Surveillance for emerging respiratory viruses (original) (raw)

Summary

Several new viral respiratory tract infectious diseases with epidemic potential that threaten global health security have emerged in the past 15 years. In 2003, WHO issued a worldwide alert for an unknown emerging illness, later named severe acute respiratory syndrome (SARS). The disease caused by a novel coronavirus (SARS-CoV) rapidly spread worldwide, causing more than 8000 cases and 800 deaths in more than 30 countries with a substantial economic impact. Since then, we have witnessed the emergence of several other viral respiratory pathogens including influenza viruses (avian influenza H5N1, H7N9, and H10N8; variant influenza A H3N2 virus), human adenovirus-14, and Middle East respiratory syndrome coronavirus (MERS-CoV). In response, various surveillance systems have been developed to monitor the emergence of respiratory-tract infections. These include systems based on identification of syndromes, web-based systems, systems that gather health data from health facilities (such as emergency departments and family doctors), and systems that rely on self-reporting by patients. More effective national, regional, and international surveillance systems are required to enable rapid identification of emerging respiratory epidemics, diseases with epidemic potential, their specific microbial cause, origin, mode of acquisition, and transmission dynamics.

This is the first in a Series of five papers on emerging respiratory tract infections

Introduction

The emergence of new human viral diseases affecting the respiratory tract continues to threaten global public health security. On March 12, 2003, WHO issued a global alert for an emerging and yet unknown illness that was subsequently known as severe acute respiratory syndrome (SARS) caused by a novel coronavirus (SARS-CoV).1 SARS-CoV caused more than 8000 cases and 800 deaths in over 30 countries with a substantial economic impact.2 Since then, several other viral respiratory pathogens (table 1)3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 have emerged including avian influenza (H5N1, H7N9, H10N8), variant influenza A H3N2 virus, human adenovirus-14, and Middle East respiratory syndrome coronavirus (MERS-CoV). Soon after the discovery of SARS, additional coronaviruses were also identified: coronavirus NL63 and coronavirus HKU1.3, 4

Table 1.

Emerging respiratory viruses

Key messages.

• •
  The emergence of several new viral respiratory tract infectious diseases with epidemic potential threatens global health security.
• •
  Emerging respiratory viruses include severe acute respiratory syndrome cornavirus (SARS-CoV), avian influenza H5N1, H7N9, and H10N8; variant influenza A H3N2 virus; human adenovirus-14; and Middle East respiratory syndrome- coronavirus (MERS-CoV).
• •
  Global surveillance systems for emerging and re-emerging respiratory viruses include active and passive surveillance systems.
• •
  Surveillance systems aim for rapid and early identification of these viruses with epidemic potential, their specific microbial cause, origin, mode of acquisition, and transmission dynamics so that effective intervention and control measures can be put in place.
• •
  Several surveillance systems are in place and include syndromic surveillance and web-based surveillance.
• •
  A good surveillance system would include the whole spectrum of disease presentation from mild to severe cases.
• •
  Future surveillance system should provide real-time early warnings by integrating clinical, laboratory, and automation of collection and dissemination of data.

Most influenza A epidemics occur in January, February, and March. However, outbreaks of influenza A Beijing/32/92 H3N2 in 1993 and Fujian/411/2002 H3N2 in 2003 happened in November and December.20 In an analysis of 335 emerging infectious diseases from 1940 to 2004, most (60%) were zoonoses and 25% were viruses, and the study showed an increase in events over time.21

In this Series paper, we review worldwide active surveillance systems for emerging and re-emerging respiratory viruses. We identify the rapid and early identification systems to allow early control measures to be put in place to prevent the spread of these pathogens. We also review the work of WHO Global Influenza Surveillance Network (GISN), Global Influenza Surveillance and Response System (GISRS), and the network of national influenza centres and laboratories.

