Effects of local and regional climatic fluctuations on dengue outbreaks in southern Taiwan

Understanding the Effect of a Changing Climate on the Re-Emergence of Mosquito-Borne Diseases in Vulnerable Small Island Nations: A Systematic Review

INTRODUCTION

Drastic changes in meteorological variables due to climate change will likely have an implication on the proliferation of vectors such as mosquitoes. Extreme weather events may therefore promote the emergence/re-emergence of mosquito-borne diseases (MBDs) and potentiate the risk of endemicity, particularly, in small island nations.

METHOD

A systematic review was chosen to methodically ascertain the knowledge gaps that exist in determining the influence of the changing climate on MBDs in small islands with vulnerable public health systems. This review was conducted using the PRISMA guidelines.

RESULTS

Following extraction of 600 articles from the databases, 16 studies were determined to meet the selection criteria. The majority of these research papers were from Sri Lanka (n = 9) while the remaining articles were distributed between islands in the Pacific and Atlantic Ocean. Several of these studies used regression modelling techniques to discuss the effect of multiple meteorological variables on the incidence of MBDs. A positive relationship was observed between temperature and the relative risk of MBDs in 72% of the papers. Rainfall enhanced dengue transmission in 84% of the studies included. All the articles discussing the effect of humidity illustrated a similar trend while wind speed was the only climatic variable demonstrating a negative relationship with MBDs.

DISCUSSION

Considering the intricate nature of the non-linear exposure-response link is crucial when estimating the lagged effect of the changing climate on MBDs transmission. Other challenges associated with bias and confounders in the selected studies as well as meteorological data accessibility, were highlighted. Therefore, it was not possible to conclusively establish that the changing climatic variables do influence the spread of MBDs which accentuated the need for conducting further studies to illustrate the effect of changing weather variables on the incidence of MBDs, with an emphasis on vulnerable small island nations.

Unraveling varying spatiotemporal patterns of Dengue Fever and associated exposure-response relationships with environmental variables in three Southeast Asian countries before and during COVID-19

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g., plant shutdowns lead to lower greenhouse gas emissions), influences the Dengue Fever (DF) vector. This provides a unique opportunity to investigate the indirect impact of COVID-19 on DF transmission and generate insights for targeted prevention measures. We aim to compare DF transmission patterns and the exposure-response relationship of environmental variables and DF incidence in the pre- and during-COVID-19 to identify variations and assess the indirect impact of COVID-19 on DF transmission. We initially visualized the overall trend of DF transmission from 2017-2022, then conducted two quantitative analyses to compare DF transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess DF seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and DF incidence. We observed a notable surge in Singapore during-COVID-19, particularly from May to August in 2020 and 2022, with cases multiplying several times compared to pre-COVID-19. All subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic patterns remained generally consistent, but seasonal variability in Singapore has become increasingly pronounced. Monthly DF incidence in three countries varied significantly. Exposure-response relationships of DF and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-DF relationship rose from about 3-17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 4-40. Our study is the first to compare DF transmission patterns and their relationship with environmental variables before and during COVID-19, demonstrating that the pandemic has affected DF transmission and altered the exposure-response relationship at both national and regional levels.

The association between the scarlet fever and meteorological factors, air pollutants and their interactions in children in northwest China

Scarlet fever (SF) is an acute respiratory transmitted disease that primarily affects children. The influence of meteorological factors and air pollutants on SF in children has been proved, but the relevant evidence in Northwest China is still lacking. Based on the weekly reported cases of SF in children in Lanzhou, northwest China, from 2014 to 2018, we used geographical detectors, distributed lag nonlinear models (DLNM), and bivariate response models to explore the influence of meteorological factors and air pollutants with SF. It was found that ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2), temperature, pressure, water vapor pressure and wind speed were significantly correlated with SF based on geographical detectors. With the median as reference, the influence of high temperature, low pressure and high pressure on SF has a risk effect (relative risk (RR) > 1), and under extreme conditions, the dangerous effect was still significant. High O3 had the strongest effect at a 6-week delay, with an RR of 5.43 (95%CI: 1.74,16.96). The risk effect of high SO2 was strongest in the week of exposure, and the maximum risk effect was 1.37 (95%CI: 1.08,1.73). The interactions showed synergistic effects between high temperatures and O3, high pressure and high SO2, high nitrogen dioxide (NO2) and high particulate matter with diameter of less than 10 μm (PM10), respectively. In conclusion, high temperature, pressure, high O3 and SO2 were the most important factors affecting the occurrence of SF in children, which will provide theoretical support for follow-up research and disease prevention policy formulation.

