Residual immunity in older people against the influenza A(H1N1)--recent experience in northern Spain

Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe

BACKGROUND

As of 16 May 2020, more than 4.5 million cases and more than 300,000 deaths from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. Reliable estimates of mortality from SARS-CoV-2 infection are essential for understanding clinical prognosis, planning healthcare capacity, and epidemic forecasting. The case-fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but it can be a misleading measure of overall mortality. The objectives of this study were to (1) simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2 mortality adjusted for biases and examine the CFR, the symptomatic case-fatality ratio (sCFR), and the infection-fatality ratio (IFR) in different geographic locations.

METHOD AND FINDINGS

We developed an age-stratified susceptible-exposed-infected-removed (SEIR) compartmental model describing the dynamics of transmission and mortality during the SARS-CoV-2 epidemic. Our model accounts for two biases: preferential ascertainment of severe cases and right-censoring of mortality. We fitted the transmission model to surveillance data from Hubei Province, China, and applied the same model to six regions in Europe: Austria, Bavaria (Germany), Baden-Württemberg (Germany), Lombardy (Italy), Spain, and Switzerland. In Hubei, the baseline estimates were as follows: CFR 2.4% (95% credible interval [CrI] 2.1%-2.8%), sCFR 3.7% (3.2%-4.2%), and IFR 2.9% (2.4%-3.5%). Estimated measures of mortality changed over time. Across the six locations in Europe, estimates of CFR varied widely. Estimates of sCFR and IFR, adjusted for bias, were more similar to each other but still showed some degree of heterogeneity. Estimates of IFR ranged from 0.5% (95% CrI 0.4%-0.6%) in Switzerland to 1.4% (1.1%-1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%-26%) in Switzerland to 34% (95% CrI 28%-40%) in Spain. A limitation of the model is that count data by date of onset are required, and these are not available in all countries.

CONCLUSIONS

We propose a comprehensive solution to the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks. The CFR is not a good predictor of overall mortality from SARS-CoV-2 and should not be used for evaluation of policy or comparison across settings. Geographic differences in IFR suggest that a single IFR should not be applied to all settings to estimate the total size of the SARS-CoV-2 epidemic in different countries. The sCFR and IFR, adjusted for right-censoring and preferential ascertainment of severe cases, are measures that can be used to improve and monitor clinical and public health strategies to reduce the deaths from SARS-CoV-2 infection.

Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe

BACKGROUND

As of 16 May 2020, more than 4.5 million cases and more than 300,000 deaths from disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been reported. Reliable estimates of mortality from SARS-CoV-2 infection are essential for understanding clinical prognosis, planning healthcare capacity, and epidemic forecasting. The case-fatality ratio (CFR), calculated from total numbers of reported cases and reported deaths, is the most commonly reported metric, but it can be a misleading measure of overall mortality. The objectives of this study were to (1) simulate the transmission dynamics of SARS-CoV-2 using publicly available surveillance data and (2) infer estimates of SARS-CoV-2 mortality adjusted for biases and examine the CFR, the symptomatic case-fatality ratio (sCFR), and the infection-fatality ratio (IFR) in different geographic locations.

METHOD AND FINDINGS

We developed an age-stratified susceptible-exposed-infected-removed (SEIR) compartmental model describing the dynamics of transmission and mortality during the SARS-CoV-2 epidemic. Our model accounts for two biases: preferential ascertainment of severe cases and right-censoring of mortality. We fitted the transmission model to surveillance data from Hubei Province, China, and applied the same model to six regions in Europe: Austria, Bavaria (Germany), Baden-Württemberg (Germany), Lombardy (Italy), Spain, and Switzerland. In Hubei, the baseline estimates were as follows: CFR 2.4% (95% credible interval [CrI] 2.1%-2.8%), sCFR 3.7% (3.2%-4.2%), and IFR 2.9% (2.4%-3.5%). Estimated measures of mortality changed over time. Across the six locations in Europe, estimates of CFR varied widely. Estimates of sCFR and IFR, adjusted for bias, were more similar to each other but still showed some degree of heterogeneity. Estimates of IFR ranged from 0.5% (95% CrI 0.4%-0.6%) in Switzerland to 1.4% (1.1%-1.6%) in Lombardy, Italy. In all locations, mortality increased with age. Among individuals 80 years or older, estimates of the IFR suggest that the proportion of all those infected with SARS-CoV-2 who will die ranges from 20% (95% CrI 16%-26%) in Switzerland to 34% (95% CrI 28%-40%) in Spain. A limitation of the model is that count data by date of onset are required, and these are not available in all countries.

