Seasonality of Influenza A(H7N9) Virus in China-Fitting Simple Epidemic Models to Human Cases

An epidemiological modeling investigation of the long-term changing dynamics of the plague epidemics in Hong Kong

Identifying epidemic-driving factors through epidemiological modeling is a crucial public health strategy that has substantial policy implications for control and prevention initiatives. In this study, we employ dynamic modeling to investigate the transmission dynamics of pneumonic plague epidemics in Hong Kong from 1902 to 1904. Through the integration of human, flea, and rodent populations, we analyze the long-term changing trends and identify the epidemic-driving factors that influence pneumonic plague outbreaks. We examine the dynamics of the model and derive epidemic metrics, such as reproduction numbers, that are used to assess the effectiveness of intervention. By fitting our model to historical pneumonic plague data, we accurately capture the incidence curves observed during the epidemic periods, which reveals some crucial insights into the dynamics of pneumonic plague transmission by identifying the epidemic driving factors and quantities such as the lifespan of flea vectors, the rate of rodent spread, as well as demographic parameters. We emphasize that effective control measures must be prioritized for the elimination of fleas and rodent vectors to mitigate future plague outbreaks. These findings underscore the significance of proactive intervention strategies in managing infectious diseases and informing public health policies.

Modeling the 2014-2015 Ebola Virus Disease Outbreaks in Sierra Leone, Guinea, and Liberia with Effect of High- and Low-risk Susceptible Individuals

Ebola virus disease (EVD) is a rare but fatal disease of humans and other primates caused by Ebola viruses. Study shows that the 2014-2015 EVD outbreak causes more than 10,000 deaths. In this paper, we propose and analyze a deterministic model to study the transmission dynamics of EVD in Sierra Leone, Guinea, and Liberia. Our analyses show that the model has two equilibria: (1) the disease-free equilibrium (DFE) which is locally asymptotically stable when the basic reproduction number ([Formula: see text]) is less than unity and unstable if it is greater than one, and (2) an endemic equilibrium (EE) which is globally asymptotically stable when [Formula: see text] is greater than unity. Furthermore, the backward bifurcation occurs, a coexistence between a stale DFE and a stable EE even if the [Formula: see text] is less than unity, which makes the disease control more strenuous and would depend on the initial size of subpopulation. By fitting to reported Ebola cases from Sierra Leone, Guinea, and Liberia in 2014-2015, our model has captured the epidemic patterns in all three countries and shed light on future Ebola control and prevention strategies.

A Note on Observation Processes in Epidemic Models

Many disease models focus on characterizing the underlying transmission mechanism but make simple, possibly naive assumptions about how infections are reported. In this note, we use a simple deterministic Susceptible-Infected-Removed (SIR) model to compare two common assumptions about disease incidence reports: Individuals can report their infection as soon as they become infected or as soon as they recover. We show that incorrect assumptions about the underlying observation processes can bias estimates of the basic reproduction number and lead to overly narrow confidence intervals.

Mechanistic modelling of the large-scale Lassa fever epidemics in Nigeria from 2016 to 2019

Lassa fever, also known as Lassa hemorrhagic fever, is a virus that has generated recurrent outbreaks in West Africa. We use mechanistic modelling to study the Lassa fever epidemics in Nigeria from 2016-19. Our model describes the interaction between human and rodent populations with the consideration of quarantine, isolation and hospitalization processes. Our model supports the phenomenon of forward bifurcation where the stability between disease-free equilibrium and endemic equilibrium exchanges. Moreover, our model captures well the incidence curves from surveillance data. In particular, our model is able to reconstruct the periodic rodent and human forces of infection. Furthermore, we suggest that the three major epidemics from 2016-19 can be modelled by properly characterizing the rodent (or human) force of infection while the estimated human force of infection also present similar patterns across outbreaks. Our results suggest that the initial susceptibility likely increased across the three outbreaks from 2016-19. Our results highlight the similarity of the transmission dynamics driving three major Lassa fever outbreaks in the endemic areas.

