Mathematical Modeling of the Effectiveness of Facemasks in Reducing the Spread of Novel Influenza A (H1N1)

On June 11, 2009, the World Health Organization declared the outbreak of novel influenza A (H1N1) a pandemic. With limited supplies of antivirals and vaccines, countries and individuals are looking at other ways to reduce the spread of pandemic (H1N1) 2009, particularly options that are cost effective and relatively easy to implement. Recent experiences with the 2003 SARS and 2009 H1N1 epidemics have shown that people are willing to wear facemasks to protect themselves against infection; however, little research has been done to quantify the impact of using facemasks in reducing the spread of disease. We construct and analyze a mathematical model for a population in which some people wear facemasks during the pandemic and quantify impact of these masks on the spread of influenza. To estimate the parameter values used for the effectiveness of facemasks, we used available data from studies on N95 respirators and surgical facemasks. The results show that if N95 respirators are only 20% effective in reducing susceptibility and infectivity, only 10% of the population would have to wear them to reduce the number of influenza A (H1N1) cases by 20%. We can conclude from our model that, if worn properly, facemasks are an effective intervention strategy in reducing the spread of pandemic (H1N1) 2009.     

The global transmission and control of influenza

New strains of influenza spread around the globe via the movement of infected individuals. The global dynamics of influenza are complicated by different patterns of influenza seasonality in different regions of the world. We have released an open-source stochastic mathematical model of the spread of influenza across 321 major, strategically located cities of the world. Influenza is transmitted between cities via infected airline passengers. Seasonality is simulated by increasing the transmissibility in each city at the times of the year when influenza has been observed to be most prevalent. The spatiotemporal spread of pandemic influenza can be understood through clusters of global transmission and links between them, which we identify using the epidemic percolation network (EPN) of the model. We use the model to explain the observed global pattern of spread for pandemic influenza A(H1N1) 2009-2010 (pandemic H1N1 2009) and to examine possible global patterns of spread for future pandemics depending on the origin of pandemic spread, time of year of emergence, and basic reproductive number (). We also use the model to investigate the effectiveness of a plausible global distribution of vaccine for various pandemic scenarios. For pandemic H1N1 2009, we show that the biggest impact of vaccination was in the temperate northern hemisphere. For pandemics starting in the temperate northern hemisphere in May or April, vaccination would have little effect in the temperate southern hemisphere and a small effect in the tropics. With the increasing interconnectedness of the world's population, we must take a global view of infectious disease transmission. Our open-source, computationally simple model can help public health officials plan for the next pandemic as well as deal with interpandemic influenza.     

Spatial Transmission of 2009 Pandemic Influenza in the US

The 2009 H1N1 influenza pandemic provides a unique opportunity for detailed examination of the spatial dynamics of an emerging pathogen. In the US, the pandemic was characterized by substantial geographical heterogeneity: the 2009 spring wave was limited mainly to northeastern cities while the larger fall wave affected the whole country. Here we use finely resolved spatial and temporal influenza disease data based on electronic medical claims to explore the spread of the fall pandemic wave across 271 US cities and associated suburban areas. We document a clear spatial pattern in the timing of onset of the fall wave, starting in southeastern cities and spreading outwards over a period of three months. We use mechanistic models to tease apart the external factors associated with the timing of the fall wave arrival: differential seeding events linked to demographic factors, school opening dates, absolute humidity, prior immunity from the spring wave, spatial diffusion, and their interactions. Although the onset of the fall wave was correlated with school openings as previously reported, models including spatial spread alone resulted in better fit. The best model had a combination of the two. Absolute humidity or prior exposure during the spring wave did not improve the fit and population size only played a weak role. In conclusion, the protracted spread of pandemic influenza in fall 2009 in the US was dominated by short-distance spatial spread partially catalysed by school openings rather than long-distance transmission events. This is in contrast to the rapid hierarchical transmission patterns previously described for seasonal influenza. The findings underline the critical role that school-age children play in facilitating the geographic spread of pandemic influenza and highlight the need for further information on the movement and mixing patterns of this age group.     

