scholarly journals Changing Climatic Factors Favor Dengue Transmission in Lahore, Pakistan

Environments ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 71
Author(s):  
Syed Ali Asad Naqvi ◽  
Bulbul Jan ◽  
Saima Shaikh ◽  
Syed Jamil Hasan Kazmi ◽  
Liaqat Ali Waseem ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Analysis ◽  
Temporal Relationship ◽  
Climatic Factors ◽  
Climatic Variability ◽  
Regression Analyses ◽  
Series Analysis ◽  
Dengue Transmission ◽  
Statistical Approaches

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.

scholarly journals Forecasting Malaria Cases Using Climatic Factors in Delhi, India: A Time Series Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Varun Kumar ◽  
Abha Mangal ◽  
Sanjeet Panesar ◽  
Geeta Yadav ◽  
Richa Talwar ◽  
...  
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Climatic Factors ◽  
Series Data ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Training Centre ◽  
Series Analysis

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.

scholarly journals Mapping air temperature using time series analysis of LST: the SINTESI approach

2013 ◽  
Vol 20 (4) ◽  
pp. 513-527 ◽  
Author(s):  
S. M. Alfieri ◽  
F. De Lorenzi ◽  
M. Menenti
Keyword(s):  
Time Series ◽  
Fourier Series ◽  
Linear Regression ◽  
Time Series Analysis ◽  
Interannual Variability ◽  
Air Temperature ◽  
Land Surface ◽  
Reference Location ◽  
Series Analysis ◽  
Annual Variability

Abstract. This paper presents a new procedure to map time series of air temperature (Ta) at fine spatial resolution using time series analysis of satellite-derived land surface temperature (LST) observations. The method assumes that air temperature is known at a single (reference) location such as in gridded climate data with grid size of the order of 35 km × 35 km. The LST spatial and temporal pattern within a grid cell has been modelled by the pixel-wise ratios r (x,y,t) of the LST at any location to the LST at a reference location. A preliminary analysis of these patterns over a decade has demonstrated that their intra-annual variability is not negligible, with significant seasonality, even if it is stable throughout the years. The intra-annual variability has been modeled using Fourier series. We have evaluated the intra-annual variability by theoretically calculating the yearly evolution of LST (t) for a range of cases as a function of terrain, land cover and hydrological conditions. These calculations are used to interpret the observed LST (x,y,t) and r (x,y,t). The inter-annual variability has been evaluated by modeling each year of observations using Fourier series and evaluating the interannual variability of Fourier coefficients. Because of the negligible interannual variability of r (x,y,t), LST (x,y,t) can be reconstructed in periods of time different from the ones when LST observations are available. Time series of Ta are generated using the ratio r (x,y,t) and a linear regression between LST and Ta. Such linear regression is applied in two ways: (a) to estimate LST at any time from observations or forecasts of Ta at the reference location; (b) to estimate Ta from LST at any location. The results presented in this paper are based on the analysis of daily MODIS LST observations over the period 2001–2010. The Ta at the reference location was gridded data at a node of a 35 km × 35 km grid. Only one node was close to our study area and was used for the work presented here. The regression of Ta on LST was determined using concurrent observations of Ta at the four available weather stations in the Valle Telesina (Italy), our study area. The accuracy of our estimates is consistent with literature and with the combined accuracy of LST and Ta. We obtained comparable error statistics when applying our method to LST data during periods different but adjacent to the periods used to model of r (x,y,t). The method has also been evaluated against Ta observations for earlier periods of time (1984–1988), although available data are rather sparse in space and time. Slightly larger deviation were obtained. In all cases five days of averages from estimated and observed Ta were compared, giving a better accuracy.

scholarly journals Time-Series Analysis of Seasonal Changes of the O Inversion Polymorphism of Drosophila subobscura

Genetics ◽  
1996 ◽  
Vol 142 (1) ◽  
pp. 179-187 ◽  
Author(s):  
Francisco Rodríguez-Trelles ◽  
Gonzalo Alvarez ◽  
Carlos Zapata
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Time Series Analysis ◽  
Seasonal Changes ◽  
Climatic Factors ◽  
Extreme Temperature ◽  
Drosophila Subobscura ◽  
Short Term ◽  
Series Analysis

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.

scholarly journals Temporal relationship between occurrences of hand, foot and mouth disease, respiratory virus detection and febrile seizures in children in tropical Singapore: a time-series analysis

2018 ◽  
Vol 147 ◽  
Author(s):  
Y. Chen ◽  
C. Y. Chong ◽  
A. R. Cook ◽  
N. T. W. Sim ◽  
P. Horby ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Influenza A ◽  
Temporal Relationship ◽  
Respiratory Virus ◽  
Foot And Mouth Disease ◽  
Mouth Disease ◽  
Influenza B ◽  
Series Analysis ◽  
Foot And Mouth

AbstractFebrile seizure (FS) in children is a common complication of infections with respiratory viruses and hand, foot and mouth disease (HFMD). We conducted a retrospective ecological time-series analysis to determine the temporal relationship between hospital attendances for FS and HFMD or respiratory virus infections. Epilepsy attendance was used as a control. Data from 2004 to 2012 FS and epilepsy hospital attendance, HFMD notifications to the Ministry of Health and from laboratory-confirmed viral respiratory infections among KK Women's and Children's Hospital inpatients were used. A multivariate linear regression analysis was conducted to evaluate the relationship between FS and the virus time series. Relative risks of FS by age were calculated using Bayesian statistical methods. Paediatric accident and emergency (A&E) attendances for FS were found to be associated with influenza A (extra 0.47 FS per influenza A case), B (extra 0.32 per influenza B case) and parainfluenza 3 (extra 0.35 per parainfluenza type 3 case). However, other viruses were not significantly associated with FS. None of the viruses were associated with epileptic seizure attendance. Influenza A, B and parainfluenza 3 viruses contributed to the burden of FS resulting in A&E attendance. Children at risk of FS should be advised to receive seasonal influenza vaccination.

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