Severe acute respiratory infection (SARI) is defined as fever of at least 100°F (37·8°C) or self-reported fever, and either a cough or a sore throat, and hospital admission.22 An influenza-like illness (ILI) is defined as acute illness with fever greater than 38°C, and cough or sore throat.22

Global surveillance

Surveillance of emerging and re-emerging respiratory viruses aims for rapid and early identification and control measures, thus preventing spread of pathogens. In 1947, WHO established its GISN, now known as the GISRS. The new name followed the adoption of the Pandemic Influenza Preparedness (PIP) framework in May 2011.23 GISRS is a network of national influenza centres and laboratories. These centres serve as laboratory-based surveillance system to monitor circulating influenza viruses and make annual recommendations on the composition of influenza vaccine for the northern and southern hemispheres. GISRS also detects as early as possible, characterises, and tracks any unusual influenza strains in human populations that could be of pandemic potential. Multiple national influenza centres (NICs) collect virus specimens in their country, do preliminary analysis, and ship representative clinical specimens and isolated viruses to WHO for advanced analysis.24 The network comprises six WHO Collaborating Centres, four WHO essential regulatory laboratories, and 141 institutions in 111 WHO member states.25 NICs are concentrated in Europe and the USA, with only a few centres in Africa, the Middle East, and parts of southeast Asia. As a result, there is an absence of knowledge about influenza epidemiology, burden of disease, and patterns of transmission in the tropics and subtropics. The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) is a worldwide initiative that involves the gathering of many networks and individuals involved in research related to the outbreaks of diseases such as avian influenza A H5N1, swine influenza A H1N1, and SARS.26 ISARIC is involved in the collaboration between different scientists to further increase our understanding of emerging respiratory diseases. ISARIC provides a collaborative platform through which worldwide, patient-oriented clinical studies can be developed, done, and disseminated, with shared protocols and a focus on clinical questions and clinical trial expertise.

Surveillance goals

The goals of surveillance are to monitor when the influenza season begins and ends, to characterise the types and subtypes of circulating strains, to monitor the clinical severity of illness, and to detect the emergence of any novel or reassortant viruses. This information also helps in selecting future vaccine strains. The surveillance also monitors the emergence of any viral resistance.27 The basic goals of influenza surveillance include description of the epidemiology of seasonal influenza and burden of disease, provision of isolates for identification of viruses and monitoring of resistance, and provision of country-specific data for programme planning and preparedness. After the re-emergence of highly-pathogenic influenza A H5N1 in 2004, another objective was to provide an early warning for outbreaks of novel influenza or agents of SARI in human beings or the circulation of a potentially new pandemic pathogen. The main aim of pandemic surveillance is the early recognition of the emergence of a novel virus so that control measures can be instituted. However, once a pandemic has begun, surveillance should switch to monitoring of the epidemiology, the characteristics of the virus, the effect of prevention and control measures, and the progression of the pandemic.28

Early warning signs for pandemic

One of the objectives of surveillance for SARI and ILI caused by influenza is the detection of early warning signs for the emergence of any novel influenza virus or respiratory virus with pandemic potential in human beings. The important functions of early warning surveillance systems are many. The early warning system is built to detect events with potential public health threat across international borders, to verify detected events, to assess the risk that an event will have global effect, to report such risk within 48 h of the event determination according to the International Health Regulations, and to work with WHO to establish any public health emergency of international concern.29 For early warning systems to work, specific triggers or signal criteria are needed for immediate reporting of possible occurrence of a single or multiple cases; such cases might be the initial indicators of the emergence of a novel respiratory virus with a pandemic potential such as H5N1 and Middle East respiratory syndrome coronavirus (MERS-CoV).

SARIs or pneumonia in health-care workers might serve as important signal events that the virus has acquired the ability to spread to human beings, as seen in the SARS epidemic. Examples of events that might signal human-to-human transmission of an emerging respiratory disease include clusters of SARI in people with social connections within a 2 week period, pneumonia in health-care workers, or people with a social or occupational connection. In addition, any change in the epidemiology of SARI cases, with a shift in the age distribution, an increase in mortality, or an increase in the number of cases, might signal the circulation of a new respiratory pathogen.30, 31

Monitoring for signals of increased activity

When the weekly influenza rates exceed the seasonal influenza threshold this increase would signal the start of a new influenza season.32 For signal detection, the model built should have autoregressive components, seasonal trends, other trends, and covariates to predict the number of cases beyond expected for a specific day. However, monitoring of ILI and SARI should take into account the trends in any existing data and not wait to have a definite signal of increased activity.

