Pandemic potential of a strain of influenza A (H1N1): early findings

Epidemiological data from the COVID-19 outbreak, real-time case information

Cases of a novel coronavirus were first reported in Wuhan, Hubei province, China, in December 2019 and have since spread across the world. Epidemiological studies have indicated human-to-human transmission in China and elsewhere. To aid the analysis and tracking of the COVID-19 epidemic we collected and curated individual-level data from national, provincial, and municipal health reports, as well as additional information from online reports. All data are geo-coded and, where available, include symptoms, key dates (date of onset, admission, and confirmation), and travel history. The generation of detailed, real-time, and robust data for emerging disease outbreaks is important and can help to generate robust evidence that will support and inform public health decision making.

Symbolic transfer entropy reveals the age structure of pandemic influenza transmission from high-volume influenza-like illness data

Existing methods to infer the relative roles of age groups in epidemic transmission can normally only accommodate a few age classes, and/or require data that are highly specific for the disease being studied. Here, symbolic transfer entropy (STE), a measure developed to identify asymmetric transfer of information between stochastic processes, is presented as a way to reveal asymmetric transmission patterns between age groups in an epidemic. STE provides a ranking of which age groups may dominate transmission, rather than a reconstruction of the explicit between-age-group transmission matrix. Using simulations, we establish that STE can identify which age groups dominate transmission even when there are differences in reporting rates between age groups and even if the data are noisy. Then, the pairwise STE is calculated between time series of influenza-like illness for 12 age groups in 884 US cities during the autumn of 2009. Elevated STE from 5 to 19 year-olds indicates that school-aged children were likely the most important transmitters of infection during the autumn wave of the 2009 pandemic in the USA. The results may be partially confounded by higher rates of physician-seeking behaviour in children compared to adults, but it is unlikely that differences in reporting rates can explain the observed differences in STE.

Coordinating the real-time use of global influenza activity data for better public health planning

Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.

A structure-free digital microfluidic platform for detection of influenza a virus by using magnetic beads and electromagnetic forces

H1N1, a subtype of influenza A virus, has emerged as a global threat in the past decades. Due to its highly infectious nature, an accurate and rapid detection assay is urgently required. Therefore, this study presents a new type of digital microfluidic platform for H1N1 virus detection by utilizing a one-aptamer/two-antibodies assay on magnetic beads. The droplets containing magnetic beads were driven by electromagnetic forces on a structure-free, super-hydrophobic surface to automate the entire assay within 40 min. With different levels of hydrophobic modification, the droplets could be easily controlled and positioned without any assisted microstructure. The tunable electromagnetic forces could be adjusted for three kinds of operating modes for the manipulations of beads and droplets, including movement of droplets containing magnetic beads, mixing of two droplets and beads extraction out of droplets. When compared with previous studies, the manipulations of droplets and magnetic particles in this study are more flexible as they can be easily adjusted by fine-tuning the magnetic flux density. Furthermore, the magnetic beads also served as three-dimensional substrates for the new enzyme-linked immunosorbent assay (ELISA)-like assay. The magnetic beads were conjugated with aptamers, which have high specificity towards H1N1 viruses such that they could be specifically captured and detected. The horseradish peroxidase-conjugated secondary antibody was then used to activate tyramide-tetramethylrhodamine (TTMR) such that fluorescent signals could be amplified. With this approach, the limit of detection was experimentally found to be 0.032 hemagglutination units/reaction, which is sensitive enough for clinical diagnostics. This kind of digital microfluidic platform with the ELISA-like assay could effectively reduce the consumption of samples and reagents such that the volume of all droplets including the H1N1 sample, antibodies, TTMR and wash buffers was only 20 μL. This is the first time that a digital microfluidic platform was demonstrated such that the entire diagnostic process for influenza A H1N1 viruses could be performed by using electromagnetic forces, which could be promising for rapid and accurate diagnosis of influenza.

Nontrivial resource requirement in the early stage for containment of epidemics

During epidemic control, containment of the disease is usually achieved through increasing a devoted resource to reduce the infectiousness. However, the impact of this resource expenditure has not been studied quantitatively. For disease spread, the recovery rate can be positively correlated with the average amount of resource devoted to infected individuals. By incorporating this relation we build a novel model and find that insufficient resource leads to an abrupt increase in the infected population size, which is in marked contrast with the continuous phase transitions believed previously. Counterintuitively, this abrupt phase transition is more pronounced in less contagious diseases. Furthermore, we find that even for a single infection source, the public resource needs to be available in a significant amount, which is proportional to the total population size, to ensure epidemic containment. Our findings provide a theoretical foundation for efficient epidemic containment strategies in the early stage.

Quantifying bias of COVID-19 prevalence and severity estimates in Wuhan, China that depend on reported cases in international travelers

Risk of COVID-19 infection in Wuhan has been estimated using imported case counts of international travelers, often under the assumption that all cases in travelers are ascertained. Recent work indicates variation among countries in detection capacity for imported cases. Singapore has historically had very strong epidemiological surveillance and contact-tracing capacity and has shown in the COVID-19 epidemic evidence of a high sensitivity of case detection. We therefore used a Bayesian modeling approach to estimate the relative imported case detection capacity for other countries compared to that of Singapore. We estimate that the global ability to detect imported cases is 38% (95% HPDI 22% - 64%) of Singapore's capacity. Equivalently, an estimate of 2.8 (95% HPDI 1.5 - 4.4) times the current number of imported cases, could have been detected, if all countries had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, we found that the ability to detect imported cases relative to Singapore among high surveillance locations is 40% (95% HPDI 22% - 67%), among intermediate surveillance locations it is 37% (95% HPDI 18% - 68%), and among low surveillance locations it is 11% (95% HPDI 0% - 42%). Using a simple mathematical model, we further find that treating all travelers as if they were residents (rather than accounting for the brief stay of some of these travelers in Wuhan) can modestly contribute to underestimation of prevalence as well. We conclude that estimates of case counts in Wuhan based on assumptions of perfect detection in travelers may be underestimated by several fold, and severity correspondingly overestimated by several fold. Undetected cases are likely in countries around the world, with greater risk in countries of low detection capacity and high connectivity to the epicenter of the outbreak.

Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2–14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3–4 days without truncation and at 5–9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data

The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2-14 days with 95% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3-4 days without truncation and at 5-9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.

