Wave Of Influenza Research Articles

Endogenous social distancing and its underappreciated impact on the epidemic curve

Social distancing is an effective strategy to mitigate the impact of infectious diseases. If sick or healthy, or both, predominantly socially distance, the epidemic curve flattens. Contact reductions may occur for different reasons during a pandemic including health-related mobility loss (severity of symptoms), duty of care for a member of a high-risk group, and forced quarantine. Other decisions to reduce contacts are of a more voluntary nature. In particular, sick people reduce contacts consciously to avoid infecting others, and healthy individuals reduce contacts in order to stay healthy. We use game theory to formalize the interaction of voluntary social distancing in a partially infected population. This improves the behavioral micro-foundations of epidemiological models, and predicts differential social distancing rates dependent on health status. The model’s key predictions in terms of comparative statics are derived, which concern changes and interactions between social distancing behaviors of sick and healthy. We fit the relevant parameters for endogenous social distancing to an epidemiological model with evidence from influenza waves to provide a benchmark for an epidemic curve with endogenous social distancing. Our results suggest that spreading similar in peak and case numbers to what partial immobilization of the population produces, yet quicker to pass, could occur endogenously. Going forward, eventual social distancing orders and lockdown policies should be benchmarked against more realistic epidemic models that take endogenous social distancing into account, rather than be driven by static, and therefore unrealistic, estimates for social mixing that intrinsically overestimate spreading.

The COVID-19 pandemic through eyes of a NYC fertility center: a unique learning experience with often unexpected results

Affecting basic tenets of human existence such as health, economic as well as personal security and, of course, reproduction, the COVID-19 pandemic transcended medical specialties and professional disciplines. Yet, six months into the pandemic, there still exists no consensus on how to combat the virus in absence of a vaccine. Facing unprecedented circumstances, and in absence of real evidence on how to proceed, our organization early in the pandemic decided to act independently from often seemingly irrational guidance and, instead, to carefully follow a quickly evolving COVID-19 literature. Here described is the, likely, unique journey of a fertility center that maintained services during peaks of COVID-19 and political unrest that followed. Closely following publicly available data, we recognized relatively early that New York City and other East Coast regions, which during the initial COVID-19 wave between March and May represented the hardest-hit areas in the country, during the second wave, beginning in June and still in progress, remained almost completely unaffected. In contrast, south western regions, almost completely unaffected by the initial wave, were severely affected in the second wave. These two distinctively different infectious phenotypes suggested two likely explanations: The country was witnessing infections with two different SARS-CoV-2 viruses and NYC (along with the East Coast) acquired during the first wave much better immunity to the virus than south western regions. Both hypotheses since have been confirmed: East and West Coasts, indeed, were initially infected by two distinctively different lineages of the virus, with the East Coast lineage being 10-times more infectious. In addition, immunologists discovered an up to this point unknown long-term anti-viral innate (cellular) immune response which offers additional and much broader anti-viral immunity than the classical adaptive immunity via immobilizing antibodies that has been known for decades. Consequently, we predict that in the U.S., even in absence of an available vaccine, COVID-19, by September–October, will be at similarly low levels as are currently seen in NYC and other East Coast regions (generally < 1% test-positivity). We, furthermore, predict that, if current mitigation measures are maintained and no newly aggressive mutation of the virus enters the country, a significant fall-wave of COVID-19, in combination with the usual fall wave of influenza, appears unlikely. To continue serving patients uninterrupted throughout the pandemic, turned for all of our center’s staff into a highly rewarding experience, garnered respect and appreciation from patients, and turned into an absolutely unique learning experience.

Mortality of the elderly population during the COVID-19 pandemic. Are there north-south differences?

