Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia

Climate and visitors as the influencing factors of dengue fever in Badung District of Bali, Indonesia

Badung district has recorded the highest dengue fever (DF) in Bali Province. This research presents the distribution of DF in Badung district and analyses its association with climate and visitors. The monthly data of DF, climate and number of visitors during January 2013 to December 2017 were analysed using Poisson Regression. A total of 10,689 new DF cases were notified from January 2013 to December 2017. DF in 2016 was recorded as the heaviest incidence. Monthly DF cases have positive association with average temperature (0.59 (95% CI: 0.56-.62)), precipitation (5.7 x 10-4 (95% CI: 3.8 x 10-4 - 7.6 x 10-4)), humidity (.014 (95% CI: 0.003-.025)) and local visitors (7.40 x 10-6 95% CI: 5.88 x 10-6 : 8.91 x 10-6). Negative association was shown between DF cases with foreign visitors (-2.18 x 10-6 (95% CI: -2.50 x 10-6 : -1.87 x 10-6)). This study underlines the urgency to integrate climate and tourism for DF surveillance.

Climate and vector-borne diseases in Indonesia: a systematic literature review and critical appraisal of evidence

Climate is widely known as an important driver to transmit vector-borne diseases (VBD). However, evidence of the role of climate variability on VBD risk in Indonesia has not been adequately understood. We conducted a systematic literature review to collate and critically review studies on the relationship between climate variability and VBD in Indonesia. We searched articles on PubMed, Scopus, and Google Scholar databases that are published until December 2021. Studies that reported the relationship of climate and VBD, such as dengue, chikungunya, Zika, and malaria, were included. For the reporting, we followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 66 out of 284 studies were reviewed. Fifty-two (78.8%) papers investigated dengue, 13 (19.7%) papers studied malaria, one (1.5%) paper discussed chikungunya, and no (0%) paper reported on Zika. The studies were predominantly conducted in western Indonesian cities. Most studies have examined the short-term effect of climate variability on the incidence of VBD at national, sub-national, and local levels. Rainfall (n = 60/66; 90.9%), mean temperature (T_mean) (n = 50/66; 75.8%), and relative humidity (RH) (n = 50/66; 75.8%) were the common climatic factors employed in the studies. The effect of climate on the incidence of VBD was heterogenous across locations. Only a few studies have investigated the long-term effects of climate on the distribution and incidence of VBD. The paucity of high-quality epidemiological data and variation in methodology are two major issues that limit the generalizability of evidence. A unified framework is required for future research to assess the impacts of climate on VBD in Indonesia to provide reliable evidence for better policymaking.

The impacts of precipitation patterns on dengue epidemics in Guangzhou city

Some studies have demonstrated that precipitation is an important risk factor of dengue epidemics. However, current studies mostly focused on a single precipitation variable, and few studies focused on the impact of precipitation patterns on dengue epidemics. This study aims to explore optimal precipitation patterns for dengue epidemics. Weekly dengue case counts and meteorological data from 2006 to 2018 in Guangzhou of China were collected. A generalized additive model with Poisson distribution was used to investigate the association between precipitation patterns and dengue. Precipitation patterns were defined as the combinations of three weekly precipitation variables: accumulative precipitation (Pre_A), the number of days with light or moderate precipitation (Pre_LMD), and the coefficient of precipitation variation (Pre_CV). We explored to identify optimal precipitation patterns for dengue epidemics. With a lead time of 10 weeks, minimum temperature, relative humidity, Pre_A, and Pre_LMD were positively associated with dengue, while Pre_CV was negatively associated with dengue. A precipitation pattern with Pre_A of 20.67-55.50 mm per week, Pre_LMD of 3-4 days per week, and Pre_CV less than 1.41 per week might be an optimal precipitation pattern for dengue epidemics in Guangzhou. The finding may be used for climate-smart early warning and decision-making of dengue prevention and control.