Assessing the impact of emergency measures in varied population density areas during a large dengue outbreak

Background

The patterns of dengue are affected by many factors, including population density and climate factors. Densely populated areas could play a role in dengue transmission due to increased human-mosquito contacts, the presence of more diverse and suitable vector habitats and breeding sites, and changes in land use. In addition to population densities, climatic factors such as temperature, relative humidity, and precipitation have been demonstrated to predict dengue patterns. To control dengue, emergency measures should focus on vector management. Most approaches to assessing emergency responses to dengue risks involve applying simulation models or describing emergency activities and the results of implementing those responses. Research using real-world data with analytical methods to evaluate emergency responses to dengue has been limited. This study investigated emergency control measures associated with dengue risks in areas with high and low population densities, considering their different control capacities.

Methodology

Data from the 2015 dengue outbreak in Kaohsiung City, Taiwan, were utilized. The government database provided information on confirmed dengue cases, emergency control measures, and climatic data. The study employed a distributed lag non-linear model (DLNM) to assess the effect of emergency control measures and their time lags on dengue risk.

Principal findings

The findings revealed that in areas with high population density, the absence of emergency measures significantly elevated the risks of dengue. However, implementing emergency measures, especially a higher number, was associated with lower risks. In contrast, in areas with low population density, the risks of dengue were only significantly elevated at the 1st week lag if no emergency control measures were implemented. When emergency activities were carried out, the risks of dengue significantly decreased only for the 1st week lag.

Conclusions

Our findings reveal distinct exposure-lag-response patterns in the associations between emergency control measures and dengue in areas with high and low population density. In regions with a high population density, implementing emergency activities during a significant dengue outbreak is crucial for reducing the risk. Conversely, in areas of low population density, the necessity of applying emergency activities may be less pronounced. The implications of this study on dengue management could provide valuable insights for health authorities dealing with limited resources.

Unraveling varying spatiotemporal patterns of dengue and associated exposure-response relationships with environmental variables in Southeast Asian countries before and during COVID-19

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g. plant shutdowns lead to lower greenhouse gas emissions), influences the dengue vector. This provides a unique opportunity to investigate the impact of COVID-19 on dengue transmission and generate insights to guide more targeted prevention measures. We aim to compare dengue transmission patterns and the exposure-response relationship of environmental variables and dengue incidence in the pre- and during-COVID-19 to identify variations and assess the impact of COVID-19 on dengue transmission. We initially visualized the overall trend of dengue transmission from 2012-2022, then conducted two quantitative analyses to compare dengue transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess dengue seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and dengue incidence. We observed that all subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic and seasonal patterns of dengue remained consistent pre- and during-COVID-19. Monthly dengue incidence in three countries varied significantly. Singapore witnessed a notable surge during-COVID-19, particularly from May to August, with cases multiplying several times compared to pre-COVID-19, while seasonality of Malaysia weakened. Exposure-response relationships of dengue and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-dengue relationship rose from about 3 to 17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 5 to 55. Our study is the first to compare dengue transmission patterns and their relationship with environmental variables before and during COVID-19, showing that COVID-19 has affected dengue transmission at both the national and regional level, and has altered the exposure-response relationship between dengue and the environment.

Improving dengue fever predictions in Taiwan based on feature selection and random forests

BACKGROUND

Dengue fever is a well-studied vector-borne disease in tropical and subtropical areas of the world. Several methods for predicting the occurrence of dengue fever in Taiwan have been proposed. However, to the best of our knowledge, no study has investigated the relationship between air quality indices (AQIs) and dengue fever in Taiwan.

RESULTS

This study aimed to develop a dengue fever prediction model in which meteorological factors, a vector index, and AQIs were incorporated into different machine learning algorithms. A total of 805 meteorological records from 2013 to 2015 were collected from government open-source data after preprocessing. In addition to well-known dengue-related factors, we investigated the effects of novel variables, including particulate matter with an aerodynamic diameter < 10 µm (PM10), PM2.5, and an ultraviolet index, for predicting dengue fever occurrence. The collected dataset was randomly divided into an 80% training set and a 20% test set. The experimental results showed that the random forests achieved an area under the receiver operating characteristic curve of 0.9547 for the test set, which was the best compared with the other machine learning algorithms. In addition, the temperature was the most important factor in our variable importance analysis, and it showed a positive effect on dengue fever at < 30 °C but had less of an effect at > 30 °C. The AQIs were not as important as temperature, but one was selected in the process of filtering the variables and showed a certain influence on the final results.

