scholarly journals Climatic factors and rotavirus infections among children under five years old in Bangladesh: time-series analysis

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
M.R. Rahaman ◽  
A. Milazzo ◽  
H. Marshall ◽  
S.M. Satter ◽  
M. Rahman ◽  
...  
2007 ◽  
Vol 136 (9) ◽  
pp. 1281-1289 ◽  
Author(s):  
M. HASHIZUME ◽  
B. ARMSTRONG ◽  
Y. WAGATSUMA ◽  
A. S. G. FARUQUE ◽  
T. HAYASHI ◽  
...  

SUMMARYAttempts to explain the clear seasonality of rotavirus infections have been made by relating disease incidence to climate factors; however, few studies have disentangled the effects of weather from other factors that might cause seasonality. We investigated the relationships between hospital visits for rotavirus diarrhoea and temperature, humidity and river level, in Dhaka, Bangladesh, using time-series analysis adjusting for other confounding seasonal factors. There was strong evidence for an increase in rotavirus diarrhoea at high temperatures, by 40·2% for each 1°C increase above a threshold (29°C). Relative humidity had a linear inverse relationship with the number of cases of rotavirus diarrhoea. River level, above a threshold (4·8 m), was associated with an increase in cases of rotavirus diarrhoea, by 5·5% per 10-cm river-level rise. Our findings provide evidence that factors associated with high temperature, low humidity and high river-level increase the incidence of rotavirus diarrhoea in Dhaka.


2019 ◽  
Vol 18 (1) ◽  
pp. 5
Author(s):  
NajlaaIbrahim Mahmood Al-Sammak ◽  
HumamGhanim Ibrahim

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Varun Kumar ◽  
Abha Mangal ◽  
Sanjeet Panesar ◽  
Geeta Yadav ◽  
Richa Talwar ◽  
...  

Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)12, was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.


Environments ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 71
Author(s):  
Syed Ali Asad Naqvi ◽  
Bulbul Jan ◽  
Saima Shaikh ◽  
Syed Jamil Hasan Kazmi ◽  
Liaqat Ali Waseem ◽  
...  

Dengue fever (DF) is a national health problem in Pakistan. It has become endemic in Lahore after its recent reemergence in 2016. This study investigates the impacts of climatic factors (temperature and rainfall) on DF transmission in the district of Lahore through statistical approaches. Initially, the climatic variability was explored using a time series analysis on climatic factors from 1970 to 2012. Furthermore, ordinary and multiple linear regression analyses were used to measure the simulating effect of climatic factors on dengue incidence from 2007 to 2012. The time series analysis revealed significant annual and monthly variability in climatic factors, which shaped a dengue-supporting environment. It also showed a positive temporal relationship between climatic factors and DF. Moreover, the regression analyses revealed a substantial monthly relationship between climatic factors and dengue incidence. The ordinary linear regression of rainfall versus dengue showed monthly R2 = 34.2%, whereas temperature versus dengue presented R2 = 38.0%. The multiple regression analysis showed a monthly significance of R2 = 44.6%. Consequently, our study shows a substantial synergism between dengue and climatic factors in Lahore. The present study could help in unveiling new ways for health prediction modeling of dengue and might be applicable in other subtropical and temperate climates.


Genetics ◽  
1996 ◽  
Vol 142 (1) ◽  
pp. 179-187 ◽  
Author(s):  
Francisco Rodríguez-Trelles ◽  
Gonzalo Alvarez ◽  
Carlos Zapata

We have studied seasonal variation (spring, early summer, last summer and autumn) of inversion polymorphisms of the O chromosome of Drosophila subobscura in a natural population over 15 years. The length of the study allowed us to investigate the temporal behavior (short-term seasonal changes and long-term directional trends) of the O arrangements by the powerful statistical method of time series analysis. It is shown that the O inversion polymorphisms varied on two different time scales: short-term seasonal changes repeated over the years superimposed on long-term directional trends. All the common arrangements (O3+4+7,  OST,  O3+4 and O3+4+8) showed significant cyclic seasonal changes, and all but one of these arrangements (O3+4+7) showed significant long-term trends. Moreover, the degree of seasonality was different for different arrangements. Thus, O3+4+7 and OST showed the highest seasonality, which accounted for ∼61 and 47% of their total variances, respectively. The seasonal changes in the frequencies of chromosome arrangements were significantly associated with the seasonal variation of the climate (temperature, rainfall, humidity and insolation). In particular, O3+4+7 and OST, the arrangements with the greatest seasonal component, showed the strongest association with all climatic factors investigated, especially to the seasonal changes of extreme temperature and humidity.


Author(s):  
Xianglin Huang ◽  
Tingbin Zhang ◽  
Guihua Yi ◽  
Dong He ◽  
Xiaobing Zhou ◽  
...  

The fragile alpine vegetation in the Tibetan Plateau (TP) is very sensitive to environmental changes, making TP one of the hotspots for studying the response of vegetation to climate change. Existing studies lack detailed description of the response of vegetation to different climatic factors using the method of multiple nested time series analysis and the method of grey correlation analysis. In this paper, based on the Normalized Difference Vegetation Index (NDVI) of TP in the growing season calculated from the MOD09A1 data product of Moderate-resolution Imaging Spectroradiometer (MODIS), the method of multiple nested time series analysis is adopted to study the variation trends of NDVI in recent 17 years, and the lag time of NDVI to climate change is analyzed using the method of Grey Relational Analysis (GRA). Finally, the characteristics of temporal and spatial differences of NDVI to different climate factors are summarized. The results indicate that: (1) the spatial distribution of NDVI values in the growing season shows a trend of decreasing from east to west, and from north to south, with a change rate of −0.13/10° E and −0.30/10° N, respectively. (2) From 2001 to 2017, the NDVI in the TP shows a slight trend of increase, with a growth rate of 0.01/10a. (3) The lag time of NDVI to air temperature is not obvious, while the NDVI response lags behind cumulative precipitation by zero to one month, relative humidity by two months, and sunshine duration by three months. (4) The effects of different climatic factors on NDVI are significantly different with the increase of the study period.


2015 ◽  
Vol 37 ◽  
pp. e2015003 ◽  
Author(s):  
Mehran Rostami ◽  
Abdollah Jalilian ◽  
Behrooz Hamzeh ◽  
Zahra Laghaei

Sign in / Sign up

Export Citation Format

Share Document