FluTE,a Publicly Available Stochastic Influenza Epidemic Simulation Model

Mathematical and computer models of epidemics have contributed to our understanding of the spread of infectious disease and the measures needed to contain or mitigate them. To help prepare for future influenza seasonal epidemics or pandemics, we developed a new stochastic model of the spread of influenza across a large population. Individuals in this model have realistic social contact networks, and transmission and infections are based on the current state of knowledge of the natural history of influenza. The model has been calibrated so that outcomes are consistent with the 1957/1958 Asian A(H2N2) and 2009 pandemic A(H1N1) influenza viruses. We present examples of how this model can be used to study the dynamics of influenza epidemics in the United States and simulate how to mitigate or delay them using pharmaceutical interventions and social distancing measures. Computer simulation models play an essential role in informing public policy and evaluating pandemic preparedness plans. We have made the source code of this model publicly available to encourage its use and further development.     

Unifying Viral Genetics and Human Transportation Data to Predict the Global Transmission Dynamics of Human Influenza H3N2

Information on global human movement patterns is central to spatial epidemiological models used to predict the behavior of influenza and other infectious diseases. Yet it remains difficult to test which modes of dispersal drive pathogen spread at various geographic scales using standard epidemiological data alone. Evolutionary analyses of pathogen genome sequences increasingly provide insights into the spatial dynamics of influenza viruses, but to date they have largely neglected the wealth of information on human mobility, mainly because no statistical framework exists within which viral gene sequences and empirical data on host movement can be combined. Here, we address this problem by applying a phylogeographic approach to elucidate the global spread of human influenza subtype H3N2 and assess its ability to predict the spatial spread of human influenza A viruses worldwide. Using a framework that estimates the migration history of human influenza while simultaneously testing and quantifying a range of potential predictive variables of spatial spread, we show that the global dynamics of influenza H3N2 are driven by air passenger flows, whereas at more local scales spread is also determined by processes that correlate with geographic distance. Our analyses further confirm a central role for mainland China and Southeast Asia in maintaining a source population for global influenza diversity. By comparing model output with the known pandemic expansion of H1N1 during 2009, we demonstrate that predictions of influenza spatial spread are most accurate when data on human mobility and viral evolution are integrated. In conclusion, the global dynamics of influenza viruses are best explained by combining human mobility data with the spatial information inherent in sampled viral genomes. The integrated approach introduced here offers great potential for epidemiological surveillance through phylogeographic reconstructions and for improving predictive models of disease control.     

A single base-pair change in 2009 H1N1 hemagglutinin increases human receptor affinity and leads to efficient airborne viral transmission in ferrets

The 2009 H1N1 influenza A virus continues to circulate among the human population as the predominant H1N1 subtype. Epidemiological studies and airborne transmission studies using the ferret model have shown that the transmission efficiency of 2009 H1N1 viruses is lower than that of previous seasonal strains and the 1918 pandemic H1N1 strain. We recently correlated this reduced transmission efficiency to the lower binding affinity of the 2009 H1N1 hemagglutinin (HA) to α2→6 sialylated glycan receptors (human receptors). Here we report that a single point mutation (Ile219→Lys; a base pair change) in the glycan receptor-binding site (RBS) of a representative 2009 H1N1 influenza A virus, A/California/04/09 or CA04/09, quantitatively increases its human receptor-binding affinity. The increased human receptor-affinity is in the same range as that of the HA from highly transmissible seasonal and 1918 pandemic H1N1 viruses. Moreover, a 2009 H1N1 virus carrying this mutation in the RBS (generated using reverse genetics) transmits efficiently in ferrets by respiratory droplets thereby reestablishing our previously observed correlation between human receptor-binding affinity and transmission efficiency. These findings are significant in the context of monitoring the evolution of the currently circulating 2009 H1N1 viruses.     

Measured dynamic social contact patterns explain the spread of H1N1v influenza

Patterns of social mixing are key determinants of epidemic spread. Here we present the results of an internet-based social contact survey completed by a cohort of participants over 9,000 times between July 2009 and March 2010, during the 2009 H1N1v influenza epidemic. We quantify the changes in social contact patterns over time, finding that school children make 40% fewer contacts during holiday periods than during term time. We use these dynamically varying contact patterns to parameterise an age-structured model of influenza spread, capturing well the observed patterns of incidence; the changing contact patterns resulted in a fall of approximately 35% in the reproduction number of influenza during the holidays. This work illustrates the importance of including changing mixing patterns in epidemic models. We conclude that changes in contact patterns explain changes in disease incidence, and that the timing of school terms drove the 2009 H1N1v epidemic in the UK. Changes in social mixing patterns can be usefully measured through simple internet-based surveys.     