Effect modification of the association between diurnal temperature range and hospitalisations for ischaemic stroke by temperature in Hefei, China

OBJECTIVES

Diurnal temperature range (DTR) is an important indicator of global climate change. Many epidemiological studies have reported the associations between high DTR and human health. This study investigated the association between DTR and hospitalisations for ischaemic stroke in Hefei, China.

STUDY DESIGN

This is an ecological study.

METHODS

Data of daily hospital admissions for ischaemic stroke and meteorological variables from 1 January 2009 to 31 December 2017 were collected in Hefei, China. A generalised additive model combined with distributed lag non-linear model was used to quantify the effects of DTR on ischaemic stroke. The interactive effect between DTR and temperature was explored with a non-parametric bivariate response surface model.

RESULTS

High DTR was associated with hospitalisations for ischaemic stroke. The adverse effect of extremely high DTR (99th percentile [17.1 °C]) occurred after 8 days (relative risk [RR] = 1.021, 95% confidence interval [CI] = 1.002, 1.041) and the maximum effect appeared after 12 days (RR = 1.029, 95% CI = 1.011, 1.046). The overall trend of the effect of DTR on ischaemic stroke was decreasing. In addition, there was a significant interactive effect of high DTR and low temperature on ischaemic stroke.

CONCLUSIONS

This study suggests that the impact of high DTR should be considered when formulating targeted measures to prevent ischaemic stroke, especially for those days with high DTR and low mean temperature.

Impact of recent climate extremes on mosquito-borne disease transmission in Kenya

Climate change and variability influence temperature and rainfall, which impact vector abundance and the dynamics of vector-borne disease transmission. Climate change is projected to increase the frequency and intensity of extreme climate events. Mosquito-borne diseases, such as dengue fever, are primarily transmitted by Aedes aegypti mosquitoes. Freshwater availability and temperature affect dengue vector populations via a variety of biological processes and thus influence the ability of mosquitoes to effectively transmit disease. However, the effect of droughts, floods, heat waves, and cold waves is not well understood. Using vector, climate, and dengue disease data collected between 2013 and 2019 in Kenya, this retrospective cohort study aims to elucidate the impact of extreme rainfall and temperature on mosquito abundance and the risk of arboviral infections. To define extreme periods of rainfall and land surface temperature (LST), we calculated monthly anomalies as deviations from long-term means (1983–2019 for rainfall, 2000–2019 for LST) across four study locations in Kenya. We classified extreme climate events as the upper and lower 10% of these calculated LST or rainfall deviations. Monthly Ae. aegypti abundance was recorded in Kenya using four trapping methods. Blood samples were also collected from children with febrile illness presenting to four field sites and tested for dengue virus using an IgG enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR). We found that mosquito eggs and adults were significantly more abundant one month following an abnormally wet month. The relationship between mosquito abundance and dengue risk follows a non-linear association. Our findings suggest that early warnings and targeted interventions during periods of abnormal rainfall and temperature, especially flooding, can potentially contribute to reductions in risk of viral transmission.

Dengue is a rapidly spreading mosquito-borne disease transmitted primarily by Aedes aegypti mosquitoes. As climate change leads to extremes in rainfall and temperature, the abundance and populations of these vectors will be affected, thus influencing transmission of dengue. Using satellite-derived climate data for Kenya, we classified months that experienced highly abnormal rainfall and temperature as extreme climate events (floods, droughts, heat waves, or cold waves). We compared the average monthly Ae. aegypti abundance and confirmed dengue counts following extreme climate months using lag periods of one month and two months, respectively. This study utilized several statistical models to account for differences among study sites and time. Floods resulted in significantly increased egg and adult abundance. Our results contributed to a better understanding of the effect of climate variability and change on dengue. As suggested by our observed increase in vector counts yet a relatively unchanged dengue infection risk, human behavior can help reduce viral transmission. Targeted interventions should be focused on both reducing vector populations and limiting human-vector contact, especially during these climate anomalies.

Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

The purpose of this study is to enhance the exploration capability of conventional Salp Swarm Algorithm (SSA) with the inducing of Levy Flight. With such modification, it will assist the SSA from trapping in local optimum. The proposed approach, which is later known as an improved SSA (iSSA) is employed in monthly dengue outbreak prediction. For that matter, monthly dataset of rainfall, humidity, temperature and number of dengue cases were employed, which render prediction information. The efficiency of the proposed algorithm is evaluated using Root Mean Square Error (RMSE), and compared against the conventional SSA and Ant Colony Optimization (ACO). The obtained results suggested that the iSSA was not only able to produce lower RMSE, but also capable to converge faster at lower rate as well.

Effects of ambient temperature and precipitation on the risk of dengue fever: A systematic review and updated meta-analysis

OBJECTIVES

We systematically reviewed the published studies on the relationship between dengue fever and meteorological factors and applied a meta-analysis to explore the effects of ambient temperature and precipitation on dengue fever.

METHODS

We completed the literature search by the end of September 1st, 2019 using databases including Science Direct, PubMed, Web of Science, and Google Scholar. We extracted relative risks (RRs) in selected studies and converted all effect estimates to the RRs per 1 °C increase in temperature and 10 mm increase in precipitation, and combined all standardized RRs together using random-effect meta-analysis.

RESULTS

Our results show that dengue fever was significantly associated with both temperature and precipitation. Our subgroup analyses suggested that the effect of temperature on dengue fever was most pronounced in high-income subtropical areas. The pooled RR of dengue fever associated with the maximum temperature was much lower than the overall effect.

CONCLUSIONS

Temperature and precipitation are important risk factors for dengue fever. Future studies should focus on factors that can distort the effects of temperature and precipitation.

Influence of coarse particulate matter on chickenpox in Jiading District, Shanghai, 2009-2018: A distributed lag non-linear time series analysis

Although the link between ambient air pollution and some infectious diseases has been studied, few studies have explored so far, the relationship between chickenpox and particulate matter. Daily chickenpox counts in Jiading District, Shanghai, were collected from 2009 to 2018. Time series analysis was conducted to describe the trends of the daily number of chickenpox cases and the concentration of particulate matter 10 μm or less (PM10). The distributed lag non-linear model (DLNM) was developed to assess the lag and non-linear relationship between the number of chickenpox cases and PM10 concentration adjusting for meteorological factors and other pollutants. Spatiotemporal scanning was used to detect the clustering of chickenpox cases. There was a concomitant relationship between the number of chickenpox cases and PM10 concentration, especially in the period of high PM10 concentration. DLNM results showed a nonlinear relationship between the number of chickenpox cases and PM10 concentration with the maximum effect of PM10 being lagged for 13-14 days, which was consistent with the average incubation period of chickenpox. PM10 was significantly associated with the daily number of chickenpox cases when above 300 μg/m³. The risk of chickenpox increased with increasing PM10 concentration and the association was strongest at the lag of 14 day (RR = 1.13, 95% CI: 1.04-1.23) for PM10 concentration of 500 μg/m³ versus 50 μg/m³. The study provides evidence that high PM10 concentration increases the risk of chickenpox spreading.

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

The aim of this study was to assess the possible association of El Niño Southern Oscillation (ENSO) and Dipole Mode Index (DMI) on chikungunya incidence overtime, including the significant reduction in cases that was observed in 2017 in Indonesia. Monthly nation-wide chikungunya case reports were obtained from the Indonesian National Disease Surveillance database, and incidence rates (IR) and case fatality rate (CFR) were calculated. Monthly data of Niño3.4 (indicator used to represent the ENSO) and DMI between 2011 and 2017 were also collected. Correlations between monthly IR and CFR and Niño3.4 and DMI were assessed using Spearman's rank correlation. We found that chikungunya case reports declined from 1972 cases in 2016 to 126 cases in 2017, a 92.6% reduction; the IR reduced from 0.67 to 0.05 cases per 100,000 population. No deaths associated with chikungunya have been recorded since its re-emergence in Indonesia in 2001. There was no significant correlation between monthly Niño3.4 and chikungunya incidence with r = -0.142 (95%CI: -0.320-0.046), p = 0.198. However, there was a significant negative correlation between monthly DMI and chikungunya incidence, r = -0.404 (95%CI: -0.229--0.554) with p < 0.001. In conclusion, our initial data suggests that the climate variable, DMI but not Niño3.4, is likely associated with changes in chikungunya incidence. Therefore, further analysis with a higher resolution of data, using the cross-wavelet coherence approach, may provide more robust evidence.

