Format

Send to

Choose Destination
PLoS Negl Trop Dis. 2014 Apr 24;8(4):e2779. doi: 10.1371/journal.pntd.0002779. eCollection 2014 Apr.

Modeling to predict cases of hantavirus pulmonary syndrome in Chile.

Author information

1
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.
2
Children's Hospital Informatics Program, Boston Children's Hospital, Boston, Massachusetts, United States of America.
3
Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America.
4
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.
5
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.

Abstract

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.

PMID:
24763320
PMCID:
PMC3998931
DOI:
10.1371/journal.pntd.0002779
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center