results

A total of 3,396,375 laboratory-confirmed COVID-19 cases were recorded across the five waves. The introduction of rapid antigen testing at the end of 2020 increased detection and modified epidemiological estimates. Overall, 11% (95% CI 10.9, 11.1) of confirmed cases were hospitalized, 20.6% (95% CI 20.5, 20.7) of the hospitalized cases were intubated, and the hospital case fatality rate was 45.1% (95% CI 44.9, 45.3). The mean in-hospital stay was 9.11 days, and patients were admitted on average 5.07 days after symptoms onset. The most recent waves dominated by the Omicron variant had the highest incidence. Hospitalization, intubation, and mean hospitalization days decreased during subsequent waves. The in-hospital case fatality rate fluctuated across waves, reaching its highest value during the second wave in winter 2020. A notable decrease in hospitalization was observed primarily among individuals ≥ 60 years. The risk of severe disease and death was positively associated with comorbidities, age, and male gender; and declined with later waves and vaccination status.

Conclusion

During the five pandemic waves, we observed an increase in the number of cases and a reduction in severity metrics. During the first three waves, the high in-hospital fatality rate was associated with hospitalization practices for critical patients with comorbidities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07800-w.

Five-Year Community Surveillance Study for Acute Respiratory Infections Using Text Messaging: Findings From the MoSAIC Study

BACKGROUND

Acute respiratory infections (ARI) are the most common infectious diseases globally. Community surveillance may provide a more comprehensive picture of disease burden than medically attended illness alone.

METHODS

In this longitudinal study conducted from 2012 to 2017 in the Washington Heights/Inwood area of New York City, we enrolled 405 households with 1915 individuals. Households were sent research text messages twice weekly inquiring about ARI symptoms. Research staff confirmed symptoms by follow-up call. If ≥2 criteria for ARI were met (fever/feverish, cough, congestion, pharyngitis, myalgias), staff obtained a mid-turbinate nasal swab in participants' homes. Swabs were tested using the FilmArray reverse transcription polymerase chain reaction (RT-PCR) respiratory panel.

RESULTS

Among participants, 43.9% were children, and 12.8% had a chronic respiratory condition. During the 5 years, 114 724 text messages were sent; the average response rate was 78.8% ± 6.8%. Swabs were collected for 91.4% (2756/3016) of confirmed ARI; 58.7% had a pathogen detected. Rhino/enteroviruses (51.9%), human coronaviruses (13.9%), and influenza (13.2%) were most commonly detected. The overall incidence was 0.62 ARI/person-year, highest (1.73) in <2 year-olds and lowest (0.46) in 18-49 year-olds. Approximately one-fourth of those with ARI sought healthcare; percents differed by pathogen, demographic factors, and presence of a chronic respiratory condition.

CONCLUSIONS

Text messaging is a novel method for community-based surveillance that could be used both seasonally as well as during outbreaks, epidemics and pandemics. The importance of community surveillance to accurately estimate disease burden is underscored by the findings of low rates of care-seeking that varied by demographic factors and pathogens.

Estimated Number of COVID-19 Infections, Hospitalizations, and Deaths Prevented Among Vaccinated Persons in the US, December 2020 to September 2021

IMPORTANCE

The number of SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths prevented among vaccinated persons, independent of the effect of reduced transmission, is a key measure of vaccine impact.

OBJECTIVE

To estimate the number of SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths prevented among vaccinated adults in the US.

DESIGN, SETTING, AND PARTICIPANTS

In this modeling study, a multiplier model was used to extrapolate the number of SARS-CoV-2 infections and COVID-19-associated deaths from data on the number of COVID-19-associated hospitalizations stratified by state, month, and age group (18-49, 50-64, and ≥65 years) in the US from December 1, 2020, to September 30, 2021. These estimates were combined with data on vaccine coverage and effectiveness to estimate the risks of infections, hospitalizations, and deaths. Risks were applied to the US population 18 years or older to estimate the expected burden in that population without vaccination. The estimated burden in the US population 18 years or older given observed levels of vaccination was subtracted from the expected burden in the US population 18 years or older without vaccination (ie, counterfactual) to estimate the impact of vaccination among vaccinated persons.

EXPOSURES

Completion of the COVID-19 vaccination course, defined as 2 doses of messenger RNA (BNT162b2 or mRNA-1273) vaccines or 1 dose of JNJ-78436735 vaccine.

MAIN OUTCOMES AND MEASURES

Monthly numbers and percentages of SARS-CoV-2 infections and COVID-19-associated hospitalizations and deaths prevented were estimated among those who have been vaccinated in the US.

RESULTS

COVID-19 vaccination was estimated to prevent approximately 27 million (95% uncertainty interval [UI], 22 million to 34 million) infections, 1.6 million (95% UI, 1.4 million to 1.8 million) hospitalizations, and 235 000 (95% UI, 175 000-305 000) deaths in the US from December 1, 2020, to September 30, 2021, among vaccinated adults 18 years or older. From September 1 to September 30, 2021, vaccination was estimated to prevent 52% (95% UI, 45%-62%) of expected infections, 56% (95% UI, 52%-62%) of expected hospitalizations, and 58% (95% UI, 53%-63%) of expected deaths in adults 18 years or older.

CONCLUSIONS AND RELEVANCE

These findings indicate that the US COVID-19 vaccination program prevented a substantial burden of morbidity and mortality through direct protection of vaccinated individuals.