The adaptation of emerging respiratory viruses to human beings might occur suddenly with widespread infection or more gradually with infection of an at-risk population. The exposure of an at-risk population to a common source results in a spillover of viruses into people. Once these viruses have gained a sustained transmission from human-to-human with an R0 of 1 or more, in certain conditions an epidemic can result.33 Monitoring of the rate at which R0 increases serves as a marker for impending epidemic.34, 35, 36 For accurate estimates of R0, a detailed outbreak and contact investigation is required.

Early detection

For early detection of new and emerging respiratory viruses, it is important to establish a programme and systems to detect the first evidence of sustained human-to-human transmission of an emerging respiratory pathogen.37 The occurrence of clusters of SARI in a localised area, the occurrence of an increased mortality or a change in the affected age group, or high sales of specific therapies for upper respiratory tract infections can be evidence of new and emerging respiratory viruses. WHO, through a number of sentinel labs, monitors and coordinates the surveillance activities for any influenza outbreaks as indicated earlier.

Syndromic surveillance

Syndromic surveillance combines cases into syndromes rather than specific diagnoses.38 Such surveillance depends on the definitions of ILI and SARI that require clinical diagnoses but might not differentiate between different etiological causes. Syndromic surveillance uses data from emergency room visits,39 discharge diagnosis,40 ambulance dispatch data that successfully identified the expected annual epidemics of influenza,41 family doctor surveillance networks,42 or general population self-reporting networks.43, 44 The use of emergency-services-based surveillance is most sensitive for severe illness and for illnesses affecting elderly individuals.41, 45, 46 A systematic analysis of syndromic surveillance for influenza and ILI in emergency departments showed that various data, such as primary complaint, discharge diagnosis, and free text analysis of the entire medical record, were used.47 Surveillance of paediatric cases with ILI might also facilitate detection of outbreaks 1–4 weeks before the peak of the disease onset.48

The largest surveillance networks are the USA DiSTRIBuTE network (no longer active) and the European triple “S” system (Syndromic Surveillance Systems in Europe), and these two systems collected large-scale emergency-department-based influenza and ILI syndromic surveillance data.49, 50 Surveillance usually provides the fastest way to identify diseases and is an excellent approach to focus appropriate response measures to any outbreak.51, 52 Syndromic surveillance systems enable a rapid response to outbreak detection.53 The establishment of the Japanese non-governmental organisation Agency for Cooperation in International Health as a sentinel surveillance system for selected targets of infectious diseases in, Africa, Asia, and South America revealed unreported infectious diseases such as influenza.54 Syndromic surveillance helps to detect the occurrence of signals of ILI that warrant further investigation. In New York, a rise in the number of cases of respiratory syndrome and fever provided the earliest indication of the occurrence of community-wide influenza activity in 2001–02 seasons.51 An advantage of sentinel syndromic surveillance is the early detection of syndromes before laboratory confirmation.51 Syndromic surveillance could depend on the presence of specific symptoms of the ILI and SARI and could also depend on the chief or primary complaints of patients. The accuracy of chief complaint had a good agreement for the syndromes of respiratory infection in reference to discharge diagnosis.38, 51 Syndromic surveillance helped detect the 2009 pandemic influenza H1N1 outbreak in the USA55 and was used in emergency departments in Canada to predict circulating respiratory viral disease such as influenza and respiratory syncytial virus.56

