“Pandemic Public Health Paradox”: Time Series Analysis of the 2009/10 Influenza A / H1N1 Epidemiology,Media Attention,Risk Perception and Public Reactions in 5 European Countries

In 2009, influenza A H₁N₁ caused the first pandemic of the 21^st century. Although a vaccine against this influenza subtype was offered before or at the onset of the second epidemic wave that caused most of the fatal cases in Europe, vaccination rates for that season were lower than expected. We propose that the contradiction between high risk of infection and low use of available prevention measures represents a pandemic public health paradox. This research aims for a better understanding of this paradox by exploring the time-dependent interplay among changing influenza epidemiology, media attention, pandemic control measures, risk perception and public health behavior among five European countries (Czech Republic, Denmark, Germany, Spain and the UK). Findings suggest that asynchronicity between media curves and epidemiological curves may potentially explain the pandemic public health paradox; media attention for influenza A H₁N₁ in Europe declined long before the epidemic reached its peak, and public risk perceptions and behaviors may have followed media logic, rather than epidemiological logic.     

The transmission dynamics of BSE and vCJD

The bovine spongiform encephalopathy (BSE) epidemic in cattle has had a huge economic impact on the agricultural industries across Europe. Furthermore, scientific evidence now strongly supporting a link between a new variant of Creutzfeldt-Jakob disease (vCJD) and consumption of BSE-infected animals has further heightened the need both to understand the transmission of these new diseases and to improve control measures to protect public health. In this paper we review work undertaken by our group using epidemiological models to understand the transmission dynamics of BSE and vCJD. We present new estimates of the future number of cases of BSE and the number of infected animals slaughtered for consumption for Great Britain, and summarise similar analyses undertaken for Northern Ireland, Ireland, Portugal and France. We also consider the epidemiological determinants of the future course of the vCJD epidemic, including the age and genetic characteristics of the confirmed cases, and present predictions of future case numbers.     

Self-Interest versus Group-Interest in Antiviral Control

Antiviral agents have been hailed to hold considerable promise for the treatment and prevention of emerging viral diseases like H5N1 avian influenza and SARS. However, antiviral drugs are not completely harmless, and the conditions under which individuals are willing to participate in a large-scale antiviral drug treatment program are as yet unknown. We provide population dynamical and game theoretical analyses of large-scale prophylactic antiviral treatment programs. Throughout we compare the antiviral control strategy that is optimal from the public health perspective with the control strategy that would evolve if individuals make their own, rational decisions. To this end we investigate the conditions under which a large-scale antiviral control program can prevent an epidemic, and we analyze at what point in an unfolding epidemic the risk of infection starts to outweigh the cost of antiviral treatment. This enables investigation of how the optimal control strategy is moulded by the efficacy of antiviral drugs, the risk of mortality by antiviral prophylaxis, and the transmissibility of the pathogen. Our analyses show that there can be a strong incentive for an individual to take less antiviral drugs than is optimal from the public health perspective. In particular, when public health asks for early and aggressive control to prevent or curb an emerging pathogen, for the individual antiviral drug treatment is attractive only when the risk of infection has become non-negligible. It is even possible that from a public health perspective a situation in which everybody takes antiviral drugs is optimal, while the process of individual choice leads to a situation where nobody is willing to take antiviral drugs.     

Curtailing transmission of severe acute respiratory syndrome within a community and its hospital

