Projecting the impact of climate change on dengue transmission in Dhaka, Bangladesh

Mathematical analysis and prediction of future outbreak of dengue on time-varying contact rate using machine learning approach

This article introduces a novel mathematical model analyzing the dynamics of Dengue in the recent past, specifically focusing on the 2023 outbreak of this disease. The model explores the patterns and behaviors of dengue fever in Bangladesh. Incorporating a sinusoidal function reveals significant mid-May to Late October outbreak predictions, aligning with the government's exposed data in our simulation. For different amplitudes (A) within a sequence of values (A = 0.1 to 0.5), the highest number of infected mosquitoes occurs in July. However, simulations project that when βM = 0.5 and A = 0.1, the peak of human infections occurs in late September. Not only the next-generation matrix approach along with the stability of disease-free and endemic equilibrium points are observed, but also a cutting-edge Machine learning (ML) approach such as the Prophet model is explored for forecasting future Dengue outbreaks in Bangladesh. Remarkably, we have fitted our solution curve of infection with the reported data by the government of Bangladesh. We can predict the outcome of 2024 based on the ML Prophet model situation of Dengue will be detrimental and proliferate 25 % compared to 2023. Finally, the study marks a significant milestone in understanding and managing Dengue outbreaks in Bangladesh.

Long-term effects of climate factors on dengue fever over a 40-year period

BACKGROUND

Dengue fever stands as one of the most extensively disseminated mosquito-borne infectious diseases worldwide. While numerous studies have investigated its influencing factors, a gap remains in long-term analysis, impeding the identification of temporal patterns, periodicity in transmission, and the development of effective prevention and control strategies. Thus, we aim to analyze the periodicity of dengue fever incidence and explore the association between various climate factors and the disease over an extended time series.

METHODS

By utilizing monthly dengue fever cases and climate data spanning four decades (1978-2018) in Guangdong province, China, we employed wavelet analysis to detect dengue fever periodicity and analyze the time-lag relationship with climate factors. Additionally, Geodetector q statistic was employed to quantify the explanatory power of each climate factor and assess interaction effects.

RESULTS

Our findings revealed a prolonged transmission period of dengue fever over the 40-year period, transitioning from August to November in the 1970s to nearly year-round in the 2010s. Moreover, we observed lags of 1.5, 3.5, and 3 months between dengue fever and temperature, relative humidity, and precipitation, respectively. The explanatory power of precipitation, temperature, relative humidity, and the Oceanic Niño Index (ONI) on dengue fever was determined to be 18.19%, 12.04%, 11.37%, and 5.17%, respectively. Dengue fever exhibited susceptibility to various climate factors, with notable nonlinear enhancement arising from the interaction of any two variables. Notably, the interaction between precipitation and humidity yielded the most significant effect, accounting for an explanatory power of 75.32%.

CONCLUSIONS

Consequently, future prevention and control strategies for dengue fever should take into account these climate changes and formulate corresponding measures accordingly. In regions experiencing the onset of high temperatures, humidity, and precipitation, it is imperative to initiate mosquito prevention and control measures within a specific window period of 1.5 months.

A perspective on the worst ever dengue outbreak 2023 in Bangladesh: What makes this old enemy so deadly, and how can we combat it?

Background and Aims

Bangladesh has been going through outbreaks of dengue fever cases every year since 2000. Yet this year's (2023) episode of dengue fever has crossed every line concerning fatality. Symptoms of the fever range from high fever, headaches, and muscle aches to deadly dengue hemorrhagic fever (DHF). The present review aims to assess the current pathogenicity and associated risk factors of recent dengue outbreaks in Bangladesh.

Methods

To perform this review work, we extracted relevant information from published articles available in PubMed, Scopus, and Google Scholar. We used dengue virus, dengue fever, and dengue outbreaks as keywords while searching for information.

Results

This Aedes mosquito-transmitted viral fever is more common in Bangladesh because of the tropical nature and immense burden of populations, resulting in convenient conditions for the reproduction of the vector. The rapid genetic transformation of this RNA virus and the resistance of its vector against insecticides have intensified the situation. The number of hospitalized patients has increased, and the case fatality rate has risen to 0.47%. Inadequate mosquito control measures, plenty of vector breeding sites, and a lack of public awareness have worsened the situation. Routine spraying of effective insecticides in high-risk zones, regular inspection of potential mosquito breeding sites, and public awareness campaigns are the keys to limiting the spread of this virus. Also, the availability of detection kits, improved hospital settings, and trained health professionals are mandatory to keep disease fatalities under control.

Conclusion

Dengue fever is a preventable disease. The successful development of a competent vaccine is now a prime need for preventing any future upsurge of the disease. Also, we recommend public awareness, vector control activities, and global collaboration to prevent spread.

Zika, chikungunya and co-occurrence in Brazil: space-time clusters and associated environmental-socioeconomic factors

Chikungunya and Zika have been neglected as emerging diseases. This study aimed to analyze the space-time patterns of their occurrence and co-occurrence and their associated environmental and socioeconomic factors. Univariate (individually) and multivariate (co-occurrence) scans were analyzed for 608,388 and 162,992 cases of chikungunya and Zika, respectively. These occurred more frequently in the summer and autumn. The clusters with the highest risk were initially located in the northeast, dispersed to the central-west and coastal areas of São Paulo and Rio de Janeiro (2018-2021), and then increased in the northeast (2019-2021). Chikungunya and Zika demonstrated decreasing trends of 13% and 40%, respectively, whereas clusters showed an increasing trend of 85% and 57%, respectively. Clusters with a high co-occurrence risk have been identified in some regions of Brazil. High temperatures are associated with areas at a greater risk of these diseases. Chikungunya was associated with low precipitation levels, more urbanized environments, and places with greater social inequalities, whereas Zika was associated with high precipitation levels and low sewage network coverage. In conclusion, to optimize the surveillance and control of chikungunya and Zika, this study's results revealed high-risk areas with increasing trends and priority months and the role of socioeconomic and environmental factors.