One study compared the Geographic Utilization of Artificial Intelligence in Real-time for Disease Identification and Alert Notification (GUARDIAN) system with the Complaint Coder (CoCo) of the Real-time Outbreak Detection System (RODS), the Symptom Coder (SyCo) of RODS, and an electronic medical record (EMR) system.57 The study showed that the GUARDIAN surveillance system was more robust in performance compared with standard EMR-based reports and the RODS systems in detection of ILI.57 Emergency department discharge diagnoses increased surveillance validity for automated and drop-in syndromic surveillance.58

The advantages and disadvantages of syndromic surveillance of ILI were discussed by the Provincial Infectious Diseases Advisory Committee.59 The main disadvantages were that not all patients visit an emergency department as their first step towards treatment, free text entry of data reduces automation of data, and start-up costs are substantial.

Surveillance in emergency departments

Chief-complaint-based emergency department surveillance systems are being used for surveillance of influenza. During the 2009 H1N1 pandemic, influenza activity in emergency departments increased 2 weeks before it did in outpatient sentinel clinics.60 The use of physician diagnosis in emergency departments proved superior to chief-complaints surveillance in the same setting.61 However, such surveillance might be influenced by the staff's knowledge of what occurrs in communities.62 By contrast, another study showed that self-reporting by patients was better than chief-complaint surveillance for prediction of the diagnosis.63 In emergency departments, increased influenza activity could be assessed by triage nurses recording complaints by categories,64 syndromic analysis of patients' chief complaints,39 and patient-based free text grouped into diagnostic groups.65, 66 Similarly, syndromic surveillance is being used in the Hajj pilgrimage for detection of any outbreaks.67

Patients' chief-complaint and triage data proved to be a good indicator of respiratory complaints.38 Information on initial patients' complaint and triage data were used in a few surveillance programmes.68, 69, 70 A computerised triage log was effective in the identification of influenza outbreaks in the first week.39 Another method of surveillance relies on nurse help-line telephone calls.71 In a study from England and Wales, surveillance of influenza based on deaths, sickness-benefit claims (SBC), laboratory reports, and observations from general practitioners showed that general practitioners' statistics and respiratory deaths were the most helpful indices for description of both size and timing of the epidemics.72

Hospitalisation and laboratory surveillance of respiratory viruses

In addition to syndromic surveillance, laboratory-confirmed influenza hospitalisations and laboratory surveillance depend on identification of the specific cause of respiratory infection; they also rely on good laboratory support for the identification of the causative agent. This conventional disease surveillance that relies on passive reporting of confirmed cases might be slow and insensitive for rapid detection of large-scale infectious disease outbreaks.73 The goals of the laboratory surveillance are provision of information on geographic distribution and secular patterns of circulating viruses, monitoring of antigenic changes in the viruses for vaccine strain selection, monitoring of antiviral resistance, and detection of novel influenza subtypes of possible pandemic potential.

Surveillance of influenza through drug sales

There are mixed results from studies looking at sales of over-the-counter drugs as an indicator of influenza activity. The earliest assessment of this indicator of influenza activity dates back to 1979.74 An increase in sales of these drugs occurred 4 weeks after the first influenza B isolate and 1 week before peak influenza activity.74 Another study assessed the sale of non-prescription drugs for three consecutive winters 1998–99, 1999–2000, and 2000–01 and did not show any correlation with increased influenza activity nationally.75 Similarly, in a study from Japan, over-the-counter drug sales did not collate with real-time detection systems for influenza epidemics.76 In a study from New York, USA, ILI over-the-counter drug sales increased during influenza epidemics and during spring and fall allergy seasons, a finding that was similar to trends in emergency departments for fever and influenza syndrome.77 In two other studies from France and Slovenia, drug sales correlated with influenza activity.78, 79

Self-reporting participatory systems

New surveillance systems such as Influenzanet, Flu Near You, FluTracking, and Go Viral are a new frontier in the collection of population symptom data (table 2). Influenzanet monitors ILI on a voluntary basis with 35 180 volunteers from ten European countries including Belgium and the Netherlands (since 2003), Portugal (since 2005), Italy (since 2008), the UK (since 2009), Denmark, France, Ireland, Spain and Sweden. This network obtains information about ILI directly from volunteers from the different countries who enter data in a web-based interphase.44, 80, 81, 82

Table 2.