Severe acute respiratory syndrome (SARS) has been transmitted extensively within hospitals, and healthcare workers (HCWs) have comprised a large proportion of SARS cases worldwide. We present a stochastic model of a SARS outbreak in a community and its hospital. For a range of basic reproductive numbers (R(0)) corresponding to conditions in different cities (but with emphasis on R(0) approximately 3 as reported for Hong Kong and Singapore), we evaluate contact precautions and case management (quarantine and isolation) as containment measures. Hospital-based contact precautions emerge as the most potent measures, with hospital-wide measures being particularly important if screening of HCWs is inadequate. For R(0) = 3, case isolation alone can control a SARS outbreak only if isolation reduces transmission by at least a factor of four and the mean symptom-onset-to-isolation time is less than 3 days. Delays of a few days in contact tracing and case identification severely degrade the utility of quarantine and isolation, particularly in high-transmission settings. Still more detrimental are delays between the onset of an outbreak and the implementation of control measures; for given control scenarios, our model identifies windows of opportunity beyond which the efficacy of containment efforts is reduced greatly. By considering pathways of transmission in our system, we show that if hospital-based transmission is not halted, measures that reduce community-HCW contact are vital to preventing a widespread epidemic. The implications of our results for future emerging pathogens are discussed.     

A Bayesian Inference Framework to Reconstruct Transmission Trees Using Epidemiological and Genetic Data

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available.     

Molecular epidemiology of the coronavirus associated with severe acute respiratory syndrome: a review of data from the Chinese University of Hong Kong

The epidemic of the severe acute respiratory syndrome (SARS) has swept through the globe with more than 8000 reported probable cases. In Hong Kong, the hardest hit areas included our teaching hospital and the Amoy Gardens apartment complex. A novel coronavirus, SARS-coronavirus (SARS-CoV), with a single-stranded plus sense RNA genome, was promptly implicated as the causative agent and subsequently fulfilled Koch's postulates. To aid the understanding of SARS-CoV, groups of investigators rapidly sequenced viral isolates around the world. We were the third group in the world to release the complete SARS-CoV genome sequence (isolate CUHK-W1) on the world-wide web. With other isolates from patients of distinct epidemiological backgrounds, we additionally sequenced four complete (CUHK-Su10, CUHK-AG01, CUHK-AG02, CUHK-AG03) and two partial SARS-CoV genomes. The reviewed data obtained from representative patients from the hospital and community outbreaks has documented the evolution of the virus in this epidemic. Their sequence variations also revealed a remarkable epidemiological correlation. We demonstrate that sequence variations in the SARS-CoV genome can be applied as a powerful molecular tool in tracing the route of transmission, when used adjunctively with standard epidemiology.     

Temporal Variability and Social Heterogeneity in Disease Transmission: The Case of SARS in Hong Kong

The extent to which self-adopted or intervention-related changes in behaviors affect the course of epidemics remains a key issue for outbreak control. This study attempted to quantify the effect of such changes on the risk of infection in different settings, i.e., the community and hospitals. The 2002–2003 severe acute respiratory syndrome (SARS) outbreak in Hong Kong, where 27% of cases were healthcare workers, was used as an example. A stochastic compartmental SEIR (susceptible-exposed-infectious-removed) model was used: the population was split into healthcare workers, hospitalized people and general population. Super spreading events (SSEs) were taken into account in the model. The temporal evolutions of the daily effective contact rates in the community and hospitals were modeled with smooth functions. Data augmentation techniques and Markov chain Monte Carlo (MCMC) methods were applied to estimate SARS epidemiological parameters. In particular, estimates of daily reproduction numbers were provided for each subpopulation. The average duration of the SARS infectious period was estimated to be 9.3 days (±0.3 days). The model was able to disentangle the impact of the two SSEs from background transmission rates. The effective contact rates, which were estimated on a daily basis, decreased with time, reaching zero inside hospitals. This observation suggests that public health measures and possible changes in individual behaviors effectively reduced transmission, especially in hospitals. The temporal patterns of reproduction numbers were similar for healthcare workers and the general population, indicating that on average, an infectious healthcare worker did not infect more people than any other infectious person. We provide a general method to estimate time dependence of parameters in structured epidemic models, which enables investigation of the impact of control measures and behavioral changes in different settings.     

The epidemiology of BSE in cattle herds in Great Britain. I. Epidemiological processes,demography of cattle and approaches to control by culling.