Modelling the impact of climate change on dengue outbreaks and future spatiotemporal shift in Pakistan

Climate change has a significant impact on the intensity and spread of dengue outbreaks. The objective of this study is to assess the number of dengue transmission suitable days (DTSD) in Pakistan for the baseline (1976-2005) and future (2006-2035, 2041-2070, and 2071-2099) periods under Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Moreover, potential spatiotemporal shift and future hotspots of DTSD due to climate change were also identified. The analysis is based on fourteen CMIP5 models that have been downscaled and bias-corrected with quantile delta mapping technique, which addresses data stationarity constraints while preserving future climate signal. The results show a higher DTSD during the monsoon season in the baseline in the study area except for Sindh (SN) and South Punjab (SP). In future periods, there is a temporal shift (extension) towards pre- and post-monsoon. During the baseline period, the top ten hotspot cities with a higher frequency of DTSD are Karachi, Hyderabad, Sialkot, Jhelum, Lahore, Islamabad, Balakot, Peshawar, Kohat, and Faisalabad. However, as a result of climate change, there is an elevation-dependent shift in DTSD to high-altitude cities, e.g. in the 2020s, Kotli, Muzaffarabad, and Drosh; in the 2050s, Garhi Dopatta, Quetta, and Zhob; and in the 2080s, Chitral and Bunji. Karachi, Islamabad, and Balakot will remain highly vulnerable to dengue outbreaks for all the future periods of the twenty-first century. Our findings also indicate that DTSD would spread across Pakistan, particularly in areas where we have never seen dengue infections previously. The good news is that the DTSD in current hotspot cities is projected to decrease in the future due to climate change. There is also a temporal shift in the region during the post- and pre-monsoon season, which provides suitable breeding conditions for dengue mosquitos due to freshwater; therefore, local authorities need to take adaption and mitigation actions.

Projecting future risk of dengue related to hydrometeorological conditions in mainland China under climate change scenarios: a modelling study

BACKGROUND

We have limited knowledge on the impact of hydrometeorological conditions on dengue incidence in China and its associated disease burden in a future with a changed climate. This study projects the excess risk of dengue caused by climate change-induced hydrometeorological conditions across mainland China.

METHODS

In this modelling study, the historical association between the Palmer drought severity index (PDSI) and dengue was estimated with a spatiotemporal Bayesian hierarchical model from 70 cities. The association combined with the dengue-transmission biological model was used to project the annual excess risk of dengue related to PDSI by 2100 across mainland China, under three representative concentration pathways ([RCP] 2·6, RCP 4·5, and RCP 8·5).

FINDINGS

93 101 dengue cases were reported between 2013 and 2019 in mainland China. Dry and wet conditions within 3 months lag were associated with increased risk of dengue. Locations with potential dengue risk in China will expand in the future. The hydrometeorological changes are projected to substantially affect the risk of dengue in regions with mid-to-low latitudes, especially the coastal areas under high emission scenarios. By 2100, the annual average increased excess risk is expected to range from 12·56% (95% empirical CI 9·54-22·24) in northwest China to 173·62% (153·15-254·82) in south China under the highest emission scenario.

INTERPRETATION

Hydrometeorological conditions are predicted to increase the risk of dengue in the future in the south, east, and central areas of mainland China in disproportionate patterns. Our findings have implications for the preparation of public health interventions to minimise the health hazards of non-optimal hydrometeorological conditions in a context of climate change.

FUNDING

National Natural Science Foundation of China.

Prediction of dengue annual incidence using seasonal climate variability in Bangladesh between 2000 and 2018

The incidence of dengue has increased rapidly in Bangladesh since 2010 with an outbreak in 2018 reaching a historically high number of cases, 10,148. A better understanding of the effects of climate variability before dengue season on the increasing incidence of dengue in Bangladesh can enable early warning of future outbreaks. We developed a generalized linear model to predict the number of annual dengue cases based on monthly minimum temperature, rainfall and sunshine prior to dengue season. Variable selection and leave-one-out cross-validation were performed to identify the best prediction model and to evaluate the model's performance. Our model successfully predicted the largest outbreak in 2018, with 10,077 cases (95% CI: [9,912-10,276]), in addition to smaller outbreaks in five different years (2003, 2006, 2010, 2012 and 2014) and successfully identified the increasing trend in cases between 2010 and 2018. We found that temperature was positively associated with the annual incidence during the late winter months (between January and March) but negatively associated during the early summer (between April and June). Our results might be suggest an optimal minimum temperature for mosquito growth of 21-23°C. This study has implications for understanding how climate variability has affected recent dengue expansion in neighbours of Bangladesh (such as northern India and Southeast Asia).

Impact of climate change and biodiversity collapse on the global emergence and spread of infectious diseases

The reality of climate change and biodiversity collapse is irrefutable in the 21st century, with urgent action required not only to conserve threatened species but also to protect human life and wellbeing. This existential threat forces us to recognise that our existence is completely dependent upon well-functioning ecosystems that sustain the diversity of life on our planet, including that required for human health. By synthesising data on the ecology, epidemiology and evolutionary biology of various pathogens, we are gaining a better understanding of factors that underlie disease emergence and spread. However, our knowledge remains rudimentary with limited insight into the complex feedback loops that underlie ecological stability, which are at risk of rapidly unravelling once certain tipping points are breached. In this paper, we consider the impact of climate change and biodiversity collapse on the ever-present risk of infectious disease emergence and spread. We review historical and contemporaneous infectious diseases that have been influenced by human environmental manipulation, including zoonoses and vector- and water-borne diseases, alongside an evaluation of the impact of migration, urbanisation and human density on transmissible diseases. The current lack of urgency in political commitment to address climate change warrants enhanced understanding and action from paediatricians - to ensure that we safeguard the health and wellbeing of children in our care today, as well as those of future generations.