CONCLUSIONS

We propose a comprehensive solution to the estimation of SARS-Cov-2 mortality from surveillance data during outbreaks. The CFR is not a good predictor of overall mortality from SARS-CoV-2 and should not be used for evaluation of policy or comparison across settings. Geographic differences in IFR suggest that a single IFR should not be applied to all settings to estimate the total size of the SARS-CoV-2 epidemic in different countries. The sCFR and IFR, adjusted for right-censoring and preferential ascertainment of severe cases, are measures that can be used to improve and monitor clinical and public health strategies to reduce the deaths from SARS-CoV-2 infection.

Sterilizing immunity to influenza virus infection requires local antigen-specific T cell response in the lungs

Sterilizing immunity is a unique immune status, which prevents effective virus infection into the host. It is different from the immunity that allows infection but with subsequent successful eradication of the virus. Pre-infection induces sterilizing immunity to homologous influenza virus challenge in ferret. In our antigen-specific experimental system, mice pre-infected with PR8 influenza virus through nasal route are likewise resistant to reinfection of the same strain of virus. The virus is cleared before establishment of effective infection. Intramuscular influenza virus injection confers protection against re-infection with facilitated virus clearance but not sterilizing immunity. Pre-infection and intramuscular injection generates comparable innate immunity and antibody response, but only pre-infection induces virus receptor reduction and efficient antigen-specific T cell response in the lungs. Pre-infection with nH1N1 influenza virus induces virus receptor reduction but not PR8-specific T cell immune response in the lungs and cannot prevent infection of PR8 influenza virus. Pre-infection with PR8 virus induced PR8-specific T cell response in the lungs but cannot prevent infection of nH1N1 virus either. These results reveal that antigen-specific T cell immunity is required for sterilizing immunity.

Changes in severity of influenza A(H1N1)pdm09 infection from pandemic to first postpandemic season, Germany

We studied risk factors for a severe clinical outcome in hospitalized patients with laboratory-confirmed influenza A(H1N1)pdm09 infection at the University Hospital Heidelberg in the pandemic and first postpandemic seasons. We identified 102 patients in 2009–10 and 76 in 2010–11. The proportion of severely diseased patients dramatically increased from 14% in 2009–10 to 46% in 2010–11 as did the mortality rate (5%–12%). Patients in the first postpandemic season were significantly older (38 vs. 18 years) and more frequently had underlying medical conditions (75% vs. 51%). Overall, 50 patients (28%) had a severe clinical outcome, resulting in 14 deaths. Multivariate analysis showed that older male patients with chronic lung disease were at increased risk for a severe clinical outcome. In summary, the proportion of patients with severe disease and fatal cases increased in the postpandemic season. Therefore, patients with suspected infections should be promptly identified and receive early treatment.

Understanding the dynamics of seasonal influenza in Italy: incidence, transmissibility and population susceptibility in a 9-year period

OBJECTIVES

Influenza surveillance systems have been established in many countries in the world, yielding timely information about the intensity and features of seasonal outbreaks. Such data have also been used to estimate epidemiological parameters and to evaluate the effect of factors on infection dynamics. However, little is known about the extent of under-reporting in surveillance data, and thus of the true influenza incidence in the population.

DESIGN

Through mathematical and statistical modelling, we analysed Italian epidemiological and virological surveillance data collected together with serological data derived from influenza vaccine clinical trials performed in Italy.