Comparative Immunogenicity of Bacterially Expressed Soluble Trimers and Nanoparticle Displayed Influenza Hemagglutinin Stem Immunogens

Current influenza vaccines need to be updated annually due to mutations in the globular head of the viral surface protein, hemagglutinin (HA). To address this, vaccine candidates have been designed based on the relatively conserved HA stem domain and have shown protective efficacy in animal models. Oligomerization of the antigens either by fusion to oligomerization motifs or display on self-assembling nanoparticle scaffolds, can induce more potent immune responses compared to the corresponding monomeric antigen due to multivalent engagement of B-cells. Since nanoparticle display can increase manufacturing complexity, and often involves one or more mammalian cell expressed components, it is important to characterize and compare various display and oligomerization scaffolds. Using a structure guided approach, we successfully displayed multiple copies of a previously designed soluble, trimeric influenza stem domain immunogen, pH1HA10, on the ferritin like protein, MsDps2 (12 copies), Ferritin (24 copies) and Encapsulin (180 copies). All proteins were expressed in Escherichia coli. The nanoparticle fusion immunogens were found to be well folded and bound to the influenza stem directed broadly neutralizing antibodies with high affinity. An 8.5 Å Cryo-EM map of Msdps2-pH1HA10 confirmed the successful design of the nanoparticle fusion immunogen. Mice immunization studies with the soluble trimeric stem and nanoparticle fusion constructs revealed that all of them were immunogenic, and protected mice against homologous (A/Belgium/145-MA/2009) and heterologous (A/Puerto Rico/8/1934) challenge with 10MLD50 mouse adapted virus. Although nanoparticle display conferred a small but statistically significant improvement in protection relative to the soluble trimer in a homologous challenge, heterologous protection was similar in both nanoparticle-stem immunized and trimeric stem immunized groups. Such rapidly producible, bacterially expressed antigens and nanoparticle scaffolds are useful modalities to tackle future influenza pandemics.

SARS-CoV-2 Attack Rate and Population Immunity in Southern New England, March 2020 to May 2021

Importance

In emergency epidemic and pandemic settings, public health agencies need to be able to measure the population-level attack rate, defined as the total percentage of the population infected thus far. During vaccination campaigns in such settings, public health agencies need to be able to assess how much the vaccination campaign is contributing to population immunity; specifically, the proportion of vaccines being administered to individuals who are already seropositive must be estimated.

Objective

To estimate population-level immunity to SARS-CoV-2 through May 31, 2021, in Rhode Island, Massachusetts, and Connecticut.

Design, Setting, and Participants

This observational case series assessed cases, hospitalizations, intensive care unit occupancy, ventilator occupancy, and deaths from March 1, 2020, to May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Data were analyzed from July 2021 to November 2021.

Exposures

COVID-19-positive test result reported to state department of health.

Main Outcomes and Measures

The main outcomes were statistical estimates, from a bayesian inference framework, of the percentage of individuals as of May 31, 2021, who were (1) previously infected and vaccinated, (2) previously uninfected and vaccinated, and (3) previously infected but not vaccinated.

Results

At the state level, there were a total of 1 160 435 confirmed COVID-19 cases in Rhode Island, Massachusetts, and Connecticut. The median age among individuals with confirmed COVID-19 was 38 years. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in these states was less than 15%, setting the stage for a large epidemic wave during winter 2020 to 2021. Population immunity estimates for May 31, 2021, were 73.4% (95% credible interval [CrI], 72.9%-74.1%) for Rhode Island, 64.1% (95% CrI, 64.0%-64.4%) for Connecticut, and 66.3% (95% CrI, 65.9%-66.9%) for Massachusetts, indicating that more than 33% of residents in these states were fully susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned owing to an estimated 34.1% (95% CrI, 32.9%-35.2%) of vaccines in Rhode Island, 24.6% (95% CrI, 24.3%-25.1%) of vaccines in Connecticut, and 27.6% (95% CrI, 26.8%-28.6%) of vaccines in Massachusetts being distributed to individuals who were already seropositive.

Conclusions and Relevance

These findings suggest that future emergency-setting vaccination planning may have to prioritize high vaccine coverage over optimized vaccine distribution to ensure that sufficient levels of population immunity are reached during the course of an ongoing epidemic or pandemic.

Estimated Incidence of Seasonal Influenza in China From 2010 to 2020 Using a Multiplier Model

This quality improvement study estimates the total incidence of seasonal influenza and associated illnesses in China from 2010 to 2020.

COVID-19 severity: A new approach to quantifying global cases and deaths

As the COVID-19 pandemic has progressed, so too has the recognition that cases and deaths have been underreported, perhaps vastly so. Here, we present an econometric strategy to estimate the true number of COVID-19 cases and deaths for 61 and 56 countries, respectively, from 1 January 2020 to 3 November 2020. Specifically, we estimate a 'structural' model based on the SIR epidemiological model extended to incorporate underreporting. The results indicate significant underreporting by magnitudes that align with existing research and conjectures by public health experts. While our approach requires some strong assumptions, these assumptions are very different from the equally strong assumptions required by other approaches addressing underreporting in the assessment of the extent of the pandemic. Thus, we view our approach as a complement to existing methods.

A modified Susceptible-Infected-Recovered model for observed under-reported incidence data

Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that the fraction of reported cases varies across all countries. For example, the reported incidence fraction for the United States of America varies from 0.3 to 0.6, while for Brazil it varies from 0.2 to 0.4.

A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data

Decisions about typhoid fever prevention and control are based on estimates of typhoid incidence and their uncertainty. Lack of specific clinical diagnostic criteria, poorly sensitive diagnostic tests, and scarcity of accurate and complete datasets contribute to difficulties in calculating age‐specific population‐level typhoid incidence. Using data from the Strategic Typhoid Alliance across Africa and Asia program, we integrated demographic censuses, healthcare utilization surveys, facility‐based surveillance, and serological surveillance from Malawi, Nepal, and Bangladesh to account for under‐detection of cases. We developed a Bayesian approach that adjusts the count of reported blood‐culture‐positive cases for blood culture detection, blood culture collection, and healthcare seeking—and how these factors vary by age—while combining information from prior published studies. We validated the model using simulated data. The ratio of observed to adjusted incidence rates was 7.7 (95% credible interval [CrI]: 6.0‐12.4) in Malawi, 14.4 (95% CrI: 9.3‐24.9) in Nepal, and 7.0 (95% CrI: 5.6‐9.2) in Bangladesh. The probability of blood culture collection led to the largest adjustment in Malawi, while the probability of seeking healthcare contributed the most in Nepal and Bangladesh; adjustment factors varied by age. Adjusted incidence rates were within or below the seroincidence rate limits of typhoid infection. Estimates of blood‐culture‐confirmed typhoid fever without these adjustments results in considerable underestimation of the true incidence of typhoid fever. Our approach allows each phase of the reporting process to be synthesized to estimate the adjusted incidence of typhoid fever while correctly characterizing uncertainty, which can inform decision‐making for typhoid prevention and control.