From pandemic to endemic: Spatial-temporal patterns of influenza-like illness incidence in a Swiss canton, 1918–1924

In pandemics, past and present, there is no textbook definition of when a pandemic is over, and how and when exactly a respiratory virus transitions from pandemic to endemic spread. In this paper we have compared the 1918/19 influenza pandemic and the subsequent spread of seasonal flu until 1924. We analysed 14,125 reports of newly stated 32,198 influenza-like illnesses from the Swiss canton of Bern. We analysed the temporal and spatial spread at the level of 497 municipalities, 9 regions, and the entire canton. We calculated incidence rates per 1000 inhabitants of newly registered cases per calendar week. Further, we illustrated the incidences of each municipality for each wave (first wave in summer 1918, second wave in fall/winter 1918/19, the strong later wave in early 1920, as well as the two seasonal waves in 1922 and 1924) on a choropleth map. We performed a spatial hotspot analysis to identify spatial clusters in each wave, using the Gi* statistic. Furthermore, we applied a robust negative binomial regression to estimate the association between selected explanatory variables and incidence on the ecological level. We show that the pandemic transitioned to endemic spread in several waves (including another strong wave in February 1920) with lower incidence and rather local spread until 1924 at least. At the municipality and regional levels, there were different patterns of spread both between pandemic and seasonal waves. In the first pandemic wave in summer 1918 the probability of higher incidence was increased in municipalities with a higher proportion of factories (OR 2.60, 95%CI 1.42–4.96), as well as in municipalities that had access to a railway station (OR 1.50, 95%CI 1.16–1.96). In contrast, the strong fall/winter wave 1918 was very widespread throughout the canton. In general, municipalities at higher altitude showed lower incidence. Our study adds to the sparse literature on incidence in the 1918/19 pandemic and subsequent years. Before Covid-19, the last pandemic that occurred in several waves and then became endemic was the 1918–19 pandemic. Such scenarios from the past can inform pandemic planning and preparedness in future outbreaks.     

A Whole Virus Pandemic Influenza H1N1 Vaccine Is Highly Immunogenic and Protective in Active Immunization and Passive Protection Mouse Models

The recent emergence and rapid spread of a novel swine-derived H1N1 influenza virus has resulted in the first influenza pandemic of this century. Monovalent vaccines have undergone preclinical and clinical development prior to initiation of mass immunization campaigns. We have carried out a series of immunogenicity and protection studies following active immunization of mice, which indicate that a whole virus, nonadjuvanted vaccine is immunogenic at low doses and protects against live virus challenge. The immunogenicity in this model was comparable to that of a whole virus H5N1 vaccine, which had previously been demonstrated to induce high levels of seroprotection in clinical studies. The efficacy of the H1N1 pandemic vaccine in protecting against live virus challenge was also seen to be equivalent to that of the H5N1 vaccine. The protective efficacy of the H1N1 vaccine was also confirmed using a severe combined immunodeficient (SCID) mouse model. It was demonstrated that mouse and guinea pig immune sera elicited following active H1N1 vaccination resulted in 100% protection of SCID mice following passive transfer of immune sera and lethal challenge. The immune responses to a whole virus pandemic H1N1 and a split seasonal H1N1 vaccine were also compared in this study. It was demonstrated that the whole virus vaccine induced a balanced Th-1 and Th-2 response in mice, whereas the split vaccine induced mainly a Th-2 response and only minimal levels of Th-1 responses. These data supported the initiation of clinical studies with the same low doses of whole virus vaccine that had previously been demonstrated to be immunogenic in clinical studies with a whole virus H5N1 vaccine.     

