Analysis of effects of meteorological variables on dengue incidence in Bangladesh using VAR and Granger causality approach

被引:0
作者
Hossain, Md. Jamal [1 ]
Sultana, Nazia [1 ]
Das, Anwesha [1 ]
Jui, Fariea Nazim [1 ]
Islam, Md. Kamrul [1 ]
Rahman, Md. Mijanoor [2 ]
Rahman, Mohammad Mafizur [3 ]
机构
[1] Noakhali Sci & Technol Univ, Dept Appl Math, Noakhali, Bangladesh
[2] Mawlana Bhashani Sci & Technol Univ, Dept Math, Tangail, Bangladesh
[3] Univ Southern Queensland, Sch Business, Toowoomba, Qld, Australia
关键词
VAR model; Granger causality; meteorological variables; dengue fever; VECM model; impulse response function; CLIMATE; COINTEGRATION; VARIABILITY; DHAKA; MODEL;
D O I
10.3389/fpubh.2024.1488742
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background Dengue fever is a serious public health issue in Bangladesh, where its incidence rises with the monsoon. Meteorological variables are believed to be responsible factors among others. Therefore, this study examines the effects of meteorological variables (temperature, rainfall, and humidity) on dengue incidence in Bangladesh. While previous studies have examined the relationship between dengue and meteorological variables using single model approaches, this study employs advanced econometric techniques to capture dynamic interactions. Furthermore, in the case of Bangladesh, this type of analysis is necessary due to the fact that dengue outbreak become one of the major issues. However, the analysis related to this issue is not available.Methods For estimation purposes, the Augmented Dickey-Fuller (ADF) test, Vector Autoregressive (VAR) model, Granger causality tests, Impulse Response Function (IRF), Variance Decomposition (VDC), and Vector Error Correction Model (VECM) are employed.Results Rainfall has a significant impact on dengue incidence compared to temperature and humidity. The Granger causality test demonstrates that rainfall and dengue incidence are causally related unidirectionally. Rainfall can potentially have a short-term and long-term effect on the incidence of dengue, as per the estimates of the VECM model.Conclusions These findings will assist policymakers in Bangladesh in developing a dengue fever early warning system depending on climate change. In order to efficiently avoid the spread of dengue in Bangladesh's dengue-endemic urban areas, this study suggests societal monitoring.
引用
收藏
页数:10
相关论文
共 39 条
[1]  
[Anonymous], 2009, Dengue: guidelines for diagnosis, treatment, prevention and control
[2]   Regional variability in relationships between climate and dengue/DHF in Indonesia [J].
Arcari, Paula ;
Tapper, Nigel ;
Pfueller, Sharron .
SINGAPORE JOURNAL OF TROPICAL GEOGRAPHY, 2007, 28 (03) :251-272
[3]   Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh [J].
Banu, Shahera ;
Hu, Wenbiao ;
Guo, Yuming ;
Hurst, Cameron ;
Tong, Shilu .
ENVIRONMENT INTERNATIONAL, 2014, 63 :137-142
[4]   Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents [J].
Caldwell, Jamie M. ;
LaBeaud, A. Desiree ;
Lambin, Eric F. ;
Stewart-Ibarra, Anna M. ;
Ndenga, Bryson A. ;
Mutuku, Francis M. ;
Krystosik, Amy R. ;
Beltran Ayala, Efrain ;
Anyamba, Assaf ;
Borbor-Cordova, Mercy J. ;
Damoah, Richard ;
Grossi-Soyster, Elysse N. ;
Heras Heras, Froilan ;
Ngugi, Harun N. ;
Ryan, Sadie J. ;
Shah, Melisa M. ;
Sippy, Rachel ;
Mordecai, Erin A. .
NATURE COMMUNICATIONS, 2021, 12 (01)
[5]   Nonlinear impacts of climatic variability on the density-dependent regulation of an insect vector of disease [J].
Chaves, Luis F. ;
Morrison, Amy C. ;
Kitron, Uriel D. ;
Scott, Thomas W. .
GLOBAL CHANGE BIOLOGY, 2012, 18 (02) :457-468
[6]   Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability [J].
Cheng, Jian ;
Bambrick, Hilary ;
Yakob, Laith ;
Devine, Gregor ;
Frentiu, Francesca D. ;
Williams, Gail ;
Li, Zhongjie ;
Yang, Weizhong ;
Hu, Wenbiao .
ENVIRONMENTAL RESEARCH, 2021, 196 (196)
[7]   Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China [J].
Cheng, Qu ;
Jing, Qinlong ;
Collender, Philip A. ;
Head, Jennifer R. ;
Li, Qi ;
Yu, Hailan ;
Li, Zhichao ;
Ju, Yang ;
Chen, Tianmu ;
Wang, Peng ;
Cleary, Eimear ;
Lai, Shengjie .
FRONTIERS IN PUBLIC HEALTH, 2023, 11
[8]   Climate and the Timing of Imported Cases as Determinants of the Dengue Outbreak in Guangzhou, 2014: Evidence from a Mathematical Model [J].
Cheng, Qu ;
Jing, Qinlong ;
Spear, Robert C. ;
Marshall, John M. ;
Yang, Zhicong ;
Gong, Peng .
PLOS NEGLECTED TROPICAL DISEASES, 2016, 10 (02)
[9]   The Global Trends and Regional Differences in Incidence of Dengue Infection from 1990 to 2019: An Analysis from the Global Burden of Disease Study 2019 [J].
Du, Min ;
Jing, Wenzhan ;
Liu, Min ;
Liu, Jue .
INFECTIOUS DISEASES AND THERAPY, 2021, 10 (03) :1625-1643
[10]   COINTEGRATION AND ERROR CORRECTION - REPRESENTATION, ESTIMATION, AND TESTING [J].
ENGLE, RF ;
GRANGER, CWJ .
ECONOMETRICA, 1987, 55 (02) :251-276