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Changing Climatic Factors Favor Dengue Transmission in Lahore, Pakistan
被引:12
|作者:
Naqvi, Syed Ali Asad
[1
]
Jan, Bulbul
[2
]
Shaikh, Saima
[3
]
Kazmi, Syed Jamil Hasan
[3
]
Waseem, Liaqat Ali
[1
]
Nasar-u-minAllah, Muhammad
[4
]
Abbas, Nasir
[5
]
机构:
[1] Govt Coll Univ Faisalabad, Dept Geog, Faisalabad 38000, Pakistan
[2] Univ Karachi, Inst Space & Planetary Astrophys, Karachi 75270, Pakistan
[3] Univ Karachi, Dept Geog, Karachi 75270, Pakistan
[4] Govt Postgrad Coll Gojra, Dept Geog, Gojra 56000, Pakistan
[5] Govt Coll Univ Lahore, Dept Geog, Lahore 54000, Pakistan
来源:
关键词:
time series;
linear regression;
rainfall;
temperature;
climate;
DF;
GLOBAL DISTRIBUTION;
FEVER;
KARACHI;
D O I:
10.3390/environments6060071
中图分类号:
X [环境科学、安全科学];
学科分类号:
08 ;
0830 ;
摘要:
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 R-2 = 34.2%, whereas temperature versus dengue presented R-2 = 38.0%. The multiple regression analysis showed a monthly significance of R-2 = 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.
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