Diurnal temperature range and childhood asthma in Hefei, China: Does temperature modify the association?

BACKGROUND

The present study aimed to understand the effect of diurnal temperature range (DTR) on childhood asthma in Hefei, China, and to explore the effect of temperature on the DTR-asthma association.

MATERIALS AND METHODS

Daily data on hospital admissions for childhood asthma, air pollutants, and weather variables in Hefei, China, from 1st January 2014 to 31st December 2015, were collected. A generalized additive model combined with a distributed lag non-linear model was used to quantify the effects of DTR on the total, age- and gender-specific hospital admissions for childhood asthma. A non-parametric bivariate response surface model, and a generalized additive model combined with a stratified parametric model were used to explore the interaction between DTR and temperature.

RESULTS

We observed that high DTR was associated with an increase in hospital admissions for childhood asthma. When DTR increased from 6.7 °C to 16.8 °C (99% percentile), hospital admissions for childhood asthma increased by 13% (relative risk: 1.13, 95% confidence interval: 1.07, 1.12). The analysis stratified, by mean temperature level, suggested that when DTR increased by 1 °C at low temperatures, asthma hospitalizations in total children, girls, boys and school-age children increased by 5.0% (95% CI: 2.6%, 7.5%), 3.7% (95% CI: 0.4%, 5.7%), 2.9% (95% CI: 0.8%, 4.4%) and 5.0% (95% CI: 2.6%, 7.5%), respectively.

CONCLUSIONS

This study suggests that the impact of high DTR should be considered among public health advice for children with existing asthma. Those days with high DTR and low mean temperature need extra attention.

Projecting the future of dengue under climate change scenarios: Progress, uncertainties and research needs

BACKGROUND

Dengue is a mosquito-borne viral disease and its transmission is closely linked to climate. We aimed to review available information on the projection of dengue in the future under climate change scenarios.

METHODS

Using five databases (PubMed, ProQuest, ScienceDirect, Scopus and Web of Science), a systematic review was conducted to retrieve all articles from database inception to 30th June 2019 which projected the future of dengue under climate change scenarios. In this review, "the future of dengue" refers to disease burden of dengue, epidemic potential of dengue cases, geographical distribution of dengue cases, and population exposed to climatically suitable areas of dengue.

RESULTS

Sixteen studies fulfilled the inclusion criteria, and five of them projected a global dengue future. Most studies reported an increase in disease burden, a wider spatial distribution of dengue cases or more people exposed to climatically suitable areas of dengue as climate change proceeds. The years 1961-1990 and 2050 were the most commonly used baseline and projection periods, respectively. Multiple climate change scenarios introduced by the Intergovernmental Panel on Climate Change (IPCC), including B1, A1B, and A2, as well as Representative Concentration Pathway 2.6 (RCP2.6), RCP4.5, RCP6.0 and RCP8.5, were most widely employed. Instead of projecting the future number of dengue cases, there is a growing consensus on using "population exposed to climatically suitable areas for dengue" or "epidemic potential of dengue cases" as the outcome variable. Future studies exploring non-climatic drivers which determine the presence/absence of dengue vectors, and identifying the pivotal factors triggering the transmission of dengue in those climatically suitable areas would help yield a more accurate projection for dengue in the future.

CONCLUSIONS

Projecting the future of dengue requires a systematic consideration of assumptions and uncertainties, which will facilitate the development of tailored climate change adaptation strategies to manage dengue.