Influenza

Influenza infection is caused by the influenza virus, a single-stranded RNA virus belonging to the Orthomyxoviridae family. Influenza viruses are classified as types A, B and C. Influenza A and B viruses can cause epidemic disease in humans and type C viruses usually cause a mild, cold-like illness. The influenza virus spreads rapidly around the world in seasonal epidemics, resulting in significant morbidity and mortality. On the 10th of July 2017, a case of confirmed Influenza A/H1N1 was reported through the immediate disease notification system from a private hospital in the Hhohho region. A 49 year old female was diagnosed of Influenza A/H1N1 after presenting with flu-like symptoms. Contacts of the index case were followed and further positive cases were identified.

Objective:

To establish morbidity patterns of influenza A/H1N1 in Swaziland from 10th July to 15th August 2017.

Public health agencies worldwide all enjoy the same mission—providing healthcare warnings, guidance, and support to the public and healthcare professionals they represent. A critical element in achieving this mission is accessing timely and comprehensive surveillance information about disease in their regions of responsibility. Advances in diagnostic technologies for infectious disease and in the wireless conveyance of information hold great promise for advancing the quality of surveillance information and in facilitating the delivery of timely, accurate, and impactful public health information. Quidel Corporation has developed a cloud–based, wireless communications system that is fully integrated with its Sofia fluorescence immunoassay (FIA) platform for rapid, point-of-care diagnosis of infectious disease. The system, called the Virena Global Wireless Surveillance System (hereinafter, Virena) provides test results to public health organizations and other appropriate entities in near-real time. Currently, more than 4,000 Sofia instruments are transmitting results automatically by Virena. This presentation describes the use of Virena in surveilling influenza in the U.S. in the 2016-2017 influenza season, when over 700,000 influenza-like-illness (ILI) patient results were transmitted. The methods employed, results, and the promise of this innovative system will be discussed.

Objective:

Demonstrate performance of the Virena Global Wireless Surveillance System, an automated platform utilized in conjunction with the Sofia FIA Analyzer, for near real-time transmission of infectious disease test results to public health and other healthcare organizations.

Transmission and amplification of influenza within schools has been purported as a driving mechanism for subsequent outbreaks in surrounding communities. However, the number of studies assessing the utility of monitoring school absenteeism as an indicator of influenza in the community is limited. ORCHARDS was initiated to evaluate the relationships between all-cause (a-Tot), illness-related (a-I), and influenza-like illness (ILI)-related absenteeism (a-ILI) within a school district and medically attended influenza A or B visits within the same community.

Objective:

The Oregon Child Absenteeism due to Respiratory Disease Study (ORCHARDS) was implemented to assess the relationships between cause-specific absenteeism within a school district and medically attended influenza visits within the same community.

Influenza is one of the significant causes of morbidity and mortality globally. Previous studies have demonstrated the benefit of laboratory surveillance and its capability to accurately detect influenza outbreaks earlier than syndromic surveillance.1-3 Current laboratory surveillance has an approximately 4-week lag due to laboratory test turn-around time, data collection and data analysis. As part of strengthening influenza virus surveillance in response to the 2009 influenza A (H1N1) pandemic, the real-time laboratory-based influenza surveillance system, the Bangkok Dusit Medical Services Surveillance System (BDMS-SS), was developed in 2010 by the Bangkok Health Research Center (BHRC). The primary objective of the BDMS-SS is to alert relevant stakeholders on the incidence trends of the influenza virus. Type-specific results along with patient demographic and geographic information were available to physicians and uploaded for public health awareness within 24 hours after patient nasopharyngeal swab was collected. This system advances early warning and supports better decision making during infectious disease events.2 The BDMS-SS operates all year round collecting results of all routinely tested respiratory clinical samples from participating hospitals from the largest group of private hospitals in Thailand.

Objective:

We describe the Bangkok Dusit Medical Services Surveillance System (BDMS-SS) and use of surveillance efforts for influenza as an example of surveillance capability in near real-time among a network of 20 hospitals in the Bangkok Dusit Medical Services group (BDMS).

Respiratory pathogens continue to present an ever increasing threat to public health. Influenza, Respiratory syncytial virus, human metapneumovirus and other respiratory viruses are major etiological agents for influenza like illnesses (ILI). Establishment of viral causes of ILI is critical for prevention and mitigation strategies to disease threats. Makerere University Walter Reed Project (MUWRP) together with the Ugandan Ministry of Health and partners undertook surveillance to determine viral causes of influenza-like illness in Uganda.

Objective:

To determine viral causes of influenza-like illness in Uganda.

Because the dynamics and severity of influenza in the US vary each season, yearly estimates of disease burden in the population are essential to evaluate interventions and allocate resources. The CDC uses data from a national health-care based surveillance system and mathematical models to estimate the overall burden of disease in the general population. Over the past decade, crowd-sourced syndromic surveillance systems have emerged as a digital data source that collects health-related information in near real-time. These systems complement traditional surveillance systems by capturing individuals who do not seek medical care and allowing for a longitudinal view of illness burden. However, because not all participants report every week and participants are more likely to report when ill, the number of weekly reports is temporally and spatially inconsistent and the estimates of disease burden and incidence may be biased. In this study, we use data from Flu Near You (FNY), a participatory surveillance system based in the US and Canada1, to estimate and compare Influenza-like Illness (ILI) ARs using different approaches to adjust for reporting biases in participatory surveillance data.

Objective:

To estimate and compare influenza attack rates (AR) in the United States (US) using different approaches to adjust for reporting biases in participatory syndromic surveillance data.

Pneumonia, an infection of the lung due to bacterial, viral or fungal pathogens, is a significant cause of morbidity and mortality worldwide. In the past few decades, the threat of emerging pathogens presenting as pneumonia, such as Severe Acute Respiratory Syndrome, avian influenza A(H5N1) and A(H7N9), and Middle East Respiratory Syndrome coronavirus has emphasised the importance of the surveillance of pneumonia and other severe respiratory infections. An unexpected increase in the number of hospital admissions for pneumonia or severe respiratory infections could be a signal of a change in the virulence of the influenza viruses or other respiratory pathogens circulating in the community, or an alert of an emerging pathogen which warrants further public health investigation. The purpose of this study was to develop a forecasting model to prospectively forecast the number of emergency department (ED) admissions due to pneumonia in Singapore, a tropical country. We hypothesise that there is complementary information between hospital-based and community-based surveillance systems. The clinical spectrum of many respiratory pathogens causing pneumonia ranges from asymptomatic or subclinical infection to severe or fatal pneumonia, and it is usually difficult to distinguish between the different pathogens in the absence of a laboratory test. Infected persons could present with varying degrees of severity of the infection, and seek treatment at different healthcare facilities. Hospital-based surveillance captures the more severe manifestation of the infection while community-based surveillance captures the less severe manifestation of the infection and enables earlier detection of the infection. Thus, the integration of information from the two surveillance systems should improve the prospective forecasting of ED admissions due to pneumonia. We also investigate if the inclusion of influenza data from the laboratory surveillance system would improve the forecasting model, since influenza circulates all-year round in Singapore and is a common aetiology for pneumonia.

Objective:

To develop a forecasting model for weekly emergency department admissions due to pneumonia using information from hospital-based, community-based and laboratory-based surveillance systems.