The Impact of Meteorological Factors on Communicable Disease Incidence and Its Projection: A Systematic Review

Background: Climate change poses a real challenge and has contributed to causing the emergence and re-emergence of many communicable diseases of public health importance. Here, we reviewed scientific studies on the relationship between meteorological factors and the occurrence of dengue, malaria, cholera, and leptospirosis, and synthesized the key findings on communicable disease projection in the event of global warming. Method: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 flow checklist. Four databases (Web of Science, Ovid MEDLINE, Scopus, EBSCOhost) were searched for articles published from 2005 to 2020. The eligible articles were evaluated using a modified scale of a checklist designed for assessing the quality of ecological studies. Results: A total of 38 studies were included in the review. Precipitation and temperature were most frequently associated with the selected climate-sensitive communicable diseases. A climate change scenario simulation projected that dengue, malaria, and cholera incidence would increase based on regional climate responses. Conclusion: Precipitation and temperature are important meteorological factors that influence the incidence of climate-sensitive communicable diseases. Future studies need to consider more determinants affecting precipitation and temperature fluctuations for better simulation and prediction of the incidence of climate-sensitive communicable diseases.

A Review of Dengue's Historical and Future Health Risk from a Changing Climate

PURPOSE OF REVIEW

The purpose of this review is to summarize research articles that provide risk estimates for the historical and future impact that climate change has had upon dengue published from 2007 through 2019.

RECENT FINDINGS

Findings from 30 studies on historical health estimates, with the majority of the studies conducted in Asia, emphasized the importance of temperature, precipitation, and relative humidity, as well as lag effects, when trying to understand how climate change can impact the risk of contracting dengue. Furthermore, 35 studies presented findings on future health risk based upon climate projection scenarios, with a third of them showcasing global level estimates and findings across the articles emphasizing the need to understand risk at a localized level as the impacts from climate change will be experienced inequitably across different geographies in the future. Dengue is one of the most rapidly spreading viral diseases in the world, with ~390 million people infected worldwide annually. Several factors have contributed towards its proliferation, including climate change. Multiple studies have previously been conducted examining the relationship between dengue and climate change, both from a historical and a future risk perspective. We searched the U.S. National Institute of Environmental Health (NIEHS) Climate Change and Health Portal for literature (spanning January 2007 to September 2019) providing historical and future health risk estimates of contracting dengue infection in relation to climate variables worldwide. With an overview of the evidence of the historical and future health risk posed by dengue from climate change across different regions of the world, this review article enables the research and policy community to understand where the knowledge gaps are and what areas need to be addressed in order to implement localized adaptation measures to mitigate the health risks posed by future dengue infection.

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.

The impact of climate change on neglected tropical diseases: a systematic review

Neglected tropical diseases (NTDs) are a diverse group of diseases that continue to affect >1 billion people, with these diseases disproportionately impacting vulnerable populations and territories. Climate change is having an increasing impact on public health in tropical and subtropical areas and across the world and can affect disease distribution and transmission in potentially diverse ways. Improving our understanding of how climate change influences NTDs can help identify populations at risk to include in future public health interventions. Articles were identified by searching electronic databases for reports of climate change and NTDs between 1 January 2010 and 1 March 2020. Climate change may influence the emergence and re-emergence of multiple NTDs, particularly those that involve a vector or intermediate host for transmission. Although specific predictions are conflicting depending on the geographic area, the type of NTD and associated vectors and hosts, it is anticipated that multiple NTDs will have changes in their transmission period and geographic range and will likely encroach on regions and populations that have been previously unaffected. There is a need for improved surveillance and monitoring to identify areas of NTD incursion and emergence and include these in future public health interventions.

Knowledge, awareness and preventive practices of dengue outbreak in Bangladesh: A countrywide study

Background

Dengue, the mosquito borne disease has become a growing public health threat in Bangladesh due to its gradual increasing morbidity and mortality since 2000. In 2019, the country witnessed the worst ever dengue outbreak. The present study was conducted to characterize the socio-economic factors and knowledge, attitude and practice (KAP) status towards dengue among the people of Bangladesh.

Method

A cross-sectional study was conducted with 1,010 randomly selected respondents from nine different administrative regions of Bangladesh between July and November 2019.

A structured questionnaire was used covering socio-demographic characteristics of the participants including their knowledge, awareness, treatment and practices regarding dengue fever. Factors associated with the knowledge and awareness of dengue were investigated separately, using multivariable logistic regression.

Results

Although majority (93.8%) of the respondents had heard about dengue, however, they had still misconceptions about Aedes breeding habitat. Around half of the study population (45.7%) had mistaken belief that Aedes can breed in dirty water and 43.1% knew that Aedes mosquito usually bites around sunrise and sunset. Fever indication was found in 36.6% of people which is the most common symptom of dengue. Among the socio-demographic variables, the level of education of the respondents was identified as an independent predictor for both knowledge (p<0.05) and awareness (p<0.05) of dengue. The preventive practice level was moderately less than the knowledge level though there was a significant association (p<0.05) existed between knowledge and preventive practices. Our study noted that TV/Radio is an effective predominant source of information about dengue fever.

Conclusion

As dengue is emerging in Bangladesh, there is an urgent need to increase health promotion activities through campaigns for eliminating the misconception and considerable knowledge gaps about dengue.

Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: A Bayesian spatial analysis

BACKGROUND

Dengue is a wide-spread mosquito-borne disease globally with a likelihood of becoming endemic in tropical Queensland, Australia. The aim of this study was to analyse the spatial variation of dengue notifications in relation to climate variability and socio-ecological factors in the tropical climate zone of Queensland, Australia.