Systematic selection between age and household structure for models aimed at emerging epidemic predictions

Numerous epidemic models have been developed to capture aspects of human contact patterns, making model selection challenging when they fit (often-scarce) early epidemic data equally well but differ in predictions. Here we consider the invasion of a novel directly transmissible infection and perform an extensive, systematic and transparent comparison of models with explicit age and/or household structure, to determine the accuracy loss in predictions in the absence of interventions when ignoring either or both social components. We conclude that, with heterogeneous and assortative contact patterns relevant to respiratory infections, the model’s age stratification is crucial for accurate predictions. Conversely, the household structure is only needed if transmission is highly concentrated in households, as suggested by an empirical but robust rule of thumb based on household secondary attack rate. This work serves as a template to guide the simplicity/accuracy trade-off in designing models aimed at initial, rapid assessment of potential epidemic severity.

Models of emerging epidemics can be exceedingly helpful in planning the response, but early on model selection is a difficult task. Here, the authors explore the joint contribution of age stratification and household structure on epidemic spread, and provides a rule of thumb to guide model choice.

Analysis Method of the Ion Current-Time Waveform Obtained from Low Aspect Ratio Solid-state Nanopores

Low aspect ratio nanopores are expected to be applied to the detection of viruses and bacteria because of their high spatial resolution. Multiphysics simulations have revealed that the ion current-time waveform obtained from low aspect ratio nanopores contains information on not only the volume of viruses and bacteria, but also the structure, surface charge, and flow dynamics. Analysis using machine learning extracts information about these analytes from the ion current-time waveform. The combination of low aspect ratio nanopores, multiphysics simulation, and machine learning has made it possible to distinguish different types of viruses and bacteria with high accuracy.

Phylogeographic reconstruction using air transportation data and its application to the 2009 H1N1 influenza A pandemic

Influenza A viruses cause seasonal epidemics and occasional pandemics in the human population. While the worldwide circulation of seasonal influenza is at least partly understood, the exact migration patterns between countries, states or cities are not well studied. Here, we use the Sankoff algorithm for parsimonious phylogeographic reconstruction together with effective distances based on a worldwide air transportation network. By first simulating geographic spread and then phylogenetic trees and genetic sequences, we confirmed that reconstructions with effective distances inferred phylogeographic spread more accurately than reconstructions with geographic distances and Bayesian reconstructions with BEAST that do not use any distance information, and led to comparable results to the Bayesian reconstruction using distance information via a generalized linear model. Our method extends Bayesian methods that estimate rates from the data by using fine-grained locations like airports and inferring intermediate locations not observed among sampled isolates. When applied to sequence data of the pandemic H1N1 influenza A virus in 2009, our approach correctly inferred the origin and proposed airports mainly involved in the spread of the virus. In case of a novel outbreak, this approach allows to rapidly analyze sequence data and infer origin and spread routes to improve disease surveillance and control.

Influenza A viruses infect up to 5 million people in recurring epidemics every year. Further, viruses of zoonotic origin constantly pose a pandemic risk. Understanding the geographical spread of these viruses, including the origin and the main spread routes between cities, states or countries, could help to monitor or contain novel outbreaks. Based on genetic sequences and sampling locations, the geographic spread can be reconstructed along a phylogenetic tree. Our approach uses a parsimonious reconstruction with air transportation data and was verified using a simulation of the 2009 H1N1 influenza A pandemic. Applied to real sequence data of the outbreak, our analysis gave detailed insights into spread patterns of influenza A viruses, highlighting the origin as well as airports mainly involved in the spread.

Comparison of the Pathogenicity in Mice of A(H1N1)pdm09 Viruses Isolated between 2009 and 2015 in Japan

The A(H1N1)pdm09 virus emerged in 2009 and continues to circulate in human populations. Recent A(H1N1)pdm09 viruses, that is, A(H1N1)pdm09 viruses circulating in the post-pandemic era, can cause more or less severe infections than those caused by the initial pandemic viruses. To evaluate the changes in pathogenicity of the A(H1N1)pdm09 viruses during their continued circulation in humans, we compared the nucleotide and amino acid sequences of ten A(H1N1)pdm09 viruses isolated in Japan between 2009 and 2015, and experimentally infected mice with each virus. The severity of infection caused by these Japanese isolates ranged from milder to more severe than that caused by the prototypic pandemic strain A/California/04/2009 (CA04/09); however, specific mutations responsible for their pathogenicity have not yet been identified.

Dendritic Cells Targeting Lactobacillus plantarum Strain NC8 with a Surface-Displayed Single-Chain Variable Fragment of CD11c Induce an Antigen-Specific Protective Cellular Immune Response

Influenza A virus (H1N1) is an acute, highly contagious respiratory virus. The use of lactic acid bacteria (LAB) to deliver mucosal vaccines against influenza virus infection is a research hot spot. In this study, two recombinant Lactobacillus plantarum strains expressing hemagglutinin (HA) alone or coexpressing aCD11c-HA to target HA protein to dendritic cells (DCs) by fusion to an anti-CD11c single-chain antibody (aCD11c) were constructed. The activation of bone marrow dendritic cells (BMDCs) by recombinant strains and the interaction of activated BMDCs and sorted CD4⁺ or CD8⁺ T cells were evaluated through flow cytometry in vitro, and cellular supernatants were assessed by using an enzyme-linked immunosorbent assay kit. The results demonstrated that, compared to the HA strain, the aCD11c-HA strain significantly increased the activation of BMDCs and increased the production of CD4⁺ gamma interferon-positive (IFN-γ⁺) T cells, CD8⁺ IFN-γ⁺ T cells, and IFN-γ in the cell culture supernatant in vitro Consistent with these results, the aCD11c-HA strain clearly increased the activation and maturation of DCs, the HA-specific responses of CD4⁺ IFN-γ⁺ T cells, CD8⁺ IFN-γ⁺ T cells, and CD8⁺ CD107a⁺ T cells, and the proliferation of T cells in the spleen, finally increasing the levels of specific antibodies and neutralizing antibodies in mice. In addition, the protection of immunized mice was observed after viral infection, as evidenced by improved weight loss, survival, and lung pathology. The adoptive transfer of CD8⁺ T cells from the aCD11c-HA mice to NOD/Lt-SCID mice resulted in a certain level of protection after influenza virus infection, highlighting the efficacy of the aCD11c targeting strategy.