COVID-19 disease courses are dynamic and in some cases, fatal. In this article, we aim to identify the periods where overall mortality is higher, and therefore to more precisely measure excess mortality. We analysed mortality rate development for the population aged 65 years and older in Germany as a whole, a south Germany region (comprising the federal states of Baden-Wuerttemberg and Bavaria) and a north Germany region (comprising the federal states of Schleswig Holstein, Mecklenburg Western-Pomerania and Brandenburg). The article analyses the mortality rates per calendar week that have been published by Germany's Federal Statistical Office (Destatis) for the first 23 calendar weeks of 2020. We compare these figures with those for the same period 2016, the last year in which there was no influenza-related excess mortality. In calendar weeks ten to 15, mortality rates for the elder population rose exceptionally in the south compared to the north Germany region as well as compared to the 2016 figures. A peak was reached in calendar weeks 14 and 15. Mortality rates peaked around two to three weeks after incidence. Since this peak, mortality rates have decreased again, but up to calendar week 18 have remained above the 2016 rates. Overall the rise in mortality rates observed appears to be related to the COVID-19 pandemic and not the annual influenza wave.

Differences in epidemic spread patterns of norovirus and influenza seasons of Germany: an application of optical flow analysis in epidemiology

This analysis presents data from a new perspective offering key insights into the spread patterns of norovirus and influenza epidemic events. We utilize optic flow analysis to gain an informed overview of a wealth of statistical epidemiological data and identify trends in movement of influenza waves throughout Germany on the NUTS 3 level (413 locations) which maps municipalities on European level. We show that Influenza and norovirus seasonal outbreak events have a highly distinct pattern. We investigate the quantitative statistical properties of the epidemic patterns and find a shifted distribution in the time between influenza and norovirus seasonal peaks of reported infections over one decade. These findings align with key biological features of both pathogens as shown in the course of this analysis.

Prediction of Mutations in H7 Hemagglutinins from Influenza A Virus

Influenza A viruses have led several pandemics and epidemics in human history. H7 subtype influenza mainly infects avian but also humans occasionally. Since the outbreak of H7N9 subtype influenza occurred in China in 2013, this virus is still circulating in domestic poultry and leading several waves of influenza. To prevent influenza, vaccination is an important strategy. However, influenza virus evolves constantly, but unpredictably. If we would have a one-to-one cause-mutation relationship, the mutation prediction would be possible. However, many external causes, which led to the mutations in the past, might not leave any trace due to the change in environments, whereas the current virus might not be subject to the historically external causes because of evolution. Furthermore, the protein should have the internal causes, which might be quite unclear and difficult to quantify, to engineer mutations. Indeed, various forces twist proteins into 3-demensional structures, whereas any perturbation could lead to a mutation. Of various internal causes for mutation, randomness in protein primary structure should play an important role in mutation. Over years, we have developed three methods to quantify the randomness within a protein primary structure; thus we build a relationship between cause, which is randomness in primary structure, and mutations, which are occurrence and non-occurrence of mutation. In this way, the cause-mutation relationship becomes the problem of classification, which can be solved using logistic regression and neural network. In this study, we apply this model to predict 1) the mutation positions in H7 hemagglutinins from influenza A virus and 2) the would-be-mutated amino-acids at predicted positions with the amino-acid mutating probability. The results show suitability and predictability in such modelling, and pave the way for further development.

Race and 1918 Influenza Pandemic in the United States: A Review of the Literature.

During epidemics, the poorest part of the population usually suffers the most. Alfred Crosby noted that the norm changed during the 1918 influenza pandemic in the US: The black population (which were expected to have higher influenza morbidity and mortality) had lower morbidity and mortality than the white population during the autumn of 1918. Crosby’s explanation for this was that black people were more exposed to a mild spring/summer wave of influenza earlier that same year. In this paper, we review the literature from the pandemic of 1918 to better understand the crossover in the role of race on mortality. The literature has used insurance, military, survey, and routine notification data. Results show that the black population had lower morbidity, and during September, October, and November, lower mortality but higher case fatality than the white population. The results also show that the black population had lower influenza morbidity prior to 1918. The reasons for lower morbidity among the black population both at baseline and during the herald and later waves in 1918 remain unclear. Results may imply that black people had a lower risk of developing the disease given exposure, but when they did get sick, they had a higher risk of dying.