Cross-sectional telephone surveys as a tool to study epidemiological factors and monitor seasonal influenza activity in Malta

Background

Seasonal influenza has major implications for healthcare services as outbreaks often lead to high activity levels in health systems. Being able to predict when such outbreaks occur is vital. Mathematical models have extensively been used to predict epidemics of infectious diseases such as seasonal influenza and to assess effectiveness of control strategies. Availability of comprehensive and reliable datasets used to parametrize these models is limited. In this paper we combine a unique epidemiological dataset collected in Malta through General Practitioners (GPs) with a novel method using cross-sectional surveys to study seasonal influenza dynamics in Malta in 2014–2016, to include social dynamics and self-perception related to seasonal influenza.

Methods

Two cross-sectional public surveys (n = 406 per survey) were performed by telephone across the Maltese population in 2014–15 and 2015–16 influenza seasons. Survey results were compared with incidence data (diagnosed seasonal influenza cases) collected by GPs in the same period and with Google Trends data for Malta. Information was collected on whether participants recalled their health status in past months, occurrences of influenza symptoms, hospitalisation rates due to seasonal influenza, seeking GP advice, and other medical information.

Results

We demonstrate that cross-sectional surveys are a reliable alternative data source to medical records. The two surveys gave comparable results, indicating that the level of recollection among the public is high. Based on two seasons of data, the reporting rate in Malta varies between 14 and 22%. The comparison with Google Trends suggests that the online searches peak at about the same time as the maximum extent of the epidemic, but the public interest declines and returns to background level. We also found that the public intensively searched the Internet for influenza-related terms even when number of cases was low.

Conclusions

Our research shows that a telephone survey is a viable way to gain deeper insight into a population’s self-perception of influenza and its symptoms and to provide another benchmark for medical statistics provided by GPs and Google Trends. The information collected can be used to improve epidemiological modelling of seasonal influenza and other infectious diseases, thus effectively contributing to public health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11862-x.

Estimated Incidence of Coronavirus Disease 2019 (COVID-19) Illness and Hospitalization-United States, February-September 2020

Background

In the United States, laboratory confirmed coronavirus disease 2019 (COVID-19) is nationally notifiable. However, reported case counts are recognized to be less than the true number of cases because detection and reporting are incomplete and can vary by disease severity, geography, and over time.

Methods

To estimate the cumulative incidence SARS-CoV-2 infections, symptomatic illnesses, and hospitalizations, we adapted a simple probabilistic multiplier model. Laboratory-confirmed case counts that were reported nationally were adjusted for sources of under-detection based on testing practices in inpatient and outpatient settings and assay sensitivity.

Results

We estimated that through the end of September, 1 of every 2.5 (95% Uncertainty Interval (UI): 2.0–3.1) hospitalized infections and 1 of every 7.1 (95% UI: 5.8–9.0) non-hospitalized illnesses may have been nationally reported. Applying these multipliers to reported SARS-CoV-2 cases along with data on the prevalence of asymptomatic infection from published systematic reviews, we estimate that 2.4 million hospitalizations, 44.8 million symptomatic illnesses, and 52.9 million total infections may have occurred in the U.S. population from February 27–September 30, 2020.

Conclusions

These preliminary estimates help demonstrate the societal and healthcare burdens of the COVID-19 pandemic and can help inform resource allocation and mitigation planning.

Evaluating Specimen Quality and Results from a Community-Wide, Home-Based Respiratory Surveillance Study

While influenza and other respiratory pathogens cause significant morbidity and mortality, the community-based burden of these infections remains incompletely understood. The development of novel methods to detect respiratory infections is essential for mitigating epidemics and developing pandemic-preparedness infrastructure.

While influenza and other respiratory pathogens cause significant morbidity and mortality, the community-based burden of these infections remains incompletely understood. The development of novel methods to detect respiratory infections is essential for mitigating epidemics and developing pandemic-preparedness infrastructure. From October 2019 to March 2020, we conducted a home-based cross-sectional study in the greater Seattle, WA, area, utilizing electronic consent and data collection instruments. Participants received nasal swab collection kits via rapid delivery within 24 hours of self-reporting respiratory symptoms. Samples were returned to the laboratory and were screened for 26 respiratory pathogens and a housekeeping gene. Participant data were recorded via online survey at the time of sample collection and 1 week later. Of the 4,572 consented participants, 4,359 (95.3%) received a home swab kit and 3,648 (83.7%) returned a nasal specimen for respiratory pathogen screening. The 3,638 testable samples had a mean RNase P relative cycle threshold (C_rt) value of 19.0 (SD, 3.4), and 1,232 (33.9%) samples had positive results for one or more pathogens, including 645 (17.7%) influenza-positive specimens. Among the testable samples, the median time between shipment of the home swab kit and completion of laboratory testing was 8.0 days (interquartile range [IQR], 7.0 to 14.0). A single adverse event occurred and did not cause long-term effects or require medical attention. Home-based surveillance using online participant enrollment and specimen self-collection is a safe and feasible method for community-level monitoring of influenza and other respiratory pathogens, which can readily be adapted for use during pandemics.

