Ying-Hen Hsieh1, Hector de Arazoza, Rachid Lounes. 1. Department of Public Health and Center for Infectious Disease Education and Research, China Medical University, Taichung, Taiwan. hsieh@mail.cmu.edu.tw
Abstract
OBJECTIVES: To investigate the temporal and regional variability of the 2001-2002 dengue outbreak in Havana City where 12 889 cases, mostly of DENV-3 type, were reported over a period of 7 months. METHODS: A simple mathematical model, the Richards model, was used to fit the weekly reported dengue case data by municipality, in order to quantify the transmissibility and temporal changes in the epidemic in each municipality via the basic reproduction number R0 . RESULTS: Model fits indicate either a 2-wave or 3-wave outbreak in all municipalities. Estimates for R0 varied greatly, from 1.97 (95% CI: 1.94, 2.01), for Arroyo Naranjo, to 61.06 (60.44, 61.68), for Boyeros, most likely due to heterogeneity in community structure, geographical locations and social networking. CONCLUSIONS: Our results illustrate the potential impact of climatological events on disease spread, further highlighting the need to be well prepared for potentially worsening disease spread in the aftermath of natural disasters such as hurricanes/typhoons.
OBJECTIVES: To investigate the temporal and regional variability of the 2001-2002 dengue outbreak in Havana City where 12 889 cases, mostly of DENV-3 type, were reported over a period of 7 months. METHODS: A simple mathematical model, the Richards model, was used to fit the weekly reported dengue case data by municipality, in order to quantify the transmissibility and temporal changes in the epidemic in each municipality via the basic reproduction number R0 . RESULTS: Model fits indicate either a 2-wave or 3-wave outbreak in all municipalities. Estimates for R0 varied greatly, from 1.97 (95% CI: 1.94, 2.01), for Arroyo Naranjo, to 61.06 (60.44, 61.68), for Boyeros, most likely due to heterogeneity in community structure, geographical locations and social networking. CONCLUSIONS: Our results illustrate the potential impact of climatological events on disease spread, further highlighting the need to be well prepared for potentially worsening disease spread in the aftermath of natural disasters such as hurricanes/typhoons.
Authors: C R Sebrango-Rodríguez; D A Martínez-Bello; L Sánchez-Valdés; P J Thilakarathne; E Del Fava; P VAN DER Stuyft; A López-Quílez; Z Shkedy Journal: Epidemiol Infect Date: 2017-06-01 Impact factor: 4.434
Authors: Rosmari Rodriguez-Roche; Hervé Blanc; Antonio V Bordería; Gisell Díaz; Rasmus Henningsson; Daniel Gonzalez; Emidalys Santana; Mayling Alvarez; Osvaldo Castro; Magnus Fontes; Marco Vignuzzi; Maria G Guzman Journal: J Virol Date: 2016-04-14 Impact factor: 5.103