CONCLUSIONS

Our study is the first to demonstrate that AQI negatively affects dengue fever occurrence in Taiwan. The proposed prediction model can be used as an early warning system for public health to prevent dengue fever outbreaks.

Investigation of the Correlation between Enterovirus Infection and the Climate Factor Complex Including the Ping-Year Factor and El Niño-Southern Oscillation in Taiwan

Enterovirus infection and enterovirus infection with severe complications (EVSC) are critical issues in several aspects. However, there is no suitable predictive tool for these infections. A climate factor complex (CFC) containing several climate factors could provide more effective predictions. The ping-year factor (PYF) and El Niño-Southern Oscillation (ENSO) are possible CFCs. This study aimed to determine the relationship between these two CFCs and the incidence of enterovirus infection. Children aged 15 years and younger with enterovirus infection and/or EVSC were enrolled between 2007 and 2022. Each year was categorized into a ping-year or non-ping-year according to the PYF. Poisson regression was used to evaluate the associations between the PYF, ENSO, and the incidence of enterovirus infection. Compared to the ping-year group, the incidence rate of enterovirus infection, the incidence rate of EVSC, and the ratio of EVSC in the non-ping-year group were 1.24, 3.38, and 2.73 times higher, respectively (p < 0.001). For every one-unit increase in La Niña, the incidence rate of enterovirus infection decreased to 0.96 times (p < 0.001). Our study indicated that CFCs could be potential predictors for enterovirus infection, and the PYF was more suitable than ENSO. Further research is needed to improve the predictive model.

Dengue in Cebu City, Philippines: A Pilot Study of Predictive Models and Visualizations for Public Health

Dengue is a global health issue, particularly in the tropical and subtropical regions of the world. Prevention is the most appropriate method to fight the spread of the virus. The objective of this research is to present a model, along with visualizations, that will enable health officials and community leaders to identify when and where potential dengue outbreaks are likely to occur. Armed with this information, local resources can be adequately deployed in an effort to use limited supplies effectively. A mathematical model that uses easily obtainable data, along with visualizations for the 80 barangays of Cebu City, Philippines, is presented. Visualizations are constructed appropriate for a generalist audience to comprehend and use for dengue mitigation. Results of this study include a model that uses readily available data to predict dengue outbreaks one month in advance and visualizations appropriate for decision-makers in public health. Additional items are identified that could enhance the explanatory power of the model, and future directions are discussed.

Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review

BACKGROUND

Creating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue.

METHODS

This scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected.

RESULTS

A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors.

CONCLUSIONS

DHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

Clinical features and transmission risk analysis of dengue virus infections in Shenzhen, During 2014-2019

Dengue fever (DF) and dengue hemorrhagic fever (DHF) are among the most common tropical diseases affecting humans. To analyze the risk of clinical and transmission of DF/DHF in Shenzhen, the surveillance on patients of all-age patients with dengue virus (DENV) infections was conducted. Our findings revealed that the majority of DENV-infected patients are young to middle-aged males, and the development of the disease is accompanied by abnormal changes in the percentages of neutrophils, lymphocytes, and basophils. Demographic analysis revealed that these patients is concentrated in areas such as Futian District, which may be due to the higher mosquito density and temperature than that in other area. Subsequent, mosquito infection experiments confirmed that the effect of temperature shift on DENV proliferation and transmission. Not only that, constant temperatures can enhance the spread of DENV, even increase the risk of epidemic. Thus, the role of innate immune response should be highlighted in the prediction of severe severity of DENV-infected patients, and temperature should be taken into account in the prevention and control of DENV.

INTRODUCTION

Dengue fever (DF) and dengue hemorrhagic fever (DHF) are among the most common tropical diseases affecting humans, and which caused by the four dengue virus serotypes (DENV 1-4).

OBJECTIVES

To analyze the risk of clinical and transmission of DF/DHF in Shenzhen.

METHODS

The surveillance on patients of all-age patients with dengue virus (DENV) infections was conducted.

RESULTS

Our findings revealed that the majority of DENV-infected patients are young to middle-aged males, and the development of the disease is accompanied by abnormal changes in the percentages of neutrophils, lymphocytes, and basophils. Demographic analysis revealed that these patients is concentrated in areas such as Futian District, which may be due to the higher mosquito density and temperature than that in other area. Subsequent, mosquito infection experiments confirmed that the effect of temperature shift on DENV proliferation and transmission. Not only that, constant temperatures can enhance the spread of DENV, even increase the risk of epidemic.