Cost-Effectiveness Analysis of Switching from Trivalent to Quadrivalent Seasonal Influenza Vaccine in Argentina

The burden of seasonal influenza disease in Argentina is considerable. The cost-effectiveness of trivalent (TIV) versus quadrivalent influenza vaccine (QIV) in Argentina was assessed. An age-stratified, static, decision-tree model compared the costs and benefits of vaccination for an average influenza season. Main outcomes included: numbers of influenza cases; general practitioner (GP) visits; complicated ambulatory cases; hospitalizations; deaths averted; and costs per quality-adjusted life years (QALYs) gained. Epidemiological data from Argentina for 2014–2019 were used to determine the proportion of A and B strain cases, and the frequency of mismatch between vaccine and circulating B strains. To manage uncertainty, one-way and probabilistic sensitivity analyses were performed. Switching from TIV to QIV would prevent 19,128 influenza cases, 16,164 GP visits, 2440 complicated ambulatory cases, 524 hospitalizations, and 82 deaths. Incremental cost–effectiveness ratios (ICERs) per QALY were 13,590 and 11,678 USD from the payer’s and societal perspectives, respectively. The greatest health benefits and direct medical cost savings would occur in ≥ 65-year-olds. One-way sensitivity analyses demonstrated the principal drivers of ICER to be vaccine acquisition costs, environmental B strain predominance, and B strain mismatch. Introducing QIV in Argentina would be beneficial and cost-effective relative to TIV, particularly in older adults.

SARIMA-modelled greater severity and mortality during the 2010/11 post-pandemic influenza season compared to the 2009 H1N1 pandemic in English hospitals

OBJECTIVE

The COVID-19 pandemic demonstrates the need for understanding pathways to healthcare demand, morbidity, and mortality of pandemic patients. We estimate H1N1 (1) hospitalization rates, (2) severity rates (length of stay, ventilation, pneumonia, and death) of those hospitalized, (3) mortality rates, and (4) time lags between infections and hospitalizations during the pandemic (June 2009 to March 2010) and post-pandemic influenza season (November 2010 to February 2011) in England.

METHODS

Estimates of H1N1 infections from a dynamic transmission model are combined with hospitalizations and severity using time series econometric analyses of administrative patient-level hospital data.

RESULTS

Hospitalization rates were 34% higher and severity rates of those hospitalized were 20%-90% higher in the post-pandemic period than the pandemic. Adults (45-64-years-old) had the highest ventilation and pneumonia hospitalization rates. Hospitalizations did not lag infection during the pandemic for the young (<24-years-old) but lagged by one or more weeks for all ages in the post-pandemic period.

DISCUSSION

The post-pandemic flu season exhibited heightened H1N1 severity, long after the pandemic was declared over. Policymakers should remain vigilant even after pandemics seem to have subsided. Analysis of administrative hospital data and epidemiological modelling estimates can provide valuable insights to inform responses to COVID-19 and future influenza and other disease pandemics.

Estimation of US SARS-CoV-2 Infections, Symptomatic Infections, Hospitalizations, and Deaths Using Seroprevalence Surveys

IMPORTANCE

Estimates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease burden are needed to help guide interventions.

OBJECTIVE

To estimate the number of SARS-CoV-2 infections, symptomatic infections, hospitalizations, and deaths in the US as of November 15, 2020.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study of respondents of all ages, data from 4 regional and 1 nationwide Centers for Disease Control and Prevention (CDC) seroprevalence surveys (April [n = 16 596], May, June, and July [n = 40 817], and August [n = 38 355]) were used to estimate infection underreporting multipliers and symptomatic underreporting multipliers. Community serosurvey data from randomly selected members of the general population were also used to validate the underreporting multipliers.

MAIN OUTCOMES AND MEASURES

SARS-CoV-2 infections, symptomatic infections, hospitalizations, and deaths. The median of underreporting multipliers derived from the 5 CDC seroprevalence surveys in the 10 states that participated in 2 or more surveys were applied to surveillance data of reported coronavirus disease 2019 (COVID-19) cases for 5 respective time periods to derive estimates of SARS-CoV-2 infections and symptomatic infections, which were summed to estimate SARS-CoV-2 infections and symptomatic infections in the US. Estimates of infections and symptomatic infections were combined with estimates of the hospitalization ratio and fatality ratio to derive estimates of SARS-CoV-2 hospitalizations and deaths. External validity of the surveys was evaluated with the April CDC survey by comparing results to 5 serosurveys (n = 22 118) that used random sampling of the general population. Internal validity of the multipliers from the 10 specific states was assessed in the August CDC survey by comparing multipliers from the 10 states to all states. A sensitivity analysis was conducted using the interquartile range of the multipliers to derive a high and low estimate of SARS-CoV-2 infections and symptomatic infections. The underreporting multipliers were then used to adjust the reported COVID-19 infections to estimate the full SARS-COV-2 disease burden.

RESULTS

Adjusting reported COVID-19 infections using underreporting multipliers derived from CDC seroprevalence studies in April (n = 16 596), May (n = 14 291), June (n = 14 159), July (n = 12 367), and August (n = 38 355), there were estimated medians of 46 910 006 (interquartile range [IQR], 38 192 705-60 814 748) SARS-CoV-2 infections, 28 122 752 (IQR, 23 014 957-36 438 592) symptomatic infections, 956 174 (IQR, 782 509-1 238 912) hospitalizations, and 304 915 (IQR, 248 253-395 296) deaths in the US through November 15, 2020. An estimated 14.3% (IQR, 11.6%-18.5%) of the US population were infected by SARS-CoV-2 as of mid-November 2020.

CONCLUSIONS AND RELEVANCE

The SARS-CoV-2 disease burden may be much larger than reported COVID-19 cases owing to underreporting. Even after adjusting for underreporting, a substantial gap remains between the estimated proportion of the population infected and the proportion infected required to reach herd immunity. Additional seroprevalence surveys are needed to monitor the pandemic, including after the introduction of safe and efficacious vaccines.