Comparison of Temporal and Spatial Dynamics of Seasonal H3N2, Pandemic H1N1 and Highly Pathogenic Avian Influenza H5N1 Virus Infections in Ferrets

Humans may be infected by different influenza A viruses-seasonal, pandemic, and zoonotic-which differ in presentation from mild upper respiratory tract disease to severe and sometimes fatal pneumonia with extra-respiratory spread. Differences in spatial and temporal dynamics of these infections are poorly understood. Therefore, we inoculated ferrets with seasonal H3N2, pandemic H1N1 (pH1N1), and highly pathogenic avian H5N1 influenza virus and performed detailed virological and pathological analyses at time points from 0.5 to 14 days post inoculation (dpi), as well as describing clinical signs and hematological parameters. H3N2 infection was restricted to the nose and peaked at 1 dpi. pH1N1 infection also peaked at 1 dpi, but occurred at similar levels throughout the respiratory tract. H5N1 infection occurred predominantly in the alveoli, where it peaked for a longer period, from 1 to 3 dpi. The associated lesions followed the same spatial distribution as virus infection, but their severity peaked between 1 and 6 days later. Neutrophil and monocyte counts in peripheral blood correlated with inflammatory cell influx in the alveoli. Of the different parameters used to measure lower respiratory tract disease, relative lung weight and affected lung tissue allowed the best quantitative distinction between the virus groups. There was extra-respiratory spread to more tissues-including the central nervous system-for H5N1 infection than for pH1N1 infection, and to none for H3N2 infection. This study shows that seasonal, pandemic, and zoonotic influenza viruses differ strongly in the spatial and temporal dynamics of infection in the respiratory tract and extra-respiratory tissues of ferrets.     

Epidemiological update on swine influenza (H1N1) in pigs

The 2009 H1N1 pandemic has slowed down its spread after initial speed of transmission. The conventional swine influenza H1N1 virus (SIV) in pig populations worldwide needs to be differentiated from pandemic H1N1 influenza virus, however it is also essential to know about the exact role of pigs in the spread and mutations taking place in pig-to-pig transmission. The present paper reviews epidemiological features of classical SIV and its differentiation with pandemic influenza.     

Pre-existing heterosubtypic immunity provides a barrier to airborne transmission of influenza viruses

Human-to-human transmission of influenza viruses is a serious public health threat, yet the precise role of immunity from previous infections on the susceptibility to airborne infection is still unknown. Using the ferret model, we examined the roles of exposure duration and heterosubtypic immunity on influenza transmission. We demonstrate that a 48 hour exposure is sufficient for efficient transmission of H1N1 and H3N2 viruses. To test pre-existing immunity, a gap of 8–12 weeks between primary and secondary infections was imposed to reduce innate responses and ensure robust infection of donor animals with heterosubtypic viruses. We found that pre-existing H3N2 immunity did not significantly block transmission of the 2009 H1N1pandemic (H1N1pdm09) virus to immune animals. Surprisingly, airborne transmission of seasonal H3N2 influenza strains was abrogated in recipient animals with H1N1pdm09 pre-existing immunity. This protection from natural infection with H3N2 virus was independent of neutralizing antibodies. Pre-existing immunity with influenza B virus did not block H3N2 virus transmission, indicating that the protection was likely driven by the adaptive immune response. We demonstrate that pre-existing immunity can impact susceptibility to heterologous influenza virus strains, and implicate a novel correlate of protection that can limit the spread of respiratory pathogens through the air.     

In Silico Structural Homology Modelling and Docking for Assessment of Pandemic Potential of a Novel H7N9 Influenza Virus and Its Ability to Be Neutralized by Existing Anti-Hemagglutinin Antibodies

