Heatwaves and dengue outbreaks in Hanoi, Vietnam: New evidence on early warning

Exceptional heatwaves and mortality in Europe: Greater impacts since the coronavirus disease 2019 outbreak

Record-breaking hot weather (exceptional heatwaves) has been increasingly common worldwide, posing a significant threat to human health. However, little is known about the effect of these exceptional heatwaves on mortality in Europe, especially since the coronavirus disease 2019 (COVID-19) outbreak, which converges with climate change to affect healthcare systems and human lives. We collected mortality data of 967 regions in 30 European countries over the last decade (2014-2023) from the Eurostat. A standard time-series analysis was used to estimate the effect of exceptional heatwaves by quasi-Poisson regression model, including the main effect (effect from heatwave intensity) and the added effect (effect from heatwave duration), on mortality for each region during two periods (before and since the COVID-19 outbreak). We used random effects meta-analysis to pool the mortality risk (i.e., relative risk [RR]) and burden (i.e., attributable fraction [AF]) associated with exceptional heatwaves, at the country level and for Europe as a whole. In Europe, the mortality burden attributable to main and added effects increased from 0.492% (95% CI: 0.488%-0.496%) to 1.276% (95% CI: 1.266%-1.285%) and from 0.307% (95% CI: 0.294%-0.318%) to 0.428% (95% CI: 0.407%-0.448%), respectively. Furthermore, substantial variations across countries were observed, with some countries such as France and Spain experiencing a large increase in the mortality burden attributable to exceptional heatwaves since the COVID-19 outbreak. Our findings underscore the urgent need for heat-health actions to consider the multi-effects of exceptional heatwaves amidst a warming climate.

Sequential label shift detection in classification data: An application to dengue fever

Classifiers have been developed to help diagnose dengue fever in patients presenting with febrile symptoms. However, classifier predictions often rely on the assumption that new observations come from the same distribution as training data. If the population prevalence of dengue changes, as would happen with a dengue outbreak, it is important to raise an alarm as soon as possible, so that appropriate public health measures can be taken and also so that the classifier can be re-calibrated. In this paper, we consider the problem of detecting such a change in distribution in sequentially-observed, unlabeled classification data. We focus on label shift changes to the distribution, where the class priors shift but the class conditional distributions remain unchanged. We reduce this problem to the problem of detecting a change in the one-dimensional classifier scores, leading to simple nonparametric sequential changepoint detection procedures. Our procedures leverage classifier training data to estimate the detection statistic, and converge to their parametric counterparts in the size of the training data. In simulated outbreaks with real dengue data, we show that our method outperforms other detection procedures in this label shift setting.

Twenty-first-century demographic and social inequalities of heat-related deaths in Brazilian urban areas

Population exposure to heat waves (HWs) is increasing worldwide due to climate change, significantly affecting society, including public health. Despite its significant vulnerabilities and limited adaptation resources to rising temperatures, South America, particularly Brazil, lacks research on the health impacts of temperature extremes, especially on the role played by socioeconomic factors in the risk of heat-related illness. Here, we present a comprehensive analysis of the effects of HWs on mortality rates in the 14 most populous urban areas, comprising approximately 35% of the country's population. Excess mortality during HWs was estimated through the observed-to-expected ratio (O/E) for total deaths during the events identified. Moreover, the interplay of intersectionality and vulnerability to heat considering demographics and socioeconomic heterogeneities, using gender, age, race, and educational level as proxies, as well as the leading causes of heat-related excess death, were assessed. A significant increase in the frequency was observed from the 1970s (0-3 HWs year-1) to the 2010s (3-11 HWs year-1), with higher tendencies in the northern, northeastern, and central-western regions. Over the 2000-2018 period, 48,075 (40,448-55,279) excessive deaths were attributed to the growing number of HWs (>20 times the number of landslides-related deaths for the same period). Nevertheless, our event-based surveillance analysis did not detect the HW-mortality nexus, reinforcing that extreme heat events are a neglected disaster in Brazil. Among the leading causes of death, diseases of the circulatory and respiratory systems and neoplasms were the most frequent. Critical regional differences were observed, which can be linked to the sharp North-South inequalities in terms of socioeconomic and health indicators, such as life expectancy. Higher heat-related excess mortality was observed for low-educational level people, blacks and browns, older adults, and females. Such findings highlight that the strengthening of primary health care combined with reducing socioeconomic, racial, and gender inequalities represents a crucial step to reducing heat-related deaths.