METHODS

Data on the number of dengue cases and climate variables including minimum temperature, maximum temperature and rainfall for the period of January 1^st, 2010 to December 31^st, 2015 were obtained for each Statistical Local Area (SLA) from Queensland Health and Australian Bureau of Meteorology, respectively. Socio-ecological data including estimated resident population, percentage of Indigenous population, housing structure (specifically terrace house), socio-economic index and land use types for each SLA were obtained from Australian Bureau of Statistics, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. To quantify the relationship between dengue, climate and socio-ecological factors, multivariate Poisson regression models in a Bayesian framework were developed with a conditional autoregressive prior structure. Posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling.

RESULTS

In the tropical climate zone of Queensland, the estimated number of dengue cases was predicted to increase by 85% [95% Credible Interval (CrI): 25%, 186%] and 7% (95% CrI: 0.1%, 14%) for a 1-mm increase in average annual rainfall and 1% increase in the proportion of terrace houses, respectively. The estimated spatial variation (structured random effects) was small compared to the remaining unstructured variation, suggesting that the inclusion of covariates resulted in no residual spatial autocorrelation in dengue data.

CONCLUSIONS

Climate and socio-ecological factors explained much of the heterogeneity of dengue transmission dynamics in the tropical climate zone of Queensland. Results will help to further understand the risk factors of dengue transmission and will provide scientific evidence in designing effective local dengue control programs in the most needed areas.

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.

Impact of Extreme Hot Climate on COVID-19 Outbreak in India

Coronavirus Disease 2019 (COVID-19) pandemic poses extreme threat to public health and economy, particularly to the nations with higher population density. The disease first reported in Wuhan, China; later, it spreads elsewhere, and currently, India emerged as COVID-19 hotspot. In India, we selected 20 densely populated cities having infection counts higher than 500 (by 15 May) as COVID-19 epicenters. Daily COVID-19 count has strong covariability with local temperature, which accounts approximately 65-85% of the explained variance; i.e., its spread depends strongly on local temperature rise prior to community transmission phase. The COVID-19 cases are clustered at temperature and humidity ranging within 27-32°C and 25-45%, respectively. We introduce a combined temperature and humidity profile, which favors rapid COVID-19 growth at the initial phase. The results are highly significant for predicting future COVID-19 outbreaks and modeling cities based on environmental conditions. On the other hand, CO2 emission is alarmingly high in South Asia (India) and entails high risk of climate change and extreme hot summer. Zoonotic viruses are sensitive to warming induced climate change; COVID-19 epicenters are collocated on CO2 emission hotspots. The COVID-19 count distribution peaks at 31.0°C, which is 1.0°C higher than current (2020) and historical (1961-1990) mean, value. Approximately, 72% of the COVID-19 cases are clustered at severe to record-breaking hot extremes of historical temperature distribution spectrum. Therefore, extreme climate change has important role in the spread of COVID-19 pandemic. Hence, a strenuous mitigation measure to abate greenhouse gas (GHG) emission is essential to avoid such pandemics in future.

Different responses of dengue to weather variability across climate zones in Queensland, Australia

BACKGROUND

Dengue is a significant public health concern in northern Queensland, Australia. This study compared the epidemic features of dengue transmission among different climate zones and explored the threshold of weather variability for climate zones in relation to dengue in Queensland, Australia.

METHODS

Daily data on dengue cases and weather variables including minimum temperature, maximum temperature and rainfall for the period of January 1, 2010 to December 31, 2015 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Climate zones shape file for Australia was also obtained from Australian Bureau of Meteorology. Kruskal-Wallis test was performed to check whether the distribution of dengue significantly differed between climate zones. Time series regression tree model was used to estimate the threshold effects of the monthly weather variables on dengue in different climate zones.

RESULTS

During the study period, the highest dengue incidence rate was found in the tropical climate zone (15.09/10,000) with the second highest in the grassland climate zone (3.49/10,000). Dengue responded differently to weather variability in different climate zones. In every climate zone, temperature was the primary predictor of dengue. However, the threshold values, type of temperature (e.g. maximum, minimum, or mean), and lag time for dengue varied between climate zones. Monthly mean temperature above 27°C at a lag of two months and monthly minimum temperature above 22°C at a lag of one month was found to be the most favourable weather condition for dengue in the tropical and subtropical climate zone, respectively. However, in the grassland climate zone, maximum temperature above 38°C at a lag of five months was found to be the ideal condition for dengue. Monthly rainfall with threshold value of 1.7 mm was found to be a significant contributor to dengue only in the tropical climate zone.

CONCLUSIONS

The temperature threshold for dengue was lower in both tropical and subtropical climate zones than in the grassland climate zone. The different temperature threshold between climate zones suggests that an early warning system would need to be developed based on local socio-ecological conditions of the climate zone to manage dengue control and intervention programs effectively.

Lag effect of climatic variables on dengue burden in India

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.

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.

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

Background

Many studies have shown associations between rising temperatures, El Niño events and dengue incidence, but the effect of sustained periods of extreme high temperatures (i.e., heatwaves) on dengue outbreaks has not yet been investigated. This study aimed to compare the short-term temperature-dengue associations during different dengue outbreak periods, estimate the dengue cases attributable to temperature, and ascertain if there was an association between heatwaves and dengue outbreaks in Hanoi, Vietnam.

Methodology/Principal findings

Dengue outbreaks were assigned to one of three categories (small, medium and large) based on the 50th, 75th, and 90th percentiles of distribution of weekly dengue cases during 2008–2016. Using a generalised linear regression model with a negative binomial link that controlled for temporal trends, temperature variation, rainfall and population size over time, we examined and compared associations between weekly average temperature and weekly dengue incidence for different outbreak categories. The same model using weeks with or without heatwaves as binary variables was applied to examine the potential effects of extreme heatwaves, defined as seven or more days with temperatures above the 95th percentile of daily temperature distribution during the study period. This study included 55,801 dengue cases, with an average of 119 (range: 0 to 1454) cases per week. The exposure-response relationship between temperature and dengue risk was non-linear and differed with dengue category. After considering the delayed effects of temperature (one week lag), we estimated that 4.6%, 11.6%, and 21.9% of incident cases during small, medium, and large outbreaks were attributable to temperature. We found evidence of an association between heatwaves and dengue outbreaks, with longer delayed effects on large outbreaks (around 14 weeks later) than small and medium outbreaks (4 to 9 weeks later). Compared with non-heatwave years, dengue outbreaks (i.e., small, moderate and large outbreaks combined) in heatwave years had higher weekly number of dengue cases (p<0.05). Findings were robust under different sensitivity analyses.

