Geographical distribution, evaluation of risk of dengue and its relationship with the El Niño Southern Oscillation in an endemic region of Peru between 2004 and 2015

The effect of weather and climate on dengue outbreak risk in Peru, 2000-2018: A time-series analysis

Background

Dengue fever is the most common arboviral disease in humans, with an estimated 50-100 million annual infections worldwide. Dengue fever cases have increased substantially in the past four decades, driven largely by anthropogenic factors including climate change. More than half the population of Peru is at risk of dengue infection and due to its geography, Peru is also particularly sensitive to the effects of El Niño Southern Oscillation (ENSO). Determining the effect of ENSO on the risk for dengue outbreaks is of particular public health relevance and may also be applicable to other Aedes-vectored viruses.

Methods

We conducted a time-series analysis at the level of the district-month, using surveillance data collected from January 2000 to September 2018 from all districts with a mean elevation suitable to survival of the mosquito vector (<2,500m), and ENSO and weather data from publicly-available datasets maintained by national and international agencies. We took a Bayesian hierarchical modeling approach to address correlation in space, and B-splines with four knots per year to address correlation in time. We furthermore conducted subgroup analyses by season and natural region.

Results

We detected a positive and significant effect of temperature (°C, RR 1.14, 95% CI 1.13, 1.15, adjusted for precipitation) and ENSO (ICEN index: RR 1.17, 95% CI 1.15, 1.20; ONI index: RR 1.04, 95% CI 1.02, 1.07) on outbreak risk, but no evidence of a strong effect for precipitation after adjustment for temperature. Both natural region and season were found to be significant effect modifiers of the ENSO-dengue effect, with the effect of ENSO being stronger in the summer and the Selva Alta and Costa regions, compared with winter and Selva Baja and Sierra regions.

Conclusions

Our results provide strong evidence that temperature and ENSO have significant effects on dengue outbreaks in Peru, however these results interact with region and season, and are stronger for local ENSO impacts than remote ENSO impacts. These findings support optimization of a dengue early warning system based on local weather and climate monitoring, including where and when to deploy such a system and parameterization of ENSO events, and provide high-precision effect estimates for future climate and dengue modeling efforts.

The theoretical importance of the El Niño Southern Oscillation to infectious disease transmission is widely accepted, however few studies have quantified this effect or its interaction with local environment. Using surveillance data on outbreaks of dengue fever in Peru, we have found evidence that El Niño events increase the risk of dengue outbreaks in the high jungle and coast, and decrease this risk in the low jungle. Our findings are likely generalizable to other viruses which are, like dengue virus, are transmitted by Aedes aegypti mosquitoes, including Zika, chikungunya, and yellow fever. As climate change is expected to increase the frequency of El Niño events, these results indicate that arbovirus outbreaks may also increase, and that El Niño events may be leveraged to predict them.

Impact of Meteorological Factors and Southern Oscillation Index on Scrub Typhus Incidence in Guangzhou, Southern China, 2006-2018

Background: Scrub typhus was epidemic in the western Pacific Ocean area and East Asia, scrub typhus epidemic in densely populated areas in southern China. To better understand the association between meteorological variables, Southern Oscillation Index (SOI), and scrub typhus incidence in Guangzhou was benefit to the control and prevention.

Methodology/Principal Findings: We collected weekly data for scrub typhus cases and meteorological variables in Guangzhou, and Southern Oscillation Index from 2006 to 2018, and used the distributed lag non-linear models to evaluate the relationships between meteorological variables, SOI and scrub typhus. The median value of each variable was set as the reference. The high-risk occupations were farmer (51.10%), house worker (17.51%), and retiree (6.29%). The non-linear relationships were observed with different lag weeks. For example, when the mean temperature was 27.7°C with1-week lag, the relative risk (RR) was highest as 1.08 (95% CI: 1.01–1.17). The risk was the highest when the relative humidity was 92.0% with 9-week lag, with the RR of 1.10 (95% CI: 1.02–1.19). For aggregate rainfall, the highest RR was 1.06 (95% CI: 1.03–1.11), when it was 83.0 mm with 4-week lag. When the SOI was 19 with 11-week lag, the highest RR was 1.06 (95% CI: 1.01–1.12). Most of the extreme effects of SOI and meteorological factors on scrub typical cases were statistically significant.

Conclusion/Significance: The high-risk occupations of scrub typhus in Guangzhou were farmer, house worker, and retiree. Meteorological factors and SOI played an important role in scrub typhus occurrence in Guangzhou. Non-linear relationships were observed in almost all the variables in our study. Approximately, mean temperature, and relative humidity positively correlated to the incidence of scrub typhus, on the contrary to atmospheric pressure and weekly temperature range (WTR). Aggregate rainfall and wind velocity showed an inverse-U curve, whereas the SOI appeared the bimodal distribution. These findings can be helpful to facilitate the development of the early warning system to prevent the scrub typhus.

Impact of the Southern Oscillation Index, Temperature, and Precipitation on Eastern Equine Encephalitis Virus Activity in Florida

Eastern equine encephalitis virus (EEEV), an Alphavirus from family Togaviridae, is a highly pathogenic arbovirus affecting the eastern United States, especially Florida. Effects of the Southern Oscillation Index (SOI), precipitation, and cooling degree days on EEEV horse case data in Florida from 2004 to 2018 were modeled using distributed lag nonlinear models (DLNMs). The analysis was conducted at statewide and regional scales. DLNMs were used to model potential delayed effects of the covariates on monthly counts of horse cases. Both models confirmed a seasonal trend in EEEV transmission and found that precipitation, cooling degree days, and the SOI were all predictors of monthly numbers of horse cases. EEEV activity in horses was associated with higher amounts of rainfall during the month of transmission at the statewide scale, as well as the prior 3 mo at the regional scale, fewer cooling degree days during the month of transmission and the preceding 3 mo and high SOI values during the month and the previous 2 mo, and SOI values in the prior 2 to 8 mo. Horse cases were lower during El Niño winters but higher during the following summer, while La Niña winters were associated with higher numbers of cases and fewer during the following summer. At the regional scale, extremely low levels of precipitation were associated with a suppression of EEEV cases for 3 mo. Given the periodicity and potential predictability of El Niño Southern Oscillation (ENSO) cycles, precipitation, and temperature, these results may provide a method for predicting EEEV risk potential in Florida.