Lag effect of climatic variables on dengue burden in India

Abstract

Dengue is a widespread vector-borne disease believed to affect between 100 and 390 million people every year. The interaction between vector, host and pathogen is influenced by various climatic factors and the relationship between dengue and climatic conditions has been poorly explored in India. This study explores the relationship between El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and dengue cases in India. Additionally, distributed lag non-linear model was used to assess the delayed effects of climatic factors on dengue cases. The weekly dengue cases reported by the Integrated Disease Surveillance Program (IDSP) over India during the period 2010–2017 were analysed. The study shows that dengue cases usually follow a seasonal pattern, with most cases reported in August and September. Both temperature and rainfall were positively associated with the number of dengue cases. The precipitation shows the higher transmission risk of dengue was observed between 8 and 15 weeks of lag. The highest relative risk (RR) of dengue was observed at 60 mm rainfall with a 12-week lag period when compared with 40 and 80 mm rainfall. The RR of dengue tends to increase with increasing mean temperature above 24 °C. The largest transmission risk of dengue was observed at 30 °C with a 0–3 weeks of lag. Similarly, the transmission risk increases more than twofold when the minimum temperature reaches 26 °C with a 2-week lag period. The dengue cases and El Niño were positively correlated with a 3–6 months lag period. The significant correlation observed between the IOD and dengue cases was shown for a 0–2 months lag period.

El Niño Southern Oscillation as an early warning tool for dengue outbreak in India

“…Therefore, fluctuations in ENSO are likely to be associated with inter-annual variation in occurrence of dengue and could even trigger the outbreaks. In a recent study, weak synchronous correlation has been found between Niño 3.4, dipole mode index (DMI; represented by anomalous sea surface temperature gradient between the western equatorial Indian Ocean), and dengue cases of 2010-2017 based on over dispersed datasets [19].…”

Effects of El Niño Southern Oscillation and Dipole Mode Index on Chikungunya Infection in Indonesia

“…There was a very weak association between ENSO and dengue incidence in Bangladesh [ 24 ]. However, in India, El Niño was positively correlated with dengue cases, with a 3–6 months lag period [ 25 ]. In Taiwan, temperature and precipitation influenced dengue incidence rates with a lag of 10–20 weeks [ 26 ].…”

“…In contrast, a negative DMI often is associated with rainfall, providing suitable conditions for Aedes aegypti breeding [ 48 ], potentially leading to increased chikungunya case reporting. Studies have shown that DMI is associated with fluctuations in the incidence of other infectious diseases, including malaria [ 23 ] and dengue [ 24 , 25 , 27 ].…”

“…These indicators link with a dry and warm season that potentially increase mosquito vector breeding and shorter virus extrinsic incubation periods [ 21 ]. The role of these indicators in other infections such as dengue, malaria, hantavirus, Rift Valley fever, cholera, and Zika also have been studied, with variable results [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. The aims of this study are (a) to investigate the reduction of chikungunya cases that occurred in Indonesian in 2017; and (b) to assess the effects of ENSO and DMI on chikungunya incidence in Indonesia, overtime, since its re-emergence.…”

Spatio-Temporal Investigations of Dengue Fever in Pakistan Through an HL7 Based Public Health Framework for Hotspot Analysis

“…However, some research has also shown negative correlation and have attributed their results to removal of breeding sites due to excessive rain [37], [38]. On the other hand, using time lags based analysis, it has also been shown that hot and dry weather followed by heavy rainfall also increases dengue incidences [39]- [41]. Therefore, the relationship of climatic factors and dengue outbreak is not as straight forward and needs further investigations by analyzing dengue dynamics in populations with high incidence rates to understand the global transmission dynamics of dengue.…”