An accurate mathematical model predicting number of dengue cases in tropics.
Dengue fever is a systemic viral infection of epidemic proportions in tropical countries. The incidence of dengue fever is ever increasing and has doubled over the last few decades. Estimated 50million new cases are detected each year and close to 10000 deaths occur each year. Epidemics are unpredic...
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ftdoajarticles:oai:doaj.org/article:075654e6fae240009570c1f248a4ced9 2023-05-15T15:12:32+02:00 An accurate mathematical model predicting number of dengue cases in tropics. Chathurangi Edussuriya Sampath Deegalla Indika Gawarammana 2021-11-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0009756 https://doaj.org/article/075654e6fae240009570c1f248a4ced9 EN eng Public Library of Science (PLoS) https://doi.org/10.1371/journal.pntd.0009756 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0009756 https://doaj.org/article/075654e6fae240009570c1f248a4ced9 PLoS Neglected Tropical Diseases, Vol 15, Iss 11, p e0009756 (2021) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2021 ftdoajarticles https://doi.org/10.1371/journal.pntd.0009756 2022-12-31T08:08:54Z Dengue fever is a systemic viral infection of epidemic proportions in tropical countries. The incidence of dengue fever is ever increasing and has doubled over the last few decades. Estimated 50million new cases are detected each year and close to 10000 deaths occur each year. Epidemics are unpredictable and unprecedented. When epidemics occur, health services are over whelmed leading to overcrowding of hospitals. At present there is no evidence that dengue epidemics can be predicted. Since the breeding of the dengue mosquito is directly influenced by environmental factors, it is plausible that epidemics could be predicted using weather data. We hypothesized that there is a mathematical relationship between incidence of dengue fever and environmental factors and if such relationship exists, new cases of dengue fever in the succeeding months can be predicted using weather data of the current month. We developed a mathematical model using machine learning technique. We used Island wide dengue epidemiology data, weather data and population density in developing the model. We used incidence of dengue fever, average rain fall, humidity, wind speed, temperature and population density of each district in the model. We found that the model is able to predict the incidence of dengue fever of a given month in a given district with precision (RMSE between 18- 35.3). Further, using weather data of a given month, the number of cases of dengue in succeeding months too can be predicted with precision (RMSE 10.4-30). Health authorities can use existing weather data in predicting epidemics in the immediate future and therefore measures to prevent new cases can be taken and more importantly the authorities can prepare local authorities for outbreaks. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 15 11 e0009756 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
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 Chathurangi Edussuriya Sampath Deegalla Indika Gawarammana An accurate mathematical model predicting number of dengue cases in tropics. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
Dengue fever is a systemic viral infection of epidemic proportions in tropical countries. The incidence of dengue fever is ever increasing and has doubled over the last few decades. Estimated 50million new cases are detected each year and close to 10000 deaths occur each year. Epidemics are unpredictable and unprecedented. When epidemics occur, health services are over whelmed leading to overcrowding of hospitals. At present there is no evidence that dengue epidemics can be predicted. Since the breeding of the dengue mosquito is directly influenced by environmental factors, it is plausible that epidemics could be predicted using weather data. We hypothesized that there is a mathematical relationship between incidence of dengue fever and environmental factors and if such relationship exists, new cases of dengue fever in the succeeding months can be predicted using weather data of the current month. We developed a mathematical model using machine learning technique. We used Island wide dengue epidemiology data, weather data and population density in developing the model. We used incidence of dengue fever, average rain fall, humidity, wind speed, temperature and population density of each district in the model. We found that the model is able to predict the incidence of dengue fever of a given month in a given district with precision (RMSE between 18- 35.3). Further, using weather data of a given month, the number of cases of dengue in succeeding months too can be predicted with precision (RMSE 10.4-30). Health authorities can use existing weather data in predicting epidemics in the immediate future and therefore measures to prevent new cases can be taken and more importantly the authorities can prepare local authorities for outbreaks. |
format |
Article in Journal/Newspaper |
author |
Chathurangi Edussuriya Sampath Deegalla Indika Gawarammana |
author_facet |
Chathurangi Edussuriya Sampath Deegalla Indika Gawarammana |
author_sort |
Chathurangi Edussuriya |
title |
An accurate mathematical model predicting number of dengue cases in tropics. |
title_short |
An accurate mathematical model predicting number of dengue cases in tropics. |
title_full |
An accurate mathematical model predicting number of dengue cases in tropics. |
title_fullStr |
An accurate mathematical model predicting number of dengue cases in tropics. |
title_full_unstemmed |
An accurate mathematical model predicting number of dengue cases in tropics. |
title_sort |
accurate mathematical model predicting number of dengue cases in tropics. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2021 |
url |
https://doi.org/10.1371/journal.pntd.0009756 https://doaj.org/article/075654e6fae240009570c1f248a4ced9 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 15, Iss 11, p e0009756 (2021) |
op_relation |
https://doi.org/10.1371/journal.pntd.0009756 https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0009756 https://doaj.org/article/075654e6fae240009570c1f248a4ced9 |
op_doi |
https://doi.org/10.1371/journal.pntd.0009756 |
container_title |
PLOS Neglected Tropical Diseases |
container_volume |
15 |
container_issue |
11 |
container_start_page |
e0009756 |
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1766343202744303616 |