Impact of COVID-19 infection control measures on influenza-related outcomes in Hong Kong

Following the announcement of coronavirus disease 2019 (COVID-19) cases in Wuhan on 31 December 2019, government officials in Hong Kong recommended the wearing of face masks as a public infection control measure against the COVID-19 virus and curtail the impact of the concurrent influenza season. The present study evaluated the influenza-related outcomes between the influenza season 2019 and 2020 in Hong Kong as a result of these infection control measures. A Monte Carlo simulation model was designed to estimate the number of influenza cases, clinic visits, hospitalization, deaths, direct medical cost and disability-adjusted life-years (DALYs) for the season 2018-2019 and 2019-2020 in six age groups: 0-5 years, 6-11 years, 12-17 years, 18-49 years, 50-64 years and ≥65 years in Hong Kong. Model inputs were derived from public data and existing literature. The model findings showed significant reduction in influenza-related cases, clinic visits, hospitalization, and deaths in 2020 versus 2019 (p < 0.05). Influenza-related direct costs in all age-groups were significantly reduced by 56%-82% (p < 0.01) in 2020 versus 2019. DALYs were also significantly decreased by 58%-85% (p < 0.01). The direct cost and DALYs avoided in 2020 was the highest among the age group of 0-5 years with a cost-saving of USD593,763 (95%CI 590,730-596,796) per 10,000 population and a DALY reduction of 57.67 (95%CI 57.54-57.83) per 10,000 population. This study illustrated the reduction of all influenza-related outcome measures in Hong Kong as a result of the implementation of public infection control measures against COVID-19.

COVID-19 pandemic and Farr's law: A global comparison and prediction of outbreak acceleration and deceleration rates

The COVID-19 outbreak has forced most of the global population to lock-down and has put in check the health services all over the world. Current predictive models are complex, region-dependent, and might not be generalized to other countries. However, a 150-year old epidemics law promulgated by William Farr might be useful as a simple arithmetical model (percent increase [R1] and acceleration [R2] of new cases and deaths) to provide a first sight of the epidemic behavior and to detect regions with high predicted dynamics. Thus, this study tested Farr's Law assumptions by modeling COVID-19 data of new cases and deaths. COVID-19 data until April 10, 2020, was extracted from available countries, including income, urban index, and population characteristics. Farr's law first (R1) and second ratio (R2) were calculated. We constructed epidemic curves and predictive models for the available countries and performed ecological correlation analysis between R1 and R2 with demographic data. We extracted data from 210 countries, and it was possible to estimate the ratios of 170 of them. Around 42·94% of the countries were in an initial acceleration phase, while 23·5% already crossed the peak. We predicted a reduction close to zero with wide confidence intervals for 56 countries until June 10 (high-income countries from Asia and Oceania, with strict political actions). There was a significant association between high R1 of deaths and high urban index. Farr's law seems to be a useful model to give an overview of COVID-19 pandemic dynamics. The countries with high dynamics are from Africa and Latin America. Thus, this is a call to urgently prioritize actions in those countries to intensify surveillance, to re-allocate resources, and to build healthcare capacities based on multi-nation collaboration to limit onward transmission and to reduce the future impact on these regions in an eventual second wave.

Impact of quadrivalent influenza vaccines in Brazil: a cost-effectiveness analysis using an influenza transmission model

BACKGROUND

Influenza epidemics significantly weight on the Brazilian healthcare system and its society. Public health authorities have progressively expanded recommendations for vaccination against influenza, particularly to the pediatric population. However, the potential mismatch between the trivalent influenza vaccine (TIV) strains and those circulating during the season remains an issue. Quadrivalent vaccines improves vaccines effectiveness by preventing any potential mismatch on influenza B lineages.

METHODS

We evaluate the public health and economic benefits of the switch from TIV to QIV for the pediatric influenza recommendation (6mo-5yo) by using a dynamic epidemiological model able to consider the indirect impact of vaccination. Results of the epidemiological model are then imputed in a health-economic model adapted to the Brazilian context. We perform deterministic and probabilistic sensitivity analysis to account for both epidemiological and economical sources of uncertainty.

RESULTS

Our results show that switching from TIV to QIV in the Brazilian pediatric population would prevent 406,600 symptomatic cases, 11,300 hospitalizations and almost 400 deaths by influenza season. This strategy would save 3400 life-years yearly for an incremental direct cost of R$169 million per year, down to R$86 million from a societal perspective. Incremental cost-effectiveness ratios for the switch would be R$49,700 per life-year saved and R$26,800 per quality-adjusted life-year gained from a public payer perspective, and even more cost-effective from a societal perspective. Our results are qualitatively similar in our sensitivity analysis.

CONCLUSIONS

Our analysis shows that switching from TIV to QIV to protect children aged 6mo to 5yo in the Brazilian influenza epidemiological context could have a strong public health impact and represent a cost-effective strategy from a public payer perspective, and a highly cost-effective one from a societal perspective.

The Jaw Epidemic: Recognition, Origins, Cures, and Prevention

Contemporary humans are living very different lives from those of their ancestors, and some of the changes have had serious consequences for health. Multiple chronic "diseases of civilization," such as cardiovascular problems, cancers, ADHD, and dementias are prevalent, increasing morbidity rates. Stress, including the disruption of traditional sleep patterns by modern lifestyles, plays a prominent role in the etiology of these diseases, including obstructive sleep apnea. Surprisingly, jaw shrinkage since the agricultural revolution, leading to an epidemic of crooked teeth, a lack of adequate space for the last molars (wisdom teeth), and constricted airways, is a major cause of sleep-related stress. Despite claims that the cause of this jaw epidemic is somehow genetic, the speed with which human jaws have changed, especially in the last few centuries, is much too fast to be evolutionary. Correlation in time and space strongly suggests the symptoms are phenotypic responses to a vast natural experiment-rapid and dramatic modifications of human physical and cultural environments. The agricultural and industrial revolutions have produced smaller jaws and less-toned muscles of the face and oropharynx, which contribute to the serious health problems mentioned above. The mechanism of change, research and clinical trials suggest, lies in orofacial posture, the way people now hold their jaws when not voluntarily moving them in speaking or eating and especially when sleeping. The critical resting oral posture has been disrupted in societies no longer hunting and gathering. Virtually all aspects of how modern people function and rest are radically different from those of our ancestors. We also briefly discuss treatment of jaw symptoms and possible clinical cures for individuals, as well as changes in society that might lead to better care and, ultimately, prevention.

Health-care seeking behavior for respiratory illness among Flu Near You participants in the United States during the 2015-16 through 2018-19 influenza season

Background

Flu Near You (FNY) is an online participatory syndromic surveillance system that collects health-related information. In this article, we summarized the healthcare-seeking behavior of FNY participants who reported influenza-like illness (ILI) symptoms.