CONCLUSION

1. Elevated levels of neutrophils, lymphocytes, basophils, and temperature are all significant risk factors for dengue transmission and pathogenesis; 2. Temperature increasing is associated with a higher risk of dengue transmission; 3. Fluctuations in temperature around 28 °C (28 ± 5 °C) would increase dengue transmission.

An Integrative Explainable Artificial Intelligence Approach to Analyze Fine-Scale Land-Cover and Land-Use Factors Associated with Spatial Distributions of Place of Residence of Reported Dengue Cases

Dengue fever is a prevalent mosquito-borne disease that burdens communities in subtropical and tropical regions. Dengue transmission is ecologically complex; several environmental conditions are critical for the spatial and temporal distribution of dengue. Interannual variability and spatial distribution of dengue transmission are well-studied; however, the effects of land cover and use are yet to be investigated. Therefore, we applied an explainable artificial intelligence (AI) approach to integrate the EXtreme Gradient Boosting and Shapley Additive Explanation (SHAP) methods to evaluate spatial patterns of the residences of reported dengue cases based on various fine-scale land-cover land-use types, Shannon's diversity index, and household density in Kaohsiung City, Taiwan, between 2014 and 2015. We found that the proportions of general roads and residential areas play essential roles in dengue case residences with nonlinear patterns. Agriculture-related features were negatively associated with dengue incidence. Additionally, Shannon's diversity index showed a U-shaped relationship with dengue infection, and SHAP dependence plots showed different relationships between various land-use types and dengue incidence. Finally, landscape-based prediction maps were generated from the best-fit model and highlighted high-risk zones within the metropolitan region. The explainable AI approach delineated precise associations between spatial patterns of the residences of dengue cases and diverse land-use characteristics. This information is beneficial for resource allocation and control strategy modification.

Effects of temperature, rainfall, and El Niño Southern Oscillations on dengue-like-illness incidence in Solomon Islands

BACKGROUND

This study investigated associations between climate variables (average temperature and cumulative rainfall), and El Niño Southern Oscillation (ENSO) and dengue-like-illness (DLI) incidence in two provinces (Western and Guadalcanal Provinces) in Solomon Islands (SI).

METHODS

Weekly DLI and meteorological data were obtained from the Ministry of Health and Medical Services SI and the Ministry of Environment, Climate Change, Disaster Management and Meteorology from 2015 to 2018, respectively. We used negative binomial generalized estimating equations to assess the effects of climate variables up to a lag of 2 months and ENSO on DLI incidence in SI.

RESULTS

We captured an upsurge in DLI trend between August 2016 and April 2017. We found the effects of average temperature on DLI in Guadalcanal Province at lag of one month (IRR: 2.186, 95% CI: 1.094-4.368). Rainfall had minor but consistent effect in all provinces. La Niña associated with increased DLI risks in Guadalcanal Province (IRR: 4.537, 95% CI: 2.042-10.083), whereas El Niño associated with risk reduction ranging from 72.8% to 76.7% in both provinces.

CONCLUSIONS

Owing to the effects of climate variability and ENSO on DLI, defining suitable and sustainable measures to control dengue transmission and enhancing community resilience against climate change in low- and middle-developed countries are important.

A systematic review of dengue outbreak prediction models: Current scenario and future directions

Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the 'Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies' ('CHARMS') framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.

Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques

Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study.

Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak

Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during coronavirus (COVID-19) pandemic. Focus on human disease outbreak prediction techniques in current literature, Marques et al. (Predictive models for decision support in the COVID-19 crisis. Springer, Switzerland, 2021) state that there are four main methods to address forecasting problem: compartmental models, classic statistical models, space-state models and machine learning models. We adopt their framework to compare our research with previous works. Besides being divided by methods, forecasting problems can also be divided by the number of variables that are considered to make predictions. Considering this number of variables, forecasting problems can be classified as univariate, causal and multivariate models. Multivariate approaches have been applied in less than 10% of research found. This research is the first attempt to evaluate, over real time-series data of 3 different countries with univariate and multivariate methods to provide a short-term prediction. In literature we found no research with that scope and aim. A comparison of univariate and multivariate methods has been conducted and we concluded that besides the strong potential of multivariate methods, in our research univariate models presented best results in almost all regions' predictions.