Association between environmental factors and dengue incidence in Lao People's Democratic Republic: a nationwide time-series study

BACKGROUND

Dengue fever is a vector-borne disease of global public health concern, with an increasing number of cases and a widening area of endemicity in recent years. Meteorological factors influence dengue transmission. This study aimed to estimate the association between meteorological factors (i.e., temperature and rainfall) and dengue incidence and the effect of altitude on this association in the Lao People's Democratic Republic (Lao PDR).

METHODS

We used weekly dengue incidence and meteorological data, including temperature and rainfall, from 18 jurisdictions in Lao PDR from 2015 to 2019. A two-stage distributed lag nonlinear model with a quasi-Poisson distribution was used to account for the nonlinear and delayed associations between dengue incidence and meteorological variables, adjusting for long-term time trends and autocorrelation.

RESULTS

A total of 55,561 cases were reported in Lao PDR from 2015 to 2019. The cumulative relative risk for the 90th percentile of weekly mean temperature (29 °C) over 22 weeks was estimated at 4.21 (95% confidence interval: 2.00-8.84), relative to the 25th percentile (24 °C). The cumulative relative risk for the weekly total rainfall over 12 weeks peaked at 82 mm (relative risk = 1.76, 95% confidence interval: 0.91-3.40) relative to no rain. However, the risk decreased significantly when heavy rain exceeded 200 mm. We found no evidence that altitude modified these associations.

CONCLUSIONS

We found a lagged nonlinear relationship between meteorological factors and dengue incidence in Lao PDR. These findings can be used to develop climate-based early warning systems and provide insights for improving vector control in the country.

Climate change, environmental extremes, and human health in Australia: challenges, adaptation strategies, and policy gaps

Climate change presents a major public health concern in Australia, marked by unprecedented wildfires, heatwaves, floods, droughts, and the spread of climate-sensitive infectious diseases. Despite these challenges, Australia's response to the climate crisis has been inadequate and subject to change by politics, public sentiment, and global developments. This study illustrates the spatiotemporal patterns of selected climate-related environmental extremes (heatwaves, wildfires, floods, and droughts) across Australia during the past two decades, and summarizes climate adaptation measures and actions that have been taken by the national, state/territory, and local governments. Our findings reveal significant impacts of climate-related environmental extremes on the health and well-being of Australians. While governments have implemented various adaptation strategies, these plans must be further developed to yield concrete actions. Moreover, Indigenous Australians should not be left out in these adaptation efforts. A collaborative, comprehensive approach involving all levels of government is urgently needed to prevent, mitigate, and adapt to the health impacts of climate change.

An outbreak of a novel lineage of dengue virus 2 in Vietnam in 2022

In 2022, a large dengue outbreak was reported in Vietnam, where dengue was endemic. A total of 1889 acute-phase serum samples were collected from patients with suspected dengue at Vung Tau General Hospital, the core hospital in Vung Tau Province, southern Vietnam. Among the 1889 samples analyzed for laboratory confirmation of dengue virus (DENV) infection, 339 positive cases were identified, of which 130 were primary infections and 209 were secondary infections. DENV-2 was the dominant serotype in both primary and secondary infection groups. Phylogenetic analysis based on sequences of the envelope protein-coding region revealed the emergence of a new DENV-2 lineage during this outbreak.

Effects of high temperatures and heatwaves on dengue fever: a systematic review and meta-analysis

BACKGROUND

Studies have shown that dengue virus transmission increases in association with ambient temperature. We performed a systematic review and meta-analysis to assess the effect of both high temperatures and heatwave events on dengue transmission in different climate zones globally.

