Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters

Abstract

Background: Influenza transmission is often associated with climatic factors. As the epidemic pattern varies geographically, the roles of climatic factors may not be unique. Previous in vivo studies revealed the direct effect of winter-like humidity on air-borne influenza transmission that dominates in regions with temperate climate, while influenza in the tropics is more effectively transmitted through direct contact.

Methodology/principal findings: Using time series model, we analyzed the role of climatic factors on the epidemiology of influenza transmission in two regions characterized by warm climate: Hong Kong (China) and Maricopa County (Arizona, USA). These two regions have comparable temperature but distinctly different rainfall. Specifically we employed Autoregressive Integrated Moving Average (ARIMA) model along with climatic parameters as measured from ground stations and NASA satellites. Our studies showed that including the climatic variables as input series result in models with better performance than the univariate model where the influenza cases depend only on its past values and error signal. The best model for Hong Kong influenza was obtained when Land Surface Temperature (LST), rainfall and relative humidity were included as input series. Meanwhile for Maricopa County we found that including either maximum atmospheric pressure or mean air temperature gave the most improvement in the model performances.

Conclusions/significance: Our results showed that including the environmental variables generally increases the prediction capability. Therefore, for countries without advanced influenza surveillance systems, environmental variables can be used for estimating influenza transmission at present and in the near future.

Figure 1. Hong Kong weekly influenza positive isolates.

Figure 2. Hong Kong fitted and predicted values as separated by the dashed line.

SARIMAX(0,1,2)(1,0,0) with LST lag 2 and 5, accumulated rainfall lag 3 and relative humidity lag 1.

Figure 3. Hong Kong environmental variables.

(A) weekly average of Land Surface Temperature (LST), (B) weekly accumulated rainfall and (C) weekly average of mean relative humidity.

Figure 4. Maricopa county influenza cases.

Figure 5. Maricopa environmental variables.

(A) LST weekly average and Mean Temperature from ground station, (B) weekly average maximum pressure and (C) weekly average of minimum and maximum relative humidity.

Figure 6. Maricopa County fitted and predicted values–as separated by the dashed line–from (A) SARIMAX with T_mean lag 1 and 7, (B) SARIMAX with P_max lag 0 and 6.