Estimating age-stratified influenza-associated invasive pneumococcal disease in England: A time-series model based on population surveillance data.

BackgroundMeasures of the contribution of influenza to Streptococcus pneumoniae infections, both in the seasonal and pandemic setting, are needed to predict the burden of secondary bacterial infections in future pandemics to inform stockpiling. The magnitude of the interaction between these two pathogens has been difficult to quantify because both infections are mainly clinically diagnosed based on signs and symptoms; a combined viral–bacterial testing is rarely performed in routine clinical practice; and surveillance data suffer from confounding problems common to all ecological studies. We proposed a novel multivariate model for age-stratified disease incidence, incorporating contact patterns and estimating disease transmission within and across groups.Methods and findingsWe used surveillance data from England over the years 2009 to 2017. Influenza infections were identified through the virological testing of samples taken from patients diagnosed with influenza-like illness (ILI) within the sentinel scheme run by the Royal College of General Practitioners (RCGP). Invasive pneumococcal disease (IPD) cases were routinely reported to Public Health England (PHE) by all the microbiology laboratories included in the national surveillance system. IPD counts at week t, conditional on the previous time point t−1, were assumed to be negative binomially distributed. Influenza counts were linearly included in the model for the mean IPD counts along with an endemic component describing some seasonal background and an autoregressive component mimicking pneumococcal transmission. Using age-specific counts, Akaike information criterion (AIC)-based model selection suggested that the best fit was obtained when the endemic component was expressed as a function of observed temperature and rainfall. Pneumococcal transmission within the same age group was estimated to explain 33.0% (confidence interval [CI] 24.9%–39.9%) of new cases in the elderly, whereas 50.7% (CI 38.8%–63.2%) of incidence in adults aged 15–44 years was attributed to transmission from another age group. The contribution of influenza on IPD during the 2009 pandemic also appeared to vary greatly across subgroups, being highest in school-age children and adults (18.3%, CI 9.4%–28.2%, and 6.07%, CI 2.83%–9.76%, respectively). Other viral infections, such as respiratory syncytial virus (RSV) and rhinovirus, also seemed to have an impact on IPD: RSV contributed 1.87% (CI 0.89%–3.08%) to pneumococcal infections in the 65+ group, whereas 2.14% (CI 0.87%–3.57%) of cases in the group of 45- to 64-year-olds were attributed to rhinovirus. The validity of this modelling strategy relies on the assumption that viral surveillance adequately represents the true incidence of influenza in the population, whereas the small numbers of IPD cases observed in the younger age groups led to significant uncertainty around some parameter estimates.ConclusionsOur estimates suggested that a pandemic wave of influenza A/H1N1 with comparable severity to the 2009 pandemic could have a modest impact on school-age children and adults in terms of IPD and a small to negligible impact on infants and the elderly. The seasonal impact of other viruses such as RSV and rhinovirus was instead more important in the older population groups.

Update: Influenza Activity in the United States During the 2018-19 Season and Composition of the 2019-20 Influenza Vaccine.

Influenza activity* in the United States during the 2018-19 season (September 30, 2018-May 18, 2019) was of moderate severity (1). Nationally, influenza-like illness (ILI)† activity began increasing in November, peaked during mid-February, and returned to below baseline in mid-April; the season lasted 21 weeks,§ making it the longest season in 10 years. Illness attributed to influenza A viruses predominated, with very little influenza B activity. Two waves of influenza A were notable during this extended season: influenza A(H1N1)pdm09 viruses from October 2018 to mid-February 2019 and influenza A(H3N2) viruses from February through May 2019. Compared with the 2017-18 influenza season, rates of hospitalization this season were lower for adults, but were similar for children. Although influenza activity is currently below surveillance baselines, testing for seasonal influenza viruses and monitoring for novel influenza A virus infections should continue year-round. Receiving a seasonal influenza vaccine each year remains the best way to protect against seasonal influenza and its potentially severe consequences.