Dengue Meteorological Determinants during Epidemic and Non-Epidemic Periods in Taiwan

The identification of the key factors influencing dengue occurrence is critical for a successful response to the outbreak. It was interesting to consider possible differences in meteorological factors affecting dengue incidence during epidemic and non-epidemic periods. In this study, the overall correlation between weekly dengue incidence rates and meteorological variables were conducted in southern Taiwan (Tainan and Kaohsiung cities) from 2007 to 2017. The lagged-time Poisson regression analysis based on generalized estimating equation (GEE) was also performed. This study found that the best-fitting Poisson models with the smallest QICu values to characterize the relationships between dengue fever cases and meteorological factors in Tainan (QICu = −8.49 × 10−3) and Kaohsiung (−3116.30) for epidemic periods, respectively. During dengue epidemics, the maximum temperature with 2-month lag (β = 0.8400, p < 0.001) and minimum temperature with 5-month lag (0.3832, p < 0.001). During non-epidemic periods, the minimum temperature with 3-month lag (0.1737, p < 0.001) and mean temperature with 2-month lag (2.6743, p < 0.001) had a positive effect on dengue incidence in Tainan and Kaohsiung, respectively.

A retrospective study of environmental predictors of dengue in Delhi from 2015 to 2018 using the generalized linear model

Dengue fever is a mosquito-borne infection with a rising trend, expected to increase further with the rise in global temperature. The study aimed to use the environmental and dengue data 2015–2018 to examine the seasonal variation and establish a probabilistic model of environmental predictors of dengue using the generalized linear model (GLM). In Delhi, dengue cases started emerging in the monsoon season, peaked in the post-monsoon, and thereafter, declined in early winter. The annual trend of dengue cases declined, but the seasonal pattern remained alike (2015–18). The Spearman correlation coefficient of dengue was significantly high with the maximum and minimum temperature at 2 months lag, but it was negatively correlated with the difference of average minimum and maximum temperature at lag 1 and 2. The GLM estimated β coefficients of environmental predictors such as temperature difference, cumulative rainfall, relative humidity and maximum temperature were significant (p < 0.01) at different lag (0 to 2), and maximum temperature at lag 2 was having the highest effect (IRR 1.198). The increasing temperature of two previous months and cumulative rainfall are the best predictors of dengue incidence. The vector control should be implemented at least 2 months ahead of disease transmission (August–November).

Dengue Early Warning System as Outbreak Prediction Tool: A Systematic Review

Early warning system (EWS) for vector-borne diseases is incredibly complex due to numerous factors originating from human, environmental, vector and the disease itself. Dengue EWS aims to collect data that leads to prompt decision-making processes that trigger disease intervention strategies to minimize the impact on a specific population. Dengue EWS may have a similar structural design, functions, and analytical approaches but different performance and ability to predict outbreaks. Hence, this review aims to summarise and discuss the evidence of different EWSs, their performance, and their ability to predict dengue outbreaks. A systematic literature search was performed of four primary databases: Scopus, Web of Science, Ovid MEDLINE, and EBSCOhost. Eligible articles were evaluated using a checklist for assessing the quality of the studies. A total of 17 studies were included in this systematic review. All EWS models demonstrated reasonably good predictive abilities to predict dengue outbreaks. However, the accuracy of their predictions varied greatly depending on the model used and the data quality. The reported sensitivity ranged from 50 to 100%, while specificity was 74 to 94.7%. A range between 70 to 96.3% was reported for prediction model accuracy and 43 to 86% for PPV. Overall, meteorological alarm indicators (temperatures and rainfall) were the most frequently used and displayed the best performing indicator. Other potential alarm indicators are entomology (female mosquito infection rate), epidemiology, population and socioeconomic factors. EWS is an essential tool to support district health managers and national health planners to mitigate or prevent disease outbreaks. This systematic review highlights the benefits of integrating several epidemiological tools focusing on incorporating climatic, environmental, epidemiological and socioeconomic factors to create an early warning system. The early warning system relies heavily on the country surveillance system. The lack of timely and high-quality data is critical for developing an effective EWS.

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.

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

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

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

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

Limited evidence for the role of environmental factors in the unusual peak of influenza in Brisbane during the 2018-2019 Australian summer

OBJECTIVE

To explore the contribution of environmental factors in the unusual pattern of influenza activity observed in Brisbane, Australia during the summer of 2018-2019.

METHODS

Distributed lag nonlinear models (DLNMs) were used to estimate the effect of environmental factors on weekly influenza incidence in Brisbane. Next generation sequencing was then employed to analyze minor and majority variants in influenza strains isolated from Brisbane children during this period.