Conclusions

The short-term association between temperature and dengue risk varied by the level of outbreaks and temperature seems more likely affect large outbreaks. Moreover, heatwaves may delay the timing and increase the magnitude of dengue outbreaks.

Dengue fever is one of the most common mosquito-borne viral diseases. Weather extremes such as El Niño event and extreme hot summer can affect dengue incidence rate and dengue outbreaks. More frequent, more intensive and longer lasting heatwaves in the 21st century is anticipated because of global warming, making it necessary to investigate the association between heatwaves and dengue outbreaks. In this study, we estimated 4.6%, 11.6%, and 21.9% of incident dengue cases during small, medium, and large outbreaks attributable to temperature in Hanoi, Vietnam. We also found evidence of an association between heatwaves and dengue outbreaks, with longer delayed effects on large outbreaks than small and medium outbreaks. Compared with non-heatwave years, dengue outbreaks in heatwave years had higher number of dengue cases. Heatwave weather may represent an emerging risk factor or predicator of dengue outbreaks in tropical regions. Future dengue prediction models incorporating heatwaves may help increase the accuracy of predictability.

A scoping review of importation and predictive models related to vector-borne diseases, pathogens, reservoirs, or vectors (1999-2016)

BACKGROUND

As globalization and climate change progress, the expansion and introduction of vector-borne diseases (VBD) from endemic regions to non-endemic regions is expected to occur. Mathematical and statistical models can be useful in predicting when and where these changes in distribution may happen. Our objective was to conduct a scoping review to identify and characterize predictive and importation models related to vector-borne diseases that exist in the global literature.

METHODS

A literature search was conducted to identify publications published between 1999 and 2016 from five scientific databases using relevant keywords. All publications had to be in English or French, and include a predictive or importation model on VBDs, pathogens, reservoirs and/or vectors. Relevance screening and data characterization were performed by two reviewers using pretested forms. The data were analyzed using descriptive statistics.

RESULTS

The search initially identified 19 710 unique articles, reports, and conference abstracts. This was reduced to 428 relevant documents after relevance screening and data charting. About half of the models used mathematical techniques, and the remainder were statistical. Most of the models were predictive (87%), rather than importation (5%). The most commonly investigated diseases were malaria and dengue fever. Around 12% of the publications did not report all the parameters used in their model. Only 29% of the models incorporated the impacts of climate change.

CONCLUSIONS

A wide variety of mathematical and statistical models on vector-borne diseases exist. Researchers creating their own mathematical and/or statistical models may be able to use this scoping review to be informed about the diseases and/or regions, parameters, model types, and methodologies used in published models.

A dengue fever predicting model based on Baidu search index data and climate data in South China

With the acceleration of global urbanization and climate change, dengue fever is spreading worldwide. Different levels of dengue fever have also occurred in China, especially in southern China, causing enormous economic losses. Unfortunately, there is no effective treatment for dengue, and the most popular dengue vaccine does not exhibit good curative effects. Therefore, we developed a Generalized Additive Mixed Model (GAMM) that gathered climate factors (mean temperature, relative humidity and precipitation) and Baidu search data during 2011-2015 in Guangzhou city to improve the accuracy of dengue fever prediction. Firstly, the time series dengue fever data were decomposed into seasonal, trend and remainder components by the seasonal-trend decomposition procedure based on loess (STL). Secondly, the time lag of variables was determined in cross-correlation analysis and the order of autocorrelation was estimated using autocorrelation (ACF) and partial autocorrelation functions (PACF). Finally, the GAMM was built and evaluated by comparing it with Generalized Additive Mode (GAM). Experimental results indicated that the GAMM (R2: 0.95 and RMSE: 34.1) has a superior prediction capability than GAM (R2: 0.86 and RMSE: 121.9). The study could help the government agencies and hospitals respond early to dengue fever outbreak.

A comprehensive review of climate change impacts, adaptation, and mitigation on environmental and natural calamities in Pakistan

The devastations and damages caused by climate change are apparent across the globe, specifically in the South Asian region where vulnerabilities to climate change among residents are high and climate change adaptation and mitigation awareness are extremely low. Pakistan's low adaptive capacity due to high poverty rate, limited financial resources and shortage of physical resources, and continual extreme climatic events including varying temperature, continual flooding, melting glaciers, saturation of lakes, earthquakes, hurricanes, storms, avalanches, droughts, scarcity of water, pest diseases, human healthcare issues, and seasonal and lifestyle changes have persistently threatened the ecosystem, biodiversity, human communities, animal habitations, forests, lands, and oceans with a potential to cause further damages in the future. The likely effect of climate change on common residents of Pakistan with comparison to the world and their per capita impact of climate change are terribly high with local animal species such as lions, vultures, dolphins, and tortoise facing extinction regardless of generating and contributing diminutively to global GHG emissions. The findings of the review suggested that GHG emissions cause climate change which has impacted agriculture livestock and forestry, weather trends and patterns, food water and energy security, and society of Pakistan. This review is a sectorial evaluation of climate change mitigation and adaption approaches in Pakistan in the aforementioned sectors and its economic costs which were identified to be between 7 to 14 billion USD per annum. The research suggested that governmental interference is essential for sustainable development of the country through strict accountability of resources and regulation implemented in the past for generating state-of-the-art climate policy.

