Outbreaks of respiratory infections – such as influenza (flu) – are a serious concern in hospital wards and clinics. Patients in these settings are particularly vulnerable to flu if their immune function is compromised (for example due to chemotherapy for cancer) and are at greater risk of severe symptoms and complications including pneumonia and death. However, management strategies for flu have both human and financial cost implications. Moving affected patients into separate rooms and closing wards may lead to bed shortages and delays to planned treatment, but failure to isolate patients with flu may facilitate further spread of infection. Accurate and timely identification of outbreaks is integral for optimising approaches to balance infection control and continuation of usual care. However, identification of outbreaks is complicated by several factors, which may lead to ineffective decision-making.
Missing an outbreak
- Many people who are infected with flu do not have characteristic symptoms such as fever, are therefore unlikely to be tested, which increases opportunities for transmission between patients, visitors and healthcare workers
- Tansmission between patients and visitors or healthcare workers may lead to the spread of flu cases around different parts of the hospital, and these cases may be incorrectly assumed to be unrelated
Incorrectly identifying an outbreak
- Flu cases have a strong seasonal pattern, meaning that they tend to cluster in time; regardless of whether or not the cases are actually related
- Current models of healthcare place together patients on specialist wards (such as oncology), who have raised risk of infection. Clusters of cases on these wards may therefore reflect both a concentration of sporadic (unrelated) cases and true outbreaks
Improved management of hospital outbreaks of flu therefore requires methods to distinguish between related and unrelated cases of infections.
ICONIC (infection response through virus genomics) is a Department of Health- and Wellcome Trust-funded project to assess the feasibility of a new approach for detecting infectious disease, such as flu.
We can use next generation sequencing (NGS) to determine the entire genome of the virus causing flu in less than a day, which offers ground-breaking opportunities for informed management of flu outbreaks. We have integrated full NGS genome data with routinely recorded hospital admissions data, which describe a patient’s journey through different hospital wards during an inpatient stay. This powerful combination uses NSG genomic data to identify highly similar clusters of cases and hospital admissions data to see how, where and when infected patients might have come into contact with each other. This method allows us to identify links between cases that would otherwise have been missed, target infection control processes to specific wards and determine whether sudden surges in case numbers reflect several smaller clusters of infection or a single uncontrolled outbreak. Hospital teams across the UK are piloting these methods and evaluating how hospital flu can be better managed to improve patient outcomes and save costs.