This paper explores the key epidemiological processes and demographic factors that determined the pattern of transmission of the aetiological agent of bovine spongiform encephalopathy (BSE) in cattle herds in Great Britain (GB). The analyses presented utilize data from published and unpublished experimental studies and from the GB central database of confirmed BSE cases. We review the experimental and epidemiological evidence that has both confirmed indirect horizontal transmission via the consumption of infectious material as the major transmission route and provided information on the duration and variability of the dose-dependent incubation period of BSE in cattle. The epidemiological and genetic data pertaining to the possible existence of maternal transmission and/or genetically variable susceptibility to infection is discussed. The demography of British cattle is characterized and the impacts of key demographic features on the observed epidemic profile are discussed. In the main BSE case database, analyses reveal that BSE cases cluster significantly at both the holding and county scale. Furthermore, analysis of longitudinal patterns reveal substantial temporal within-holding correlation. Such clustering of cases suggests a highly heterogeneous infection process. The paper ends with a discussion of how analyses of spatio-temporal clustering inform the design of targeted culling programmes aimed at reducing future disease incidence. We show how the retrospective implementation of culling policies on the BSE case database allows the qualitative evaluation of policy performance, but that model predictions of future trends in case incidence are required to estimate the precise impact of any current or future programme.     

Scientific research progress of COVID‐19/SARS‐CoV‐2 in the first five months

A cluster of pneumonia (COVID‐19) cases have been found in Wuhan China in late December, 2019, and subsequently, a novel coronavirus with a positive stranded RNA was identified to be the aetiological virus (severe acute respiratory syndrome coronavirus 2, SARS‐CoV‐2), which has a phylogenetic similarity to severe acute respiratory syndrome coronavirus (SARS‐CoV). SARS‐CoV‐2 transmits mainly through droplets and close contact and the elder or people with chronic diseases are high‐risk population. People affected by SARS‐CoV‐2 can be asymptomatic, which brings about more difficulties to control the transmission. COVID‐19 has become pandemic rapidly after onset, and so far the infected people have been above 2 000 000 and more than 130 000 died worldwide according to COVID‐19 situation dashboard of World Health Organization ( https://covid19.who.int ). Here, we summarized the current known knowledge regarding epidemiological, pathogenesis, pathology, clinical features, comorbidities and treatment of COVID‐19/ SARS‐CoV‐2 as reference for the prevention and control COVID‐19.     

Bayesian modelling of an epidemic of severe acute respiratory syndrome

This paper analyses data arising from a SARS epidemic in Shanxi province of China involving a total of 354 people infected with SARS-CoV between late February and late May 2003. Using Bayesian inference, we have estimated critical epidemiological determinants. The estimated mean incubation period was 5.3 days (95% CI 4.2–6.8 days), mean time to hospitalisation was 3.5 days (95% CI 2.8–3.6 days), mean time from symptom onset to recovery was 26 days (95% CI 25–27 days) and mean time from symptom onset to death was 21 days (95% CI 16–26 days). The reproduction ratio was estimated to be 4.8 (95% CI 2.2–8.8) in the early part of the epidemic (February and March 2003) reducing to 0.75 (95% CI 0.65–0.85) in the later part of the epidemic (April and May 2003). The infectivity of symptomatic SARS cases in hospital and in the community was estimated. Community SARS cases caused transmission to others at an estimated rate of 0.4 per infective per day during the early part of the epidemic, reducing to 0.2 in the later part of the epidemic. For hospitalised patients, the daily infectivity was approximately 0.15 early in the epidemic, but fell to 0.0006 in the later part of the epidemic. Despite the lower daily infectivity level for hospitalised patients, the long duration of the hospitalisation led to a greater number of transmissions within hospitals compared with the community in the early part of the epidemic, as estimated by this study. This study investigated the individual infectivity profile during the symptomatic period, with an estimated peak infectivity on the ninth symptomatic day.     

