Statistical Analysis of Climate Factors Influencing Dengue Incidences in Colombo, Sri Lanka: Poisson and Negative Binomial Regression Approach

Chandrakantha, Leslie

doi:10.29322/ijsrp.9.02.2019.p8616

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Their work showed that, based on cross-correlations, dengue incidence had the most significant associations with maximum monthly temperature, minimum monthly temperature, relative humidity, and rainfall, at a lag of two months. Chandrakantha [9] also identified that rainfall data within a two-month lag period were a significant predictor in dengue incidences in Colombo, Sri Lanka. Their work was based on Poisson and negative binomial regression modeling.…”

Section: Introductionmentioning

confidence: 92%

Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach

Chandrakantha

2019

Stats

Self Cite

View full text Add to dashboard Cite

Dengue fever is a mosquito-borne viral disease prevalent in more than one hundred tropical and subtropical countries. Annually, an estimated 390 million infections occur worldwide. It is transmitted by the bite of an Aedes mosquito infected with the virus. It has become a major public health challenge in recent years for many countries, including Sri Lanka. It is known that climate factors such as rainfall, temperature, and relative humidity influence the generation of mosquito offspring, thus increasing dengue incidences. Identifying the climate factors that affect the spread of dengue fever would be helpful in order for the relevant authorities to take necessary actions. The objective of this study is to build a model for predicting the likelihood of having high dengue incidences based on climate factors. A logistic regression approach was utilized for model formulation. This study found a significant association between high numbers of dengue incidences and rainfall. Furthermore, it was observed that the influence of rainfall on dengue incidences was expected to be visible after some lag period.

show abstract

Section: Introductionmentioning

confidence: 92%

Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach

Chandrakantha

2019

Stats

Self Cite

View full text Add to dashboard Cite

show abstract

“…Identifying the association between dengue incidents and meteorological factors is also useful for the development of dengue warning systems, and hence, the administration can set out the dengue control measures promptly. A recent study (Chandrakantha, 2019) shows how climatological factors affecting the spread of dengue in the city of Colombo, Sri Lanka over the period from 2010 to 2018 using the Poisson and negative binomial regression approach. The study didn't consider the time lag and has been analysed in small geographical areas.…”

Section: Open Access Articlementioning

confidence: 99%

Identifying Extreme Behaviour and Fitting Empirical Models for Dengue Incidents of Selected Regions in Sri Lanka

Nisansala

Wijekoon

2021

J. Univ. Ruhuna

View full text Add to dashboard Cite

Dengue fever is one of the most significant mosquito-borne diseases caused by a virus. Numerous methods available to predict dengue incidents are mainly focused on the mean features of events. However, understanding the extreme behaviour of dengue incidents is important, and that will allow sufficient time to take the necessary decisions and actions to safeguard the situation for local authorities. Therefore, this study mainly focuses to model the risk of rare dengue events, that is, extreme dengue events, and to identify the best-fitted distributions for the study areas. Further, the weather-based dengue empirical models for dengue incidents were fitted using climatological factors to forecast potential outbreaks. The weekly dengue incidents and climatology data (rainfall, temperature, and relative humidity) from January 2010 to December 2018 for seven administrative districts were collected from the Epidemiology Unit of the Ministry of Health (MoH), and the Meteorology Department of Sri Lanka, respectively. The Extreme value theory (EVT) was used to analyse the extreme dengue incidents, and the negative binomial generalized linear model was used to fit weather-based dengue empirical models. Various lag times between dengue and weather variables were analysed to identify the optimal dengue forecasting period. The best fitted empirical models for dengue incidents were identified for the selected districts. The Generalized Linear Negative Binomial (GLNB) models with monsoon season as a covariate, lag 0 model is the suitable model for Colombo and Gampaha districts, and lag 1 model is the suitable for Kurunegala whereas lag 2 model is the best for Anuradhapura with highest prediction accuracy. For Badulla district, lag 2 model without having monsoon season as a covariate shows highest prediction accuracy. The prediction accuracy is the same for the models with or without having the monsoon season as a covariate for Kandy (lag 2) and Ratnapura (lag 3) districts.

show abstract

Modelo de Poisson e suas generalizações aplicadas a dados de dengue, Brasil

Freitas

Oliveira

Filho

et al. 2020

RSD

View full text Add to dashboard Cite

Objetivo: analisar e comparar o comportamento semanal de casos de dengue nos cinco municípios mais populosos das mesorregiões Pernambucanas, a saber Caruaru, Palmares, Recife, Petrolina e Serra Talhada. Método: utilizaram-se os registros epidemiológicos semanais de dengue, no período de 2009 a 2018, disponibilizados através da Serviço de Informação ao Cidadão (SIC). Foram aplicados modelos de probabilidade, mais precisamente, os modelos Poisson e suas generalizações. Resultados: o modelo Binomial Negativo se sobressaiu em relação ao Modelo Quase-Poisson, reduzindo os parâmetros de dispersões com mais precisão devida à natureza dos dados superdispersos. Além disso, as análises indicaram que a precipitação e temperatura foram fatores significativos que afetou no número de casos em alguns municípios. Conclusão: a modelagem permitiu ser uma ferramenta útil para as autoridades locais planejarem tomadas de decisões e intervenção nos períodos mais propício de proliferação.

show abstract

Statistical Analysis of Climate Factors Influencing Dengue Incidences in Colombo, Sri Lanka: Poisson and Negative Binomial Regression Approach

Cited by 5 publications

References 15 publications

Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach

Risk Prediction Model for Dengue Transmission Based on Climate Data: Logistic Regression Approach

Identifying Extreme Behaviour and Fitting Empirical Models for Dengue Incidents of Selected Regions in Sri Lanka

Modelo de Poisson e suas generalizações aplicadas a dados de dengue, Brasil

Contact Info

Product

Resources

About