Insight into vaccination and meteorological factors on daily COVID-19 cases and mortality in Bangladesh

被引:8
作者
Hasan, Mohammad Nayeem [1 ,2 ]
Islam, Md Aminul [3 ,4 ,15 ]
Sangkham, Sarawut [5 ]
Werkneh, Adhena Ayaliew [6 ]
Hossen, Foysal [3 ]
Haque, Md Atiqul [7 ,8 ]
Alam, Mohammad Morshad [9 ]
Rahman, Md Arifur [3 ]
Mukharjee, Sanjoy Kumar [3 ]
Chowdhury, Tahmid Anam [10 ]
Sosa-Hernandez, Juan Eduardo [11 ]
Jakariya, Md [12 ]
Ahmed, Firoz [3 ]
Bhattacharya, Prosun [13 ]
Sarkodie, Samuel Asumadu [14 ]
机构
[1] Shahjalal Univ Sci & Technol, Dept Stat, Sylhet, Bangladesh
[2] Food Hungry, Joint Rohingya Response Program, Coxs Bazar, Bangladesh
[3] Noakhali Sci & Technol Univ, Dept Microbiol, COVID 19 Diagnost Lab, Noakhali 3814, Bangladesh
[4] President Abdul Hamid Med Coll, Dept Microbiol, Adv Mol Lab, Karimganj, Kishoreganj, Bangladesh
[5] Univ Phayao, Sch Publ Hlth, Dept Environm Hlth, Muang Dist 56000, Phayao, Thailand
[6] Mekelle Univ, Coll Hlth Sci, Sch Publ Hlth, Dept Environm Hlth, POB 1871, Mekelle, Ethiopia
[7] China Agr Univ, Coll Vet Med, Key Lab Anim Epidemiol & Zoonoses, Minist Agr & Rural Affairs, Beijing, Peoples R China
[8] Hajee Mohammad Danesh Sci & Technol Univ, Fac Vet & Anim Sci, Dept Microbiol, Dinajpur 5200, Bangladesh
[9] World Bank, Hlth Nutr & Populat Global Practice, Dhaka 1207, Bangladesh
[10] Shahjalal Univ Sci & Technol, Dept Geog & Environm, Sylhet 3114, Bangladesh
[11] Tecnol Monterrey, Sch Engn & Sci, Monterrey 64849, Mexico
[12] North South Univ, Dept Environm Sci & Management, Dhaka 1229, Bangladesh
[13] KTH Royal Inst Technol, Dept Sustainable Dev Environm Sci & Engn, COVID 19 Res KTH, Teknikringen 10B, SE-10044 Stockholm, Sweden
[14] Nord Univ, Business Sch HHN, Post Box 1490, N-8049 Bodo, Norway
[15] President Abdul Hamid Med Coll & Hosp, Dept Microbiol, Adv Mol Lab, PAHMC, Kishoreganj 2310, Bangladesh
关键词
Meteorological factors; COVID-19; Vaccination; Mathematical models; Bangladesh; Temperature and rainfall; SARS-CoV-2; TEMPERATURE; RAINFALL; WEATHER;
D O I
10.1016/j.gsd.2023.100932
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The ongoing COVID-19 contagious disease caused by SARS-CoV-2 has disrupted global public health, businesses, and economies due to widespread infection, with 676.41 million confirmed cases and 6.77 million deaths in 231 countries as of February 07, 2023. To control the rapid spread of SARS-CoV-2, it is crucial to determine the potential determinants such as meteorological factors and their roles. This study examines how COVID-19 cases and deaths changed over time while assessing meteorological characteristics that could impact these disparities from the onset of the pandemic. We used data spanning two years across all eight administrative divisions, this is the first of its kind--showing a connection between meteorological conditions, vaccination, and COVID-19 in-cidences in Bangladesh. We further employed several techniques including Simple Exponential Smoothing (SES), Auto-Regressive Integrated Moving Average (ARIMA), Auto-Regressive Integrated Moving Average with explanatory variables (ARIMAX), and Automatic forecasting time-series model (Prophet). We further analyzed the effects of COVID-19 vaccination on daily cases and deaths. Data on COVID-19 cases collected include eight administrative divisions of Bangladesh spanning March 8, 2020, to January 31, 2023, from available online servers. The meteorological data include rainfall (mm), relative humidity (%), average temperature (degrees C), surface pressure (kPa), dew point (degrees C), and maximum wind speed (m/s). The observed wind speed and surface pressure show a significant negative impact on COVID-19 cases (-0.89, 95% confidence interval (CI): 1.62 to-0.21) and (-1.31, 95%CI: 2.32 to-0.29), respectively. Similarly, the observed wind speed and surface pressure show a significant negative impact on COVID-19 deaths (-0.87, 95% CI: 1.54 to-0.21) and (-3.11, 95%CI: 4.44 to-1.25), respectively. The impact of meteorological factors is almost similar when vaccination information is included in the model. However, the impact of vaccination in both cases and deaths model is significantly negative (for cases: 1.19, 95%CI: 2.35 to-0.38 and for deaths: 1.55, 95%CI: 2.88 to-0.43). Accordingly, vaccination effectively reduces the number of new COVID-19 cases and fatalities in Bangladesh. Thus, these results could assist future researchers and policymakers in the assessment of pandemics, by making thorough efforts that account for COVID-19 vaccinations and meteorological conditions.