Methods

We applied inverse probability weighting to calculate age-adjusted estimates of the percentage of FNY participants in the United States who sought health care for ILI symptoms during the 2015–2016 through 2018–2019 influenza season and compared seasonal trends across different demographic and regional subgroups, including age group, sex, census region, and place of care using adjusted χ ² tests.

Results

The overall age-adjusted percentage of FNY participants who sought healthcare for ILI symptoms varied by season and ranged from 22.8% to 35.6%. Across all seasons, healthcare seeking was highest for the <18 and 65+ years age groups, women had a greater percentage compared with men, and the South census region had the largest percentage while the West census region had the smallest percentage.

Conclusions

The percentage of FNY participants who sought healthcare for ILI symptoms varied by season, geographical region, age group, and sex. FNY compliments existing surveillance systems and informs estimates of influenza-associated illness by adding important real-time insights into healthcare-seeking behavior.

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

BACKGROUND

Seasonal influenza poses a significant public health and economic burden, associated with the outcome of infection and resulting complications. The true burden of the disease is difficult to capture due to the wide range of presentation, from asymptomatic cases to non-respiratory complications such as cardiovascular events, and its seasonal variability. An understanding of the magnitude of the true annual incidence of influenza is important to support prevention and control policy development and to evaluate the impact of preventative measures such as vaccination.

METHODS

We use a dynamic disease transmission model, laboratory-confirmed influenza surveillance data, and randomized-controlled trial (RCT) data to quantify the underestimation factor, expansion factor, and symptomatic influenza illnesses in the US and Canada during the 2011-2012 and 2012-2013 influenza seasons.

RESULTS

Based on 2 case definitions, we estimate between 0.42-3.2% and 0.33-1.2% of symptomatic influenza illnesses were laboratory-confirmed in Canada during the 2011-2012 and 2012-2013 seasons, respectively. In the US, we estimate between 0.08-0.61% and 0.07-0.33% of symptomatic influenza illnesses were laboratory-confirmed in the 2011-2012 and 2012-2013 seasons, respectively. We estimated the symptomatic influenza illnesses in Canada to be 0.32-2.4 million in 2011-2012 and 1.8-8.2 million in 2012-2013. In the US, we estimate the number of symptomatic influenza illnesses to be 4.4-34 million in 2011-2012 and 23-102 million in 2012-2013.

CONCLUSIONS

We illustrate that monitoring a representative group within a population may aid in effectively modelling the transmission of infectious diseases such as influenza. In particular, the utilization of RCTs in models may enhance the accuracy of epidemiological parameter estimation.

Projected Population Benefit of Increased Effectiveness and Coverage of Influenza Vaccination on Influenza Burden in the United States

BACKGROUND

Vaccination is the best way to prevent influenza; however, greater benefits could be achieved. To help guide research and policy agendas, we aimed to quantify the magnitude of influenza disease that would be prevented through targeted increases in vaccine effectiveness (VE) or vaccine coverage (VC).

METHODS

For 3 influenza seasons (2011-12, 2015-16, and 2017-18), we used a mathematical model to estimate the number of prevented influenza-associated illnesses, medically attended illnesses, and hospitalizations across 5 age groups. Compared with estimates of prevented illness during each season, given observed VE and VC, we explored the number of additional outcomes that would have been prevented from a 5% absolute increase in VE or VC or from achieving 60% VE or 70% VC.

RESULTS

During the 2017-18 season, compared with the burden already prevented by influenza vaccination, a 5% absolute VE increase would have prevented an additional 1 050 000 illnesses and 25 000 hospitalizations (76% among those aged ≥65 years), while achieving 60% VE would have prevented an additional 190 000 hospitalizations. A 5% VC increase would have resulted in 785 000 fewer illnesses (56% among those aged 18-64 years) and 11 000 fewer hospitalizations; reaching 70% would have prevented an additional 39 000 hospitalizations.

CONCLUSIONS

Small, attainable improvements in effectiveness or VC of the influenza vaccine could lead to substantial additional reductions in the influenza burden in the United States. Improvements in VE would have the greatest impact in reducing hospitalizations in adults aged ≥65 years, and VC improvements would have the largest benefit in reducing illnesses in adults aged 18-49 years.

Active surveillance documents rates of clinical care seeking due to respiratory illness

Background

Respiratory viral infections are a leading cause of disease worldwide. However, the overall community prevalence of infections has not been properly assessed, as standard surveillance is typically acquired passively among individuals seeking clinical care.

Methods

We conducted a prospective cohort study in which participants provided daily diaries and weekly nasopharyngeal specimens that were tested for respiratory viruses. These data were used to analyze healthcare seeking behavior, compared with cross‐sectional ED data and NYC surveillance reports, and used to evaluate biases of medically attended ILI as signal for population respiratory disease and infection.

Results

The likelihood of seeking medical attention was virus‐dependent: higher for influenza and metapneumovirus (19%‐20%), lower for coronavirus and RSV (4%), and 71% of individuals with self‐reported ILI did not seek care and half of medically attended symptomatic manifestations did not meet the criteria for ILI. Only 5% of cohort respiratory virus infections and 21% of influenza infections were medically attended and classifiable as ILI. We estimated 1 ILI event per person/year but multiple respiratory infections per year.

Conclusion

Standard, healthcare‐based respiratory surveillance has multiple limitations. Specifically, ILI is an incomplete metric for quantifying respiratory disease, viral respiratory infection, and influenza infection. The prevalence of respiratory viruses, as reported by standard, healthcare‐based surveillance, is skewed toward viruses producing more severe symptoms. Active, longitudinal studies are a helpful supplement to standard surveillance, can improve understanding of the overall circulation and burden of respiratory viruses, and can aid development of more robust measures for controlling the spread of these pathogens.

Effects of Influenza Vaccination in the United States During the 2017-2018 Influenza Season

BACKGROUND

The severity of the 2017-2018 influenza season in the United States was high, with influenza A(H3N2) viruses predominating. Here, we report influenza vaccine effectiveness (VE) and estimate the number of vaccine-prevented influenza-associated illnesses, medical visits, hospitalizations, and deaths for the 2017-2018 influenza season.