Impacts of exposure to ambient temperature on burden of disease: a systematic review of epidemiological evidence

Ambient temperature is an important determinant of mortality and morbidity, making it necessary to assess temperature-related burden of disease (BD) for the planning of public health policies and adaptive responses. To systematically review existing epidemiological evidence on temperature-related BD, we searched three databases (PubMed, Web of Science, and Scopus) on 1 September 2018. We identified 97 studies from 56 counties for this review, of which 75 reported the fraction or number of health outcomes (include deaths and diseases) attributable to temperature, and 22 reported disability-adjusted life years (include years of life lost and years lost due to disability) related to temperature. Non-optimum temperatures (i.e., heat and cold) were responsible for > 2.5% of mortality in all included high-income countries/regions, and > 3.0% of mortality in all included middle-income countries. Cold was mostly reported to be the primary source of mortality burden from non-optimum temperatures, but the relative role of three different temperature exposures (i.e., heat, cold, and temperature variability) in affecting morbidity and mortality remains unclear so far. Under the warming climate scenario, almost all projections assuming no population adaptation suggested future increase in heat-related but decrease in cold-related BD. However, some studies emphasized the great uncertainty in future pattern of temperature-related BD, largely depending on the scenarios of climate, population adaptation, and demography. We also identified important discrepancies and limitations in current research methodologies employed to measure temperature exposures and model temperature-health relationship, and calculate the past and project future temperature-related BD. Overall, exposure to non-optimum ambient temperatures has become and will continue to be a considerable contributor to the global and national BD, but future research is still needed to develop a stronger methodological framework for assessing and comparing temperature-related BD across different settings.

An ecological study of eosinophilic meningitis caused by the nematode, Angiostrongylus cantonensis (Chen, 1935) (Nematoda: Metastrongylidae)

Climate change and other weather factors are associated with several infectious diseases, but are rarely reported as being associated with nematode infection. Eosinophilic meningitis (EOM) is an emerging disease worldwide caused by the nematode, Angiostrongylus cantonensis. It is transmitted through various agents such as snails and slugs. Temperature and rainfall are associated with snail population. There have been no previous studies on the relationship between weather and EOM. This was an ecological study. Numbers of EOM patients and weather data in Thailand's Loei province from 2006 to 2017 were obtained using a national database. A Spearman correlation was used to explore the relationship between EOM and weather variables. We developed a Poisson time series model combined with a distributed lag model (DLM) for estimating the effects of weather on EOM. We also created an autoregressive integrated moving average with exogeneous variable (ARIMAX) model for predicting future EOM cases over the following 12 months. There were 1126 EOM patients in the study. Among several weather factors, wind was significantly negatively correlated with the number of EOM patients (r_s: -0.204, 95% CI: -0.361 to -0.058; p value: 0.014). The ARIMAX(3, 0, 0) model with wind speed as a variable was appropriate for predicting the number of EOM patients. The predicted and actual numbers of EOM patients in 2018 were highly concordant. In conclusion, wind speed is significantly negatively correlated with the number of EOM patients.

Dengue fever in Punjab, Pakistan: Knowledge, perception and adaptation among urban adults

Climate change and weather variations are strongly associated with the incidence of dengue fever, outbreak risk in Pakistan and other developing countries. Knowledge and adaptation measures can affect the incidence and outbreak risk of dengue fever. The present study attempted to determine the knowledge, perception and adaptation to dengue fever by the respondents in Faisalabad, Pakistan employing cross-sectional data. The respondents who suffered and those that did not suffer from dengue fever have reported that electronic and print media were important sources of awareness about dengue fever. Around 59% respondents who did not suffer from dengue fever reported knowledge of being affected by dengue fever and 67% did not perceive that the symptoms of dengue fever would appear after mosquito biting. Logit model was employed to examine the factors affecting the adaptation measures to reduce the incidence of dengue fever. Education, family size, adults, income and perception were significantly related adaptation to dengue fever. The respondents that suffered from dengue fever were highly probable to use the adaptation measures compared to the respondents that did not suffer from dengue fever. Findings of the study might be helpful for the public health authorities to devise policies on improving awareness of dengue fever among the masses and sensitizing population to use adaptation measures.

El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers

The El Niño Southern Oscillation (ENSO) is an important mode of climatic variability that exerts a discernible impact on ecosystems and society through alterations in climate patterns. For this reason, ENSO has attracted much interest in the climate and health science community, with many analysts investigating ENSO health links through considering the degree of dependency of the incidence of a range of climate diseases on the occurrence of El Niño events. Because of the mounting interest in the relationship between ENSO as a major mode of climatic variability and health, this paper presents an overview of the basic characteristics of the ENSO phenomenon and its climate impacts, discusses the use of ENSO indices in climate and health research, and outlines the present understanding of ENSO health associations. Also touched upon are ENSO-based seasonal health forecasting and the possible impacts of climate change on ENSO and the implications this holds for future assessments of ENSO health associations. The review concludes that there is still some way to go before a thorough understanding of the association between ENSO and health is achieved, with a need to move beyond analyses undertaken through a purely statistical lens, with due acknowledgement that ENSO is a complex non-canonical phenomenon, and that simple ENSO health associations should not be expected.

Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study

BACKGROUND

Over the last 5 years (2013-2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016.

METHODS AND FINDINGS

Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure-lag-response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure-lag-response functions of these indicators-rather than linear effects for individual lags-more appropriately described the climate-disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika.

CONCLUSION

We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region.

Open data mining for Taiwan's dengue epidemic

By using a quantitative approach, this study examines the applicability of data mining technique to discover knowledge from open data related to Taiwan's dengue epidemic. We compare results when Google trend data are included or excluded. Data sources are government open data, climate data, and Google trend data. Research findings from analysis of 70,914 cases are obtained. Location and time (month) in open data show the highest classification power followed by climate variables (temperature and humidity), whereas gender and age show the lowest values. Both prediction accuracy and simplicity decrease when Google trends are considered (respectively 0.94 and 0.37, compared to 0.96 and 0.46). The article demonstrates the value of open data mining in the context of public health care.