The unpredictable nature of pandemic influenza and difficulties in early prediction of pandemic potential of new isolates present a major challenge for health planners. Vaccine manufacturers, in particular, are reluctant to commit resources to development of a new vaccine until after a pandemic is declared. We hypothesized that a structural bioinformatics approach utilising homology-based molecular modelling and docking approaches would assist prediction of pandemic potential of new influenza strains alongside more traditional laboratory and sequence-based methods. The newly emerged Chinese A/Hangzhou/1/2013 (H7N9) influenza virus provided a real-life opportunity to test this hypothesis. We used sequence data and a homology-based approach to construct a 3D-structural model of H7-Hangzhou hemagglutinin (HA) protein. This model was then used to perform docking to human and avian sialic acid receptors to assess respective binding affinities. The model was also used to perform docking simulations with known neutralizing antibodies to assess their ability to neutralize the newly emerged virus. The model predicted H7N9 could bind to human sialic acid receptors thereby indicating pandemic potential. The model also confirmed that existing antibodies against the HA head region are unable to neutralise H7N9 whereas antibodies, e.g. Cr9114, targeting the HA stalk region should bind with high affinity to H7N9. This indicates that existing stalk antibodies initially raised against H5N1 or other influenza A viruses could be therapeutically beneficial in prevention and/or treatment of H7N9 infections. The subsequent publication of the H7N9 HA crystal structure confirmed the accuracy of our in-silico structural model. Antibody docking studies performed using the H7N9 HA crystal structure supported the model''s prediction that existing stalk antibodies could cross-neutralise the H7N9 virus. This study demonstrates the value of using in-silico structural modelling approaches to complement physical studies in characterization of new influenza viruses.     

Human mobility networks, travel restrictions, and the global spread of 2009 H1N1 pandemic

After the emergence of the H1N1 influenza in 2009, some countries responded with travel-related controls during the early stage of the outbreak in an attempt to contain or slow down its international spread. These controls along with self-imposed travel limitations contributed to a decline of about 40% in international air traffic to/from Mexico following the international alert. However, no containment was achieved by such restrictions and the virus was able to reach pandemic proportions in a short time. When gauging the value and efficacy of mobility and travel restrictions it is crucial to rely on epidemic models that integrate the wide range of features characterizing human mobility and the many options available to public health organizations for responding to a pandemic. Here we present a comprehensive computational and theoretical study of the role of travel restrictions in halting and delaying pandemics by using a model that explicitly integrates air travel and short-range mobility data with high-resolution demographic data across the world and that is validated by the accumulation of data from the 2009 H1N1 pandemic. We explore alternative scenarios for the 2009 H1N1 pandemic by assessing the potential impact of mobility restrictions that vary with respect to their magnitude and their position in the pandemic timeline. We provide a quantitative discussion of the delay obtained by different mobility restrictions and the likelihood of containing outbreaks of infectious diseases at their source, confirming the limited value and feasibility of international travel restrictions. These results are rationalized in the theoretical framework characterizing the invasion dynamics of the epidemics at the metapopulation level.     

Higher level of replication efficiency of 2009 (H1N1) pandemic influenza virus than those of seasonal and avian strains: kinetics from epithelial cell culture and computational modeling

The pathogenicity and transmission of influenza A viruses are likely determined in part by replication efficiency in human cells, which is the net effect of complex virus-host interactions. H5N1 avian, H1N1 seasonal, and H1N1 2009 pandemic influenza virus strains were compared by infecting human differentiated bronchial epithelial cells in air-liquid interface cultures at relatively low virus particle/cell ratios. Differential equation and computational models were used to characterize the in vitro kinetic behaviors of the three strains. The models were calibrated by fitting experimental data in order to estimate difficult-to-measure parameters. Both models found marked differences in the relative values of p, the virion production rate per cell, and R(0), an index of the spread of infection through the monolayer, with the values for the strains in the following rank order (from greatest to least): pandemic strain, followed by seasonal strain, followed by avian strain, as expected. In the differential equation model, which treats virus and cell populations as well mixed, R(0) and p varied proportionately for all 3 strains, consistent with a primary role for productivity. In the spatially explicit computational model, R(0) and p also varied proportionately except that R(0) derived for the pandemic strain was reduced, consistent with constrained viral spread imposed by multiple host defenses, including mucus and paracrine antiviral effects. This synergistic experimental-computational strategy provides relevant parameters for identifying and phenotyping potential pandemic strains.     