METHODS

A systematic literature search was conducted in PubMed, Scopus, Embase, and Web of Science from January 1990 to September 20, 2022. We included peer reviewed original observational studies using ecological time series, case crossover, or case series study designs reporting the association of high temperatures and heatwave with dengue and comparing risks over different exposures or time periods. Studies classified as case reports, clinical trials, non-human studies, conference abstracts, editorials, reviews, books, posters, commentaries; and studies that examined only seasonal effects were excluded. Effect estimates were extracted from published literature. A random effects meta-analysis was performed to pool the relative risks (RRs) of dengue infection per 1 °C increase in temperature, and further subgroup analyses were also conducted. The quality and strength of evidence were evaluated following the Navigation Guide systematic review methodology framework. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO).

FINDINGS

The study selection process yielded 6367 studies. A total of 106 studies covering more than four million dengue cases fulfilled the inclusion criteria; of these, 54 studies were eligible for meta-analysis. The overall pooled estimate showed a 13% increase in risk of dengue infection (RR = 1.13; 95% confidence interval (CI): 1.11-1.16, I2 = 98.0%) for each 1 °C increase in high temperatures. Subgroup analyses by climate zones suggested greater effects of temperature in tropical monsoon climate zone (RR = 1.29, 95% CI: 1.11-1.51) and humid subtropical climate zone (RR = 1.20, 95% CI: 1.15-1.25). Heatwave events showed association with an increased risk of dengue infection (RR = 1.08; 95% CI: 0.95-1.23, I2 = 88.9%), despite a wide confidence interval. The overall strength of evidence was found to be "sufficient" for high temperatures but "limited" for heatwaves. Our results showed that high temperatures increased the risk of dengue infection, albeit with varying risks across climate zones and different levels of national income.

INTERPRETATION

High temperatures increased the relative risk of dengue infection. Future studies on the association between temperature and dengue infection should consider local and regional climate, socio-demographic and environmental characteristics to explore vulnerability at local and regional levels for tailored prevention.

FUNDING

Australian Research Council Discovery Program.

Climate Crises and Developing Vector-Borne Diseases: A Narrative Review

Background

Climate change based on temperature, humidity and wind can improve many characteristics of the arthropod carrier life cycle, including survival, arthropod population, pathogen communication, and the spread of infectious agents from vectors. This study aimed to find association between content of disease followed climate change we demonstrate in humans.

Methods

All the articles from 2016 to 2021 associated with global climate change and the effect of vector-borne disease were selected form databases including PubMed and the Global Biodiversity information facility database. All the articles selected for this short review were English.

Results

Due to the high burden of infectious diseases and the growing evidence of the possible effects of climate change on the incidence of these diseases, these climate changes can potentially be involved with the COVID-19 epidemic. We highlighted the evidence of vector-borne diseases and the possible effects of climate change on these communicable diseases.

Conclusion

Climate change, specifically in rising temperature system is one of the world's greatest concerns already affected pathogen-vector and host relation. Lice parasitic, fleas, mites, ticks, and mosquitos are the prime public health importance in the transmission of virus to human hosts.

Impact of extreme weather on dengue fever infection in four Asian countries: A modelling analysis

