登革热病毒(DENV)是一种包膜病毒,单链RNA病毒,黄病毒科(引起登革热)的成员,和节肢动物传播的人类病毒感染。孟加拉国以一些亚洲最脆弱的登革热疫情而闻名,随着气候变化,它的位置,密集的人口是主要的贡献者。对于DENV爆发特征的猜测,确定气象因素如何与病例数量相关至关重要。本研究采用5种时间序列模型对登革热病例进行趋势观察和预测。当前基于数据的研究还应用了四个统计模型来测试登革热阳性病例与气象参数之间的关系。美国国家航空航天局的数据集用于气象参数,每日DENV病例是从卫生服务总局(DGHS)开放访问网站获得的。在学习期间,DENV病例的平均值为882.26±3993.18,每天确诊病例的最小值为0至最大值为52,636例.气候变量与登革热发病率之间的Spearman等级相关系数表明,每日登革热病例与风速之间没有实质性关系。温度,和表面压力(Spearman'srho;r=-0.007,p>0.05;r=0.085,p>0.05;r=-0.086,p>0.05)。尽管如此,每日登革热病例与露点之间存在显著关系,相对湿度,和降雨量(r=0.158,p<0.05;r=0.175,p<0.05;r=0.138,p<0.05)。使用ARIMAX和GA模型,登革热病例与风速的关系为-666.50[95%CI:-1711.86至378.86]和-953.05[-2403.46至497.36],分别。在GLM模型中也确定了登革热病例与风速之间的类似负相关关系(IRR=0.98)。在ARIMAX和GA模型中,露点和表面压力也呈负相关,分别,但GLM模型显示出正相关。此外,温度和相对湿度与登革热病例呈正相关(分别为105.71和57.39,在ARIMAX,633.86,以及GA模型中的200.03)。相比之下,在GLM模型中,温度和相对湿度均与登革热病例呈负相关。在泊松回归模型中,在所有季节,风速与登革热病例都有显著的负面影响。在所有季节,温度和降雨与登革热病例呈显着正相关。气象因素与最近爆发数据之间的关联是我们知道孟加拉国使用最大时间序列模型的第一项研究。通过这些发现,将来有可能对DENV爆发采取综合措施,这可以帮助其他研究人员和政策制定者。
Dengue virus (DENV) is an enveloped, single-stranded RNA virus, a member of the Flaviviridae family (which causes Dengue fever), and an arthropod-transmitted human viral infection. Bangladesh is well known for having some of Asia\'s most vulnerable Dengue outbreaks, with climate change, its location, and it\'s dense population serving as the main contributors. For speculation about DENV outbreak characteristics, it is crucial to determine how meteorological factors correlate with the number of cases. This study used five time series models to observe the trend and forecast Dengue cases. Current data-based research has also applied four statistical models to test the relationship between Dengue-positive cases and meteorological parameters. Datasets were used from NASA for meteorological parameters, and daily DENV cases were obtained from the Directorate General of Health Service (DGHS) open-access websites. During the study period, the mean of DENV cases was 882.26 ± 3993.18, ranging between a minimum of 0 to a maximum of 52,636 daily confirmed cases. The Spearman\'s rank correlation coefficient between climatic variables and Dengue incidence indicated that no substantial relationship exists between daily Dengue cases and wind speed, temperature, and surface pressure (Spearman\'s rho; r = -0.007, p > 0.05; r = 0.085, p > 0.05; and r = -0.086, p > 0.05, respectively). Still, a significant relationship exists between daily Dengue cases and dew point, relative humidity, and rainfall (r = 0.158, p < 0.05; r = 0.175, p < 0.05; and r = 0.138, p < 0.05, respectively). Using the ARIMAX and GA models, the relationship for Dengue cases with wind speed is -666.50 [95% CI: -1711.86 to 378.86] and -953.05 [-2403.46 to 497.36], respectively. A similar negative relation between Dengue cases and wind speed was also determined in the GLM model (IRR = 0.98). Dew point and surface pressure also represented a negative correlation in both ARIMAX and GA models, respectively, but the GLM model showed a positive association. Additionally, temperature and relative humidity showed a positive correlation with Dengue cases (105.71 and 57.39, respectively, in the ARIMAX, 633.86, and 200.03 in the GA model). In contrast, both temperature and relative humidity showed negative relation with Dengue cases in the GLM model. In the Poisson regression model, windspeed has a substantial significant negative connection with Dengue cases in all seasons. Temperature and rainfall are significantly and positively associated with Dengue cases in all seasons. The association between meteorological factors and recent outbreak data is the first study where we are aware of the use of maximum time series models in Bangladesh. Taking comprehensive measures against DENV outbreaks in the future can be possible through these findings, which can help fellow researchers and policymakers.