Pandemic 1918 Influenza Virus Does Not Cause Lethal Infection in Rhesus or Cynomolgus Macaques.

The 1918 H1N1 influenza pandemic was among the most severe in history, taking the lives of approximately 50 million people worldwide, and novel prophylactic vaccines are urgently needed to prevent another pandemic. Given that macaques are physiologically relevant preclinical models of human immunology that have advanced the clinical treatment of infectious diseases, a lethal pandemic influenza challenge model would provide a stringent platform for testing new influenza vaccine concepts. To this end, we infected rhesus macaques and Mauritian cynomolgus macaques with highly pathogenic 1918 H1N1 influenza virus and assessed pathogenesis and disease severity. Despite infection with a high dose of 1918 influenza delivered via multiple routes, rhesus macaques demonstrated minimal signs of disease, with only intermittent viral shedding. Cynomolgus macaques infected via intrabronchial instillation demonstrated mild symptoms, with disease severity depending on the infection dose. Cynomolgus macaques infected with a high dose of 1918 influenza delivered via multiple routes experienced moderate disease characterized by consistent viral shedding, pulmonary infiltrates, and elevated inflammatory cytokine levels. However, 1918 influenza was uniformly nonlethal in these two species, demonstrating that this isolate is insufficiently pathogenic in rhesus and Mauritian cynomolgus macaques to support testing novel prophylactic influenza approaches where protection from severe disease combined with a lethal outcome is desired as a highly stringent indication of vaccine efficacy. IMPORTANCE The world remains at risk of an influenza pandemic, and the development of new therapeutic and preventative modalities is critically important for minimizing human death and suffering during the next influenza pandemic. Animal models are central to the development of new therapies and vaccine approaches. In particular, nonhuman primates like rhesus and cynomolgus macaques are highly relevant preclinical models given their physiological and immunological similarities to humans. Unfortunately, there remains a scarcity of macaque models of pandemic influenza with which to test novel antiviral modalities. Here, we demonstrate that even at the highest doses tested, 1918 influenza was not lethal in these two macaque species, suggesting that they are not ideal for the development and testing of novel pandemic influenza-specific vaccines and therapies. Therefore, other physiologically relevant nonhuman primate models of pandemic influenza are needed.

Traveling Waves and Estimation of Minimal Wave Speed for a Diffusive Influenza Model with Multiple Strains.

Antiviral treatment remains one of the key pharmacological interventions against influenza pandemic. However, widespread use of antiviral drugs brings with it the danger of drug resistance evolution. To assess the risk of the emergence and diffusion of resistance, in this paper, we develop a diffusive influenza model where influenza infection involves both drug-sensitive and drug-resistant strains. We first analyze its corresponding reaction model, whose reproduction numbers and equilibria are derived. The results show that the sensitive strains can be eliminated by treatment. Then, we establish the existence of the three kinds of traveling waves starting from the disease-free equilibrium, i.e., semi-traveling waves, strong traveling waves and persistent traveling waves, from which we can get some useful information (such as whether influenza will spread, asymptotic speed of propagation, the final state of the wavefront). On the other hand, we discuss three situations in which semi-traveling waves do not exist. When the control reproduction number [Formula: see text] is larger than 1, the conditions for the existence and nonexistence of traveling waves are determined completely by the reproduction numbers [Formula: see text], [Formula: see text] and the wave speed c. Meanwhile, we give an interval estimation of minimal wave speed for influenza transmission, which has important guiding significance for the control of influenza in reality. Our findings demonstrate that the control of influenza depends not only on the rates of resistance emergence and transmission during treatment, but also on the diffusion rates of influenza strains, which have been overlooked in previous modeling studies. This suggests that antiviral treatment should be implemented appropriately, and infected individuals (especially with the resistant strain) should be tested and controlled effectively. Finally, we outline some future directions that deserve further investigation.

An operational epidemiological model for calibrating agent-based simulations of pandemic influenza outbreaks.