METHODS

We used national age-specific estimates of 2017-2018 influenza vaccine coverage and disease burden. We estimated VE against medically attended reverse-transcription polymerase chain reaction-confirmed influenza virus infection in the ambulatory setting using a test-negative design. We used a compartmental model to estimate numbers of influenza-associated outcomes prevented by vaccination.

RESULTS

The VE against outpatient, medically attended, laboratory-confirmed influenza was 38% (95% confidence interval [CI], 31%-43%), including 22% (95% CI, 12%-31%) against influenza A(H3N2), 62% (95% CI, 50%-71%) against influenza A(H1N1)pdm09, and 50% (95% CI, 41%-57%) against influenza B. We estimated that influenza vaccination prevented 7.1 million (95% CrI, 5.4 million-9.3 million) illnesses, 3.7 million (95% CrI, 2.8 million-4.9 million) medical visits, 109 000 (95% CrI, 39 000-231 000) hospitalizations, and 8000 (95% credible interval [CrI], 1100-21 000) deaths. Vaccination prevented 10% of expected hospitalizations overall and 41% among young children (6 months-4 years).

CONCLUSIONS

Despite 38% VE, influenza vaccination reduced a substantial burden of influenza-associated illness, medical visits, hospitalizations, and deaths in the United States during the 2017-2018 season. Our results demonstrate the benefit of current influenza vaccination and the need for improved vaccines.

School dismissal as a pandemic influenza response: When, where and for how long?

We used individual-based computer simulation models at community, regional and national levels to evaluate the likely impact of coordinated pre-emptive school dismissal policies during an influenza pandemic. Such policies involve three key decisions: when, over what geographical scale, and how long to keep schools closed. Our evaluation includes uncertainty and sensitivity analyses, as well as model output uncertainties arising from variability in serial intervals and presumed modifications of social contacts during school dismissal periods. During the period before vaccines become widely available, school dismissals are particularly effective in delaying the epidemic peak, typically by 4–6 days for each additional week of dismissal. Assuming the surveillance is able to correctly and promptly diagnose at least 5–10% of symptomatic individuals within the jurisdiction, dismissals at the city or county level yield the greatest reduction in disease incidence for a given dismissal duration for all but the most severe pandemic scenarios considered here. Broader (multi-county) dismissals should be considered for the most severe and fast-spreading (1918-like) pandemics, in which multi-month closures may be necessary to delay the epidemic peak sufficiently to allow for vaccines to be implemented.

Epidemiology of respiratory viruses in Saudi Arabia: toward a complete picture

Acute lower respiratory tract infection is a major health problem that affects more than 15% of the total population of Saudi Arabia each year. Epidemiological studies conducted over the last three decades have indicated that viruses are responsible for the majority of these infections. The epidemiology of respiratory viruses in Saudi Arabia is proposed to be affected mainly by the presence and mobility of large numbers of foreign workers and the gathering of millions of Muslims in Mecca during the Hajj and Umrah seasons. Knowledge concerning the epidemiology, circulation pattern, and evolutionary kinetics of respiratory viruses in Saudi Arabia are scant, with the available literature being inconsistent. This review summarizes the available data on the epidemiology and evolution of respiratory viruses. The demographic features associated with Middle East respiratory syndrome-related coronavirus infections are specifically analyzed for a better understanding of the epidemiology of this virus. The data support the view that continuous entry and exit of pilgrims and foreign workers with different ethnicities and socioeconomic backgrounds in Saudi Arabia is the most likely vehicle for global dissemination of respiratory viruses and for the emergence of new viruses (or virus variants) capable of greater dissemination.

A Control Banding Framework for Protecting the US Workforce from Aerosol Transmissible Infectious Disease Outbreaks with High Public Health Consequences

Recent high-profile infectious disease outbreaks illustrate the importance of selecting appropriate control measures to protect a wider range of employees, other than those in healthcare settings. In such settings, where routine exposure risks are often high, control measures may be more available, routinely implemented, and studied for effectiveness. In the absence of evidence-based guidelines or established best practices for selecting appropriate control measures, employers may unduly rely on personal protective equipment (PPE) because of its wide availability and pervasiveness as a control measure, circumventing other effective options for protection. Control banding is one approach that may be used to assign job tasks into risk categories and prioritize the application of controls. This article proposes an initial control banding framework for workers at all levels of risk and incorporates a range of control options, including PPE. Using the National Institutes of Health (NIH) risk groups as a surrogate for toxicity and combining the exposure duration with the exposure likelihood, we can generate the risk of a job task to the worker.

Bias of influenza vaccine effectiveness estimates from test-negative studies conducted during an influenza pandemic

Test-negative (TN) studies have become the most widely used study design for the estimation of influenza vaccine effectiveness (VE) and are easily incorporated into existing influenza surveillance networks. We seek to determine the bias of TN-based VE estimates during a pandemic using a dynamic probability model. The model is used to evaluate and compare the bias of VE estimates under various sources of bias when vaccination occurs after the beginning of an outbreak, such as during a pandemic. The model includes two covariates (health status and health awareness), which may affect the probabilities of vaccination, developing influenza and non-influenza acute respiratory illness (ARI), and seeking medical care. Specifically, we evaluate the bias of VE estimates when (1) vaccination affects the probability of developing a non-influenza ARI; (2) vaccination affects the probability of seeking medical care; (3) a covariate (e.g. health status) is related to both the probabilities of vaccination and developing an ARI; and (4) a covariate (e.g. health awareness) is related to both the probabilities of vaccination and of seeking medical care. We considered two outcomes against which the vaccine is supposed to protect: symptomatic influenza and medically-attended influenza. When vaccination begins during an outbreak, we found that the effect of delayed onset of vaccination is unpredictable. VE estimates from TN studies were biased regardless of the source of bias present. However, if the core assumption of the TN design is satisfied, that is, if vaccination does not affect the probability of non-influenza ARI, then TN-based VE estimates against medically-attended influenza will only suffer from small (<0.05) to moderate bias (≥0.05 and <0.10). These results suggest that if sources of bias listed above are ruled out, TN studies are a valid study design for the estimation of VE during a pandemic.