The rapid spread of dengue fever (DF) infection has posed severe threats to global health. Environmental factors, such as weather conditions, are believed to regulate DF spread. While previous research reported inconsistent change of DF risk with varying weather conditions, few of them evaluated the impact of extreme weather conditions on DF infection risk. This study aims to examine the short-term associations between extreme temperatures, extreme rainfall, and DF infection risk in South and Southeast Asia. A total of 35 locations in Singapore, Malaysia, Sri Lanka, and Thailand were included, and weekly DF data, as well as the daily meteorological data from 2012 to 2020 were collected. A two-stage meta-analysis was used to estimate the overall effect of extreme weather conditions on the DF infection risk. Location-specific associations were obtained by the distributed lag nonlinear models. The DF infection risk appeared to increase within 1-3 weeks after extremely high temperature (e.g. lag week 2: RR = 1.074, 95 % CI: 1.022-1.129, p = 0.005). Compared with no rainfall, extreme rainfall was associated with a declined DF risk (RR = 0.748, 95 % CI: 0.620-0.903, p = 0.003), and most of the impact was across 0-3 weeks lag. In addition, the DF risk was found to be associated with more intensive extreme weathers (e.g. seven extreme rainfall days per week: RR = 0.338, 95 % CI: 0.120-0.947, p = 0.039). This study provides more evidence in support of the impact of extreme weather conditions on DF infection and suggests better preparation of DF control measures according to climate change.

Spatio-temporal dynamics of three diseases caused by Aedes-borne arboviruses in Mexico

Background

The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus (CHIKV), Dengue virus (DENV), and Zika virus (ZIKV) clusters in Mexico.

Methods

We present an integrated analysis of Aedes-borne diseases (ABDs), the local climate, and the socio-demographic profiles of 2469 municipalities in Mexico. We used SaTScan to detect spatial clusters and utilize the Pearson correlation coefficient, Randomized Dependence Coefficient, and SHapley Additive exPlanations to analyze the influence of socio-demographic and climatic factors on the prevalence of ABDs. We also compare six machine learning techniques, including XGBoost, decision tree, Support Vector Machine with Radial Basis Function kernel, K nearest neighbors, random forest, and neural network to predict risk factors of ABDs clusters.

Results

DENV is the most prevalent of the three diseases throughout Mexico, with nearly 60.6% of the municipalities reported having DENV cases. For some spatiotemporal clusters, the influence of socio-economic attributes is larger than the influence of climate attributes for predicting the prevalence of ABDs. XGBoost performs the best in terms of precision-measure for ABDs prevalence.

Conclusions

Both socio-demographic and climatic factors influence ABDs transmission in different regions of Mexico. Future studies should build predictive models supporting early warning systems to anticipate the time and location of ABDs outbreaks and determine the stand-alone influence of individual risk factors and establish causal mechanisms.

Drought-heatwave nexus in Brazil and related impacts on health and fires: A comprehensive review

Climate change is drastically altering the frequency, duration, and severity of compound drought-heatwave (CDHW) episodes, which present a new challenge in environmental and socioeconomic sectors. These threats are of particular importance in low-income regions with growing populations, fragile infrastructure, and threatened ecosystems. This review synthesizes emerging progress in the understanding of CDHW patterns in Brazil while providing insights about the impacts on fire occurrence and public health. Evidence is mounting that heatwaves are becoming increasingly linked with droughts in northeastern and southeastern Brazil, the Amazonia, and the Pantanal. In those regions, recent studies have begun to build a better understanding of the physical mechanisms behind CDHW events, such as the soil moisture-atmosphere coupling, promoted by exceptional atmospheric blocking conditions. Results hint at a synergy between CDHW events and high fire activity in the country over the last decades, with the most recent example being the catastrophic 2020 fires in the Pantanal. Moreover, we show that HWs were responsible for increasing mortality and preterm births during record-breaking droughts in southeastern Brazil. This work paves the way for a more in-depth understanding on CDHW events and their impacts, which is crucial to enhance the adaptive capacity of different Brazilian sectors.