Measured voluntary avoidance behaviour during the 2009 A/H1N1 epidemic

Managing infectious disease is among the foremost challenges for public health policy. Interpersonal contacts play a critical role in infectious disease transmission, and recent advances in epidemiological theory suggest a central role for adaptive human behaviour with respect to changing contact patterns. However, theoretical studies cannot answer the following question: are individual responses to disease of sufficient magnitude to shape epidemiological dynamics and infectious disease risk? We provide empirical evidence that Americans voluntarily reduced their time spent in public places during the 2009 A/H1N1 swine flu, and that these behavioural shifts were of a magnitude capable of reducing the total number of cases. We simulate 10 years of epidemics (2003–2012) based on mixing patterns derived from individual time-use data to show that the mixing patterns in 2009 yield the lowest number of total infections relative to if the epidemic had occurred in any of the other nine years. The World Health Organization and other public health bodies have emphasized an important role for ‘distancing’ or non-pharmaceutical interventions. Our empirical results suggest that neglect for voluntary avoidance behaviour in epidemic models may overestimate the public health benefits of public social distancing policies.     

Network theory and SARS: predicting outbreak diversity

Many infectious diseases spread through populations via the networks formed by physical contacts among individuals. The patterns of these contacts tend to be highly heterogeneous. Traditional "compartmental" modeling in epidemiology, however, assumes that population groups are fully mixed, that is, every individual has an equal chance of spreading the disease to every other. Applications of compartmental models to Severe Acute Respiratory Syndrome (SARS) resulted in estimates of the fundamental quantity called the basic reproductive number R0--the number of new cases of SARS resulting from a single initial case--above one, implying that, without public health intervention, most outbreaks should spark large-scale epidemics. Here we compare these predictions to the early epidemiology of SARS. We apply the methods of contact network epidemiology to illustrate that for a single value of R0, any two outbreaks, even in the same setting, may have very different epidemiological outcomes. We offer quantitative insight into the heterogeneity of SARS outbreaks worldwide, and illustrate the utility of this approach for assessing public health strategies.     

Preparedness for SARS in the UK in 2003

Severe acute respiratory syndrome (SARS) has been described as the first major emerging infectious disease of the twenty-first century. Having initially emerged, almost unnoticed, in southern China, it rapidly spread across the globe. It severely tested national public health and health systems. However, it also resulted in rapid, intensive international collaboration, led by the World Health Organization, to elucidate its characteristics and cause and to contain its spread. The UK mounted a vigorous public health response. Some particular issues concerned: the practicalities of implementing exit screening had this been required; the likely efficacy of this and other control measures; the legal base for public health action; and the surge capacity in all systems should the disease have taken hold in the UK. We have used this experience of 2003 to inform our preparation of a framework for an integrated, escalating response to a future re-emergence of SARS according to the levels of disease activity worldwide. Recent cases confirm that SARS has not "gone away". We cannot be complacent about our contingency planning.     

BSE in Northern Ireland: epidemiological patterns past,present and future.

By 30 January 1998, there had been 170,259 confirmed cases of BSE in Great Britain (GB), 1766 confirmed cases in Northern Ireland (NI) (2 January 1998), and 276 confirmed cases in the Republic of Ireland (31 January 1998). Analysis of the epidemiological patterns in the NI epidemic reveals significant clustering of cases in herds and counties. The observed clustering of cases within herds results in lower per capita incidence of BSE in previously unaffected herds, providing support for the introduction of a certified herd scheme in NI. By fitting a backcalculation model to the case data, we can estimate the number of animals infected with the aetiological agent of BSE and project the number of future cases. We predict that the epidemic will decline rapidly, with approximately 99 cases (95% confidence interval 30,504) occurring in the five year period 1997-2001.     