Excess influenza hospital admissions and costs due to the 2009 H1N1 pandemic in England

Influenza pandemics considerably burden affected health systems due to surges in inpatient admissions and associated costs. Previous studies underestimate or overestimate 2009/2010 influenza A/H1N1 pandemic hospital admissions and costs. We robustly estimate overall and age-specific weekly H1N1 admissions and costs between June 2009 and March 2011 across 170 English hospitals. We calculate H1N1 admissions and costs as the difference between our administrative data of all influenza-like-illness patients (seasonal and pandemic alike) and a counterfactual of expected weekly seasonal influenza admissions and costs established using time-series models on prepandemic (2004-2008) data. We find two waves of H1N1 admissions: one pandemic wave (June 2009-March 2010) with 10,348 admissions costing £20.5 million and one postpandemic wave (November 2010-March 2011) with 11,775 admissions costing £24.8 million. Patients aged 0-4 years old have the highest H1N1 admission rate, and 25- to 44- and 65+-year-olds have the highest costs. Our estimates are up to 4.3 times higher than previous reports, suggesting that the pandemic's burden on hospitals was formerly underassessed. Our findings can help hospitals manage unexpected surges in admissions and resource use due to pandemics.

Population based hospitalization burden of laboratory-confirmed hand, foot and mouth disease caused by multiple enterovirus serotypes in Southern China

BACKGROUND

Hand, foot and mouth disease (HFMD) is spread widely across Asia, and the hospitalization burden is currently not well understood. Here, we estimated serotype-specific and age-specific hospitalization rates of HFMD in Southern China.

METHODS

We enrolled pediatric HFMD patients admitted to 3/3 county-level hospitals, and 3/23 township-level hospitals in Anhua county, Hunan (CN). Samples were collected to identify enterovirus serotypes by RT-PCRs between October 2013 and September 2016. Information on other eligible, but un-enrolled, patients were retrospectively collected from the same six hospitals. Monthly numbers of all-cause hospitalizations were collected from each of the 23 township-level hospitals to extrapolate hospitalizations associated with HFMD among these.

RESULTS

During the three years, an estimated 3,236 pediatric patients were hospitalized with lab-confirmed HFMD, and among these only one case was severe. The mean hospitalization rate was 660 (95% CI: 638-684) per 100,000 person-years for lab-confirmed HFMD, with higher rates among CV-A16 and CV-A6 associated HFMD (213 vs 209 per 100,000 person-years), and lower among EV-A71, CV-A10 and other enterovirus associated HFMD (134, 39 and 66 per 100,000 person-years respectively, p<0.001). Children aged 12-23 months had the highest hospitalization rates (3,594/100,000 person-years), followed by those aged 24-35 months (1,828/100,000 person-years) and 6-11 months (1,572/100,000 person-years). Compared with other serotypes, CV-A6-associated hospitalizations were evident at younger ages.

CONCLUSIONS

Our study indicates a substantial hospitalization burden associated with non-severe HFMD in a rural county in southern China. Future mitigation policies should take into account the disease burden identified, and optimize interventions for HFMD.

Estimating dengue under-reporting in Puerto Rico using a multiplier model

Dengue is a mosquito-borne viral illness that causes a variety of health outcomes, from a mild acute febrile illness to potentially fatal severe dengue. Between 2005 and 2010, the annual number of suspected dengue cases reported to the Passive Dengue Surveillance System (PDSS) in Puerto Rico ranged from 2,346 in 2006 to 22,496 in 2010. Like other passive surveillance systems, PDSS is subject to under-reporting. To estimate the degree of under-reporting in Puerto Rico, we built separate inpatient and outpatient probability-based multiplier models, using data from two different surveillance systems—PDSS and the enhanced dengue surveillance system (EDSS). We adjusted reported cases to account for sensitivity of diagnostic tests, specimens with indeterminate results, and differences between PDSS and EDSS in numbers of reported dengue cases. In addition, for outpatients, we adjusted for the fact that less than 100% of medical providers submit diagnostic specimens from suspected cases. We estimated that a multiplication factor of between 5 (for 2010 data) to 9 (for 2006 data) must be used to correct for the under-reporting of the number of laboratory-positive dengue inpatients. Multiplication factors of between 21 (for 2010 data) to 115 (for 2008 data) must be used to correct for the under-reporting of laboratory-positive dengue outpatients. We also estimated that, after correcting for underreporting, the mean annual rate, for 2005–2010, of medically attended dengue in Puerto Rico to be between 2.1 (for dengue inpatients) to 7.8 (for dengue outpatients) per 1,000 population. These estimated rates compare to the reported rates of 0.4 (dengue outpatients) to 0.1 (dengue inpatients) per 1,000 population. The multipliers, while subject to limitations, will help public health officials correct for underreporting of dengue cases, and thus better evaluate the cost-and-benefits of possible interventions.

The number of global cases of dengue has increased an estimated 30-fold from 1962 to 2012, and two-fifths of the world’s population are thought to be at risk for dengue. It has been recently estimated that the global incidence of dengue is between 50 and 100 million cases per year. These estimates of burden and impact are, however, are not considered very reliable. It has been previously established and reported that there is notable under-reporting of clinical cases of dengue, even those who sought medical treatment. This includes under-reporting of those hospitalized with laboratory-confirmed dengue. This lack of reliable estimates hampers efforts of public health officials in determining the of burden of disease and the costs-and-benefits of potential interventions. We estimated that multiplication factors ranging from 5 to 9 must be used to correct for under-reporting of laboratory-positive dengue inpatient cases reported to public health officials in Puerto Rico. Multiplication factors ranging from 21 to 115 must be used to correct for the underreporting of laboratory-positive dengue outpatients. Our results illustrate the need for, and thus potential benefits of, using our methodology to estimate the degree of under-reporting in passive dengue systems during epidemic and non-epidemic years.