Emergence of waterfowl-originated gene cassettes in HPAI H7N9 viruses caused severe human infection in Fujian, China.

BackgroundHighly pathogenic avian influenza (HPAI) A(H7N9) virus emerged and caused human infections during the 2016‐2017 epidemic wave of influenza A(H7N9) viruses in China. We report a human infection with HPAI H7N9 virus and six environmental isolates in Fujian Province, China.MethodsEnvironmental surveillance was conducted in live poultry markets and poultry farms in Fujian, China. Clinical and epidemiologic data and samples were collected. Real‐time RT‐PCRs were conducted for each sample, and H7‐positive samples were isolated using embryonated chicken eggs. Full genomes of the isolates were obtained by next‐generation sequencing. Phylogenetic analysis and antigenic analysis were conducted.ResultsA 59‐year‐old man who raised about 1000 ducks was identified as HPAI H7N9 infection. Six HPAI H7 viruses were isolated from environmental samples, including five H7N9 viruses and one H7N6 virus. Phylogenetic results showed the human and environmental viruses are highly genetically diverse and containing significantly different gene constellation from that of other HPAI H7N9 previously reported. The internal genes derived from H7N9/H9N2, H5N6, and the Eurasian wild‐bird gene pool, indicating waterfowl‐originated genotypes, have emerged in HPAI H7N9/N6 viruses and caused human infection.ConclusionThe new genotypes raise the concern that these HPAI H7 viruses might transmit back into migratory birds and spread to other countries as the HPAI H5Nx viruses. Considering their capability of causing severe infections in both human and poultry, the HPAI H7 viruses in this study pose a risk to public health and the poultry industry and highlight the importance of sustained surveillance of these viruses.

Developments in life expectancy in Germany. Current trends.

Since the beginning of the 1990s, life expectancy in Germany has increased by 4.2 years among women (to 83.2 years), and by 5.9 years among men (to 78.4 years). This rise is related to the increasing convergence of life expectancy in Germany’s new and old federal states. Recently, life expectancy among women in the new federal states has even risen slightly above the level found in the old federal states. In addition, differences between socioeconomic groups continue to be observed in Germany. Women in the highest income group have a 4.4-year longer life expectancy than women in the lowest income group. Similarly, an 8.6-year difference exists between men in the highest income group and men in the lowest income group. Influenza waves can adversely affect the development of life expectancy in certain calendar years. In comparison to other European countries, Germany has a mid-range life expectancy: the current difference between life expectancy in Germany and Switzerland (the European country with the highest life expectancy) is 2.7 years.

Closure of live bird markets leads to the spread of H7N9 influenza in China.

Following the emergence of H7N9 influenza in March 2013, local animal and public health authorities in China have been closing live bird markets as a measure to try to control the H7N9 influenza epidemic. The role of live bird market (LBM) closure on the spread of N7N9 influenza following the closure of LBMs during March to May 2013 (the first wave) and October 2013 to March 2014 (the second wave) is described in this paper. Different provinces implemented closure actions at different times, and intensive media reports on H7N9 in different provinces started at different times. Local broiler prices dropped dramatically in places with outbreaks and more live chickens were transported to other LBMs in neighboring areas without human cases from infected areas when live bird markets were being closed. There were six clusters of human infection from March to May 2013 and October 2013 to March 2014 and there may have been intensive poultry transportation among cluster areas. These findings provide evidence that the closure of LBMs in early waves of H7N9 influenza had resulted in expansion of H7N9 infection to uninfected areas. This suggests that provincial authorities in inland provinces should be alert to the risks of sudden changes in movement patterns for live birds after LBM closure or increased publicity about LBM closure.

