Spatiotemporal epidemiology and forecasting of dengue in the state of Punjab, India: Study protocol
Tài liệu tham khảo
van der Aalst, 2016
Albarakat, 2019, Comparison of normalized difference vegetation index derived from Landsat, MODIS, and AVHRR for the Mesopotamian marshes between 2002 and 2018, Remote Sens (Basel), 11, 1245, 10.3390/rs11101245
Azil, 2011, Dengue vector surveillance programs: a review of methodological diversity in some endemic and epidemic countries, Asia Pac. J. Public Health, 23, 827, 10.1177/1010539511426595
Banu, 2011, Dengue transmission in the Asia-Pacific region: impact of climate change and socio-environmental factors, Trop. Med. Int. Health, 16, 598, 10.1111/j.1365-3156.2011.02734.x
Bouzillé, 2018, Leveraging hospital big data to monitor flu epidemics, Comput. Methods Programs Biomed., 154, 153, 10.1016/j.cmpb.2017.11.012
Chae, 2018, Predicting infectious disease using deep learning and big data, IJERPH, 15, 1596, 10.3390/ijerph15081596
2015
2021
2021
Fan, 2014, A Systematic review and meta-analysis of dengue risk with temperature change, IJERPH, 12, 1, 10.3390/ijerph120100001
Farrar, 2014
2021
Hung, 2020, Using routine health information data for research in low- and middle-income countries: a systematic review, BMC Health Serv. Res., 20, 790, 10.1186/s12913-020-05660-1
Last, 2001
Louis, 2014, Modeling tools for dengue risk mapping - a systematic review, Int. J. Health Geogr., 13, 50, 10.1186/1476-072X-13-50
Künn, 2015, The challenges of linking survey and administrative data, Izawol, 10.15185/izawol.214
Minale, 2018, Mapping malaria risk using geographic information systems and remote sensing: the case of Bahir Dar City, Ethiopia. Geospat Health, 13
Morin, 2013, Climate and Dengue Transmission: evidence and Implications, Environ. Health Perspect., 121, 1264, 10.1289/ehp.1306556
Murhekar, 2019, Burden of dengue infection in India, 2017: a cross-sectional population based serosurvey, Lancet Glob. Health, 7, e1065, 10.1016/S2214-109X(19)30250-5
2019
2021
2021
2020
2021
2018
Ong, 2018, Mapping dengue risk in Singapore using Random Forest, PLoS Negl. Trop Dis., 12, 10.1371/journal.pntd.0006587
Ooi, 2009, Global spread of epidemic dengue: the influence of environmental change, Fut. Virol., 4, 571, 10.2217/fvl.09.55
Pei, 2018, Forecasting the spatial transmission of influenza in the United States, Proc. Natl Acad. Sci. USA, 115, 2752, 10.1073/pnas.1708856115
Phanitchat, 2019, Spatial and temporal patterns of dengue incidence in northeastern Thailand 2006–2016, BMC Infect. Dis., 19, 743, 10.1186/s12879-019-4379-3
2020
Sánchez-González, 2018, Prediction of dengue outbreaks in Mexico based on entomological, meteorological and demographic data, PLoS ONE, 13, 10.1371/journal.pone.0196047
Singh, 2019, Bio-eco-social determinants of Aedes breeding in field practice area of a medical college in Pune, Maharashtra, Indian J. Public Health, 63, 324, 10.4103/ijph.IJPH_296_18
Stolerman, 2019, Forecasting dengue fever in Brazil: an assessment of climate conditions, PLoS ONE, 14, 10.1371/journal.pone.0220106
Sun, 2018, Evaluation and correction of GPM IMERG precipitation products over the capital circle in northeast China at multiple spatiotemporal scales, Adv. Meteorol., 10.1155/2018/4714173
Swain, 2019, Distribution of and associated factors for dengue burden in the state of Odisha, India during 2010–2016, Infect. Dis. Poverty, 8, 31, 10.1186/s40249-019-0541-9
Titus Muurlink, 2018, Long-term predictors of dengue outbreaks in Bangladesh: a data mining approach, Infect. Dis. Modell., 3, 322
Vector-borne diseases. 2020. https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases (accessed January 19, 2021).
Verma, 2018, Google search trends predicting disease outbreaks: an analysis from India, Healthc Inform Res, 24, 300, 10.4258/hir.2018.24.4.300
Volkova, 2017, Forecasting influenza-like illness dynamics for military populations using neural networks and social media, PLoS ONE, 12, 10.1371/journal.pone.0188941
Wang, 2019, A combination of climatic conditions determines major within-season dengue outbreaks in Guangdong Province, China. Parasites Vect., 12, 45, 10.1186/s13071-019-3295-0
Wangdi, 2018, Spatial and temporal patterns of dengue infections in Timor-Leste, 2005–2013, Parasites Vect., 11
Withanage, 2018, A forecasting model for dengue incidence in the District of Gampaha, Sri Lanka. Parasites Vectors, 11, 262, 10.1186/s13071-018-2828-2
2020
Xu, 2020, Forecast of dengue cases in 20 Chinese cities based on the deep learning method, Int. J. Environ. Res. Public Health, 17, E453, 10.3390/ijerph17020453
Xu, 2020, High relative humidity might trigger the occurrence of the second seasonal peak of dengue in the Philippines, Sci. Total Environ., 708, 10.1016/j.scitotenv.2019.134849
Yuan, 2019, A systematic review of aberration detection algorithms used in public health surveillance, J. Biomed. Inform., 94, 10.1016/j.jbi.2019.103181
Zambrana, 2018, Seroprevalence, risk factor, and spatial analyses of Zika virus infection after the 2016 epidemic in Managua, Nicaragua. Proc Natl Acad Sci USA, 115, 9294, 10.1073/pnas.1804672115
Zhang, 2018, Multi-step prediction for influenza outbreak by an adjusted long short-term memory, Epidemiol. Infect., 146, 809, 10.1017/S0950268818000705
Zhang, 2016, Developing a time series predictive model for dengue in Zhongshan, China based on weather and guangzhou dengue surveillance data, PLoS Negl Trop Dis, 10, 10.1371/journal.pntd.0004473
Zheng, 2019, Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China, Infect. Dis. Poverty, 8, 24, 10.1186/s40249-019-0533-9