Infectious disease pandemic planning and response: Incorporating decision analysis

Freya Shearer and co-authors discuss the use of decision analysis in planning for infectious disease pandemics.

Prediction of COVID-19 spread by sliding mSEIR observer

The outbreak of COVID-19 has brought unprecedented challenges not only in China but also in the whole world. Thousands of people have lost their lives, and the social operating system has been affected seriously. Thus, it is urgent to study the determinants of the virus and the health conditions in specific populations and to reveal the strategies and measures in preventing the epidemic spread. In this study, we first adopt the long short-term memory algorithm to predict the infected population in China. However, it gives no interpretation of the dynamics of the spread process. Also the long-term prediction error is too large to be accepted. Thus, we introduce the susceptible-exposed-infected-removed (SEIR) model and further the metapopulation SEIR (mSEIR) model to capture the spread process of COVID-19. By using a sliding window algorithm, we suggest that the parameter estimation and the prediction of the SEIR populations are well performed. In addition, we conduct extensive numerical experiments to show the trend of the infected population for several provinces. The results may provide some insight into the research of epidemics and the understanding of the spread of the current COVID-19.

Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases

Understanding the state of the COVID-19 pandemic relies on infection and mortality data. Yet official data may underestimate the actual cases due to limited symptoms and testing capacity. We offer a simulation-based approach which combines various sources of data to estimate the magnitude of outbreak. Early in the epidemic we applied the method to Iran's case, an epicenter of the pandemic in winter 2020. Estimates using data up to March 20th, 2020, point to 916,000 (90% UI: 508 K, 1.5 M) cumulative cases and 15,485 (90% UI: 8.4 K, 25.8 K) total deaths, numbers an order of magnitude higher than official statistics. Our projections suggest that absent strong sustaining of contact reductions the epidemic may resurface. We also use data and studies from the succeeding months to reflect on the quality of original estimates. Our proposed approach can be used for similar cases elsewhere to provide a more accurate, early, estimate of outbreak state. © 2020 System Dynamics Society.

Spatially Adjusted Time-varying Reproductive Numbers: Understanding the Geographical Expansion of Urban Dengue Outbreaks

The basic reproductive number (R0) is a fundamental measure used to quantify the transmission potential of an epidemic in public health practice. However, R0 cannot reflect the time-varying nature of an epidemic. A time-varying effective reproductive number Rt can provide more information because it tracks the subsequent evolution of transmission. However, since it neglects individual-level geographical variations in exposure risk, Rt may smooth out interpersonal heterogeneous transmission potential, obscure high-risk spreaders, and hence hamper the effectiveness of control measures in spatial dimension. Therefore, this study proposes a new method for quantifying spatially adjusted (time-varying) reproductive numbers that reflects spatial heterogeneity in transmission potential among individuals. This new method estimates individual-level effective reproductive numbers (Rj) and a summarized indicator for population-level time-varying reproductive number (Rt). Data from the five most severe dengue outbreaks in southern Taiwan from 1998-2015 were used to demonstrate the ability of the method to highlight early spreaders contributing to the geographic expansion of dengue transmission. Our results show spatial heterogeneity in the transmission potential of dengue among individuals and identify the spreaders with the highest Rj during the epidemic period. The results also reveal that super-spreaders are usually early spreaders that locate at the edges of the epidemic foci, which means that these cases could be the drivers of the expansion of the outbreak. Therefore, our proposed method depicts a more detailed spatial-temporal dengue transmission process and identifies the significant role of the edges of the epidemic foci, which could be weak spots in disease control and prevention.

Burden of Middle East respiratory syndrome coronavirus infection in Saudi Arabia

MERS-coronavirus infection is currently responsible for considerable morbidity and mortality in Saudi Arabia. Understanding its burden, as an emerging infectious disease, is vital for devising appropriate control strategies. In this study, the burden of MERS-CoV was estimated over 31months period from June 6, 2012 to January 5, 2015. The total number of patients was 835; 528 (63.2%) patients were male, 771 (92.3%) patients were ≥25 years of age, and 210 (25.1%) patients were healthcare workers. A total of 751 (89.9%) patients required hospitalization. The median duration between onset of illness and hospitalization was 2 days (interquartile range, 0-5). The median length of hospital stay was 14 days (IQR, 6-27). The overall case fatality rate was 43.1%. Basic reproductive number was 0.9. Being Saudi, non-healthcare workers, and age ≥65 years were significantly associated with higher mortality. In conclusion, MERS-CoV infection caused a substantial health burden in Saudi Arabia.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models

Stochastic transmission dynamic models are needed to quantify the uncertainty in estimates and predictions during outbreaks of infectious diseases. We previously developed a calibration method for stochastic epidemic compartmental models, called Multiple Shooting for Stochastic Systems (MSS), and demonstrated its competitive performance against a number of existing state-of-the-art calibration methods. The existing MSS method, however, lacks a mechanism against filter degeneracy, a phenomenon that results in parameter posterior distributions that are weighted heavily around a single value. As such, when filter degeneracy occurs, the posterior distributions of parameter estimates will not yield reliable credible or prediction intervals for parameter estimates and predictions. In this work, we extend the MSS method by evaluating and incorporating two resampling techniques to detect and resolve filter degeneracy. Using simulation experiments, we demonstrate that an extended MSS method produces credible and prediction intervals with desired coverage in estimating key epidemic parameters (e.g. mean duration of infectiousness and R0) and short- and long-term predictions (e.g. one and three-week forecasts, timing and number of cases at the epidemic peak, and final epidemic size). Applying the extended MSS approach to a humidity-based stochastic compartmental influenza model, we were able to accurately predict influenza-like illness activity reported by U.S. Centers for Disease Control and Prevention from 10 regions as well as city-level influenza activity using real-time, city-specific Google search query data from 119 U.S. cities between 2003 and 2014.