Protective effect of pneumococcal conjugate vaccination on the short-term association between low temperatures and childhood pneumonia hospitalizations: Interrupted time-series and case-crossover analyses in Matlab, Bangladesh

Studies have shown that ambient extreme temperatures (heat and cold) were associated with an increased risk of childhood pneumonia, but the evidence is very limited in low-middle-income countries. It also remains unknown whether pneumococcal conjugate vaccine (PCV) could prevent temperature-related childhood pneumonia. This study collected data on ambient temperature and hospitalizations for childhood pneumonia in Matlab, Bangladesh from 2012 to 2016. Interrupted time series (ITS) analysis was employed to assess the impact of PCV (10-valent) intervention on childhood pneumonia hospitalizations. A time-stratified case-crossover analysis with a conditional logistic regression was performed to examine the association of childhood pneumonia hospitalizations with extreme temperatures and heatwaves before and after PCV10 intervention. Subgroup analyses were conducted to explore the modification effects of seasons, age, gender, and socioeconomic levels on temperature-related childhood pneumonia hospitalizations. We found that after PCV10 intervention, there was a sharp decrease in hospitalizations for childhood pneumonia (relative risk (RR): 0.59, 95% confidence interval (CI): 0.43-0.83). During the study period, heat effects on childhood pneumonia appeared immediately on the current day (odds ratio (OR): 1.28; 95% CI: 1.02-1.60, lag 0), while cold effects appeared 4 weeks later (OR: 1.53, 95% CI: 1.06-2.22, lag 28). Importantly, cold effects decreased significantly after PCV10 (p-value＜0.05), but heat and heatwave effects increased after PCV10 (p-value＜0.05). Particularly, children from families with a middle or low socioeconomic level, boys, and infants were more susceptible to heat-related pneumonia. This study suggests that PCV10 intervention in Bangladesh may help decrease cold-related not heat-related childhood pneumonia.

Deep learning models for forecasting dengue fever based on climate data in Vietnam

Background

Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam.

Objective

This study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change.

Methods

Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997–2013 were used to train models, which were then evaluated using data from 2014–2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).

Results and discussion

LSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features.

Conclusion

This study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years.

Dengue fever (DF) represents a significant health burden worldwide and in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam. This study aimed to use deep learning models to develop a prediction model of DF rates in Vietnam using a wide range of climate factors as input variables to inform public health responses for outbreak prevention in the context of future climate change. The study found that LSTM-ATT outperformed competing models, scoring average places of 1.60 for RMSE-based ranking and 1.90 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 12 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreaks up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. This is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich climate features, and it demonstrates the usefulness of deep learning models for climate-based DF forecasting.

The 2017 Dengue virus 1 outbreak in northern Vietnam was caused by a locally circulating virus group

Background

Dengue virus (DENV) is a member of insect vector-borne viruses, and it causes dengue fever. Southeast Asia is the epi-center of dengue fever in the world. The characterization of the virus is essential to identify the transmission and evolution of DENV.

Objectives

In 2017, there was an outbreak of Dengue virus type 1 (DENV1) in northern Vietnam and the neighboring countries. To identify the genetic character of the outbreak virus in the area, we conducted whole-genome sequencing analysis on the samples positive for the DENV1 along with real-time PCR.

Study design

In total, 1026 blood samples were collected from patients with suspected dengue fever in Ha Nam and Hai Duong province, nearby areas of the capital of Vietnam. After screening by real-time PCR, 40 of DENV1 positive samples were subjected to whole-genome sequencing, and 28 complete coding sequences were obtained.

Results

All 28 sequences were genotype I of DENV1, which is dominant in the southeast and East Asian countries. The phylogenetic analysis of the E region showed that they fell into a single cluster with the reported sequences from Vietnam between 2009 and 2016, in which the isolates from other countries are very rare. Our results suggested that the 2017 outbreak in the area was caused by locally circulating viruses.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41182-021-00386-0.

Community Knowledge and Attitudes about the Transmission of Dengue Haemorrhagic Fever and Its Relationship to Prevention Behaviour in Palembang, South Sumatra, Indonesia