Shifting patterns of seasonal influenza epidemics

Seasonal waves of influenza display a complex spatiotemporal pattern resulting from the interplay of biological, sociodemographic, and environmental factors. At country level many studies characterized the robust properties of annual epidemics, depicting a typical season. Here we analyzed season-by-season variability, introducing a clustering approach to assess the deviations from typical spreading patterns. The classification is performed on the similarity of temporal configurations of onset and peak times of regional epidemics, based on influenza-like-illness time-series in France from 1984 to 2014. We observed a larger variability in the onset compared to the peak. Two relevant classes of clusters emerge: groups of seasons sharing similar recurrent spreading patterns (clustered seasons) and single seasons displaying unique patterns (monoids). Recurrent patterns exhibit a more pronounced spatial signature than unique patterns. We assessed how seasons shift between these classes from onset to peak depending on epidemiological, environmental, and socio-demographic variables. We found that the spatial dynamics of influenza and its association with commuting, previously observed as a general property of French influenza epidemics, apply only to seasons exhibiting recurrent patterns. The proposed methodology is successful in providing new insights on influenza spread and can be applied to incidence time-series of different countries and different diseases.

Zoonotic Influenza and Human Health-Part 1: Virology and Epidemiology of Zoonotic Influenzas.

Zoonotic influenza viruses are those that cross the animal-human barrier and can cause disease in humans, manifesting from minor respiratory illnesses to multiorgan dysfunction. They have also been implicated in the causation of deadly pandemics in recent history. The increasing incidence of infections caused by these viruses worldwide has necessitated focused attention to improve both diagnostic as well as treatment modalities. In this first part of a two-part review, we describe the structure of zoonotic influenza viruses, the relationship between mutation and pandemic capacity, pathogenesis of infection, and also discuss history and epidemiology. We are currently witnessing the fifth and the largest wave of the avian influenza A(H7N9) epidemic. Also in circulation are a number of other zoonotic influenza viruses, including avian influenza A(H5N1) and A(H5N6); avian influenza A(H7N2); and swine influenza A(H1N1)v, A(H1N2)v, and A(H3N2)v viruses. Most recently, the first human case of avian influenza A(H7N4) infection has been documented. By understanding the virology and epidemiology of emerging zoonotic influenzas, we are better prepared to face a new pandemic. However, continued effort is warranted to build on this knowledge in order to efficiently combat the constant threat posed by the zoonotic influenza viruses.

Antigenic Drift of Influenza A(H7N9) Virus Hemagglutinin

Since the emergence of influenza A(H7N9) virus in 2013, there have been 5 waves of influenza A(H7N9) epidemics in China. However, evolution of the hemagglutinin (HA) protein antigenicity has not been systematically investigated. To better understand how antigenic drift in HA proteins of influenza (A)H7N9 virus occurs, 902 influenza A(H7N9) virus HA protein sequences from a public database were retrieved and analyzed. Fifty-three mutants with single amino acid substitutions in HA protein were introduced into pseudoviruses, and their antigenic characteristics were analyzed using pseudovirus-based assays. The frequencies of 9 mutations incrementally increased over the past 5 years, with mutations identified at multiple sites. While mean neutralization titers of most variants remained unchanged, 3 mutations, A143V, A143T, and R148K, displayed a median 4-fold lower susceptibility to neutralization by antisera against influenza A/Anhui/1/2013(H7N9) virus. Notably, A143V and A143T were located outside the previously reported antigenic sites. The most dominant variant (A143V/R148K) in the most recent season constituted 74.11% of all mutations and demonstrated a 10-fold reduction in its reactivity to influenza A/Anhui/1/2013(H7N9) virus antisera. Importantly, compared with the DNA construct without the corresponding HA protein mutation, DNA vaccine encoding the A143V/R148K mutant induced a 5-fold increase in the neutralizing activity against this circulating virus. An appropriate vaccine strain should be considered in response to increasing antigenic drift in influenza A(H7N9) virus HA protein.

Conjunction of factors triggering waves of seasonal influenza.

