Modeling to predict cases of hantavirus pulmonary syndrome in Chile.
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.


METHODS
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.
Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America; Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America.
Elaine O
EO
Nsoesie
Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
Sumiko R
SR
Mekaru
Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America.
Naren
N
Ramakrishnan
Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America.
Madhav V
MV
Marathe
Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.
John S
JS
Brownstein
D002677:Chile / Q000453:epidemiology; D002980:Climate; D055907:Climatic Processes; D018804:Hantavirus Pulmonary Syndrome / Q000453:epidemiology*; D006801:Humans; D006813:Humidity; D015233:Models, Statistical; D013696:Temperature