BACKGROUND: PSN 3 M Plus is a long-running program in Indonesia for the prevention and control of dengue hemorrhagic fever (DHF). AIM: This study aimed to determine the knowledge, behavior, attitudes, and beliefs of the community toward PSN 3 M Plus in preventing and controlling the spread of DHF. METHODS: A cluster random sampling method was used to recruit 200 respondents in endemic areas and 100 respondents in sporadic locations of Indonesia from August 2020 to February 2021. The respondents were interviewed directly by interviewers and the relationships between demographics and characteristics with the practice of PSN 3M Plus prevention behavior on the incidence of DHF were analyzed. RESULTS: Most respondents had good knowledge regarding the cause of DHF. Although respondents recognized and understood the dangers of and how to control DHF, most did not follow PSN 3 M Plus and believed that fogging was the most effective control measure. There was a significant relationship between the characteristics of the respondents in terms of education, occupation, and attitude on vector control practice. CONCLUSION: Although community environmental modifications can be a cost-effective approach to reduce the incidence of DHF, there is a need to raise public awareness regarding preventive vector control measures as good knowledge does not guarantee good compliance with PSN 3M Plus recommendations.

Impact of Climate Variability and Incidence on Dengue Hemorrhagic Fever in Palembang City, South Sumatra, Indonesia

Dengue hemorrhagic fever (DHF) is a dengue virus infection transmitted by Aedes spp. Climate has a profound influence on mosquito breeding. Palembang has the highest rate of DHF in South Sumatra. This study aimed to investigate the relationship between the components of climate factors and the incidence of DHF in Palembang. This study was cross-sectional, with an observational analytic approach. The Palembang City Health Office compiled data on DHF incidence rates from 2016 to 2020. Climatic factor data (rainfall, number of rainy days, temperature, humidity, wind speed, sun irradiance) were collected from the Climatology Station Class I Palembang - BMKG Station and Task Force that same year. The Spearman test was used to conduct the correlation test. Between 2016 and 2020, there were 3,398 DHF patients. From January to May, DHF increased. There was a significant correlation between rainfall (r = 0.320; p = 0.005), number of rainy days (r = 0.295; p = 0.020), temperature (r = 0.371; p = 0.040), and humidity (r = 0.221; p = 0.024), wind speed (r= 0.76; p = 0.492), and sunlight (r = 0.008; p = 0.865). Rainfall, the number of rainy days, and temperature were three climatic factors determining the increase in dengue incidence in Palembang.

Extreme weather events and dengue outbreaks in Guangzhou, China: a time-series quasi-binomial distributed lag non-linear model

Dengue transmission is climate-sensitive and permissive conditions regularly cause large outbreaks in Asia-Pacific area. As climate change progresses, extreme weather events such as heatwaves and unusually high rainfall are predicted more intense and frequent, but their impacts on dengue outbreaks remain unclear so far. This paper aimed to investigate the relationship between extreme weather events (i.e., heatwaves, extremely high rainfall and extremely high humidity) and dengue outbreaks in China. We obtained daily number of locally acquired dengue cases and weather factors for Guangzhou, China, for the period 2006-2015. The definition of dengue outbreaks was based on daily number of locally acquired cases above the threshold (i.e., mean + 2SD of daily distribution of dengue cases during peaking period). Heatwave was defined as ≥2 days with temperature ≥ 95th percentile, and extreme rainfall and humidity defined as daily values ≥95th percentile during 2006-2015. A generalized additive model was used to examine the associations between extreme weather events and dengue outbreaks. Results showed that all three extreme weather events were associated with increased risk of dengue outbreaks, with a risk increase of 115-251% around 6 weeks after heatwaves, 173-258% around 6-13 weeks after extremely high rainfall, and 572-587% around 6-13 weeks after extremely high humidity. Each extreme weather event also had good capacity in predicting dengue outbreaks, with the model's sensitivity, specificity, accuracy, and area under the receiver operating characteristics curve all exceeding 86%. This study found that heatwaves, extremely high rainfall, and extremely high humidity could act as potential drivers of dengue outbreaks.

The effects of maximum ambient temperature and heatwaves on dengue infections in the tropical city-state of Singapore - A time series analysis

BACKGROUND

Global incidence of dengue has surged rapidly over the past decade. Each year, an estimated 390 million infections occur worldwide, with Asia-Pacific countries bearing about three-quarters of the global dengue disease burden. Global warming may influence the pattern of dengue transmission. While previous studies have shown that extremely high temperatures can impede the development of the Aedes mosquito, the effect of such extreme heat over a sustained period, also known as heatwaves, has not been investigated in a tropical climate setting.

