Comparing models for early warning systems of neglected tropical diseases.
Early warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases who...
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ftdoajarticles:oai:doaj.org/article:85b0679624d3464097077fa392cb1c8b 2023-05-15T15:15:14+02:00 Comparing models for early warning systems of neglected tropical diseases. Luis Fernando Chaves Mercedes Pascual 2007-10-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0000033 https://doaj.org/article/85b0679624d3464097077fa392cb1c8b EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC2041810?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0000033 https://doaj.org/article/85b0679624d3464097077fa392cb1c8b PLoS Neglected Tropical Diseases, Vol 1, Iss 1, p e33 (2007) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2007 ftdoajarticles https://doi.org/10.1371/journal.pntd.0000033 2022-12-31T11:22:37Z Early warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations.In the present paper, an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented. Also, a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis, a neglected tropical disease, as an example. The comparison of the different models using the predictive R(2) for forecasts of "out-of-fit" data (data that has not been used to fit the models) shows that for the several linear and non-linear models tested, the best results were obtained for seasonal autoregressive (SAR) models that incorporate climatic covariates. An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach. While the autoregressive part of the model is not significant, the exogenous forcing due to climate is always statistically significant. Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter.this study illustrates a protocol for the development of EWS that includes three main steps: (i) the fitting of different models using several methodologies, (ii) the comparison of models based on the predictability of "out-of-fit" data, and (iii) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLoS Neglected Tropical Diseases 1 1 e33 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
spellingShingle |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Luis Fernando Chaves Mercedes Pascual Comparing models for early warning systems of neglected tropical diseases. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Early warning systems (EWS) are management tools to predict the occurrence of epidemics of infectious diseases. While climate-based EWS have been developed for malaria, no standard protocol to evaluate and compare EWS has been proposed. Additionally, there are several neglected tropical diseases whose transmission is sensitive to environmental conditions, for which no EWS have been proposed, though they represent a large burden for the affected populations.In the present paper, an overview of the available linear and non-linear tools to predict seasonal time series of diseases is presented. Also, a general methodology to compare and evaluate models for prediction is presented and illustrated using American cutaneous leishmaniasis, a neglected tropical disease, as an example. The comparison of the different models using the predictive R(2) for forecasts of "out-of-fit" data (data that has not been used to fit the models) shows that for the several linear and non-linear models tested, the best results were obtained for seasonal autoregressive (SAR) models that incorporate climatic covariates. An additional bootstrapping experiment shows that the relationship of the disease time series with the climatic covariates is strong and consistent for the SAR modeling approach. While the autoregressive part of the model is not significant, the exogenous forcing due to climate is always statistically significant. Prediction accuracy can vary from 50% to over 80% for disease burden at time scales of one year or shorter.this study illustrates a protocol for the development of EWS that includes three main steps: (i) the fitting of different models using several methodologies, (ii) the comparison of models based on the predictability of "out-of-fit" data, and (iii) the assessment of the robustness of the relationship between the disease and the variables in the model selected as best with an objective criterion. |
format |
Article in Journal/Newspaper |
author |
Luis Fernando Chaves Mercedes Pascual |
author_facet |
Luis Fernando Chaves Mercedes Pascual |
author_sort |
Luis Fernando Chaves |
title |
Comparing models for early warning systems of neglected tropical diseases. |
title_short |
Comparing models for early warning systems of neglected tropical diseases. |
title_full |
Comparing models for early warning systems of neglected tropical diseases. |
title_fullStr |
Comparing models for early warning systems of neglected tropical diseases. |
title_full_unstemmed |
Comparing models for early warning systems of neglected tropical diseases. |
title_sort |
comparing models for early warning systems of neglected tropical diseases. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2007 |
url |
https://doi.org/10.1371/journal.pntd.0000033 https://doaj.org/article/85b0679624d3464097077fa392cb1c8b |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 1, Iss 1, p e33 (2007) |
op_relation |
http://europepmc.org/articles/PMC2041810?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0000033 https://doaj.org/article/85b0679624d3464097077fa392cb1c8b |
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https://doi.org/10.1371/journal.pntd.0000033 |
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PLoS Neglected Tropical Diseases |
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e33 |
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