Infectious diseases spreading on a metapopulation network coupled with its second-neighbor network

Traditional infectious diseases models on metapopulation networks focus on direct transportations (e.g., direct flights), ignoring the effect of indirect transportations. Based on global aviation network, we turn the problem of indirect flights into a question of second neighbors, and propose a susceptible-infectious-susceptible model to study disease transmission on a connected metapopulation network coupled with its second-neighbor network (SNN). We calculate the basic reproduction number, which is independent of human mobility, and we prove the global stability of disease-free and endemic equilibria of the model. Furthermore, the study shows that the behavior that all travelers travel along the SNN may hinder the spread of disease if the SNN is not connected. However, the behavior that individuals travel along the metapopulation network coupled with its SNN contributes to the spread of disease. Thus for an emerging infectious disease, if the real network and its SNN keep the same connectivity, indirect transportations may be a potential threat and need to be controlled. Our work can be generalized to high-speed train and rail networks, which may further promote other research on metapopulation networks.

Using Clinical Research Networks to Assess Severity of an Emerging Influenza Pandemic

Background

Early clinical severity assessments during the 2009 influenza A H1N1 pandemic (pH1N1) overestimated clinical severity due to selection bias and other factors. We retrospectively investigated how to use data from the International Network for Strategic Initiatives in Global HIV Trials, a global clinical influenza research network, to make more accurate case fatality ratio (CFR) estimates early in a future pandemic, an essential part of pandemic response.

Methods

We estimated the CFR of medically attended influenza (CFR_MA) as the product of probability of hospitalization given confirmed outpatient influenza and the probability of death given hospitalization with confirmed influenza for the pandemic (2009–2011) and post-pandemic (2012–2015) periods. We used literature survey results on health-seeking behavior to convert that estimate to CFR among all infected persons (CFR_AR).

Results

During the pandemic period, 5.0% (3.1%–6.9%) of 561 pH1N1-positive outpatients were hospitalized. Of 282 pH1N1-positive inpatients, 8.5% (5.7%–12.6%) died. CFR_MA for pH1N1 was 0.4% (0.2%–0.6%) in the pandemic period 2009–2011 but declined 5-fold in young adults during the post-pandemic period compared to the level of seasonal influenza in the post-pandemic period 2012–2015. CFR for influenza-negative patients did not change over time. We estimated the 2009 pandemic CFR_AR to be 0.025%, 16-fold lower than CFR_MA.

Conclusions

Data from a clinical research network yielded accurate pandemic severity estimates, including increased severity among younger people. Going forward, clinical research networks with a global presence and standardized protocols would substantially aid rapid assessment of clinical severity.

Clinical Trials Registration

NCT01056354 and NCT010561.

Oseltamivir Is Effective against 1918 Influenza Virus Infection of Macaques but Vulnerable to Escape

Oseltamivir phosphate is used as a first line of defense in the event of an influenza pandemic prior to vaccine administration. Treatment failure through selection and replication of drug-resistant viruses is a known complication in the field and was also demonstrated in our study with spread of resistant 1918 influenza virus in multiple respiratory tissues. This emphasizes the importance of early treatment and the possibility that noncompliance may exacerbate treatment effectiveness. It also demonstrates the importance of implementing combination therapy and vaccination strategies as soon as possible in a pandemic situation.

The 1918 influenza virus, subtype H1N1, was the causative agent of the most devastating pandemic in the history of infectious diseases. In vitro studies have confirmed that extreme virulence is an inherent property of this virus. Here, we utilized the macaque model for evaluating the efficacy of oseltamivir phosphate against the fully reconstructed 1918 influenza virus in a highly susceptible and relevant disease model. Our findings demonstrate that oseltamivir phosphate is effective in preventing severe disease in macaques but vulnerable to virus escape through emergence of resistant mutants, especially if given in a treatment regimen. Nevertheless, we conclude that oseltamivir would be highly beneficial to reduce the morbidity and mortality rates caused by a highly pathogenic influenza virus although it would be predicted that resistance would likely emerge with sustained use of the drug.

Can the emergency department triage category and clinical presentation predict hospitalization of H1N1 patients?

Background

Human H1N1 Influenza A virus was first reported in 2009 when seasonal outbreaks consistently occurred around the world. H1N1 patients present to the emergency departments (ED) with flu-like symptoms extending up to severe respiratory symptoms that require hospital admission. Developing a prediction model for patient outcomes is important to select patients for hospital admission. To date, there is no available data to guide the hospital admission of H1N1 patients based on their initial presentation.

Objective

The aim of this study was to investigate the predictors of hospital admission of H1N1 patients presenting in the ED.

Methods

We conducted a retrospective review of all laboratory-confirmed H1N1 cases presenting to the ED of a tertiary university hospital in the Eastern region of Saudi Arabia within the period from November 2015 to January 2016. We retrieved data of the initial triage category, vital signs, and presenting symptoms. Multivariate logistic regression analysis was performed to evaluate risk factors for hospital admission among H1N1patients presented to the ED.

Results

We identified 333 patients with laboratory-confirmed H1N1. Patients were classified into two groups: admitted group (n=80; 24%) and non-admitted group (n=253; 76%). Sixty patients (75%) were triaged under category IV. Triage category of level III and less were the most predictive for hospital admission. Multivariate regression analysis showed that of all vital signs, tachypnea was a significant risk factor for hospital admission (OR=1.1; 95% CI 1.02 to 1.13, p<0.01). The association between lower triage category and hospital stay was statistically significant (χ² =6.068, p=0.037). Also, patients with dyspnea were 4.5 times more likely to have longer hospital stay (OR=4.5; 95% CI 1.2 to 17.1, p=0.025).

Conclusion

Lower triage category and increased respiratory rate predict the need for hospital admission of H1N1 infected patients; while patients with dyspnea or bronchial asthma are likely to stay longer in the hospital. Further prospective studies are needed to evaluate the accuracy of using the CTAS and other clinical parameters in predicting hospitalization of H1N1 patients during outbreaks.

Seasonal dynamics of influenza viruses and age distribution of infected individuals across nine seasons covering 2009-2018 in Taiwan

BACKGROUND/PURPOSE

A swine-origin influenza A/H1N1 virus (termed A/H1N1pdm) caused a pandemic in 2009 and has continuously circulated in the human population. To investigate its possible ecological effects on circulating influenza strains, the seasonal patterns of influenza viruses and the respective age distribution of infected patients were studies.

METHODS

The data obtained from national influenza surveillance systems in Taiwan from July 2009 to June 2018 were analyzed.

