OBJECTIVES: This study was conducted to determine the predicting factors of Crimean Congo hemorrhagic fever (CCHF) in Zabol and Zahedan, from where more than 60% of all national cases are reported, in order to improve CCHF disease surveillance and to target control efforts. METHODS: Data were collected from the National Reference Laboratory on Arboviruses and Viral Hemorrhagic Fevers, the national meteorology organization, the veterinary organization, and the national statistics center of Iran. A Poisson regression analysis was applied for the temporal modeling of human samples between 2000 and 2006. The modeling fitness was checked with data from 2007. RESULTS: This modeling revealed that the disease occurrence followed a seasonal pattern. The maximum temperature and relative humidity in previous months was found to positively affect the occurrence of the disease. Variables such as the level of livestock imports and the number of slaughtered animals were also found to be influential in the occurrence of the disease. The pseudo R(2) was 0.51 in the final model. CONCLUSIONS: The model predicted the number of cases 1 month in advance with more or less acceptable accuracy. Therefore, it appears that the model might be useful as part of an early warning system.
OBJECTIVES: This study was conducted to determine the predicting factors of Crimean Congo hemorrhagic fever (CCHF) in Zabol and Zahedan, from where more than 60% of all national cases are reported, in order to improve CCHF disease surveillance and to target control efforts. METHODS: Data were collected from the National Reference Laboratory on Arboviruses and Viral Hemorrhagic Fevers, the national meteorology organization, the veterinary organization, and the national statistics center of Iran. A Poisson regression analysis was applied for the temporal modeling of human samples between 2000 and 2006. The modeling fitness was checked with data from 2007. RESULTS: This modeling revealed that the disease occurrence followed a seasonal pattern. The maximum temperature and relative humidity in previous months was found to positively affect the occurrence of the disease. Variables such as the level of livestock imports and the number of slaughtered animals were also found to be influential in the occurrence of the disease. The pseudo R(2) was 0.51 in the final model. CONCLUSIONS: The model predicted the number of cases 1 month in advance with more or less acceptable accuracy. Therefore, it appears that the model might be useful as part of an early warning system.
Authors: Arefeh Mousavi; Ali Ardalan; Amirhossein Takian; Abbas Ostadtaghizadeh; Kazem Naddafi; Alireza Massah Bavani Journal: J Environ Health Sci Eng Date: 2020-04-02
Authors: Lina González Gordon; Paul R Bessell; Egbe F Nkongho; Victor N Ngwa; Vincent N Tanya; Melissa Sander; Lucy Ndip; Kenton L Morgan; Ian G Handel; Stella Mazeri; Barend MdeC Bronsvoort; Robert F Kelly Journal: PLoS Negl Trop Dis Date: 2022-03-21