Climate variability and dengue hemorrhagic fever in Southeast Sulawesi Province, Indonesia

To determine the association of climatic factors and dengue hemorrhagic fever and to develop the prediction approach of future dengue transmission. The study used totally monthly dengue hemorrhagic fever cases at Health Office Kendari, Southeast Sulawesi, Indonesia. Monthly meteorological data, consisting of temperature, rainfall, and humidity, was obtained from the Meteorology, Climatology and Geophysics Agency in Kendari district. All data analysis, including Spearman and Poisson distribution, was carried out in R Studio (version 3.3.2) utilizing the R statistical language version 2.15. The highest rate of dengue hemorrhagic fever cases was found in January, February, and March. Temperature averages at lag 2 (p = 0.53, p < 0.0001), lag 3 (p = 0.59, p < 0.0001), and lag 4 (p = 0.41, p < 0.01)) correlated with the incident rate of DHF. The average temperature at lag 2 was found to have a positive impact on the incidence of DHF by Poisson function. This study provides preliminary evidence of the influence of climatic factors on dengue transmission.

Spatial-temporal distribution of dengue and climate characteristics for two clusters in Sri Lanka from 2012 to 2016

Dengue is a vector-borne disease causing high morbidity and mortality in tropical and subtropical countries. Urbanization, globalization, and lack of effective mosquito control have lead to dramatically increased frequency and magnitude of dengue epidemic in the past 40 years. The virus and the mosquito vectors keep expanding geographically in the tropical regions of the world. Using the hot spot analysis and the spatial-temporal clustering method, we investigated the spatial-temporal distribution of dengue in Sri Lanka from 2012 to 2016 to identify spatial-temporal clusters and elucidate the association of climatic factors with dengue incidence. We detected two important spatial-temporal clusters in Sri Lanka. Dengue incidences were predicted by combining historical dengue incidence data with climate data, and hot and cold spots were forecasted using the predicted dengue incidences to identify areas at high risks. Targeting the hot spots during outbreaks instead of all the regions can save resources and time for public health authorities. Our study helps better understand how climatic factors impact spatial and temporal spread of dengue virus. Hot spot prediction helps public health authorities forecast future high risk areas and direct control measures to minimize cost on health, time, and economy.

Assessing spatio-temporal trend of vector breeding and dengue fever incidence in association with meteorological conditions

Th aim of this study is to investigate spatio-temporal trends of dengue vector breeding and epidemic (disease incidence) influenced by climatic factors. The spatio-temporal (low-, medium-, and high-intensity periods) evaluation of entomological and epidemiological investigations along with climatic factors like rainfall (RF), temperature (T_max), relative humidity (RH), and larval indexing was conducted to develop correlations in the area of Lahore, Pakistan. The vector abundance and disease transmission trend was geo-tagged for spatial insight. The sufficient rainfall events and optimum temperature and relative humidity supported dengue vector breeding with high larval indices for water-related containers (27-37%). Among temporal analysis, the high-intensity period exponentially projected disease incidence followed by post-rainfall impacts. The high larval incidence that was observed in early high-intensity periods effected the dengue incidence. The disease incidence had a strong association with RF (r = 0.940, α = 0.01). The vector larva occurrence (r = 0.017, α = 0.05) influenced the disease incidence. Similarly, RH (r = 0.674, α = 0.05) and average T_max (r = 0.307, α = 0.05) also induced impact on the disease incidence. In this study, the vulnerability to dengue fever highly correlates with meteorological factors during high-intensity period. It provides area-specific understanding of vector behavior, key containers, and seasonal patterns of dengue vector breeding and disease transmission which is essential for preparing an effective prevention plan against the vector.

Impacts of ambient temperature on the burden of bacillary dysentery in urban and rural Hefei, China

Bacillary dysentery continues to be a major health issue in developing countries and ambient temperature is a possible environmental determinant. However, evidence about the risk of bacillary dysentery attributable to ambient temperature under climate change scenarios is scarce. We examined the attributable fraction (AF) of temperature-related bacillary dysentery in urban and rural Hefei, China during 2006-2012 and projected its shifting pattern under climate change scenarios using a distributed lag non-linear model. The risk of bacillary dysentery increased with the temperature rise above a threshold (18·4 °C), and the temperature effects appeared to be acute. The proportion of bacillary dysentery attributable to hot temperatures was 18·74% (95 empirical confidence interval (eCI): 8·36-27·44%). Apparent difference of AF was observed between urban and rural areas, with AF varying from 26·87% (95% eCI 16·21-36·68%) in urban area to -1·90% (95 eCI -25·03 to 16·05%) in rural area. Under the climate change scenarios alone (1-4 °C rise), the AF from extreme hot temperatures (>31·2 °C) would rise greatly accompanied by the relatively stable AF from moderate hot temperatures (18·4-31·2 °C). If climate change proceeds, urban area may be more likely to suffer from rapidly increasing burden of disease from extreme hot temperatures in the absence of effective mitigation and adaptation strategies.

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

BACKGROUND

Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data.

METHODOLOGY AND PRINCIPAL FINDINGS

A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29).

CONCLUSIONS

Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou.

Time-Lagging Interplay Effect and Excess Risk of Meteorological/Mosquito Parameters and Petrochemical Gas Explosion on Dengue Incidence

In Kaohsiung, a metropolitan city in Taiwan at high risk of dengue epidemic, weather factors combined with an accidental petrochemical gas explosion (PGE) may affect mosquito‒human dynamics in 2014. Generalized estimating equations with lagged-time Poisson regression analyses were used to evaluate the effect of meteorological/mosquito parameters and PGE on dengue incidences (2000–2014) in Kaohsiung. Increased minimum temperatures rendered a 2- and 3-month lagging interactive effect on higher dengue risks, and higher rainfall exhibited a 1- and 2-month lagging interplay effect on lower risks (interaction, P ≤ 0.001). The dengue risk was significantly higher than that in a large-scale outbreak year (2002) from week 5 after PGE accident in 2014 (2.9‒8.3-fold for weeks 5‒22). The greatest cross-correlation of dengue incidences in the PGE-affected and PGE-neighboring districts was identified at weeks 1 after the PGE (r_s = 0.956, P < 0.001). Compared with the reference years, the combined effect of minimum temperature, rainfall, and PGE accounted for 75.1% of excess dengue risk in 2014. In conclusion, time-lagging interplay effects from minimum temperature and rainfall may be respectively associated with early and near environments facilitating dengue transmission. Events that interact with weather and influence mosquito‒human dynamics, such as PGEs, should not be ignored in dengue prevention and control.

