1. Modeling and Roles of Meteorological Factors in Outbreaks of Highly Pathogenic Avian Influenza H5N1
- Author
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M. Z. Islam, Nitish C. Debnath, Mat Yamage, and Paritosh Kumar Biswas
- Subjects
Veterinary medicine ,Atmospheric Science ,Epidemiology ,medicine.disease_cause ,Disease Outbreaks ,Avian Influenza A Virus ,Moving average ,Zoonoses ,Geoinformatics ,Influenza A virus ,Medicine and Health Sciences ,Public and Occupational Health ,Autoregressive integrated moving average ,Avian influenza A viruses ,Multidisciplinary ,Geography ,virus diseases ,Medical microbiology ,Infectious Diseases ,Veterinary Diseases ,Medicine ,Environmental Health ,Research Article ,Computer and Information Sciences ,Science ,Biology ,Microbiology ,Environmental Epidemiology ,Animal Influenza ,Meteorology ,Covariate ,Influenza, Human ,medicine ,Influenza viruses ,Humans ,Environmental Systems Modeling ,Biology and life sciences ,Influenza A Virus, H5N1 Subtype ,Viral pathogens ,Outbreak ,Seasonality ,Veterinary Virology ,Models, Theoretical ,medicine.disease ,Influenza A virus subtype H5N1 ,Microbial pathogens ,Health Care ,Earth Sciences ,Veterinary Science ,Orthomyxoviruses - Abstract
The highly pathogenic avian influenza A virus subtype H5N1 (HPAI H5N1) is a deadly zoonotic pathogen. Its persistence in poultry in several countries is a potential threat: a mutant or genetically reassorted progenitor might cause a human pandemic. Its world-wide eradication from poultry is important to protect public health. The global trend of outbreaks of influenza attributable to HPAI H5N1 shows a clear seasonality. Meteorological factors might be associated with such trend but have not been studied. For the first time, we analyze the role of meteorological factors in the occurrences of HPAI outbreaks in Bangladesh. We employed autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to assess the roles of different meteorological factors in outbreaks of HPAI. Outbreaks were modeled best when multiplicative seasonality was incorporated. Incorporation of any meteorological variable(s) as inputs did not improve the performance of any multivariable models, but relative humidity (RH) was a significant covariate in several ARIMA and SARIMA models with different autoregressive and moving average orders. The variable cloud cover was also a significant covariate in two SARIMA models, but air temperature along with RH might be a predictor when moving average (MA) order at lag 1 month is considered.
- Published
- 2014