RESULTS

Depending on the season, the reporting rate estimates ranged between 20% and 33% of the total incidence with higher reporting rates in seasons dominated by A/H3N2 virus. Despite a generally higher number of individuals immune against A/H3N2 viruses, effective reproduction ratios were quite similar in all seasons varying between 1·2 and 1·4. We observed an age-dependent transmissibility for different subtypes: susceptible children were more likely than susceptible adults and elderly to get infected when A/H1N1 or B strains were circulating, while no clear age-dependence was found for A/H3N2. We also perform sensitivity analysis under different assumptions for vaccine effectiveness, generation time (GT) and model variants; we found that the overall results in predicted patterns were extremely similar, with a slightly better fit obtained with shorter GTs.

CONCLUSIONS

Our results provide relevant information on the influenza dynamics to fine-tune intervention strategies and for data collection improvement.

Geographic prioritization of distributing pandemic influenza vaccines

Pandemic influenza is an international public health concern. In light of the persistent threat of H5N1 avian influenza and the recent pandemic of A/H1N1swine influenza outbreak, public health agencies around the globe are continuously revising their preparedness plans. The A/H1N1 pandemic of 2009 demonstrated that influenza activity and severity might vary considerably among age groups and locations, and the distribution of an effective influenza vaccine may be significantly delayed and staggered. Thus, pandemic influenza vaccine distribution policies should be tailored to the demographic and spatial structures of communities. Here, we introduce a bi-criteria decision-making framework for vaccine distribution policies that is based on a geospatial and demographically-structured model of pandemic influenza transmission within and between counties of Arizona in the Unites States. Based on data from the 2009-2010 H1N1 pandemic, the policy predicted to reduce overall attack rate most effectively is prioritizing counties expected to experience the latest epidemic waves (a policy that may be politically untenable). However, when we consider reductions in both the attack rate and the waiting period for those seeking vaccines, the widely adopted pro rata policy (distributing according to population size) is also predicted to be an effective strategy.

Optimal H1N1 vaccination strategies based on self-interest versus group interest

Background

Influenza vaccination is vital for reducing H1N1 infection-mediated morbidity and mortality. To reduce transmission and achieve herd immunity during the initial 2009-2010 pandemic season, the US Centers for Disease Control and Prevention (CDC) recommended that initial priority for H1N1 vaccines be given to individuals under age 25, as these individuals are more likely to spread influenza than older adults. However, due to significant delay in vaccine delivery for the H1N1 influenza pandemic, a large fraction of population was exposed to the H1N1 virus and thereby obtained immunity prior to the wide availability of vaccines. This exposure affects the spread of the disease and needs to be considered when prioritizing vaccine distribution.

Methods

To determine optimal H1N1 vaccine distributions based on individual self-interest versus population interest, we constructed a game theoretical age-structured model of influenza transmission and considered the impact of delayed vaccination.

Results

Our results indicate that if individuals decide to vaccinate according to self-interest, the resulting optimal vaccination strategy would prioritize adults of age 25 to 49 followed by either preschool-age children before the pandemic peak or older adults (age 50-64) at the pandemic peak. In contrast, the vaccine allocation strategy that is optimal for the population as a whole would prioritize individuals of ages 5 to 64 to curb a growing pandemic regardless of the timing of the vaccination program.

Conclusions

Our results indicate that for a delayed vaccine distribution, the priorities that are optimal at a population level do not align with those that are optimal according to individual self-interest. Moreover, the discordance between the optimal vaccine distributions based on individual self-interest and those based on population interest is even more pronounced when vaccine availability is delayed. To determine optimal vaccine allocation for pandemic influenza, public health agencies need to consider both the changes in infection risks among age groups and expected patterns of adherence.

Effectiveness of seasonal 2008-2009, 2009-2010 and pandemic vaccines, to prevent influenza hospitalizations during the autumn 2009 influenza pandemic wave in Castellón, Spain. A test-negative, hospital-based, case-control study

We estimate the impact of the two previous influenza seasonal vaccines and the pandemic vaccine on risk of A (H1N1) 2009 laboratory confirmed hospitalizations during the autumn 2009 pandemic wave in Castellón, Spain. We conducted a test-negative, hospital-based, case-control study. Influenza A (H1N1) 2009 infection was detected in 147 (44%) of 334 patients hospitalized for a presumptive influenza related illness. No effect was observed for the 2008-2009 and 2009-2010 seasonal influenza vaccines. However, the pandemic vaccine was associated with an adjusted vaccine effectiveness of 90% (95% CI, 48-100%). Pandemic vaccines were effective in preventing pandemic influenza associated hospitalizations.

