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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Autores principales:	Chathurangi Edussuriya, Sampath Deegalla, Indika Gawarammana
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/075654e6fae240009570c1f248a4ced9
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spelling	oai:doaj.org-article:075654e6fae240009570c1f248a4ced92021-12-02T20:23:26ZAn accurate mathematical model predicting number of dengue cases in tropics.1935-27271935-273510.1371/journal.pntd.0009756https://doaj.org/article/075654e6fae240009570c1f248a4ced92021-11-01T00:00:00Zhttps://doi.org/10.1371/journal.pntd.0009756https://doaj.org/toc/1935-2727https://doaj.org/toc/1935-2735Dengue 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.Chathurangi EdussuriyaSampath DeegallaIndika GawarammanaPublic Library of Science (PLoS)articleArctic medicine. Tropical medicineRC955-962Public aspects of medicineRA1-1270ENPLoS Neglected Tropical Diseases, Vol 15, Iss 11, p e0009756 (2021)
institution	DOAJ
collection	DOAJ
language	EN
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 Chathurangi Edussuriya Sampath Deegalla Indika Gawarammana An accurate mathematical model predicting number of dengue cases in tropics.
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
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://doaj.org/article/075654e6fae240009570c1f248a4ced9
work_keys_str_mv	AT chathurangiedussuriya anaccuratemathematicalmodelpredictingnumberofdenguecasesintropics AT sampathdeegalla anaccuratemathematicalmodelpredictingnumberofdenguecasesintropics AT indikagawarammana anaccuratemathematicalmodelpredictingnumberofdenguecasesintropics AT chathurangiedussuriya accuratemathematicalmodelpredictingnumberofdenguecasesintropics AT sampathdeegalla accuratemathematicalmodelpredictingnumberofdenguecasesintropics AT indikagawarammana accuratemathematicalmodelpredictingnumberofdenguecasesintropics
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An accurate mathematical model predicting number of dengue cases in tropics.

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