David Muscatello is an Associate Professor in infectious diseases epidemiology. He has a PhD in the epidemiology of pandemic and seasonal influenza. He also has many years' experience in government as an epidemiologist specialising in acute disease surveillance using administrative databases, public health intelligence and biostatistics including time series analysis. He served in the New South Wales government response to both the COVID-19 and 2009 influenza pandemics and has served on the...
David Muscatello is an Associate Professor in infectious diseases epidemiology. He has a PhD in the epidemiology of pandemic and seasonal influenza. He also has many years' experience in government as an epidemiologist specialising in acute disease surveillance using administrative databases, public health intelligence and biostatistics including time series analysis. He served in the New South Wales government response to both the COVID-19 and 2009 influenza pandemics and has served on the Australian National Influenza Surveillance Committee. David is also a graduate of the New South Wales Public Health Officer Training Program and has supervised and trained numerous Public Health Officer and Biostatistical trainees. He is particularly interested in the use of time series analysis for estimating mortality and morbidity from infectious and other diseases and for assessing the impact of health policies on populations. He contributes to the World Health Organization's (WHO) activities for estimating the global burden of deaths and hospitalisations attributable to influenza.
- Publications
- Grants
- Research Activities
- Teaching and Supervision
2021-2025 NHMRC Investigator Grant (EL1)
The PEARL project
With ever-increasing availability of electronic health and related records, there is a need to unify previously independent sources of information for infectious disease surveillance. This project aims to investigate the benefits of integrating multiple sources of information for surveillance of important epidemic respiratory infections, COVID-19 and influenza.
In 2022, the epidemiological landscape of respiratory infections is highly unusual in the recent history of humanity. It is characterised by:
- A pandemic of SARS-CoV-2 virus that has killed at least 6 million. It continues to cause major social disruption and economic cost after 2 years.
- Virtual disappearance globally of influenza infections, and now a sharp return.
- Decline in incidence of other common respiratory infections associated with stay at home orders and varying and unusual epidemic rebound following loosening of restrictions.
The PEARL (pandemic and epidemic risk assessment using linked data) database contains the probabilistically linked short-term (28 day) health outcome records for a cohort of persons presenting to emergency departments in New South Wales (NSW), Australia, population >8,000,000. The cohort consists of people with a respiratory infection or respiratory infection-like illness, that could be caused by COVID-19 or pandemic or seasonal influenza. Linkage is with notified confirmed COVID-19 and influenza infections, ambulance despatch and electronic medical records, hospital admission and death records. Intensive care admission status is included.
This 5 year (from 2021) National Health and Medical Research Council-funded research project will establish and use the PEARL database as a research asset. The database can be used to emulate real-time public health surveillance of persons presenting to NSW emergency departments with fever, unspecified infections, symptoms of respiratory infections and some specific respiratory infections. Through record linkage, we aim to demonstrate how future surveillance systems using integrated, electronic medical and other records can provide a much richer and more useful source of surveillance information and epidemic risk assessment.
Objectives
1. Create the PEARL database as an epidemic research resource.
2. Using the PEARL database identify early epidemic severity characteristics for severe influenza and COVID-19 epidemics in NSW.
3. To develop an analytic approach for locally-specific epidemic risk assessments using temporospatial statistical methods.
4. Using the PEARL database, demonstrate how linked data can be used to estimate the added burden on health services and deaths due to both known and unknown influenza and COVID-19 infections during epidemic periods.
Linked data Inclusions
|
Dataset |
Period |
Linkage inclusions |
|
NSW Emergency Department Data Collection |
Jan 2005 – Dec 2024 |
Primary cohort of persons presenting with respiratory infection or infection-like illness |
|
NSW Ambulance despatch and eMR |
Jan 2009 – Dec 2024 |
Ambulance 000 call date on the same or previous day of the arrival date of the EDDC presentation |
|
NSW Admitted Patient Data Collection |
Jan 2005 – Dec 2024 |
Admission Date within 0 to 28 days, inclusive, of the arrival date of the EDDC presentation |
|
NSW Notifiable Conditions Information Management System – Influenza and COVID-19 |
Jan 2005 – Dec 2024 |
Calculated_onset_date within -28 to +28 days, inclusive, of the EDDC presentation |
|
ABS Cause of death unit record file and NSW death registrations |
Jan 2005 – Dec 2024 |
Date of death within 0 to 28 days, inclusive, of the arrival date of the EDDC presentation |
-
Dataset
-
NSW Emergency Department Data Collection
-
Period
-
Jan 2005 – Dec 2024
-
Linkage inclusions
-
Primary cohort of persons presenting with respiratory infection or infection-like illness
-
-
Dataset
-
NSW Ambulance despatch and eMR
-
Period
-
Jan 2009 – Dec 2024
-
Linkage inclusions
-
Ambulance 000 call date on the same or previous day of the arrival date of the EDDC presentation
-
-
Dataset
-
NSW Admitted Patient Data Collection
-
Period
-
Jan 2005 – Dec 2024
-
Linkage inclusions
-
Admission Date within 0 to 28 days, inclusive, of the arrival date of the EDDC presentation
-
-
Dataset
-
NSW Notifiable Conditions Information Management System – Influenza and COVID-19
-
Period
-
Jan 2005 – Dec 2024
-
Linkage inclusions
-
Calculated_onset_date within -28 to +28 days, inclusive, of the EDDC presentation
-
-
Dataset
-
ABS Cause of death unit record file and NSW death registrations
-
Period
-
Jan 2005 – Dec 2024
-
Linkage inclusions
-
Date of death within 0 to 28 days, inclusive, of the arrival date of the EDDC presentation
-
My Research Supervision
My Teaching
Outbreak investigation PHCM9731, Bioterrorism and Health Intelligence PHCM9789, Internships PHCM9143