Using several longitudinal datasets describing putative factors affecting influenza incidence and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of influenza epidemics. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions. The strongest predictor groups are as follows, ranked by importance: (1) the host population's socio- and ethno-demographic properties; (2) weather variables pertaining to specific humidity, temperature, and solar radiation; (3) the virus' antigenic drift over time; (4) the host population'€™s land-based travel habits, and; (5) recent spatio-temporal dynamics, as reflected in the influenza wave auto-correlation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies.

Comparison of the three waves of avian influenza A(H7N9) virus circulation since live poultry markets were permanently closed in the main urban areas in Zhejiang Province, July 2014-June 2017.

BackgroundThe sudden increase in the number of human cases infected with avian influenza A(H7N9) virus after September 2016 raised global concern.ObjectivesTo assess the changes in epidemiological characteristics of H7N9 cases since the massive closure of live poultry markets (LPMs) in the main urban areas in Zhejiang province.MethodsWe used descriptive statistics to compare epidemiological characteristics of the three distinct waves of H7N9 cases in Zhejiang province. The rural or urban cases were defined according to the location where the patients had exposure within 2 weeks before illness onset.ResultsBetween July 2014 and June 2017, 166 H7N9 cases were reported in Zhejiang province, with 45, 34, and 87 cases reported in the third, fourth, and fifth wave, respectively. Across the three waves, most reported cases were from rural areas. A similar percentage of cases in all three waves reported exposure to LPMs, raising poultry at home or around the house, as well as occupational exposure. Compared to the third (80.00%) and fourth wave (70.59%), a significantly larger proportion of cases from the non‐LPMs closure areas were observed in the fifth wave (89.66%) (P = .034).ConclusionEpidemiological characteristics of human cases infected with avian influenza A(H7N9) virus had generally remained unchanged since the massive closure of LPMs in the main urban area of Zhejiang province. The sudden increase in the number of H7N9 cases in the fifth wave was mainly attributed to the excessive cases reported from areas where LPMs were not permanently closed.

Changing Geographic Patterns and Risk Factors for Avian Influenza A(H7N9) Infections in Humans, China.

The fifth epidemic wave of avian influenza A(H7N9) virus in China during 2016–2017 demonstrated a geographic range expansion and caused more human cases than any previous wave. The factors that may explain the recent range expansion and surge in incidence remain unknown. We investigated the effect of anthropogenic, poultry, and wetland variables on all epidemic waves. Poultry predictor variables became much more important in the last 2 epidemic waves than they were previously, supporting the assumption of much wider H7N9 transmission in the chicken reservoir. We show that the future range expansion of H7N9 to northern China may increase the risk of H7N9 epidemic peaks coinciding in time and space with those of seasonal influenza, leading to a higher risk of reassortments than before, although the risk is still low so far.

Human exposure to live poultry among residents during the second wave of avian influenza A (H7N9) epidemic in Beijing, 2013-2014

Objective: To investigate human exposure to live poultry (poultry feeding and purchasing) in the residents in Beijing and related factors during the second wave of avian influenza A(H7N9) epidemic during 2013-2014, and provide scientific evidence for avian influenza prevention and control. Methods: A total of 7 366 adults aged ≥18 years were selected through multi-stage stratified sampling in Beijing for a questionnaire survey. Logistic regression model was used to analyze the influence factors of human exposure to live poultry. Results: The live poultry feeding rate and live poultry purchasing rate in residents in Beijing in the past year were 5.3% (95%CI: 4.8%-5.8%) and 6.0% (95%CI: 5.5%-6.5%) respectively. Logistic regression analysis indicated that lower educational level of primary school and below, (OR=1.82, 95%CI: 1.22-2.72); being farmer (OR=2.49, 95%CI:1.89-3.29) or being unemployed (OR=1.65, 95%CI: 1.08-2.52); being non local resident (OR=1.54, 95%CI: 1.10-2.16); living in suburban area (OR=2.36, 95%CI: 1.77-3.16); having one child (OR=1.76, 95%CI: 1.42-2.17) or ≥2 children (OR=2.15, 95%CI: 1.43-3.22) in the family were the risk factors associated with feeding poultry compared with higher educational level of college and above, being employed, being local resident, living in urban area and having no child. And being farmer (OR=1.61, 95%CI: 1.27-2.02); being non local resident (OR=1.76, 95%CI: 1.31-2.35); living in suburban area (OR=2.05, 95%CI: 1.61-2.61); having one child (OR=1.24, 95%CI: 1.02-1.52) or ≥2 children (OR=1.78, 95%CI: 1.21-2.63) were the risk factors for purchasing live poultry. Conclusion: Some residents living in Beijing still have exposure to live poultry, and targeted measures should be taken to reduce the exposure to poultry.