[Pregnancy and pandemic influenza A(H1N1) 2009. Current concepts for anaesthesia and critical care medicine]

Pregnant women are particularly vulnerable to the pandemic influenza A(H1N1) 2009. Indeed, they are at high risk of developing a severe or fatal form of the disease. The physiological changes and the "immune deviation" from cellular to humoral immunity occurring during pregnancy are hypotheses to explain this vulnerability. Severe forms, mainly viral pneumonias, require an urgent prescription of an effective antiviral therapy. Preventive measures, mainly vaccination, are essential to avoid the appearance of these severe forms.

Community-based measures for mitigating the 2009 H1N1 pandemic in China

Since the emergence of influenza A/H1N1 pandemic virus in March–April 2009, very stringent interventions including Fengxiao were implemented to prevent importation of infected cases and decelerate the disease spread in mainland China. The extent to which these measures have been effective remains elusive. We sought to investigate the effectiveness of Fengxiao that may inform policy decisions on improving community-based interventions for management of on-going outbreaks in China, in particular during the Spring Festival in mid-February 2010 when nationwide traveling will be substantially increased. We obtained data on initial laboratory-confirmed cases of H1N1 in the province of Shaanxi and used Markov-chain Monte-Carlo (MCMC) simulations to estimate the reproduction number. Given the estimates for the exposed and infectious periods of the novel H1N1 virus, we estimated a mean reproduction number of 1.68 (95% CI 1.45–1.92) and other A/H1N1 epidemiological parameters. Our results based on a spatially stratified population dynamical model show that the early implementation of Fengxiao can delay the epidemic peak significantly and prevent the disease spread to the general population but may also, if not implemented appropriately, cause more severe outbreak within universities/colleges, while late implementation of Fengxiao can achieve nothing more than no implementation. Strengthening local control strategies (quarantine and hygiene precaution) is much more effective in mitigating outbreaks and inhibiting the successive waves than implementing Fengxiao. Either strong mobility or high transport-related transmission rate during the Spring Festival holiday will not reverse the ongoing outbreak, but both will result in a large new wave. The findings suggest that Fengxiao and travel precautions should not be relaxed unless strict measures of quarantine, isolation, and hygiene precaution practices are put in place. Integration and prompt implementation of these interventions can significantly reduce the overall attack rate of pandemic outbreaks.

The 1918 influenza pandemic: lessons for 2009 and the future

The 1918 to 1919 H1N1 influenza pandemic is among the most deadly events in recorded human history, having killed an estimated 50 to 100 million persons. Recent H5N1 avian influenza epizootics associated with sporadic human fatalities have heightened concern that a new influenza pandemic, one at least as lethal as that of 1918, could be developing. In early 2009, a novel pandemic H1N1 influenza virus appeared, but it has not exhibited unusually high pathogenicity. Nevertheless, because this virus spreads globally, some scientists predict that mutations will increase its lethality. Therefore, to accurately predict, plan, and respond to current and future influenza pandemics, we must first better-understand the events and experiences of 1918. Although the entire genome of the 1918 influenza virus has been sequenced, many questions about the pandemic it caused remain unanswered. In this review, we discuss the origin of the 1918 pandemic influenza virus, the pandemic's unusual epidemiologic features and the causes and demographic patterns of fatality, and how this information should impact our response to the current 2009 H1N1 pandemic and future pandemics. After 92 yrs of research, fundamental questions about influenza pandemics remain unanswered. Thus, we must remain vigilant and use the knowledge we have gained from 1918 and other influenza pandemics to direct targeted research and pandemic influenza preparedness planning, emphasizing prevention, containment, and treatment.