Uncertainty of pandemic influenza viruses continue to cause major preparedness challenges for public health policymakers. Decisions to mitigate influenza outbreaks often involve tradeoff between the social costs of interventions (e.g., school closure) and the cost of uncontrolled spread of the virus. To achieve a balance, policymakers must assess the impact of mitigation strategies once an outbreak begins and the virus characteristics are known. Agent-based (AB) simulation is a useful tool for building highly granular disease spread models incorporating the epidemiological features of the virus as well as the demographic and social behavioral attributes of tens of millions of affected people. Such disease spread models provide excellent basis on which various mitigation strategies can be tested, before they are adopted and implemented by the policymakers. However, to serve as a testbed for the mitigation strategies, the AB simulation models must be operational. A critical requirement for operational AB models is that they are amenable for quick and simple calibration. The calibration process works as follows: the AB model accepts information available from the field and uses those to update its parameters such that some of its outputs in turn replicate the field data. In this paper, we present our epidemiological model based calibration methodology that has a low computational complexity and is easy to interpret. Our model accepts a field estimate of the basic reproduction number, and then uses it to update (calibrate) the infection probabilities in a way that its effect combined with the effects of the given virus epidemiology, demographics, and social behavior results in an infection pattern yielding a similar value of the basic reproduction number. We evaluate the accuracy of the calibration methodology by applying it for an AB simulation model mimicking a regional outbreak in the US. The calibrated model is shown to yield infection patterns closely replicating the input estimates of the basic reproduction number. The calibration method is also tested to replicate an initial infection incidence trend for a H1N1 outbreak like that of 2009.

The impact of ambient air pollution on an influenza model with partial immunity and vaccination.

In this paper, we investigate the effects of ambient air pollution (AAP) on the spread of influenza in an AAP-dependent dynamic influenza model. The value of this study lies in two aspects. Mathematically, we establish the threshold dynamics in the term of the basic reproduction number $ \mathcal{R}_0 $: If $ \mathcal{R}_0 < 1 $, the disease will go to extinction, while if $ \mathcal{R}_0 > 1 $, the disease will persist. Epidemiologically, based on the statistical data in Huaian, China, we find that, in order to control the prevalence of influenza, we must increase the vaccination rate, the recovery rate and the depletion rate, and decrease the rate of the vaccine wearing off, the uptake coefficient, the effect coefficient of AAP on transmission rate and the baseline rate. To put it simply, we must change our traveling plan and stay at home to reduce the contact rate or increase the close-contact distance and wear protective masks to reduce the influence of the AAP on the influenza transmission.

Sliding dynamics and bifurcations of a human influenza system under logistic source and broken line control strategy.

This paper proposes a non-smooth human influenza model with logistic source to describe the impact on media coverage and quarantine of susceptible populations of the human influenza transmission process. First, we choose two thresholds $ I_{T} $ and $ S_{T} $ as a broken line control strategy: Once the number of infected people exceeds $ I_{T} $, the media influence comes into play, and when the number of susceptible individuals is greater than $ S_{T} $, the control by quarantine of susceptible individuals is open. Furthermore, by choosing different thresholds $ I_{T} $ and $ S_{T} $ and using Filippov theory, we study the dynamic behavior of the Filippov model with respect to all possible equilibria. It is shown that the Filippov system tends to the pseudo-equilibrium on sliding mode domain or one endemic equilibrium or bistability endemic equilibria under some conditions. The regular/virtulal equilibrium bifurcations are also given. Lastly, numerical simulation results show that choosing appropriate threshold values can prevent the outbreak of influenza, which implies media coverage and quarantine of susceptible individuals can effectively restrain the transmission of influenza. The non-smooth system with logistic source can provide some new insights for the prevention and control of human influenza.

Impact of weekday social contact patterns on the modeling of influenza transmission, and determination of the influenza latent period.