Worldwide networks of surveillance and their websites

Flu Near You is a website-based survey about symptoms of ILI that can be completed by anyone older than 13 years of age. The website is administered by Healthmap of Boston Children's Hospital in partnership with the American Public Health Association and the Skoll Global Threats Fund. In Australia, FluTracking is an online health surveillance system for the detection of influenza. In addition to reporting symptoms of influenza, specific websites also provide participants with kits including the sample collection materials so that participants can provide a nasal swab and saliva sample for influenza testing.

One of the challenges in the case of outbreaks is the high demand for specific supplies such as beds, storage areas, haemodynamic monitors, mechanical ventilators, and specialised personnel.83 An online cumulative-sum-based model named Overcrowd-Severe-Respiratory-Disease-Index was based on the Modified Overcrowd Index. The model simultaneously monitors and informs the demand of required supplies and personnel and generates early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables.83 BioDiaspora is an easy-to-use, customisable, intelligent web application that predicts the effect of infectious diseases worldwide by integration of data on outbreaks, human populations, animal and insect populations, environmental and climatic conditions, and commercial air travel. BioDiaspora has an easy-to-access, web-based, global information system solution that can generate and communicate intelligence about global infectious disease threats in real time and that integrates global epidemic intelligence from HealthMap.84

Informal surveillance and epidemic intelligence

Epidemic intelligence is a key component of modern surveillance of emerging infectious diseases. Epidemic intelligence is an ad-hoc detection and analysis of unstructured information available on the internet. This information relies on official and informal sources. Epidemic intelligence was developed in the 1990s after the development of the internet,85 and several systems exist (Table 2, Table 3).85, 86, 87, 88, 89, 90, 91, 92, 93 The Program for Monitoring Emerging Diseases (ProMED) mail is an internet-based reporting system designed for rapid distribution of information on infectious disease outbreaks. ProMED mail was started in August, 1994, to monitor emerging infectious diseases worldwide. ProMED mail provides early warning of outbreaks of emerging and re-emerging diseases. ProMED is an event-based, informal surveillance system where information is received from many official and unofficial sources such as WHO, health-care workers, ministries of health, lay public, the media, laboratories, and local health officials. On Feb 10, 2003, a request for information was posted on ProMED in relation to an epidemic in Guangzhou.95 This epidemic became known as SARS. On Sept 20, 2012, ProMED-mail reported the identification of a novel coronavirus (nCoV), later known as MERS-CoV, from a fatal case of severe respiratory illness with renal failure.5, 96, 97

Table 3.

Description of different surveillance system

A team of researchers, epidemiologists, and software developers at Boston Children's Hospital founded HealthMap in 2006. This web-based approach provides informal sources for disease outbreak monitoring and real-time surveillance of emerging public health threats. HealthMap is available as a website, and as a mobile app, Outbreaks Near Me, and both deliver real-time intelligence on a broad range of emerging infectious diseases for a diverse audience, including libraries, local health departments, governments, and international travellers.

The Global Public Health Intelligence Network (GPHIN) is a Canadian initiative that draws on the capacity of the internet and worldwide news coverage of health events.93 GPHIN creates an early warning of outbreaks by monitoring internet media, including news wires and websites, to detect and report disease outbreaks.94

Google Flu Trend is a web-based site that estimates ILI incidence on the basis of influenza-related queries made by millions of users around the world online in search for health data related to influenza.98 Use of Google Flu Trend in emergency departments predicted the 2009 H1N1 outbreak in Manitoba,99 other emergency rooms,100 and South Korea.101 Google Flu Trend results strongly correlated with ILI data from the USA,98, 102, 103 Australia,104 Canada,99 and China105 and Google Flu Trend was the only external information system to provided the most accurate influenza predictions with different prediction models.106 Google Flu Trend results were less reliable during the 2009 influenza H1N1 pandemic in many countries including New Zealand, Singapore, and the USA.107, 108, 109 Such inconsistency might result from a change in internet search behaviour and the change in age-related internet use.110, 111, 112 Google Flu Trend might not provide reliable surveillance for seasonal or pandemic influenza, and the result obtained from this surveillance method should be interpreted with caution.113 Google Flu Trend also performed poorly compared with laboratory-confirmed influenza.114 The correlation of Google Flu Trend with influenza incidence was most profound in European countries where the internet is most frequently used for health-related searching.115