Characteristics and outcomes of a cohort hospitalized for pandemic and seasonal influenza in Germany based on nationwide inpatient data.

RationaleFrom June of 2009 to August of 2010 the influenza subtype H1N1pdm09 caused a worldwide pandemic. The impact on populations and health care systems around the globe evolved differently. Substantial data come from the German national surveillance network in an outpatient and private practice setting, while information on hospitalized patients in Germany is rather limited.MethodsData from the Federal Statistics Office comprising health insurance claims of the entire nationwide inpatient sample from 2005 to 2012 were used to identify patients who were hospitalized for laboratory-confirmed influenza and to analyse demographical aspects, comorbidities, hospitalization duration, outcomes and ventilator use during the pandemic and seasonal waves of influenza.Measurements and main resultsA number of 34,493 admissions for laboratory-confirmed influenza occurred during waves between 2005 and 2012. During the pandemic seasonal waves, the number of hospitalizations vastly surpassed the level that was seen in any of the seasonal waves. A major demographic shift was seen with respect to patient age, as younger patients (< 60 years old) were more frequently hospitalized. Mean length of stay was shorter (149 vs. 193 hours), mean time on ventilation tended to be shorter (261 vs. 305 hours) in young children (< 4 years old) and longer (393 vs. 339 hours) in the elderly (> 60 years old). Time to ventilation was shorter in non-fatal cases (328 vs. 349 hours) and longer in fatal cases (419 vs. 358 hours). Logistic regression was used to show the impact of comorbidities and co-diagnoses on mortality and the need for ventilation, as well as differences between pandemic and seasonal influenza.ConclusionsInpatient data suggest differences in patient populations during pandemic and seasonal influenza. Younger patients were more frequently hospitalized. Differences with respect to the presence of certain comorbidities and co-diagnoses, length of stay, time to ventilation and ventilation time could be identified.

Effects of reactive social distancing on the 1918 influenza pandemic.

The 1918 influenza pandemic was characterized by multiple epidemic waves. We investigated reactive social distancing, a form of behavioral response where individuals avoid potentially infectious contacts in response to available information on an ongoing epidemic or pandemic. We modelled its effects on the three influenza waves in the United Kingdom. In previous studies, human behavioral response was modelled by a Power function of the proportion of recent influenza mortality in a population, and by a Hill function, which is a function of the number of recent influenza mortality. Using a simple epidemic model with a Power function and one common set of parameters, we provided a good model fit for the observed multiple epidemic waves in London boroughs, Birmingham and Liverpool. We further applied the model parameters from these three cities to all 334 administrative units in England and Wales and including the population sizes of individual administrative units. We computed the Pearson’s correlation between the observed and simulated for each administrative unit. We found a median correlation of 0.636, indicating that our model predictions are performing reasonably well. Our modelling approach is an improvement from previous studies where separate models are fitted to each city. With the reduced number of model parameters used, we achieved computational efficiency gain without over-fitting the model. We also showed the importance of reactive behavioral distancing as a potential non-pharmaceutical intervention during an influenza pandemic. Our work has both scientific and public health significance.