Characterization In Vitro and In Vivo of Pandemic (H1N1) 2009 Influenza Viruses Isolated from Patients

The first influenza pandemic of the 21st century was caused by novel H1N1 viruses that emerged in early 2009. Molecular evolutionary analyses of the 2009 pandemic influenza A H1N1 [A(H1N1)pdm09] virus revealed two major clusters, cluster I and cluster II. Although the pathogenicity of viruses belonging to cluster I, which became extinct by the end of 2009, has been examined in a nonhuman primate model, the pathogenic potential of viruses belonging to cluster II, which has spread more widely in the world, has not been studied in this animal model. Here, we characterized two Norwegian isolates belonging to cluster II, namely, A/Norway/3568/2009 (Norway3568) and A/Norway/3487-2/2009 (Norway3487), which caused distinct clinical symptoms, despite their genetic similarity. We observed more efficient replication in cultured cells and delayed virus clearance from ferret respiratory organs for Norway3487 virus, which was isolated from a severe case, compared with the efficiency of replication and time of clearance of Norway3568 virus, which was isolated from a mild case. Moreover, Norway3487 virus to some extent caused more severe lung damage in nonhuman primates than did Norway3568 virus. Our data suggest that the distinct replicative and pathogenic potentials of these two viruses may result from differences in their biological properties (e.g., the receptor-binding specificity of hemagglutinin and viral polymerase activity).     

Eurasian-origin gene segments contribute to the transmissibility, aerosol release, and morphology of the 2009 pandemic H1N1 influenza virus

The epidemiological success of pandemic and epidemic influenza A viruses relies on the ability to transmit efficiently from person-to-person via respiratory droplets. Respiratory droplet (RD) transmission of influenza viruses requires efficient replication and release of infectious influenza particles into the air. The 2009 pandemic H1N1 (pH1N1) virus originated by reassortment of a North American triple reassortant swine (TRS) virus with a Eurasian swine virus that contributed the neuraminidase (NA) and M gene segments. Both the TRS and Eurasian swine viruses caused sporadic infections in humans, but failed to spread from person-to-person, unlike the pH1N1 virus. We evaluated the pH1N1 and its precursor viruses in a ferret model to determine the contribution of different viral gene segments on the release of influenza virus particles into the air and on the transmissibility of the pH1N1 virus. We found that the Eurasian-origin gene segments contributed to efficient RD transmission of the pH1N1 virus likely by modulating the release of influenza viral RNA-containing particles into the air. All viruses replicated well in the upper respiratory tract of infected ferrets, suggesting that factors other than viral replication are important for the release of influenza virus particles and transmission. Our studies demonstrate that the release of influenza viral RNA-containing particles into the air correlates with increased NA activity. Additionally, the pleomorphic phenotype of the pH1N1 virus is dependent upon the Eurasian-origin gene segments, suggesting a link between transmission and virus morphology. We have demonstrated that the viruses are released into exhaled air to varying degrees and a constellation of genes influences the transmissibility of the pH1N1 virus.     

The viral nucleoprotein determines Mx sensitivity of influenza A viruses

Host restriction factors play a crucial role in preventing trans-species transmission of viral pathogens. In mammals, the interferon-induced Mx GTPases are powerful antiviral proteins restricting orthomyxoviruses. Hence, the human MxA GTPase may function as an efficient barrier against zoonotic introduction of influenza A viruses into the human population. Successful viruses are likely to acquire adaptive mutations allowing them to evade MxA restriction. We compared the 2009 pandemic influenza A virus [strain A/Hamburg/4/09 (pH1N1)] with a highly pathogenic avian H5N1 isolate [strain A/Thailand/1(KAN-1)/04] for their relative sensitivities to human MxA and murine Mx1. The H5N1 virus was highly sensitive to both Mx GTPases, whereas the pandemic H1N1 virus was almost insensitive. Substitutions of the viral polymerase subunits or the nucleoprotein (NP) in a polymerase reconstitution assay demonstrated that NP was the main determinant of Mx sensitivity. The NP of H5N1 conferred Mx sensitivity to the pandemic H1N1 polymerase, whereas the NP of pandemic H1N1 rendered the H5N1 polymerase insensitive. Reassortant viruses which expressed the NP of H5N1 in a pH1N1 genetic background and vice versa were generated. Congenic Mx1-positive mice survived intranasal infection with these reassortants if the challenge virus contained the avian NP. In contrast, they succumbed to infection if the NP of pH1N1 origin was present. These findings clearly indicate that the origin of NP determines Mx sensitivity and that human influenza viruses acquired adaptive mutations to evade MxA restriction. This also explains our previous observations that human and avian influenza A viruses differ in their sensitivities to Mx.