RESULTS

The A/H1N1pdm and A/H3N2 strains usually caused a higher ratio of severe to mild cases than influenza B. New variants of A/H1N1pdm and A/H3N2 emerged accompanied by a large epidemic peak. However, the new influenza B variants intended to circulate for several seasons before causing a large epidemic. The major group of outpatients affected by A/H1N1pdm were aged 13-23 years in the pandemic wave, and the age range of infected individuals gradually shifted to 24-49 and 0-6 years across seasons; A/H1N1pdm-infected inpatients were aged 24-49 years in 2009-2011, and the age range gradually switched to older groups aged 50-65 and >65 years. Individuals aged 0-6 or 24-49 years accounted for the majority of A/H3N2-infected outpatients across seasons, whereas most of the inpatients affected by A/H3N2 were aged >65 years.

CONCLUSION

Understanding the effects of new variants and changes in dominant circulating viral strains on the age distribution of the affected human population, disease severity and epidemic levels is useful for the establishment of fine-tuned strategies for further improvement of influenza control.

Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning Epidemic

The Zika epidemic in the Americas has challenged surveillance and control. As the epidemic appears to be waning, it is unclear whether transmission is still ongoing, which is exacerbated by discrepancies in reporting. To uncover locations with lingering outbreaks, we investigated travel-associated Zika cases to identify transmission not captured by reporting. We uncovered an unreported outbreak in Cuba during 2017, a year after peak transmission in neighboring islands. By sequencing Zika virus, we show that the establishment of the virus was delayed by a year and that the ensuing outbreak was sparked by long-lived lineages of Zika virus from other Caribbean islands. Our data suggest that, although mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, such measures need to be maintained to be effective. Our study highlights how Zika virus may still be "silently" spreading and provides a framework for understanding outbreak dynamics. VIDEO ABSTRACT.

The use and reporting of airline passenger data for infectious disease modelling: a systematic review

BackgroundA variety of airline passenger data sources are used for modelling the international spread of infectious diseases. Questions exist regarding the suitability and validity of these sources.AimWe conducted a systematic review to identify the sources of airline passenger data used for these purposes and to assess validation of the data and reproducibility of the methodology.MethodsArticles matching our search criteria and describing a model of the international spread of human infectious disease, parameterised with airline passenger data, were identified. Information regarding type and source of airline passenger data used was collated and the studies' reproducibility assessed.ResultsWe identified 136 articles. The majority (n = 96) sourced data primarily used by the airline industry. Governmental data sources were used in 30 studies and data published by individual airports in four studies. Validation of passenger data was conducted in only seven studies. No study was found to be fully reproducible, although eight were partially reproducible.LimitationsBy limiting the articles to international spread, articles focussed on within-country transmission even if they used relevant data sources were excluded. Authors were not contacted to clarify their methods. Searches were limited to articles in PubMed, Web of Science and Scopus.ConclusionWe recommend greater efforts to assess validity and biases of airline passenger data used for modelling studies, particularly when model outputs are to inform national and international public health policies. We also recommend improving reporting standards and more detailed studies on biases in commercial and open-access data to assess their reproducibility.

Simple framework for real-time forecast in a data-limited situation: the Zika virus (ZIKV) outbreaks in Brazil from 2015 to 2016 as an example

Background

In 2015–2016, Zika virus (ZIKV) caused serious epidemics in Brazil. The key epidemiological parameters and spatial heterogeneity of ZIKV epidemics in different states in Brazil remain unclear. Early prediction of the final epidemic (or outbreak) size for ZIKV outbreaks is crucial for public health decision-making and mitigation planning. We investigated the spatial heterogeneity in the epidemiological features of ZIKV across eight different Brazilian states by using simple non-linear growth models.

Results

We fitted three different models to the weekly reported ZIKV cases in eight different states and obtained an R² larger than 0.995. The estimated average values of basic reproduction numbers from different states varied from 2.07 to 3.41, with a mean of 2.77. The estimated turning points of the epidemics also varied across different states. The estimation of turning points nevertheless is stable and real-time. The forecast of the final epidemic size (attack rate) is reasonably accurate, shortly after the turning point. The knowledge of the epidemic turning point is crucial for accurate real-time projection of the outbreak.

Conclusions

Our simple models fitted the epidemic reasonably well and thus revealed the spatial heterogeneity in the epidemiological features across Brazilian states. The knowledge of the epidemic turning point is crucial for real-time projection of the outbreak size. Our real-time estimation framework is able to yield a reliable prediction of the final epidemic size.

Electronic supplementary material

The online version of this article (10.1186/s13071-019-3602-9) contains supplementary material, which is available to authorized users.

Outbreak analytics: a developing data science for informing the response to emerging pathogens

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. This theme issue is linked with the earlier issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'.

Reactive school closure weakens the network of social interactions and reduces the spread of influenza

School-closure policies are considered one of the most promising nonpharmaceutical interventions for mitigating seasonal and pandemic influenza. However, their effectiveness is still debated, primarily due to the lack of empirical evidence about the behavior of the population during the implementation of the policy. Over the course of the 2015 to 2016 influenza season in Russia, we performed a diary-based contact survey to estimate the patterns of social interactions before and during the implementation of reactive school-closure strategies. We develop an innovative hybrid survey-modeling framework to estimate the time-varying network of human social interactions. By integrating this network with an infection transmission model, we reduce the uncertainty surrounding the impact of school-closure policies in mitigating the spread of influenza. When the school-closure policy is in place, we measure a significant reduction in the number of contacts made by students (14.2 vs. 6.5 contacts per day) and workers (11.2 vs. 8.7 contacts per day). This reduction is not offset by the measured increase in the number of contacts between students and nonhousehold relatives. Model simulations suggest that gradual reactive school-closure policies based on monitoring student absenteeism rates are capable of mitigating influenza spread. We estimate that without the implemented reactive strategies the attack rate of the 2015 to 2016 influenza season would have been 33% larger. Our study sheds light on the social mixing patterns of the population during the implementation of reactive school closures and provides key instruments for future cost-effectiveness analyses of school-closure policies.