AIM

We examined the short-term relationships between maximum ambient temperature and heatwaves and reported dengue infections in Singapore, via ecological time series analysis, using data from 2009 to 2018.

METHODS

We studied the effect of two measures of extreme heat - (i) heatwaves and (ii) maximum ambient temperature. We used a negative binomial regression, coupled with a distributed lag nonlinear model, to examine the immediate and lagged associations of extreme temperature on dengue infections, on a weekly timescale. We adjusted for long-term trend, seasonality, rainfall and absolute humidity, public holidays and autocorrelation.

RESULTS

We observed an overall inhibitive effect of heatwaves on the risk of dengue infections, and a parabolic relationship between maximum temperature and dengue infections. A 1 °C increase in maximum temperature from 31 °C was associated with a 13.1% (Relative Risk (RR): 0.868, 95% CI: 0.798, 0.946) reduction in the cumulative risk of dengue infections over six weeks. Weeks with 3 heatwave days were associated with a 28.3% (RR: 0.717, 95% CI: 0.608, 0.845) overall reduction compared to weeks with no heatwave days. Adopting different heatwaves specifications did not substantially alter our estimates.

CONCLUSION

Extreme heat was associated with decreased dengue incidence. Findings from this study highlight the importance of understanding the temperature dependency of vector-borne diseases in resource planning for an anticipated climate change scenario.

Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability

BACKGROUND

Previous studies have shown associations between local weather factors and dengue incidence in tropical and subtropical regions. However, spatial variability in those associations remains unclear and evidence is scarce regarding the effects of weather extremes.

OBJECTIVES

We examined spatial variability in the effects of various weather conditions on the unprecedented dengue outbreak in Guangdong province of China in 2014 and explored how city characteristics modify weather-related risk.

METHODS

A Bayesian spatial conditional autoregressive model was used to examine the overall and city-specific associations of dengue incidence with weather conditions including (1) average temperature, temperature variation, and average rainfall; and (2) weather extremes including numbers of days of extremely high temperature and high rainfall (both used 95th percentile as the cut-off). This model was run for cumulative dengue cases during five months from July to November (accounting for 99.8% of all dengue cases). A further analysis based on spatial variability was used to validate the modification effects by economic, demographic and environmental factors.

RESULTS

We found a positive association of dengue incidence with average temperature in seven cities (relative risk (RR) range: 1.032 to 1.153), a positive association with average rainfall in seven cities (RR range: 1.237 to 1.974), and a negative association with temperature variation in four cities (RR range: 0.315 to 0.593). There was an overall positive association of dengue incidence with extremely high temperature (RR:1.054, 95% credible interval (CI): 1.016 to 1.094), without evidence of variation across cities, and an overall positive association of dengue with extremely high rainfall (RR:1.505, 95% CI: 1.096 to 2.080), with seven regions having stronger associations (RR range: 1.237 to 1.418). Greater effects of weather conditions appeared to occur in cities with higher economic level, lower green space coverage and lower elevation.

CONCLUSIONS

Spatially varied effects of weather conditions on dengue outbreaks necessitate area-specific dengue prevention and control measures. Extremes of temperature and rainfall have strong and positive associations with dengue outbreaks.

