A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India.
Background The elimination programme for visceral leishmaniasis (VL) in India has seen great progress, with total cases decreasing by over 80% since 2010 and many blocks now reporting zero cases from year to year. Prompt diagnosis and treatment is critical to continue progress and avoid epidemics in...
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ftdoajarticles:oai:doaj.org/article:eb96383a587c4b7a95ed7da028640a2c 2023-05-15T15:16:51+02:00 A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India. Emily S Nightingale Lloyd A C Chapman Sridhar Srikantiah Swaminathan Subramanian Purushothaman Jambulingam Johannes Bracher Mary M Cameron Graham F Medley 2020-07-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0008422 https://doaj.org/article/eb96383a587c4b7a95ed7da028640a2c EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0008422 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008422 https://doaj.org/article/eb96383a587c4b7a95ed7da028640a2c PLoS Neglected Tropical Diseases, Vol 14, Iss 7, p e0008422 (2020) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2020 ftdoajarticles https://doi.org/10.1371/journal.pntd.0008422 2022-12-31T13:56:21Z Background The elimination programme for visceral leishmaniasis (VL) in India has seen great progress, with total cases decreasing by over 80% since 2010 and many blocks now reporting zero cases from year to year. Prompt diagnosis and treatment is critical to continue progress and avoid epidemics in the increasingly susceptible population. Short-term forecasts could be used to highlight anomalies in incidence and support health service logistics. The model which best fits the data is not necessarily most useful for prediction, yet little empirical work has been done to investigate the balance between fit and predictive performance. Methodology/principal findings We developed statistical models of monthly VL case counts at block level. By evaluating a set of randomly-generated models, we found that fit and one-month-ahead prediction were strongly correlated and that rolling updates to model parameters as data accrued were not crucial for accurate prediction. The final model incorporated auto-regression over four months, spatial correlation between neighbouring blocks, and seasonality. Ninety-four percent of 10-90% prediction intervals from this model captured the observed count during a 24-month test period. Comparison of one-, three- and four-month-ahead predictions from the final model fit demonstrated that a longer time horizon yielded only a small sacrifice in predictive power for the vast majority of blocks. Conclusions/significance The model developed is informed by routinely-collected surveillance data as it accumulates, and predictions are sufficiently accurate and precise to be useful. Such forecasts could, for example, be used to guide stock requirements for rapid diagnostic tests and drugs. More comprehensive data on factors thought to influence geographic variation in VL burden could be incorporated, and might better explain the heterogeneity between blocks and improve uniformity of predictive performance. Integration of the approach in the management of the VL programme would be an important step to ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 14 7 e0008422 |
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ftdoajarticles |
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English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Emily S Nightingale Lloyd A C Chapman Sridhar Srikantiah Swaminathan Subramanian Purushothaman Jambulingam Johannes Bracher Mary M Cameron Graham F Medley A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Background The elimination programme for visceral leishmaniasis (VL) in India has seen great progress, with total cases decreasing by over 80% since 2010 and many blocks now reporting zero cases from year to year. Prompt diagnosis and treatment is critical to continue progress and avoid epidemics in the increasingly susceptible population. Short-term forecasts could be used to highlight anomalies in incidence and support health service logistics. The model which best fits the data is not necessarily most useful for prediction, yet little empirical work has been done to investigate the balance between fit and predictive performance. Methodology/principal findings We developed statistical models of monthly VL case counts at block level. By evaluating a set of randomly-generated models, we found that fit and one-month-ahead prediction were strongly correlated and that rolling updates to model parameters as data accrued were not crucial for accurate prediction. The final model incorporated auto-regression over four months, spatial correlation between neighbouring blocks, and seasonality. Ninety-four percent of 10-90% prediction intervals from this model captured the observed count during a 24-month test period. Comparison of one-, three- and four-month-ahead predictions from the final model fit demonstrated that a longer time horizon yielded only a small sacrifice in predictive power for the vast majority of blocks. Conclusions/significance The model developed is informed by routinely-collected surveillance data as it accumulates, and predictions are sufficiently accurate and precise to be useful. Such forecasts could, for example, be used to guide stock requirements for rapid diagnostic tests and drugs. More comprehensive data on factors thought to influence geographic variation in VL burden could be incorporated, and might better explain the heterogeneity between blocks and improve uniformity of predictive performance. Integration of the approach in the management of the VL programme would be an important step to ... |
format |
Article in Journal/Newspaper |
author |
Emily S Nightingale Lloyd A C Chapman Sridhar Srikantiah Swaminathan Subramanian Purushothaman Jambulingam Johannes Bracher Mary M Cameron Graham F Medley |
author_facet |
Emily S Nightingale Lloyd A C Chapman Sridhar Srikantiah Swaminathan Subramanian Purushothaman Jambulingam Johannes Bracher Mary M Cameron Graham F Medley |
author_sort |
Emily S Nightingale |
title |
A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India. |
title_short |
A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India. |
title_full |
A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India. |
title_fullStr |
A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India. |
title_full_unstemmed |
A spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in India. |
title_sort |
spatio-temporal approach to short-term prediction of visceral leishmaniasis diagnoses in india. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2020 |
url |
https://doi.org/10.1371/journal.pntd.0008422 https://doaj.org/article/eb96383a587c4b7a95ed7da028640a2c |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 14, Iss 7, p e0008422 (2020) |
op_relation |
https://doi.org/10.1371/journal.pntd.0008422 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0008422 https://doaj.org/article/eb96383a587c4b7a95ed7da028640a2c |
op_doi |
https://doi.org/10.1371/journal.pntd.0008422 |
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PLOS Neglected Tropical Diseases |
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14 |
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7 |
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e0008422 |
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