Parameter identification for a stochastic SEIRS epidemic model: case study influenza

A recent parameter identification technique, the local lagged adapted generalized method of moments, is used to identify the time-dependent disease transmission rate and time-dependent noise for the stochastic susceptible, exposed, infectious, temporarily immune, susceptible disease model (SEIRS) with vital rates. The stochasticity appears in the model due to fluctuations in the time-dependent transmission rate of the disease. All other parameter values are assumed to be fixed, known constants. The method is demonstrated with US influenza data from the 2004-2005 through 2016-2017 influenza seasons. The transmission rate and noise intensity stochastically work together to generate the yearly peaks in infections. The local lagged adapted generalized method of moments is tested for forecasting ability. Forecasts are made for the 2016-2017 influenza season and for infection data in year 2017. The forecast method qualitatively matches a single influenza season. Confidence intervals are given for possible future infectious levels.

Optimizing targeted vaccination across cyber-physical networks: an empirically based mathematical simulation study

Targeted vaccination, whether to minimize the forward transmission of infectious diseases or their clinical impact, is one of the 'holy grails' of modern infectious disease outbreak response, yet it is difficult to achieve in practice due to the challenge of identifying optimal targets in real time. If interruption of disease transmission is the goal, targeting requires knowledge of underlying person-to-person contact networks. Digital communication networks may reflect not only virtual but also physical interactions that could result in disease transmission, but the precise overlap between these cyber and physical networks has never been empirically explored in real-life settings. Here, we study the digital communication activity of more than 500 individuals along with their person-to-person contacts at a 5-min temporal resolution. We then simulate different disease transmission scenarios on the person-to-person physical contact network to determine whether cyber communication networks can be harnessed to advance the goal of targeted vaccination for a disease spreading on the network of physical proximity. We show that individuals selected on the basis of their closeness centrality within cyber networks (what we call 'cyber-directed vaccination') can enhance vaccination campaigns against diseases with short-range (but not full-range) modes of transmission.

Increased infection severity in downstream cities in infectious disease transmission and tourists surveillance analysis

Infectious disease severely threatens human life. Human mobility and travel patterns influence the spread of infection between cities and countries. We find that the infection severity in downstream cities during outbreaks is related to transmission rate, recovery rate, travel rate, travel duration and the average number of person-to-person contacts per day. The peak value of the infected population in downstream cities is slightly higher than that in source cities. However, as the number of cities increases, the severity increase percentage during outbreaks between end and source cities is constant. The surveillance of important nodes connecting cities, such as airports and train stations, can help delay the occurrence time of infection outbreaks. The city-entry surveillance of hub cities is not only useful to these cities, but also to cities that are strongly connected (i.e., have a high travel rate) to them. The city-exit surveillance of hub cities contributes to other downstream cities, but only slightly to itself. Surveillance conducted in hub cities is highly efficient in controlling infection transmission. Only strengthening the individual immunity of frequent travellers is not efficient for infection control. However, reducing the number of person-to-person contacts per day effectively limits the spread of infection.

Size and Flexibility Define the Inhibition of the H3N2 Influenza Endonuclease Enzyme by Calix[n]arenes

Inhibition of H3N2 influenza PA endonuclease activity by a panel of anionic calix[n]arenes and β-cyclodextrin sulfate has been studied. The joint experimental and theoretical results reveal that the larger, more flexible and highly water-soluble sulfonato-calix[n]arenes have high inhibitory activity, with para-sulfonato-calix[8]arene, SC8, having an IC₅₀ value of 6.4 μM. Molecular docking calculations show the SC8 can interact at both the polyanion binding site and also the catalytic site of H3N2 influenza PA endonuclease.

Are we prepared for the next influenza pandemic? Lessons from modelling different preparedness policies against four pandemic scenarios

In the event of a novel influenza strain that is markedly different to the current strains circulating in humans, the population have little/no immunity and infection spreads quickly causing a global pandemic. Over the past century, there have been four major influenza pandemics: the 1918 pandemic ("Spanish Flu"), the 1957-58 pandemic (the "Asian Flu"), the 1967-68 pandemic (the "Hong Kong Flu") and the 2009 pandemic (the "Swine flu"). To inform planning against future pandemics, this paper investigates how different is the net-present value of employing pre-purchase and responsive- purchased vaccine programmes in presence and absence of anti-viral drugs to scenarios that resemble these historic influenza pandemics. Using the existing literature and in discussions with policy decision makers in the UK, we first characterised the four past influenza pandemics by their transmissibility and infection-severity. For these combinations of parameters, we then projected the net-present value of employing pre-purchase vaccine (PPV) and responsive-purchase vaccine (RPV) programmes in presence and absence of anti-viral drugs. To differentiate between PPV and RPV policies, we changed the vaccine effectiveness value and the time to when the vaccine is first available. Our results are "heat-map" graphs displaying the benefits of different strategies in pandemic scenarios that resemble historic influenza pandemics. Our results suggest that immunisation with either PPV or RPV in presence of a stockpile of effective antiviral drugs, does not have positive net-present value for all of the pandemic scenarios considered. In contrast, in the absence of effective antivirals, both PPV and RPV policies have positive net-present value across all the pandemic scenarios. Moreover, in all considered circumstances, vaccination was most beneficial if started sufficiently early and covered sufficiently large number of people. When comparing the two vaccine programmes, the RPV policy allowed a longer timeframe and lower coverage to attain the same benefit as the PPV policy. Our findings suggest that responsive-purchase vaccination policy has a bigger window of positive net-present value when employed against each of the historic influenza pandemic strains but needs to be rapidly available to maximise benefit. This is important for future planning as it suggests that future preparedness policies may wish to consider utilising timely (i.e. responsive-purchased) vaccines against emerging influenza pandemics.

Emerging Challenges and Opportunities in Infectious Disease Epidemiology

Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners.

Transportan-derived cell-penetrating peptide delivers siRNA to inhibit replication of influenza virus in vivo

Introduction

In this study, we report on the development of an effective delivery system for siRNAs; a novel cell-penetrating peptide (CPP), T9(dR), obtained from transportan (TP), was used for in vivo and in vitro testing.

Methods

In this study, toxicity of T9(dR) and TP and efficient delivery of siRNA were tested in 293T, MDCK, RAW, and A549 cells. Furthermore, T9(dR)- and TP-delivered siRNAs against nucleoprotein (NP) gene segment of influenza virus (siNP) were studied in both cell lines and mice.

