A combination of annual and nonannual forces drive respiratory disease in the tropics

Abstract

Introduction: It is well known that influenza and other respiratory viruses are wintertime-seasonal in temperate regions. However, respiratory disease seasonality in the tropics is less well understood. In this study, we aimed to characterise the seasonality of influenza-like illness (ILI) and influenza virus in Ho Chi Minh City, Vietnam.

Methods: We monitored the daily number of ILI patients in 89 outpatient clinics from January 2010 to December 2019. We collected nasal swabs and tested for influenza from a subset of clinics from May 2012 to December 2019. We used spectral analysis to describe the periodic signals in the system. We evaluated the contribution of these periodic signals to predicting ILI and influenza patterns through lognormal and gamma hurdle models.

Results: During 10 years of community surveillance, 66 799 ILI reports were collected covering 2.9 million patient visits; 2604 nasal swabs were collected, 559 of which were PCR-positive for influenza virus. Both annual and nonannual cycles were detected in the ILI time series, with the annual cycle showing 8.9% lower ILI activity (95% CI 8.8% to 9.0%) from February 24 to May 15. Nonannual cycles had substantial explanatory power for ILI trends (ΔAIC=183) compared with all annual covariates (ΔAIC=263) in lognormal regression. Near-annual signals were observed for PCR-confirmed influenza but were not consistent over time or across influenza (sub)types. The explanatory power of climate factors for ILI and influenza virus trends was weak.

Conclusion: Our study reveals a unique pattern of respiratory disease dynamics in a tropical setting influenced by both annual and nonannual drivers, with influenza dynamics showing near-annual periodicities. Timing of vaccination campaigns and hospital capacity planning may require a complex forecasting approach.

Keywords: epidemiology; public health; respiratory infections.

Figure 1

Daily ILI ζ- score (grey line) and 7-day smoothed ILI ζ- score (black line) from 2010-01-01 to 2019-12-31. The mean of ILI ζ- score in each year is shown as a blue horizontal dashed line.

Figure 2

Nonannual and annual cycles in ILI ζ- score. (A) Pearson autocorrelation function (ACF) of ILI ζ- score timeseries, split across different study periods. Horizontal dashed lines label the regions where ACF is significantly different from 0 (p < 0.05). Vertical dashed lines label the peak lag period of ACF between 150 to 450 days. Annual cycle is labeled with a black dot. Periods are inclusive so “2010-2015” spans six years. (B) The shift of ACF values from nonannual cycles to annual cycles. The x-axis denotes the last year included in the time series, and the y-axis shows the ACF value. ACF values of annual cycles (circles) and nonannual cycles (triangles) are shown. The nonannual cycle showed stronger signals prior to 2017.

Figure 3

Nonannual and annual cycles in ILI ζ- score. (A) The AIC of cyclic step functions for ILI ζ-score for different numbers of steps and different cycle lengths. AIC is lowest for a cycle of 210 days or 365 days. (B) Contribution of each predictor is calculated as the AIC difference when removing the predictor from the fitted lognormal model. “Temp” denotes temperature, “RF” denotes rainfall, and “AH” denotes absolute humidity. (C) Predicted values of ILI ζ-score (red) and 95% prediction intervals (orange) of the full model including both cycles; the observed ILI ζ-score is shown in grey.

Figure 4

ILI+ time series and periodic signals. (A) The 7-day smoothed overall ILI+ stacked by subtypes. (B) ACF plot of the entire time series of total ILI+. Vertical dashed line labels show ACF peaks at 358-day lag and the subsequent cycles. Black points show annual cycles. (C) The peak lag of ACF varied between 338 and 377 days when varying the length of the included ILI+ time series. (D) Discrete Fourier transform of the entire time series of overall ILI+. The black circle labels the dominant 324-day cycle.

Figure 5

Cycles in ILI+. (A) AIC heatmap for cycle length and the number of steps allowed in each cycle. Near-annual cycle lengths (330-day and 385-day) have the best fits. (B) AIC contribution of each predictor in a gamma hurdle model, measured as the AIC difference when removing the predictor from the final model. “Temp” denotes temperature, “RF” denotes rainfall, and “AH” denotes absolute humidity. (C) The predicted ILI+ (red) with the 95% prediction interval (orange) is shown with the observed ILI+ (grey).