RESULTS

There were limited marked differences in the environmental factors observed in Brisbane between the 2018-2019 summer period and the same period of the proceeding years, with the exception of significant reduction in rainfall. DLNM showed that reduced rainfall in Brisbane (at levels consistent with the 2018-2019 period) correlated with a dramatic increase in the relative risk of influenza. Sulfur dioxide (SO₂) levels were also increased in the 2018-2019 period, although these levels did not correlate with an increased risk of influenza. Sequencing of a limited number of pediatric influenza virus strains isolated during the 2018-2019 showed numerous mutations within the viral HA.

CONCLUSIONS

Taken together, these data suggest a limited role for key environmental factors in the influenza activity observed in Brisbane, Australia during the summer of 2018-2019. One alternative explanation may that viral factors, in addition to other factors not studied herein, contributed to the unusual influenza season. Our findings provide fundamental information that may be beneficial to a better understanding of the seasonal trends of influenza virus.

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.

A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles

Transmission of dengue fever depends on a complex interplay of human, climate and mosquito dynamics, which often change in time and space. It is well known that its disease dynamics are highly influenced by multiple factors including population susceptibility to infection as well as by microclimates: small-area climatic conditions which create environments favourable for the breeding and survival of mosquitoes. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology to any location or input data, it may prove valuable for public health decision-making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.

Original Article: Decline of notified dengue infections in Indonesia in 2017: Discussion of the possible determinants

This study was conducted to quantify the trend in dengue notifications in the country in 2017 and to explore the possible determinants. Annual nation-wide dengue notification data were obtained from the National Disease Surveillance of Ministry of Health of Indonesia. Annual incidence rate (IR) and case fatality rate (CFR) in 2017 and the previous years were quantified and compared. Correlations between annual larva free index (LFI), implementation coverage of integrated vector management (IVM), El Niño Southern Oscillation (Niño3.4), Dipole Mode Index (DMI), Zika virus seropositivity and the percent change in IR and CFR of dengue were examined. The change of dengue IR and CFRs were mapped. In 2017, dengue IR was declined by 71% (22.55 per 100,000 population) compared to 2016 (77.96 per 100,000 population) while the CFR was slightly reduced from 0.79% to 0.75%. Reduction in IR and CFR occurred in 94.1% and 70.1% out of 34 provinces, respectively. The trend of dengue IR seems to be influenced by Niño3.4 but there is no clear evidence that Niño3.4 is the main reason for dengue reduction in 2017. It is difficult to elucidate that the reduction of dengue in 2017 was associated with previous Zika outbreaks. In conclusion, there was a significant reduction on dengue notifications in Indonesia in 2017. Further investigation is needed to look at the role of climate on the decline of dengue IR at finer temporal scale. In addition, study on the role of cross-protective immunity generated by Zika infection on dengue incidence is also warranted.

Modeling the association between Aedes aegypti ovitrap egg counts, multi-scale remotely sensed environmental data and arboviral cases at Puntarenas, Costa Rica (2017-2018)

Problems with vector surveillance are a major barrier for the effective control of vector-borne disease transmission through Latin America. Here, we present results from a 80-week longitudinal study where Aedes aegypti (L.) (Diptera: Culicidae) ovitraps were monitored weekly at 92 locations in Puntarenas, a coastal city in Costa Rica with syndemic Zika, chikungunya and dengue transmission. We used separate models to investigate the association of either Ae. aegypti-borne arboviral cases or Ae. aegypti egg counts with remotely sensed environmental variables. We also evaluated whether Ae. aegypti-borne arboviral cases were associated with Ae. aegypti egg counts. Using cross-correlation and time series modeling, we found that arboviral cases were not significantly associated with Ae. aegypti egg counts. Through model selection we found that cases had a non-linear response to multi-scale (1-km and 30-m resolution) measurements of temperature standard deviation (SD) with a lag of up to 4 weeks, while simultaneously increasing with finely-grained NDVI (30-m resolution). Meanwhile, median ovitrap Ae. aegypti egg counts increased, and respectively decreased, with temperature SD (1-km resolution) and EVI (30-m resolution) with a lag of 6 weeks. A synchrony analysis showed that egg counts had a travelling wave pattern, with synchrony showing cyclic changes with distance, a pattern not observed in remotely sensed data with 30-m and 10-m resolution. Spatially, using generalized additive models, we found that eggs were more abundant at locations with higher temperatures and where EVI was leptokurtic during the study period. Our results suggest that, in Puntarenas, remotely sensed environmental variables are associated with both Ae. aegypti-borne arbovirus transmission and Ae. aegypti egg counts from ovitraps.

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.

