Spatio-temporal variability of malaria infection in Chahbahar County, Iran: association with the ENSO and rainfall variability.
Seyed Mohammad Jafar NazemosadatReza ShafieiHabib GhaedaminiMohsen NajjariZahra Nazemosadat-ArsanjaniGholam Reza HatamPublished in: Environmental science and pollution research international (2022)
Malaria is one of the most widespread communicable diseases in the southeast regions of Iran, particularly the Chabahar County. Although the outbreak of this disease is a climate-related phenomenon, a comprehensive analysis of the malaria-climate relationship has not yet been investigated in Iran. The aims of this study are as follows: a) analyzing the seasonal characteristics of the various species of the infection; b) differentiating between number of patients during El Niño and La Niña and also during the wet and dry years. The monthly malaria statistics collected from twelve health centers were firstly averaged into seasonal scale and then composited with the corresponding data of the ground-based meteorological records, Southern Oscillation Index (SOI), and the satellite-based rainfall data. The proper statistical tests were used to detect differences in the number of patients between El Niño and La Niña and also between the adopted wet and dry episodes. Infection rate from the highest to the lowest was associated with summer, autumn, spring, and winter, respectively. Plasmodium falciparum, P. vivax, and the other species were responsible for 22%, 75%, and 3% of the sickness, respectively. The outbreak of P. falciparum/P. vivax occurs during autumn/summer. Due to the malaria eradication programs in urban areas, infection statistics collected from the rural areas were found to be more climate-related than that of urban regions. For rural/urban areas, the infection statistics exhibited a significant decline/increase during El Niño episodes. In autumn, spring, and winter, the patient number has significantly increased/decreased during the dry/wet years, respectively. These relationships were, however, reversed in summer.
Keyphrases
- plasmodium falciparum
- end stage renal disease
- ejection fraction
- chronic kidney disease
- climate change
- newly diagnosed
- healthcare
- peritoneal dialysis
- machine learning
- big data
- prognostic factors
- mental health
- high frequency
- patient reported outcomes
- artificial intelligence
- case report
- helicobacter pylori
- transition metal
- health information
- data analysis
- social media