Identifying Risk Factors Of A(H7N9) Outbreak by Wavelet Analysis and Generalized Estimating Equation

Five epidemic waves of A(H7N9) occurred between March 2013 and May 2017 in China. However, the potential risk factors associated with disease transmission remain unclear. To address the spatial–temporal distribution of the reported A(H7N9) human cases (hereafter referred to as “cases”), statistical description and geographic information systems were employed. Based on long-term observation data, we found that males predominated the majority of A(H7N9)-infected individuals and that most males were middle-aged or elderly. Further, wavelet analysis was used to detect the variation in time-frequency between A(H7N9) cases and meteorological factors. Moreover, we formulated a Poisson regression model to explore the relationship among A(H7N9) cases and meteorological factors, the number of live poultry markets (LPMs), population density and media coverage. The main results revealed that the impact factors of A(H7N9) prevalence are manifold, and the number of LPMs has a significantly positive effect on reported A(H7N9) cases, while the effect of weekly average temperature is significantly negative. This confirms that the interaction of multiple factors could result in a serious A(H7N9) outbreak. Therefore, public health departments adopting the corresponding management measures based on both the number of LPMs and the forecast of meteorological conditions are crucial for mitigating A(H7N9) prevalence.

Spatiotemporal Variation and Hotspot Detection of the Avian Influenza A(H7N9) Virus in China, 2013⁻2017

This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from 19 February 2013 to 30 September 2017 extracted from Centre for Health Protection of the Department of Health (CHP) and electronic databases managed by China's Center for Disease Control (CDC) and provincial CDCs synthetically using the Geographic Information System (GIS) software ArcMap™ 10.2 and SaTScan. Based on the multiple analyses of the A(H7N9) epidemics, there was a strong seasonal pattern in A(H7N9) virus infection, with high activity in the first quarter of the year, especially in January, February, and April, and a gradual dying out in the third quarter. Spatial distribution analysis indicated that Eastern China contained the most severely affected areas, such as Zhejiang Province, and the distribution shifted from coastline areas to more inland areas over time. In addition, the cases exhibited local spatial aggregation, with high-risk areas most found in the southeast coastal regions of China. Shanghai, Jiangsu, Zhejiang, and Guangdong were the high-risk epidemic areas, which should arouse the attention of local governments. A strong cluster from 9 April 2017 to 24 June 2017 was also identified in Northern China, and there were many secondary clusters in Eastern and Southern China, especially in Zhejiang, Fujian, Jiangsu, and Guangdong Provinces. Our results suggested that the spatial-temporal clustering of H7N9 in China is fundamentally different, and is expected to contribute to accumulating knowledge on the changing temporal patterns and spatial dissemination during the fifth epidemic and provide data to enable adequate preparation against the next epidemic.

Avian Influenza A (H7N9) Model Based on Poultry Transport Network in China

In order to analyze the spread of avian influenza A (H7N9), we construct an avian influenza transmission model from poultry (including poultry farm, backyard poultry farm, live-poultry wholesale market, and wet market) to human according to poultry transport network. We obtain the threshold value for the prevalence of avian influenza A (H7N9) and also give the existence and number of the boundary equilibria and endemic equilibria in different conditions. We can see that poultry transport network plays an important role in controlling avian influenza A (H7N9). Finally, numerical simulations are presented to illustrate the effects of poultry in different places on avian influenza. In order to reduce human infections in China, our results suggest that closing the retail live-poultry market or preventing the poultry of backyard poultry farm into the live-poultry market is feasible in a suitable condition.

A review on current trends in the treatment of human infection with H7N9-avian influenza A

The H7N9 subtype of avian influenza is an enzootic and airborne virus which caused an influenza outbreak in China. Infected individuals mostly worked with poultry, suggesting H7N9 virus-infected poultry as the primary source of human infection. Significantly increased levels of proinflammatory mediators (chemokines, cytokines) during virus infection could hamper the immune system and aggravate the infection. Severe cases are marked by fulminant pneumonia, acute respiratory distress syndrome (ARDS) and encephalopathy. Left untreated, the condition may rapidly progress to multi-organ failure and death. Reverse transcription polymerase chain reaction (rRT-PCR) is the gold standard diagnostic test for H7N9 avian influenza. Use of neurominidase inhibitor antivirals remain the main treatment. New antivirals are developed to counteract neurominidase inhibitor resistance H7N9 viral strains. Corticosteroid use in viral pneumonia may provoke mortality and longer viral shedding time. Subjects at high risk of contracting avian influenza H7N9 infection are recommended to receive annual seasonal influenza vaccination.

