کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5588959 | 1569429 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison
ترجمه فارسی عنوان
پیش بینی های احتمالی انتقال تراخم در سطح منطقه: یک مقایسه مدل آماری
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کلمات کلیدی
تراکوما، حذف، پیش بینی، مقایسه مدل،
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
بوم شناسی، تکامل، رفتار و سامانه شناسی
چکیده انگلیسی
The World Health Organization and its partners are aiming to eliminate trachoma as a public health problem by 2020. In this study, we compare forecasts of TF prevalence in 2011 for 7 different statistical and mechanistic models across 9 de-identified trachoma endemic districts, representing 4 unique trachoma endemic countries. We forecast TF prevalence between 1-6 years ahead in time and compare the 7 different models to the observed 2011 data using a log-likelihood score. An SIS model, including a district-specific random effect for the district-specific transmission coefficient, had the highest log-likelihood score across all 9 districts and was therefore the best performing model. While overall the deterministic transmission model was the least well performing model, although it did comparably well to the other models for 8 of 9 districts. We perform a statistically rigorous comparison of the forecasting ability of a range of mathematical and statistical models across multiple endemic districts between 1 and 6 years ahead of the last collected TF prevalence data point in 2011, assessing results against surveillance data. This study is a step towards making statements about likelihood and time to elimination with regard to the WHO GET2020 goals.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Epidemics - Volume 18, March 2017, Pages 48-55
Journal: Epidemics - Volume 18, March 2017, Pages 48-55
نویسندگان
Amy Pinsent, Fengchen Liu, Michael Deiner, Paul Emerson, Ana Bhaktiari, Travis C. Porco, Thomas Lietman, Manoj Gambhir,