The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda

Despite improved control measures, Ebola remains a serious public health risk in African regions where recurrent outbreaks have been observed since the initial epidemic in 1976. Using epidemic modeling and data from two well-documented Ebola outbreaks (Congo 1995 and Uganda 2000), we estimate the number of secondary cases generated by an index case in the absence of control interventions R0. Our estimate of R0 is 1.83 (SD 0.06) for Congo (1995) and 1.34 (SD 0.03) for Uganda (2000). We model the course of the outbreaks via an SEIR (susceptible-exposed-infectious-removed) epidemic model that includes a smooth transition in the transmission rate after control interventions are put in place. We perform an uncertainty analysis of the basic reproductive number R0 to quantify its sensitivity to other disease-related parameters. We also analyse the sensitivity of the final epidemic size to the time interventions begin and provide a distribution for the final epidemic size. The control measures implemented during these two outbreaks (including education and contact tracing followed by quarantine) reduce the final epidemic size by a factor of 2 relative the final size with a 2-week delay in their implementation.     

Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data

When responding to infectious disease outbreaks, rapid and accurate estimation of the epidemic trajectory is critical. However, two common data collection problems affect the reliability of the epidemiological data in real time: missing information on the time of first symptoms, and retrospective revision of historical information, including right censoring. Here, we propose an approach to construct epidemic curves in near real time that addresses these two challenges by 1) imputation of dates of symptom onset for reported cases using a dynamically-estimated “backward” reporting delay conditional distribution, and 2) adjustment for right censoring using the NobBS software package to nowcast cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number (R_t) in real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We evaluate how these real-time estimates compare with more complete epidemiological data that became available later. We explore the impact of the different assumptions on the estimates, and compare our estimates with those obtained from commonly used surveillance approaches. Our framework can help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health systems in other locations.     

BAsE-Seq: a method for obtaining long viral haplotypes from short sequence reads

Epidemiologists aim to inform the design of public health interventions with evidence on the evolution, emergence and spread of infectious diseases. Sequencing of pathogen genomes, together with date, location, clinical manifestation and other relevant data about sample origins, can contribute to describing nearly every aspect of transmission dynamics, including local transmission and global spread. The analyses of these data have implications for all levels of clinical and public health practice, from institutional infection control to policies for surveillance, prevention and treatment. This review highlights the range of epidemiological questions that can be addressed from the combination of genome sequence and traditional ‘line lists’ (tables of epidemiological data where each line includes demographic and clinical features of infected individuals). We identify opportunities for these data to inform interventions that reduce disease incidence and prevalence. By considering current limitations of, and challenges to, interpreting these data, we aim to outline a research agenda to accelerate the genomics-driven transformation in public health microbiology.     

国外防治重症急性呼吸综合征的措施概况

目前重症急性呼吸综合征(SARS)的疫情仍处于不稳定状态,并且呈现蔓延之势,引起了全球的重视。为了有效地控制其流行,打赢与SARS的战争,包括中国在内的许多国家和地区纷纷采取了强有力的预防和治疗措施,竭尽全力将疫情控制在最小的范围之内。本通过介绍世界各国针对SARS所采取的行之有效的反应措施,了解一些相关情况及经验,以开阔我们的眼界,同时冀望对建立和完善我国在突发性传染病、环境灾难以及生化恐怖袭击等公共危机突发事件的应急反应机制上提供借鉴意义。     

Bayesian analysis of severe acute respiratory syndrome: the 2003 Hong Kong epidemic

This paper analyzes data arising from a Severe Acute Respiratory Syndrome (SARS) epidemic in Hong Kong in 2003 involving 1755 cases. A discrete time stochastic model that uses a back-projection approach is proposed. Markov Chain Monte Carlo (MCMC) methods are developed for estimation of model parameters. The algorithm is further extended to integrate numerically over unobserved variables of the model. Applying the method to SARS data from Hong Kong, a value of 3.88 with a posterior standard deviation of 0.09 was estimated for the basic reproduction number. An estimate of the transmission parameter at the beginning of the epidemic was also obtained as 0.149 with a posterior standard deviation of 0.003. A reduction in the transmission parameter during the course of the epidemic forced the effective reproduction number to cross the threshold value of one, seven days after control interventions were introduced. At the end of the epidemic, the effective reproduction number was as low as 0.001 suggesting that the epidemic was brought under control by the intervention measures introduced.