Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison
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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017-03-01
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| Series: | Epidemics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1755436516300718 |
| _version_ | 1818238246954467328 |
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| author | Amy Pinsent Fengchen Liu Michael Deiner Paul Emerson Ana Bhaktiari Travis C. Porco Thomas Lietman Manoj Gambhir |
| author_facet | Amy Pinsent Fengchen Liu Michael Deiner Paul Emerson Ana Bhaktiari Travis C. Porco Thomas Lietman Manoj Gambhir |
| author_sort | Amy Pinsent |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T12:38:37Z |
| format | Article |
| id | doaj.art-1d5c0642280849568519590e703eca73 |
| institution | Directory Open Access Journal |
| issn | 1755-4365 1878-0067 |
| language | English |
| last_indexed | 2024-12-12T12:38:37Z |
| publishDate | 2017-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Epidemics |
| spelling | doaj.art-1d5c0642280849568519590e703eca732022-12-22T00:24:15ZengElsevierEpidemics1755-43651878-00672017-03-0118C485510.1016/j.epidem.2017.01.007Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparisonAmy Pinsent0Fengchen Liu1Michael Deiner2Paul Emerson3Ana Bhaktiari4Travis C. Porco5Thomas Lietman6Manoj Gambhir7Department of Public Health and Preventative Medicine, Monash University, Melbourne, AustraliaF.I. Proctor Foundation, University of California San Francisco, San Francisco, CA, USAF.I. Proctor Foundation, University of California San Francisco, San Francisco, CA, USAInternational Trachoma Initiative, Atlanta, GA, USAInternational Trachoma Initiative, Atlanta, GA, USAF.I. Proctor Foundation, University of California San Francisco, San Francisco, CA, USAF.I. Proctor Foundation, University of California San Francisco, San Francisco, CA, USADepartment of Public Health and Preventative Medicine, Monash University, Melbourne, AustraliaThe 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.http://www.sciencedirect.com/science/article/pii/S1755436516300718TrachomaEliminationForecastingModel comparison |
| spellingShingle | Amy Pinsent Fengchen Liu Michael Deiner Paul Emerson Ana Bhaktiari Travis C. Porco Thomas Lietman Manoj Gambhir Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison Epidemics Trachoma Elimination Forecasting Model comparison |
| title | Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison |
| title_full | Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison |
| title_fullStr | Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison |
| title_full_unstemmed | Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison |
| title_short | Probabilistic forecasts of trachoma transmission at the district level: A statistical model comparison |
| title_sort | probabilistic forecasts of trachoma transmission at the district level a statistical model comparison |
| topic | Trachoma Elimination Forecasting Model comparison |
| url | http://www.sciencedirect.com/science/article/pii/S1755436516300718 |
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