The 2014 autochthonous dengue fever outbreak in Tokyo: A case series study and assessment of the causes and preventive measures

Objective

In 2014, an autochthonous dengue fever outbreak occurred around the Yoyogi Park in Japan for the first time in 70 years. Despite no local cases reported since then, the risk of another outbreak remains high. This study reviews the autochthonous dengue fever cases of the outbreak, investigates its causes, and delineates preventive measures against autochthonous dengue epidemics.

Methods

We conducted a case series study of 15 patients who visited our institution during the 2014 outbreak. We collected and evaluated data on the surveillance of vector mosquitoes, weather, pest control, travelers’ origins and destinations, and imported dengue fever cases using reports made by public institutions.

Results

All patients recovered with supportive treatments and none met the diagnostic criteria for severe dengue infection. Twelve patients with positive real-time polymerase chain reactions were confirmed as having dengue virus-1 infections. We found no obvious associations between the number of mosquitoes and the weather, or between the number of imported dengue fever cases and that of travelers. Insect growth regulator (IGR) against vector mosquitoes has been used since 2014 for pest control, but the number of larvae has not declined in the Yoyogi Park, although that of imagoes has been relatively suppressed.

Conclusion

The 2014 outbreak emerged without particularly favorable climate conditions for vector mosquitoes. We found no obvious associations between the number of travelers or the imported dengue fever cases and the outbreak, but the increasing number of travelers may contribute to another outbreak. Pest control, including IGR, remains essential for infection control.

•

We studied 15 patients with autochthonous dengue fever during a local outbreak.

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This outbreak emerged without favorable climate conditions for mosquitoes.

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There were no increased number of travelers or imported dengue cases.

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Pest control using insect growth regulator can be effective for infection control.

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

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

The influence of meteorological variables on the oviposition dynamics of Aedes aegypti (Diptera: Culicidae) in four environmentally distinct areas in northeast Brazil

BACKGROUND Fluctuations in climate have been associated with variations in mosquito abundance. OBJECTIVES To analyse the influence of precipitation, temperature, solar radiation, wind speed and humidity on the oviposition dynamics of Aedes aegypti in three distinct environmental areas (Brasília Teimosa, Morro da Conceição/Alto José do Pinho and Dois Irmãos/Pintos) of the city of Recife and the Fernando de Noronha Archipelago northeastern Brazil. METHODS Time series study using a database of studies previously carried out in the areas. The eggs were collected using spatially distributed geo-referenced sentinel ovitraps (S-OVTs). Meteorological satellite data were obtained from the IRI climate data library. The association between meteorological variables and egg abundance was analysed using autoregressive models. FINDINGS Precipitation was positively associated with egg abundance in three of the four study areas with a lag of one month. Higher humidity (β = 45.7; 95% CI: 26.3 - 65.0) and lower wind speed (β = -125.2; 95% CI: -198.8 - -51.6) were associated with the average number of eggs in the hill area. MAIN CONCLUSIONS The effect of climate variables on oviposition varied according to local environmental conditions. Precipitation was a main predictor of egg abundance in the study settings.

Impact of Climate Variability and Abundance of Mosquitoes on Dengue Transmission in Central Vietnam

Dengue fever is an important arboviral disease in many countries. Its incidence has increased during the last decade in central Vietnam. Most dengue studies in Vietnam focused on the northern area (Hanoi) and southern regions but not on central Vietnam. Dengue transmission dynamics and relevant environmental risk factors in central Vietnam are not understood. This study aimed to evaluate spatiotemporal patterns of dengue fever in central Vietnam and effects of climatic factors and abundance of mosquitoes on its transmission. Dengue and mosquito surveillance data were obtained from the Department of Vector Control and Border Quarantine at Nha Trang Pasteur Institute. Geographic Information System and satellite remote sensing techniques were used to perform spatiotemporal analyses and to develop climate models using generalized additive models. During 2005-2018, 230,458 dengue cases were reported in central Vietnam. Da Nang and Khanh Hoa were two major hotspots in the study area. The final models indicated the important role of Indian Ocean Dipole, multivariate El Niño-Southern Oscillation index, and vector index in dengue transmission in both regions. Regional climatic variables and mosquito population may drive dengue transmission in central Vietnam. These findings provide important information for developing an early dengue warning system in central Vietnam.

High relative humidity might trigger the occurrence of the second seasonal peak of dengue in the Philippines

BACKGROUND

Dengue in some regions has a bimodal seasonal pattern, with a first big seasonal peak followed by a second small seasonal peak. The factors associated with the second small seasonal peak remain unclear.