引用
收藏
页数:11
相关论文
共 63 条
[1]  
Adhikari R., 2013, An introductory study on time series modeling and forecasting
[2]   Investigation of effective climatology parameters on COVID-19 outbreak in Iran [J].
Ahmadi, Mohsen ;
Sharifi, Abbas ;
Dorosti, Shadi ;
Ghoushchi, Saeid Jafarzadeh ;
Ghanbari, Negar .
SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 729
[3]   First detection of SARS-CoV-2 genetic material in the vicinity of COVID-19 isolation Centre in Bangladesh: Variation along the sewer network [J].
Ahmed, Firoz ;
Islam, Md Aminul ;
Kumar, Manish ;
Hossain, Maqsud ;
Bhattacharya, Prosun ;
Islam, Md Tahmidul ;
Hossen, Foysal ;
Hossain, Md Shahadat ;
Islam, Md Sydul ;
Uddin, Md Main ;
Islam, Md Nur ;
Bahadur, Newaz Mohammed ;
Didar-Ul-Alam, Md ;
Reza, Hasan Mahmud ;
Jakariya, Md .
SCIENCE OF THE TOTAL ENVIRONMENT, 2021, 776
[4]   Climate factors and incidence of Middle East respiratory syndrome coronavirus [J].
Altamimi, Asmaa ;
Ahmed, Anwar E. .
JOURNAL OF INFECTION AND PUBLIC HEALTH, 2020, 13 (05) :704-708
[5]  
[Anonymous], 2021, SEX SPECIFIC EPIDEMI, DOI [10.1101/2021.07.05.21259933, DOI 10.1101/2021.07.05.21259933]
[6]   Weather: driving force behind the transmission of severe acute respiratory syndrome in China? [J].
Bi, P. ;
Wang, J. ;
Hiller, J. E. .
INTERNAL MEDICINE JOURNAL, 2007, 37 (08) :550-554
[7]   Application of machine learning time series analysis for prediction COVID-19 pandemic [J].
Chaurasia V. ;
Pal S. .
Research on Biomedical Engineering, 2022, 38 (01) :35-47
[8]   Ambient temperature and subsequent COVID-19 mortality in the OECD countries and individual United States [J].
Christophi, Costas A. ;
Sotos-Prieto, Mercedes ;
Lan, Fan-Yun ;
Delgado-Velandia, Mario ;
Efthymiou, Vasilis ;
Gaviola, Gabriel C. ;
Hadjivasilis, Alexandros ;
Hsu, Yu-Tien ;
Kyprianou, Aikaterini ;
Lidoriki, Irene ;
Wei, Chih-Fu ;
Rodriguez-Artalejo, Fernando ;
Kales, Stefanos N. .
SCIENTIFIC REPORTS, 2021, 11 (01)
[9]   Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning [J].
Chyon, Fuad Ahmed ;
Suman, Md Nazmul Hasan ;
Fahim, Md Rafiul Islam ;
Ahmmed, Md Sazol .
JOURNAL OF VIROLOGICAL METHODS, 2022, 301
[10]   Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing [J].
De Livera, Alysha M. ;
Hyndman, Rob J. ;
Snyder, Ralph D. .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2011, 106 (496) :1513-1527