Predicting COVID-19 transmission across cities: Insights from inter-city dynamics and temperature effects in Sydney, Melbourne, and Brisbane.

Objectives: This study explores the role of public health interventions and temperature variability in driving COVID-19 transmission across Sydney, Melbourne, and Brisbane, and predicting outbreaks in one city based on trends in another.

Study Design: Ecological time-series study design.

Methods: Daily COVID-19 case notifications, temperature and public health intervention data were collected for Sydney, Melbourne, and Brisbane cities. Time series cross-correlation (TSCC) analysis was used to explore temporal patterns and trends between cities. Seasonal decomposition analysis was used to explore trends and seasonality of the time series, followed by multivariable regression to explore the inter-city relationship while accounting for seasonality and trends.

Results: TSCC analysis revealed a significant positive correlation between COVID-19 cases in Sydney and Melbourne (r = 0.886, p < .0001) with a 5-day lag, Sydney and Brisbane with a 7-day lag (r = 0.945, p < .0001) and between Melbourne and Brisbane up to 5 days lag, with the strongest correlation occurring at lag 0 (r = 0.912, p < .0001). Using seasonally adjusted values, we found that a one-unit increase in Sydney's seasonally adjusted cases at 7 days was associated with 0.38 (95 % CI: 0.37-0.39) increase in Brisbane cases (R = 0.901, p < .0001), and one-unit increase in Sydney adjusted cases at lag 5 (R = 0.799, p < .0001), Melbourne cases increased by 0.95 (95 % CI: 0.92-0.99).

Conclusions: These findings underscore the potential of using inter-city associations to develop early warning systems, enhancing the timeliness and effectiveness of public health responses. Incorporating weather factors and intervention measures such as economic support and containment policies can further refine predictive models, offering valuable tools for managing future pandemics.