Modeling influenza seasonality in the tropics and subtropics

Abstract

Climate drivers such as humidity and temperature may play a key role in influenza seasonal transmission dynamics. Such a relationship has been well defined for temperate regions. However, to date no models capable of capturing the diverse seasonal pattern in tropical and subtropical climates exist. In addition, multiple influenza viruses could cocirculate and shape epidemic dynamics. Here we construct seven mechanistic epidemic models to test the effect of two major climate drivers (humidity and temperature) and multi-strain co-circulation on influenza transmission in Hong Kong, an influenza epidemic center located in the subtropics. Based on model fit to long-term influenza surveillance data from 1998 to 2018, we found that a simple model incorporating the effect of both humidity and temperature best recreated the influenza epidemic patterns observed in Hong Kong. The model quantifies a bimodal effect of absolute humidity on influenza transmission where both low and very high humidity levels facilitate transmission quadratically; the model also quantifies the monotonic but nonlinear relationship with temperature. In addition, model results suggest that, at the population level, a shorter immunity period can approximate the co-circulation of influenza virus (sub)types. The basic reproductive number R0 estimated by the best-fit model is also consistent with laboratory influenza survival and transmission studies under various combinations of humidity and temperature levels. Overall, our study has developed a simple mechanistic model capable of quantifying the impact of climate drivers on influenza transmission in (sub)tropical regions. This model can be applied to improve influenza forecasting in the (sub)tropics in the future.

Fig 1. Influenza epidemics observed in Hong Kong during 1998–2018 and the corresponding mean daily temperature and AH.

Upper panel: stacked barplot of the weekly ILI+ incidence time series. Segments of the bar represent different virus (sub)types circulating during the week. Lower panel: daily mean temperature and specific humidity (a measure of AH) observed during 1998–2018.

Fig 2. Model performance.

Boxes and whiskers show the median (thick horizontal lines), interquartile range and 95% CI of RMSE (1^st row), average RMSE (2^nd row), correlation (3^rd row) and average correlation (4^th row) of the top 1000 parameter combinations for each model, during the training (red) and testing (green) period, separately.

Fig 3

Top 10 model fits for three climate forcing models: AH/T (A), AH/T/Vary (B), and AH/T/Strain (C). Black crosses show observed ILI+; the colored lines run through the crosses are the top 10 model estimates. The vertical dash line indicates a pandemic (2009). The shaded region represents testing years (2013–2018), while the rest are the training years.

Fig 4. Estimated relationship between influenza transmission with AH and temperature.

We use the basic reproductive number (R₀) to represent the level of influenza transmission. Each point shows the estimated R₀ at different specific humidity, a measure of AH, (and temperature if included) calculated per the AH/T model (left) or the AH model (right) using the top 10 parameter combinations for the corresponding model. For the AH/T model (left panel), the color of the point shows the concurrent temperature included in the model to moderate the relationship between R₀ and specific humidity.

Fig 5. Comparison of the reproductive numbers estimated by the AH/T model with laboratory observed virus survival rate and transmission rate in guinea pigs.

Left panel plots the viral survival rate (A) and transmission rate (C) against R₀ calculated using Eq 3 and best-fit parameters for the AH/T model. Right panel plots the viral survival rate (B) and transmission rate (D) against R_e, where R_e is calculated as R₀ multiplied by the estimated mean population susceptibility during the study period. The grey vertical line indicates where R_e = 1. The viral survival data came from Harper 1961 [35] and transmission rate data came from Lowen et al. 2007 and 2008 [39,40].