Multi-pathogen situational assessment and forecasting of respiratory disease in Aotearoa New Zealand

TL;DR

This paper presents two real-time models for assessing and forecasting three respiratory pathogens during New Zealand's 2025 winter, aiding public health responses with weekly epidemic trend estimates and forecasts.

Contribution

Introduction of two models for real-time assessment and forecasting of multiple respiratory pathogens in New Zealand's winter season, integrated into a public health reporting system.

Findings

Models provided reasonable real-time epidemic trend estimates.

Forecasts of case incidence were generally accurate.

Identified areas for model improvement in future assessments.

Abstract

Real-time analysis of epidemic trends and forecasts can help support public health planning and the response to seasonal respiratory disease. Here, we present two models that were used in a 2025 New Zealand winter situational assessment programme for three respiratory pathogens: SARS-CoV-2, influenza and respiratory syncytial virus (RSV). These models were run weekly from May to October 2025 on real-time disease surveillance data and provided a quantitative representation of the current epidemic trend, along with estimates of the epidemic growth rate and 28-day ahead forecasts of case incidence. Model results and interpretation were provided in weekly reports to public health partners as part of a trans-Tasman winter programme run by the Australia--Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA). We compare in-season results that were included in these reports to a retrospective analysis of the complete data for the season. We conclude that real-time analyses performed reasonably well, and identify some areas for improvement in future winter situational assessment programmes.