Time series analysis of dengue cases reporting to a tertiary care hospital
DOI:
https://doi.org/10.18203/2394-6040.ijcmph20191844Keywords:
Dengue, Time series, Forecast, Seasonal trend, Climate changeAbstract
Background: Dengue fever is a major public health problem, the concern is high as the disease is closely related to climate change.
Methods: This was a retrospective study, conducted for 1 year in a tertiary care hospital in the city of Mumbai. Data of Dengue cases and climate for the city of Mumbai between 2011 and 2015 were obtained. Data was analysed using SPSS- time series analysis and forecasting model.
Results: 33% cases belonged to the 21-30 years, proportion of men affected were more than women. A seasonal distribution of cases was observed. A strong correlation was noted between the total number of cases reported and (a) mean monthly rainfall and (b) number of days of rainfall. ARIMA model was used for forecasting.
Conclusions: The trend analysis along with forecasting model helps in being prepared for the year ahead.
References
World Health organisation. Climate change and health. WHO 2018. https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health. Accessed on 12 February 2019.
Rao Mutheneni S, Morse AP, Caminade C, Murty Upadhyayula S. Dengue burden in India: recent trends and importance of climatic parameters. Emerg Microbes Infect. 2017;6:e70.
Das S, Sarfraz A, Jaiswal N, Das P. Impediments of reporting dengue cases in India. J Infect Public Health. 2017;10:494–8.
WHO. Dengue and severe dengue. World Heal Organ 2012. Available at: http://www.who.int/ mediacentre/factsheets/fs117/en/. Accessed on 26 February 2019.
Gupta N, Srivastava S, Jain A, Chaturvedi UC. Dengue in India. Indian J Med Res. 2012;136:373–90.
Halstead SB. Dengue hemorrhagic fever - a public health concern and a field of research. Bull WHO. 1980;58:1–21.
Johansson MA, Dominici F, Glass GE. Local and Global Effects of Climate on Dengue Transmission in Puerto Rico. PLoS Negl Trop Dis. 2009;3:e382.
K P. Park’s Textbook of Preventive and Social Medicine. 24th ed. Jabalpur: M/s Banarsidas Bhanot; 2017: 261-271.
Gupta E, Dar L, Kapoor G, Broor S. The changing epidemiology of dengue in Delhi, India. Virol J. 2006;3:92.
Murhekar M, Joshua V, Kanagasabai K, Shete V, Ravi M, Ramachandran R, et al. Epidemiology of dengue fever in India, based on laboratory surveillance data, 2014–2017. Int J Infect Dis. 2019.
Ceccato P, Connor SJ, Jeanne I, Thomson MC. Application of geographical information system and remote sensing in Malaria risk. Parassitologia. 2005;47:81–96.
Choi Y, Tang CS, Mciver L, Hashizume M, Chan V, Abeyasinghe RR, et al. Effects of weather factors on dengue fever incidence and implications for interventions in Cambodia. BMC Public Health. 2016;16:241.
Lal V, Gupta S, Gupta O, Bhatnagar S. Forecasting incidence of dengue in Rajasthan, using time series analyses. Indian J Public Health. 2013;56:281–5.