Results

Gel retardation showed that T9(dR) effectively condensed siRNA into nanoparticles sized between 350 and 550 nm when the mole ratio of T9(dR) to siRNA was ≥4:1. In vitro studies demonstrated that T9(dR) successfully delivered siRNA with low cellular toxicity into several cell lines. It was also observed that T9(dR)-delivered siRNAs inhibited replication of influenza virus more efficiently as compared to that delivered by TP into the MDCK and A549 cells. It was also noticed that when given a combined tail vein injection of siNP and T9(dR) or TP, all mice in the 50 nmol siNP group infected with PR8 influenza virus survived and showed weight recovery at 2 weeks post-infection.

Conclusion

This study indicates that T9(dR) is a promising siRNA delivery tool with potential application for nucleotide drug delivery.

Association of meteorological factors with seasonal activity of influenza A subtypes and B lineages in subtropical western China

The seasonality of individual influenza subtypes/lineages and the association of influenza epidemics with meteorological factors in the tropics/subtropics have not been well understood. The impact of the 2009 H1N1 pandemic on the prevalence of seasonal influenza virus remains to be explored. Using wavelet analysis, the periodicities of A/H3N2, seasonal A/H1N1, A/H1N1pdm09, Victoria and Yamagata were identified, respectively, in Panzhihua during 2006–2015. As a subtropical city in southwestern China, Panzhihua is the first industrial city in the upper reaches of the Yangtze River. The relationship between influenza epidemics and local climatic variables was examined based on regression models. The temporal distribution of influenza subtypes/lineages during the pre-pandemic (2006–2009), pandemic (2009) and post-pandemic (2010–2015) years was described and compared. A total of 6892 respiratory specimens were collected and 737 influenza viruses were isolated. A/H3N2 showed an annual cycle with a peak in summer–autumn, while A/H1N1pdm09, Victoria and Yamagata exhibited an annual cycle with a peak in winter–spring. Regression analyses demonstrated that relative humidity was positively associated with A/H3N2 activity while negatively associated with Victoria activity. Higher prevalence of A/H1N1pdm09 and Yamagata was driven by lower absolute humidity. The role of weather conditions in regulating influenza epidemics could be complicated since the diverse viral transmission modes and mechanism. Differences in seasonality and different associations with meteorological factors by influenza subtypes/lineages should be considered in epidemiological studies in the tropics/subtropics. The development of subtype- and lineage-specific prevention and control measures is of significant importance.

Atypical influenza A(H1N1)pdm09 strains caused an influenza virus outbreak in Saudi Arabia during the 2009-2011 pandemic season

BACKGROUND

The triple assortment influenza A(H1N1) virus emerged in spring 2009 and disseminated worldwide, including Saudi Arabia. This study was carried out to characterize Saudi influenza isolates in relation to the global strains and to evaluate the potential role of mutated residues in transmission, adaptation, and the pathogenicity of the virus.

METHODS

Nasopharyngeal samples (n = 6492) collected between September 2009 to March 2011 from patients with influenza-like illness were screened by PCR for influenza A(H1N1). Phylogenetic and Molecular evolutionary analysis were carried out to place the Saudi strains in relation to the global strains followed by Mutation analysis of surface and internal proteins.

RESULTS

Concatenated whole-genome phylogenetic analysis along with hemagglutinin (HA) signature changes, that is, Aspartic Acid (D) at position 187, P83S, S203T, and R223Q confirmed that the Saudi strains belong to the antigenic category of A/California/07/2009. However, phylogenetic analysis revealed unusual strains of A(H1N1) circulating in Saudi Arabia, not belonging to any of known clades, appearing in five distinct groups well supported by group-specific mutations and novel mutation complexes. These cases had characteristic inter- and intragroup substitution patterns while few of their closest matches showed up as sporadic cases the world over. Specific mutation patterns were detected within the functional domains of internal proteins PB2, PB1, PA, NP, NS1, and M2 having a putative role in viral fitness and virulence. Bayesian coalescent MCMC analysis revealed that Saudi strains belonged to cluster 2 of A(H1N1)pdm09 and spread a month later as compared to other strains of this cluster.

CONCLUSION

Influenza outbreak in Saudi Arabia during 2009-2011 was caused by atypical strains of influenza A(H1N1)pdm09, probably introduced in this community on multiple occasions. To understand the antigenic significance of these novel point mutations and mutation complexes require functional studies, which will be crucial for risk assessment of emergent strains and defining infection control measures.

How Modelling Can Enhance the Analysis of Imperfect Epidemic Data

Mathematical models play an increasingly important role in our understanding of the transmission and control of infectious diseases. Here, we present concrete examples illustrating how mathematical models, paired with rigorous statistical methods, are used to parse data of different levels of detail and breadth and estimate key epidemiological parameters (e.g., transmission and its determinants, severity, impact of interventions, drivers of epidemic dynamics) even when these parameters are not directly measurable, when data are limited, and when the epidemic process is only partially observed. Finally, we assess the hurdles to be taken to increase availability and applicability of these approaches in an effort to ultimately enhance their public health impact.

Many data can be used to estimate the transmission potential of a pathogen, including descriptions of the transmission chains, human cluster sizes, sources of infection, and epidemic curves.

An important agenda in public health is understanding the impact of control methods. However, the dynamic nature of epidemics makes this task challenging. Models can disentangle the natural course of outbreaks from the effect of external factors.

In the absence of reliable surveillance data, models can reconstruct epidemic history by combining age-specific seroprevalence data with an understanding of the natural history of infection.

Mechanisms of immunity are hard to observe at an individual level, yet they affect population-level dynamics. Models can tease out such signatures.

Morbidity and mortality can be difficult to estimate when many infections are unobserved and severe infections are reported more often. Models can be used to correct for under-reporting and selection bias.

Persistent spatial patterns of interacting contagions

The spread of infectious diseases, rumors, fashions, and innovations are complex contagion processes, embedded in network and spatial contexts. While the studies in the former context are intensively expanded, the latter remains largely unexplored. In this paper, we investigate the pattern formation of an interacting contagion, where two infections, A and B, interact with each other and diffuse simultaneously in space. The contagion process for each follows the classical susceptible-infected-susceptible kinetics, and their interaction introduces a potential change in the secondary infection propensity compared to the baseline reproduction number R_{0}. We show that the nontrivial spatial infection patterns arise when the susceptible individuals move faster than the infected and the interaction between the two infections is neither too competitive nor too cooperative. Interestingly, the system exhibits pattern hysteresis phenomena, i.e., quite different parameter regions for patterns exist in the direction of increasing or decreasing R_{0}. Decreasing R_{0} reveals remarkable enhancement in contagion prevalence, meaning that the eradication becomes difficult compared to the single-infection or coinfection without space. Linearization analysis supports our observations, and we have identified the required elements and dynamical mechanism, which suggests that these patterns are essentially Turing patterns. Our work thus reveals new complexities in interacting contagions and paves the way for further investigation because of its relevance to both biological and social contexts.

