A nowcasting framework for correcting for reporting delays in malaria surveillance.

Area of Science:

• Epidemiology
• Public Health
• Infectious Disease Surveillance

Background:

• Reporting delays to national surveillance systems hinder infectious disease control, impacting decision-making and resource allocation.
• Malaria surveillance is particularly challenged by reporting lags in rural, remote, and poorly connected regions, such as those in Guyana.
• Effective infectious disease control requires accurate and timely data, which is often compromised by reporting delays.

Purpose of the Study:

• To analyze 13 years of malaria surveillance data in Guyana to identify factors associated with reporting time lags.
• To develop and validate nowcasting methods for estimating malaria cases that occurred but were not yet reported.
• To enhance the accuracy of malaria case estimates for improved public health interventions.

Main Methods:

• Analysis of 13 years of historical malaria surveillance data from Guyana.
• Development of nowcasting models utilizing historical reporting delay patterns.
• Retrospective assessment of model performance using data available at the time of estimation.

Main Results:

• Identification of key correlates contributing to time lags in malaria case reporting.
• Nowcasting methods demonstrated substantial improvements in malaria case estimation accuracy.
• The best performing models achieved up to two-fold error reduction in estimating malaria cases in specific regions.

Conclusions:

• Nowcasting methods can effectively estimate unreported malaria cases by leveraging historical reporting delay patterns.
• This approach offers a generalizable tool to enhance infectious disease surveillance in endemic countries.
• The developed methods are being implemented to guide malaria elimination and resource allocation strategies in Guyana.