Influenza in Africa

The exact epidemiology of ILI and SARI is not well known in Africa and the Middle East. In a study from several countries, from Madagascar to Senegal, the epidemiology and virology of influenza viruses showed variation in relation to spatiotemporal circulation of the different virus types, subtypes, and strains.116 In 2008, the sentinel surveillance system in Madagascar showed that of 26 669 fever cases, 11·1% were ILI.117 The availability of seasonal influenza vaccine in Africa was reported to be 45% of 31 countries who responded to the questionnaire sent by the investigators in one study, and that vaccine coverage data were available for four of 14 countries that reported availability of seasonal influenza vaccine.118 The importance of having laboratory influenza virus surveillance was highlighted in a study from west Africa where genetic sequencing of 2009 pandemic influenza A H1N1 viruses during 2009–13 showed persistence of two viral lineages.119

Challenges for emerging respiratory viruses surveillance

The challenges for the surveillance of any emerging respiratory viruses, especially at the beginning of any outbreak, are the difficulties in the identification of the causative agent and the large number of samples received. Ideally, routine cultures might provide the answer for any emerging virus identification; such techniques would require additional safety measures. Comprehensive multiplexed PCR reactions might help in the identification of various agents without the use of biosafety level 3 laboratories.120 The combined use of culture, rapid antigen detection assays, and molecular assays are often effective.121, 122 The use of a combination of these techniques will decrease the number of samples from patients being tested at one time.123

Further improvement of surveillance systems to cover diverse areas of the world including developed and developing countries is clearly needed. Such an objective could be accomplished by capacity building. The experience in Laos is an excellent example.124 There was a clear coordination and collaboration between multisector interests such as human and animal health, the Government of Laos, and the international partner community through the Lao National Avian and Human Influenza Coordinating Office (NAHICO) resulting in the translation of experience into practical steps to deal with emerging viral infections.124 The collateral impact of the influenza investment in advance of overall public health capacity in Laos has been pronounced, and this could also happen to other resource-limited countries. Real-time data should be displayed on the internet to allow immediate access. The immediate availability of data would help health-care policy makers in the preparation for any epidemics.

Search strategy and selection criteria

We did a literature search of the electronic database PubMed and Google Scholar (1980–2014) with the following search terms: “surveillance AND influenza”, “influenza”, “influenza-like illness”, “surveillance” “internet-based surveillance” “syndromic surveillance”, “respiratory” “viruses” and “emerging”. We complemented the search with publications from WHO, Centre for Disease Control and Prevention, and Google Scholar. We also reviewed studies cited by articles identified in this search. We included only studies in human beings and in the English language.

Acknowledgments

Acknowledgments

AZ is supported by the UK European Union FW7 Rid-RTI programme grant; European Developing Countries Clinical trials Partnership (EDCTP) TB NEAT, PANACEA and REMox grants; UBS Optimus Foundation, Switzerland; and NIHR Biomedical Research Centre, University College London Hospitals, London, UK.

Contributors

ZAM and AZ initiated The Lancet Infectious Diseases respiratory tract infections series. ZAM, AZ, DH, and JAA-T developed the series articles outlines and assigned lead authors. ZAM and JAA-T coordinated the writing of this Series paper and wrote the draft outline, subsequent and final drafts of the manuscript. All authors contributed relevant text and tables on their expert sections and contributed to finalisation of the manuscript.

Declaration of interests

All authors declare no conflicts of interest.

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