Efficacy of A/H1N1/2009 split inactivated influenza A vaccine (GC1115) in mice and ferrets

To evaluate the efficacy of a non-adjuvant A/H1N1/2009 influenza A vaccine (GC1115), we demonstrated the immunogenicity and protective efficacy of GC1115 in mouse and ferret models. The immunogenicity of GC1115 was confirmed after intramuscular administration of 1.875, 3.75, 7.5, and 15 μg hemagglutinin antigen (HA) in mice and 7.5, 15, and 30 μg HA in ferrets at 3-week intervals. A single immunization with GC1115 at HA doses > 7.5 μg induced detectable seroconversion in most mice, and all mice given a second dose exhibited high antibody responses in a dose-dependent manner. The mice in the mock (PBS) and 1.875 μg HA immunized groups succumbed by 13 days following A/California/ 04/09 infection, while all mice in groups given more than 3.75 μg HA were protected from lethal challenge with the A/California/04/09 virus. In ferrets, although immunization with even a single dose of 15 or 30 μg of HA induced detectable HI antibodies, all ferrets given two doses of vaccine seroconverted and exhibited HI titers greater than 80 units. Following challenge with A/California/04/09, the mock (PBS) immunized ferrets showed influenza-like clinical symptoms, such as increased numbers of coughs, elevated body temperature, and body weight loss, for 7 days, while GC1115- immunized ferrets showed attenuated clinical symptoms only for short time period (3-4 days). Further, GC1115-immunized ferrets displayed significantly lower viral titers in the upper respiratory tract (nasal cavity) than the mock vaccinated group in a dose-dependent manner. Taken together, this study demonstrates the immunogenicity and protective efficacy of GC1115 as a non-adjuvanted vaccine.

Precision epidemiology for infectious disease control

Advances in genomics and computing are transforming the capacity for the characterization of biological systems, and researchers are now poised for a precision-focused transformation in the way they prepare for, and respond to, infectious diseases. This includes the use of genome-based approaches to inform molecular diagnosis and individual-level treatment regimens. In addition, advances in the speed and granularity of pathogen genome generation have improved the capability to track and understand pathogen transmission, leading to potential improvements in the design and implementation of population-level public health interventions. In this Perspective, we outline several trends that are driving the development of precision epidemiology of infectious disease and their implications for scientists' ability to respond to outbreaks.

Epidemic curves made easy using the R package incidence

The epidemiological curve (epicurve) is one of the simplest yet most useful tools used by field epidemiologists, modellers, and decision makers for assessing the dynamics of infectious disease epidemics. Here, we present the free, open-source package incidence for the R programming language, which allows users to easily compute, handle, and visualise epicurves from unaggregated linelist data. This package was built in accordance with the development guidelines of the R Epidemics Consortium (RECON), which aim to ensure robustness and reliability through extensive automated testing, documentation, and good coding practices. As such, it fills an important gap in the toolbox for outbreak analytics using the R software, and provides a solid building block for further developments in infectious disease modelling. incidence is available from https://www.repidemicsconsortium.org/incidence.

Current and future influenza vaccines

Although antiviral drugs and vaccines have reduced the economic and healthcare burdens of influenza, influenza epidemics continue to take a toll. Over the past decade, research on influenza viruses has revealed a potential path to improvement. The clues have come from accumulated discoveries from basic and clinical studies. Now, virus surveillance allows researchers to monitor influenza virus epidemic trends and to accumulate virus sequences in public databases, which leads to better selection of candidate viruses for vaccines and early detection of drug-resistant viruses. Here we provide an overview of current vaccine options and describe efforts directed toward the development of next-generation vaccines. Finally, we propose a plan for the development of an optimal influenza vaccine.

Temporal patterns of influenza A subtypes and B lineages across age in a subtropical city, during pre-pandemic, pandemic, and post-pandemic seasons

Background

Seasonal patterns of influenza A subtypes and B lineages in tropical/subtropical regions across age have remained to be explored. The impact of the 2009 H1N1 pandemic on seasonal influenza activity have not been well understood.

Methods

Based on a national sentinel hospital-based influenza surveillance system, the epidemiology of influenza virus during 2006/07–2015/16 was characterized in the subtropical city, Chengdu. Chengdu is one of the most populous cities in southwestern China, where the first reported case of A/H1N1pdm09 in mainland China was identified. Wavelet analysis was applied to identify the periodicities of A/H3N2, seasonal A/H1N1, A/H1N1pdm09, Victoria, and Yamagata across age, respectively. The persistence and age distribution patterns were described during the pre-pandemic (2006/07–2008/09), pandemic (2009/10), and post-pandemic (2010/11–2015/16) seasons.

Results

A total of 10,981 respiratory specimens were collected, of which 2516 influenza cases were identified. Periodicity transition from semi-annual cycles to an annual cycle was observed for composite influenza virus as well as A/H3N2 along in Chengdu since the 2009 H1N1 pandemic. Semi-annual cycles of composite influenza virus and A/H3N2 along were observed again during 2014/15–2015/16, coinciding with the emergence and predominance of A/H3N2 significant antigenic drift groups. However, A/H1N1pdm09, Victoria, and Yamagata generally demonstrated an annual winter-spring peak in non-pandemic seasons. Along with periodicity transitions, age groups with higher positive rates shifted from school-aged children and adults to adults and the elderly for A/H1N1pdm09 during 2009/10–2010/11 and for A/H3N2 during 2014/15–2015/16.

Conclusions

Differences in periodicity and age distribution by subtype/lineage and by season highlight the importance of increasing year-round influenza surveillance and developing subtype/lineage- and age-specific prevention and control measures. Changes of periodicity and age shifts should be considered in public health response to influenza pandemics and epidemics. In addition, it is suggested to use quadrivalent influenza vaccines to provide protection against both influenza B lineages.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-3689-9) contains supplementary material, which is available to authorized users.