Climate forcing and infectious disease transmission in urban landscapes: integrating demographic and socioeconomic heterogeneity

Urbanization and climate change are the two major environmental challenges of the 21st century. The dramatic expansion of cities around the world creates new conditions for the spread, surveillance, and control of infectious diseases. In particular, urban growth generates pronounced spatial heterogeneity within cities, which can modulate the effect of climate factors at local spatial scales in large urban environments. Importantly, the interaction between environmental forcing and socioeconomic heterogeneity at local scales remains an open area in infectious disease dynamics, especially for urban landscapes of the developing world. A quantitative and conceptual framework on urban health with a focus on infectious diseases would benefit from integrating aspects of climate forcing, population density, and level of wealth. In this paper, we review what is known about these drivers acting independently and jointly on urban infectious diseases; we then outline elements that are missing and would contribute to building such a framework.

A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control

OBJECTIVE

To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta region (MDR).

METHODS

We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalised linear-distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the β-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis.

RESULTS

The north-eastern MDR was identified as the high-risk cluster. A 1 °C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95% CI, 9-13) and 7% (95% CI, 6-8), respectively. A 1% rise in humidity increased dengue risk 0.9% (95% CI, 0.2-1.4) at lag 1-4 and 0.8% (95% CI, 0.2-1.4) at lag 5-8 weeks. Similarly, a 1-mm increase in rainfall increased dengue risk 0.1% (95% CI, 0.05-0.16) at lag 1-4 and 0.11% (95% CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%).

CONCLUSION

This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings.

Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.

Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh

Dengue dynamics are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors. Several studies examined the role of El Niño Southern Oscillation (ENSO) in dengue incidence. However, the role of Indian Ocean Dipole (IOD), a coupled ocean atmosphere phenomenon in the Indian Ocean, which controls the summer monsoon rainfall in the Indian region, remains unexplored. Here, we examined the effects of ENSO and IOD on dengue incidence in Bangladesh. According to the wavelet coherence analysis, there was a very weak association between ENSO, IOD and dengue incidence, but a highly significant coherence between dengue incidence and local climate variables (temperature and rainfall). However, a distributed lag nonlinear model (DLNM) revealed that the association between dengue incidence and ENSO or IOD were comparatively stronger after adjustment for local climate variables, seasonality and trend. The estimated effects were nonlinear for both ENSO and IOD with higher relative risks at higher ENSO and IOD. The weak association between ENSO, IOD and dengue incidence might be driven by the stronger effects of local climate variables such as temperature and rainfall. Further research is required to disentangle these effects.

A study of the correlation between dengue and weather in Kandy City, Sri Lanka (2003 -2012) and lessons learned

Background

Weather variables affect dengue transmission. This study aimed to identify a dengue weather correlation pattern in Kandy, Sri Lanka, compare the results with results of similar studies, and establish ways for better control and prevention of dengue.

Method

We collected data on reported dengue cases in Kandy and mid-year population data from 2003 to 2012, and calculated weekly incidences. We obtained daily weather data from two weather stations and converted it into weekly data. We studied correlation patterns between dengue incidence and weather variables using the wavelet time series analysis, and then calculated cross-correlation coefficients to find magnitudes of correlations.

Results

We found a positive correlation between dengue incidence and rainfall in millimeters, the number of rainy and wet days, the minimum temperature, and the night and daytime, as well as average, humidity, mostly with a five- to seven-week lag. Additionally, we found correlations between dengue incidence and maximum and average temperatures, hours of sunshine, and wind, with longer lag periods. Dengue incidences showed a negative correlation with wind run.

Conclusion

Our results showed that rainfall, temperature, humidity, hours of sunshine, and wind are correlated with local dengue incidence. We have suggested ways to improve dengue management routines and to control it in these times of global warming. We also noticed that the results of dengue weather correlation studies can vary depending on the data analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/s40249-015-0075-8) contains supplementary material, which is available to authorized users.

The emergence of dengue in Bangladesh: epidemiology, challenges and future disease risk

Dengue occurred sporadically in Bangladesh from 1964 until a large epidemic in 2000 established the virus. We trace dengue from the time it was first identified in Bangladesh and identify factors favourable to future dengue haemorrhagic fever epidemics. The epidemic in 2000 was likely due to introduction of a dengue virus strain from a nearby endemic country, probably Thailand. Cessation of dichlorodiphenyltrichloroethane (DDT) spraying, climatic, socio-demographic, and lifestyle factors also contributed to epidemic transmission. The largest number of cases was notified in 2002 and since then reported outbreaks have generally declined, although with increased notifications in alternate years. The apparent decline might be partially due to public awareness with consequent reduction in mosquito breeding and increased prevalence of immunity. However, passive hospital-based surveillance has changed with mandatory serological confirmation now required for case reporting. Further, a large number of cases remain undetected because only patients with severe dengue require hospitalisation. Thus, the reduction in notification numbers may be an artefact of the surveillance system. Indeed, population-based serological survey indicates that dengue transmission continues to be common. In the future, the absence of active interventions, unplanned urbanisation, environmental deterioration, increasing population mobility, and economic factors will heighten dengue risk. Projected increases in temperature and rainfall may exacerbate this.