Modeling to Predict Cases of Hantavirus Pulmonary Syndrome in Chile

Author Summary Hantavirus pulmonary syndrome (HPS) is a severe disease present in Chile, Argentina and other countries in the Americas. Mortality rates for HPS can be as high as 60% for some outbreaks. Although hantavirus outbreaks tend to be small, the high death rate, unavailability of a vaccine, and occurrence of infections in rural regions where individuals are least likely to have appropriate healthcare make HPS forecasting an important public health issue in Chile and other countries. We present an approach for modeling and forecasting confirmed HPS cases in Chile. Seasonal time series models that predict future cases based on previous cases appear reasonable. However, adding climate variables such as precipitation, which is thought to indirectly influence outbreaks of hantavirus slightly improves the model fit. To further improve the current models to make them more useful for public health preparedness/interventions, data at the regional level with reliable predictions several months into the future are needed. Background Hantavirus pulmonary syndrome (HPS) is a life threatening disease transmitted by the rodent Oligoryzomys longicaudatus in Chile. Hantavirus outbreaks are typically small and geographically confined. Several studies have estimated risk based on spatial and temporal distribution of cases in relation to climate and environmental variables, but few have considered climatological modeling of HPS incidence for monitoring and forecasting purposes. Methodology Monthly counts of confirmed HPS cases were obtained from the Chilean Ministry of Health for 2001-2012. There were an estimated 667 confirmed HPS cases. The data suggested a seasonal trend, which appeared to correlate with changes in climatological variables such as temperature, precipitation, and humidity. We considered several Auto Regressive Integrated Moving Average (ARIMA) time-series models and regression models with ARIMA errors with one or a combination of these climate variables as covariates. We adopted an information-theoretic approach to model ranking and selection. Data from 2001-2009 were used in fitting and data from January 2010 to December 2012 were used for one-step-ahead predictions. Results We focused on six models. In a baseline model, future HPS cases were forecasted from previous incidence; the other models included climate variables as covariates. The baseline model had a Corrected Akaike Information Criterion (AICc) of 444.98, and the top ranked model, which included precipitation, had an AICc of 437.62. Although the AICc of the top ranked model only provided a 1.65% improvement to the baseline AICc, the empirical support was 39 times stronger relative to the baseline model. Conclusions Instead of choosing a single model, we present a set of candidate models that can be used in modeling and forecasting confirmed HPS cases in Chile. The models can be improved by using data at the regional level and easily extended to other countries with seasonal incidence of HPS.