Human social contact patterns show marked day-of-week variations, with a higher frequency of contacts occurring during weekdays when children are in school, and adults are in contact with co-workers, than typically occur on weekends. Using epidemic modeling, we show that using the average of social contacts during the week in the model yields virtually identical predictions of epidemic final size and the timing of the epidemic incidence peak as a model that incorporates weekday social contact patterns. This is true of models with a constant weekly average contact rate throughout the year, and also of models that assume seasonality of transmission. Our modeling studies reveal, however, that weekday social contact patterns can produce substantial weekday variations in an influenza incidence curve, and the pattern of variation is sensitive to the influenza latent period. The possible observability of weekday patterns in daily influenza incidence data opens up an interesting avenue of further inquiry that can shed light on the latent period of pandemic influenza. The duration of the latent period must be known with precision in order to design effective disease intervention strategies, such as use of antivirals. For a hypothetical influenza pandemic, we thus perform a simulation study to determine the number of cases needed to observe the weekday variation pattern in influenza epidemic incidence data. Our studies suggest that these patterns should be observable at 95% confidence in daily influenza hospitalization data from large cities over 75% of the time. Using 2009 A(H1N1) daily case data recorded by a large hospital in Santiago, Chile, we show that significant weekday incidence patterns are evident. From these weekday incidence patterns, we estimate the latent period of influenza to be [0.04, 0.60] days (95% CI). This method for determination of the influenza latent period in a community setting is novel, and unique in its approach.

Multistability of a Two-Dimensional Map Arising in an Influenza Model.

In this paper, we propose and analyze a nonsmoothly two-dimensional map arising in a seasonal influenza model. Such map consists of both linear and nonlinear dynamics depending on where the map acts on its domain. The map exhibits a complicated and unpredictable dynamics such as fixed points, period points, chaotic attractors, or multistability depending on the ranges of a certain parameters. Surprisingly, bistable states include not only the coexistence of a stable fixed point and stable period three points but also that of stable period three points and a chaotic attractor. Among other things, we are able to prove rigorously the coexistence of the stable equilibrium and stable period three points for a certain range of the parameters. Our results also indicate that heterogeneity of the population drives the complication and unpredictability of the dynamics. Specifically, the most complex dynamics occur when the underlying basic reproduction number with respect to our model is an intermediate value and the large portion of the population in the same compartment changes in states the following season.

Near-optimal control and threshold behavior of an avian influenza model with spatial diffusion on complex networks.

Near-optimization is as sensible and important as optimization for both theory and applications. This paper concerns the near-optimal control of an avian influenza model with saturation on heterogeneous complex networks. Firstly, the basic reproduction number $ \mathcal{R}_{0} $ is defined for the model, which can be used to govern the threshold dynamics of influenza disease. Secondly, the near-optimal control problem was formulated by slaughtering poultry and treating infected humans while keeping the loss and cost to a minimum. Thanks to the maximum condition of the Hamiltonian function and the Ekeland's variational principle, we establish both necessary and sufficient conditions for the near-optimality by several delicate estimates for the state and adjoint processes. Finally, a number of examples presented to illustrate our theoretical results.

Experimental Infection Using Mouse-Adapted Influenza B Virus in a Mouse Model.

Every year, influenza B viruses (IBVs) contribute to annual illness, and infection can lead to serious respiratory disease among humans. More attention is needed in several areas, such as increasing virulence or pathogenicity of circulating B viruses and developing vaccines against current influenza. Since preclinical trials of anti-influenza drugs are mainly conducted in mice, we developed an appropriate infection model, using an antigenically-relevant IBV strain, for furtherance of anti-influenza drug testing and influenza vaccine protective efficacy analysis. A Victoria lineage (clade 1A) IBV was serially passaged 17 times in BALB/c mice, and adaptive amino acid substitutions were found in hemagglutinin (HA) (T214I) and neuraminidase (NA) (D432N). By electron microscopy, spherical and elliptical IBV forms were noted. Light microscopy showed that mouse-adapted IBVs caused influenza pneumonia on day 6 post inoculation. We evaluated the illness pathogenicity, viral load, and histopathological features of mouse-adapted IBVs and estimated anti-influenza drugs and vaccine efficiency in vitro and in vivo. Assessment of an investigational anti-influenza drug (oseltamivir ethoxysuccinate) and an influenza vaccine (Ultrix®, SPBNIIVS, Saint Petersburg, Russia) showed effectiveness against the mouse-adapted influenza B virus.