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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Main Authors: Chathurangi Edussuriya, Sampath Deegalla, Indika Gawarammana
Format: Article in Journal/Newspaper
Language:English
Published: Public Library of Science (PLoS) 2021
Subjects:
Online Access:https://doaj.org/article/1709048e40be434b98c0f85f9ed7d328
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spelling ftdoajarticles:oai:doaj.org/article:1709048e40be434b98c0f85f9ed7d328 2023-05-15T15:18:23+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://doaj.org/article/1709048e40be434b98c0f85f9ed7d328 EN eng Public Library of Science (PLoS) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575180/?tool=EBI https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 https://doaj.org/article/1709048e40be434b98c0f85f9ed7d328 PLoS Neglected Tropical Diseases, Vol 15, Iss 11 (2021) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2021 ftdoajarticles 2022-12-31T11:51:31Z 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. Author summary 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 50 million new cases are detected each year and close to 10000 deaths occur ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic
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
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. Author summary 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 50 million new cases are detected each year and close to 10000 deaths occur ...
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://doaj.org/article/1709048e40be434b98c0f85f9ed7d328
geographic Arctic
geographic_facet Arctic
genre Arctic
genre_facet Arctic
op_source PLoS Neglected Tropical Diseases, Vol 15, Iss 11 (2021)
op_relation https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575180/?tool=EBI
https://doaj.org/toc/1935-2727
https://doaj.org/toc/1935-2735
1935-2727
1935-2735
https://doaj.org/article/1709048e40be434b98c0f85f9ed7d328
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