Structural and Molecular Characterization of the Hemagglutinin from the Fifth-Epidemic-Wave A(H7N9) Influenza Viruses

The avian influenza A(H7N9) virus continues to cause human infections in China and is a major ongoing public health concern. Five epidemic waves of A(H7N9) infection have occurred since 2013, and the recent fifth epidemic wave saw the emergence of two distinct lineages with elevated numbers of human infection cases and broader geographic distribution of viral diseases compared to the first four epidemic waves. Moreover, highly pathogenic avian influenza (HPAI) A(H7N9) viruses were also isolated during the fifth epidemic wave. Here, we present a detailed structural and biochemical analysis of the surface hemagglutinin (HA) antigen from viruses isolated during this recent epidemic wave. Results highlight that, compared to the 2013 virus HAs, the fifth-wave virus HAs remained a weak binder to human glycan receptor analogs. We also studied three mutations, V177K-K184T-G219S, that were recently reported to switch a 2013 A(H7N9) HA to human-type receptor specificity. Our results indicate that these mutations could also switch the H7 HA receptor preference to a predominantly human binding specificity for both fifth-wave H7 HAs analyzed in this study.IMPORTANCE The A(H7N9) viruses circulating in China are of great public health concern. Here, we report a molecular and structural study of the major surface proteins from several recent A(H7N9) influenza viruses. Our results improve the understanding of these evolving viruses and provide important information on their receptor preference that is central to ongoing pandemic risk assessment.

The temporal distribution of new H7N9 avian influenza infections based on laboratory-confirmed cases in Mainland China, 2013-2017

In this study, estimates of the growth rate of new infections, based on the growth rate of new laboratory-confirmed cases, were used to provide a statistical basis for in-depth research into the epidemiological patterns of H7N9 epidemics. The incubation period, interval from onset to laboratory confirmation, and confirmation time for all laboratory-confirmed cases of H7N9 avian influenza in Mainland China, occurring between January 2013 and June 2017, were used as the statistical data. Stochastic processes theory and maximum likelihood were used to calculate the growth rate of new infections. Time-series analysis was then performed to assess correlations between the time series of new infections and new laboratory-confirmed cases. The rate of new infections showed significant seasonal fluctuation. Laboratory confirmation was delayed by a period of time longer than that of the infection (average delay, 13 days; standard deviation, 6.8 days). At the lags of −7.5 and −15 days, respectively, the time-series of new infections and new confirmed cases were significantly correlated; the cross correlation coefficients (CCFs) were 0.61 and 0.16, respectively. The temporal distribution characteristics of new infections and new laboratory-confirmed cases were similar and strongly correlated.

AVIAN INFLUENZA A H7N9 VIRUS HAS BEEN ESTABLISHED IN CHINA

In March 2013, a novel avian-origin influenza A H7N9 virus was identified among human patients in China and a total of 124 human cases with 24 related deaths were confirmed by May 2013. From November 2013 to July 2017, H7N9 broke out four more times in China. A deterministic model is proposed to study the transmission dynamics of the avian influenza A H7N9 virus between wild and domestic birds and from birds to humans, and is applied to simulate the open data on numbers of the infected human cases and related deaths reported from March to May 2013 and from November 2013 to June 2014 by the Chinese Center for Disease Control and Prevention. The basic reproduction number [Formula: see text] is estimated and sensitivity analysis of [Formula: see text] in terms of model parameters is performed. Taking into account the fact that it broke out again from November 2014 to June 2015, from November 2015 to July 2016, and from October 2016 to July 2017, we believe that H7N9 virus has been well established in birds and will likely cause regular outbreaks in humans again in the future. Control measures for the future spread of H7N9 include (i) reducing the transmission opportunities between wild birds and domestic birds, (ii) closing or monitoring the retail live-poultry markets in the infected areas, and (iii) culling the infected domestic birds in the epidemic regions.

