tetano
Editor, Senior Moderator
J Health Care Poor Underserved. 2011;22(4 Suppl):39-60.
Epidemiology of Influenza A 2009 H1N1 Virus Pandemic in the U.S.
Abdalla E, Habtemariam T, Nganwa D, Dibaba AB, Gerbi G, Vinaida R, Tameru B.
Abstract
Abstract:A novel influenza, Influenza A 2009 H1N1 virus outbreak, emerged in mid-April 2009, and by December 2009, spread across the world. This epidemiologic analysis uses the epidemiologic problem oriented approach to gather information for, and develop a quantitative risk assessment model that evaluates the likelihood of an influenza outbreak in the U.S., utilizing weekly incidence rates (WIR) and case fatality rates (CFR) stratified by age and the 10 U.S. regions. In addition ArcGIS was used to show variability regarding morbidity rate and WIR. Using data from the Centers for Disease Control and Prevention (CDC) Emerging Infections Program and applying Monte Carlo simulations with @Risk software, the analysis revealed morbidity to be highest in region 8 (1.27?10-2) and lowest in region 2 (0.13?10-2). Heat map results revealed WIR were higher in younger children, for all regions. Case fatality rates were 10 times greater in younger people than in older people and children. Using these different temporal-spatial epidemiologic methods can help decision-makers identify high-risk population clusters and help in prevention and control of pandemics.
PMID:
22102305
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/22102305
Epidemiology of Influenza A 2009 H1N1 Virus Pandemic in the U.S.
Abdalla E, Habtemariam T, Nganwa D, Dibaba AB, Gerbi G, Vinaida R, Tameru B.
Abstract
Abstract:A novel influenza, Influenza A 2009 H1N1 virus outbreak, emerged in mid-April 2009, and by December 2009, spread across the world. This epidemiologic analysis uses the epidemiologic problem oriented approach to gather information for, and develop a quantitative risk assessment model that evaluates the likelihood of an influenza outbreak in the U.S., utilizing weekly incidence rates (WIR) and case fatality rates (CFR) stratified by age and the 10 U.S. regions. In addition ArcGIS was used to show variability regarding morbidity rate and WIR. Using data from the Centers for Disease Control and Prevention (CDC) Emerging Infections Program and applying Monte Carlo simulations with @Risk software, the analysis revealed morbidity to be highest in region 8 (1.27?10-2) and lowest in region 2 (0.13?10-2). Heat map results revealed WIR were higher in younger children, for all regions. Case fatality rates were 10 times greater in younger people than in older people and children. Using these different temporal-spatial epidemiologic methods can help decision-makers identify high-risk population clusters and help in prevention and control of pandemics.
PMID:
22102305
[PubMed - in process]
http://www.ncbi.nlm.nih.gov/pubmed/22102305