Implementing the World Health Organization Pandemic Influenza Severity Assessment framework-Singapore's experience

De-escalation strategies for non-pharmaceutical interventions following infectious disease outbreaks: a rapid review and a proposed dynamic de-escalation framework

BACKGROUND

The severity of COVID-19, as well as the speed and scale of its spread, has posed a global challenge. Countries around the world have implemented stringent non-pharmaceutical interventions (NPI) to control transmission and prevent health systems from being overwhelmed. These NPI have had profound negative social and economic impacts. With the timeline to worldwide vaccine roll-out being uncertain, governments need to consider to what extent they need to implement and how to de-escalate these NPI. This rapid review collates de-escalation criteria reported in the literature to provide a guide to criteria that could be used as part of de-escalation strategies globally.

METHODS

We reviewed literature published since 2000 relating to pandemics and infectious disease outbreaks. The searches included Embase.com (includes Embase and Medline), LitCovid, grey literature searching, reference harvesting and citation tracking. Over 1,700 documents were reviewed, with 39 documents reporting de-escalation criteria included in the final analysis. Concepts retrieved through a thematic analysis of the included documents were interlinked to build a conceptual dynamic de-escalation framework.

RESULTS

We identified 52 de-escalation criteria, the most common of which were clustered under surveillance (cited by 43 documents, 10 criteria e.g. ability to actively monitor confirmed cases and contact tracing), health system capacity (cited by 30 documents, 11 criteria, e.g. ability to treat all patients within normal capacity) and epidemiology (cited by 28 documents, 7 criteria, e.g. number or changes in case numbers). De-escalation is a gradual and bi-directional process, and resurgence of infections or emergence of variants of concerns can lead to partial or full re-escalation(s) of response and control measures in place. Hence, it is crucial to rely on a robust public health surveillance system.

CONCLUSIONS

This rapid review focusing on de-escalation within the context of COVID-19 provides a conceptual framework and a guide to criteria that countries can use to formulate de-escalation plans.

A novel data-driven methodology for influenza outbreak detection and prediction

Influenza is an infectious disease that leads to an estimated 5 million cases of severe illness and 650,000 respiratory deaths worldwide each year. The early detection and prediction of influenza outbreaks are crucial for efficient resource planning to save patient’s lives and healthcare costs. We propose a new data-driven methodology for influenza outbreak detection and prediction at very local levels. A doctor’s diagnostic dataset of influenza-like illness from more than 3000 clinics in Malaysia is used in this study because these diagnostic data are reliable and can be captured promptly. A new region index (RI) of the influenza outbreak is proposed based on the diagnostic dataset. By analysing the anomalies in the weekly RI value, potential outbreaks are identified using statistical methods. An ensemble learning method is developed to predict potential influenza outbreaks. Cross-validation is conducted to optimize the hyperparameters of the ensemble model. A testing data set is used to provide an unbiased evaluation of the model. The proposed methodology is shown to be sensitive and accurate at influenza outbreak prediction, with average of 75% recall, 74% precision, and 83% accuracy scores across five regions in Malaysia. The results are also validated by Google Flu Trends data, news reports, and surveillance data released by World Health Organization.

Implementing the World Health Organization Pandemic Influenza Severity Assessment framework-Singapore's experience

BACKGROUND

We report our experience in evaluating the severity of local influenza epidemics using the World Health Organization Pandemic Influenza Severity Assessment framework.

METHODS

We assessed the severity of influenza by monitoring indicators of influenza transmissibility, seriousness of disease and impact on healthcare resource utilisation. Indicators were described by various parameters collected weekly from eight government hospitals, 20 government and 30 private primary care clinics, and the national public health laboratory. Transmissibility and seriousness of disease indicators were each represented by multiple parameters, and alert thresholds were set at the 70th and 90th percentile of a parameter's past 2-year surveillance data. We derived a collective measure for each indicator using the average percentile rank of the related parameters. Alert thresholds for the single impact parameter were set at predefined values and evaluated for its sensitivity, specificity and positive predictive value.

RESULTS

For the transmissibility and seriousness of disease parameters, calculation of the percentile rank was simple and independent of a parameter's underlying distribution. For the impact parameter, predefined alert thresholds had high sensitivity and specificity (>80%) but low positive predictive value (15%-30%). Assessment scales were used to qualitatively classify the activity of an indicator as low, moderate or high together with a confidence level.

CONCLUSION

We applied different methods for threshold setting depending on the attributes of each parameter and indicator. For indicators represented by multiple parameters, an aggregated assessment of the indicator's level of activity and confidence level of the assessment was needed for effective reporting.