A Potent Germline-like Human Monoclonal Antibody Targets a pH-Sensitive Epitope on H7N9 Influenza Hemagglutinin

The H7N9 influenza virus causes high-mortality disease in humans but no effective therapeutics are available. Here we report a human monoclonal antibody, m826, that binds to H7 hemagglutinin (HA) and protects against H7N9 infection. m826 binds to H7N9 HA with subnanomolar affinity at acidic pH and 10-fold lower affinity at neutral pH. The high-resolution (1.9 Å) crystal structure of m826 complexed with H7N9 HA indicates that m826 binds an epitope that may be fully exposed upon pH-induced conformational changes in HA. m826 fully protects mice against lethal challenge with H7N9 virus through mechanisms likely involving antibody-dependent cell-mediated cytotoxicity. Interestingly, immunogenetic analysis indicates that m826 is a germline antibody, and m826-like sequences can be identified in H7N9-infected patients, healthy adults, and newborn babies. These m826 properties offer a template for H7N9 vaccine immunogens, a promising candidate therapeutic, and a tool for exploring mechanisms of virus infection inhibition by antibodies.

Modelling the transmission and control strategies of varicella among school children in Shenzhen, China

Objectives

Varicella (chickenpox) is a highly transmissible childhood disease. Between 2010 and 2015, it displayed two epidemic waves annually among school populations in Shenzhen, China. However, their transmission dynamics remain unclear and there is no school-based vaccination programme in Shenzhen to-date. In this study, we developed a mathematical model to compare a school-based vaccination intervention scenario with a baseline (i.e. no intervention) scenario.

Methods

Data on varicella reported cases were downloaded from the Infectious Disease Reporting Information Management System. We obtained the population size, age structure of children aged 15 or under, the class and school distribution from Shenzhen Education Bureau. We developed an Agent-Based Susceptible-Exposed-Infectious-Recovered (ABM-SEIR) Model that considered within-class, class-to-class and out-of-school transmission modes. The intervention scenario was that school-wide vaccination intervention occurred when an outbreak threshold was reached within a school. We varied this threshold level from five to ten cases. We compared the reduction of disease outbreak size and estimated the key epidemiological parameters under the intervention strategy.

Results

Our ABM-SEIR model provided a good model fit to the two annual varicella epidemic waves from 2013 to 2015. The transmission dynamics displayed strong seasonality. Our results suggested that a school-based vaccination strategy could effectively prevent large outbreaks at different thresholds.

Conclusions

There was a considerable increase in reported varicella cases from 2013 to 2015 in Shenzhen. Our modelling study provided important theoretical support for disease control decision making during school outbreaks and the development of a school-based vaccination programme.

ANALYSIS OF AVIAN INFLUENZA A (H7N9) MODEL BASED ON THE LOW PATHOGENICITY IN POULTRY

The avian influenza A (H7N9) virus is one subtype of influenza viruses, which has previously been isolated only in birds. Recently, an outbreak of a new avian influenza (H7N9) in China has resulted in numerous infections and high mortality in the humans. The H7N9 virus is low pathogenic in poultry and high pathogenic in human and that is critically different from other avian influenza viruses. An increasing number of the new H7N9 cases and the high mortality have caused a serious global concern. Here, based on the reported data, we propose and analyze an SE-SEIS avian–human influenza model. We prove the global stability results for both the disease-free equilibrium point and the endemic equilibrium point by using a general Bendixson–Dulac theorem. Our reported theoretical results of this paper are expected to help in exploring the development of efficient methods to controlling the spread of avian influenza A(H7N9).

Inference and forecast of H7N9 influenza in China, 2013 to 2015

The recent emergence of A(H7N9) avian influenza poses a significant challenge to public health in China and around the world; however, understanding of the transmission dynamics and progression of influenza A(H7N9) infection in domestic poultry, as well as spillover transmission to humans, remains limited. Here, we develop a mathematical model–Bayesian inference system which combines a simple epidemic model and data assimilation method, and use it in conjunction with data on observed human influenza A(H7N9) cases from 19 February 2013 to 19 September 2015 to estimate key epidemiological parameters and to forecast infection in both poultry and humans. Our findings indicate a high outbreak attack rate of 33% among poultry but a low rate of chicken-to-human spillover transmission. In addition, we generated accurate forecasts of the peak timing and magnitude of human influenza A(H7N9) cases. This work demonstrates that transmission dynamics within an avian reservoir can be estimated and that real-time forecast of spillover avian influenza in humans is possible.