The effect of global warming on mortality

There is a significant relationship between ambient temperature and mortality. In healthy individuals with no underlying co-morbid conditions, there is an efficient heat regulation system which enables the body to effectively handle thermal stress. However, in vulnerable groups, especially in elderly over the age of 65 years, infants and individuals with co-morbid cardiovascular and/or respiratory conditions, there is a deficiency in thermoregulation. When temperatures exceed a certain limit, being cold winter spells or heat waves, there is an increase in the number of deaths. In particular, it has been shown that at temperatures above 27 °C, the daily mortality rate increases more rapidly per degree rise compared to when it drops below 27 °C. This is especially of relevance with the current emergency of global warming. Besides the direct effect of temperature rises on human health, global warming will have a negative impact on primary producers and livestock, leading to malnutrition, which will in turn lead to a myriad of health related issues. This is further exacerbated by environmental pollution. Public health measures that countries should follow should include not only health-related information strategies aiming to reduce the exposure to heat for vulnerable individuals and the community, but improved urban planning and reduction in energy consumption, among many others. This will reduce the carbon footprint and help avert global warming, thus reducing mortality.

Changes in the Magnitude of the Individual and Combined Effects of Contaminants, Warming, and Predators on Tropical Cladocerans across 11 Generations

A massive challenge in ecotoxicology is assessing how the interaction of contaminants, climate change, and biotic stressors shapes the structure and functions of natural populations. Furthermore, it is not known whether contemporary evolutionary responses to multiple stressors across multigenerations may alter the interaction of these stressors. To address these issues, we exposed Moina dubia to lead (Pb, 50 μg/L) under two temperatures (25 and 28 °C) with/without predator cues from climbing perch (Anabas testudineus) for 11 generations (F1-F11). We assessed changes in M. dubia fitness, including development time, adult size, lifespan, fecundity, and neonate production. We found strong negative effects of Pb, elevated temperature, and predator cues on the fitness of M. dubia. Strikingly, Pb-induced reduction in the performance of M. dubia was stronger at 25 °C and in the absence of predator cues. The individual and interactive effects of Pb, temperature, and predator cues on M. dubia were stronger across F1-F9 and generally leveled off in F10-F11. Our results highlight the high vulnerability of M. dubia to multiple stressors, thus weakening top-down control on algal blooms in eutrophic lakes. Our study underscores the importance of integrating evolutionary responses in realistic ecotoxicological risk assessments of contaminants interacting with climatic and biotic stressors.

Predominant secondary dengue infection among Vietnamese adults mostly without warning signs and severe disease

BACKGROUND

The morbidity in dengue fever is dependent on the dengue virus (DENV) serotypes, the patient age, predisposing immunogenic markers and the frequency of primary and secondary infections. This study aims to distinguish acute primary from secondary dengue infections of Vietnamese adults and to assess the association of viremia and anti-dengue immunoglobulin levels with clinical outcomes.

STUDY DESIGN

Viral RNA, dengue serotypes and levels of anti-dengue IgM and IgG of hospitalized adult cases were determined in EDTA-plasma samples prospectively collected during three consecutive years of dengue infection in Hanoi. Patients admitted to hospital within 7 days of their 1st reported fever were included. Primary infections were anti-dengue IgG enzyme-linked immunosorbent assay (ELISA) negative on both day of hospital entry (day 0) and day two or three of hospitalization (day 2 or 3) with a positive anti-dengue IgM on either day 0 or day 2 or 3 hospitalization. The secondary infections were anti-dengue IgG ELISA positive on both day 0 and day 2 or 3 with positive anti-dengue IgM ELISA on either day 0 or day 2 or 3.

RESULTS

The hospitalized dengue fever cases between October 2016 and March 2019 were predominantly secondary infections (74%, 68% and 77%, respectively) with DENV-1 (60% and 65%) and DENV-2 (22% and 26%) serotypes determined in the latter two years. The viremia in primary infection was significantly higher than that in secondary infection (P < 0.01) and positively correlated with the days of hospital stay. In secondary infections, platelet counts were lower than in primary infections (P = 0.04) and IgG levels in secondary infection negatively correlated with platelet counts (Spearman's r = -0.22, P < 0.01).

CONCLUSIONS

Our results indicate high rates of secondary infection with DENV1 and DENV2 serotypes. Anti-dengue immunoglobulins negatively correlate with hospital stay and platelet counts with few warning signs or severe disease. Further investigations of specific antibodies in adults which predict auto-inflammatory activity after the recovery from dengue infection are warranted.