METHODS

Monthly data on dengue cases in the Philippines and its 17 regions from 2008 to 2017 were collected and underwent a time series seasonal decomposition analysis. The associations of monthly average mean temperature, average relative humidity, and total rainfall with dengue in 19 provinces were assessed with a generalized additive model. Logistic regression and a classification and regression tree (CART) model were used to identify the factors associated with the second seasonal peak of dengue.

RESULTS

Dengue incidence rate in the Philippines increased substantially in the period 2013-2017, particularly for the regions in south Philippines. Dengue peaks in south Philippines predominantly occurred in August, with the peak in the national capital region (NCR) (i.e., Metropolitan Manila) occurring in September. The association between mean temperature and dengue appeared J-shaped or upside-down-V-shaped, and the association between relative humidity (or rainfall) and dengue was heterogeneous across different provinces (e.g., J shape, reverse J shape, or upside-down V shape, etc). Relative humidity was the only factor associated with the second seasonal peak of dengue (odds ratio: 1.144; 95% confidence interval: 1.023-1.279; threshold: 77%).

CONCLUSIONS

Dengue control and prevention resources are increasingly required in regions beyond the NCR, and relative humidity can be used as a predictor of the second seasonal peak of dengue in the Philippines.

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

BACKGROUND

Although the association between dengue in Bali, Indonesia, and imported dengue in Australia has been widely asserted, no study has quantified this association so far.

METHODS

Monthly data on dengue and climatic factors over the past decade for Bali and Jakarta as well as monthly data on imported dengue in Australia underwent a three-stage analysis. Stage I: a quasi-Poisson regression with distributed lag non-linear model was used to assess the associations of climatic factors with dengue in Bali. Stage II: a generalized additive model was used to quantify the association of dengue in Bali with imported dengue in Australia with and without including the number of travelers in log scale as an offset. Stage III: the associations of mean temperature and rainfall (two climatic factors identified in stage I) in Bali with imported dengue in Australia were examined using stage I approach.

RESULTS

The number of dengue cases in Bali increased with increasing mean temperature, and, up to a certain level, it also increased with increasing rainfall but dropped off for high levels of rainfall. Above a monthly incidence of 1.05 cases per 100,000, dengue in Bali was almost linearly associated with imported dengue in Australia at a lag of one month. Mean temperature (relative risk (RR) per 0.5 °C increase: 2.95, 95% confidence interval (CI): 1.87, 4.66) and rainfall (RR per 7.5 mm increase: 3.42, 95% CI: 1.07, 10.92) in Bali were significantly associated with imported dengue in Australia at a lag of four months.

CONCLUSIONS

This study suggests that climatic factors (i.e., mean temperature and rainfall) known to be conducive of dengue transmission in Bali can provide an early warning with 4-month lead time for Australia in order to mitigate future outbreaks of local dengue in Australia. This study also provides a template and framework for future surveillance of travel-related infectious diseases globally.

Risk Assessment of Dengue Autochthonous Infections in Tokyo during Summer, Especially in the Period of the 2020 Olympic Games

An outbreak of autochthonous dengue fever occurred in the summer of 2014 in Tokyo, Japan. Numerous participants and spectators from abroad are expected to visit Tokyo in the summer of 2020. This study aims to analyze the risk of autochthonous dengue infections in Tokyo in summer and also assess the additional risk in the Olympiad using a mathematical model. A stochastic transmission model was developed with the cooperation of seasonal factors that greatly influence the transmission cycle of dengue virus, and stochastic simulations were conducted for each scenario provided adequately. This study found that (i) the incidence of dengue autochthonous infections is predicted to occur in a small number of cases; (ii) the local climate greatly influences the scale of dengue autochthonous infections; (iii) the incidence reaches its peak in August and early September; and (iv) the possibility of progressing to dengue outbreak is rare. In the Olympiad to be held in the summer of 2020, an additional risk of dengue autochthonous infections will amount to double compared with that in other years.

The influence of weather and weather variability on mosquito abundance and infection with West Nile virus in Harris County, Texas, USA

Early warning systems for vector-borne diseases (VBDs) prediction are an ecological application where data from the interface of several environmental components can be used to predict future VBD transmission. In general, models for early warning systems only consider average environmental conditions ignoring variation in weather variables, despite the prediction from Schmalhausen's law about the importance of environmental variability for biological systems. We present results from a long-term mosquito surveillance program from Harris County, Texas, USA, where we use time series analysis techniques to study the abundance and West Nile virus (WNV) infection patterns in the local primary vector, Culex quinquefasciatus Say. We found that, as predicted by Schmalhausen's law, mosquito abundance was associated with the standard deviation and kurtosis of environmental variables. By contrast, WNV infection rates were associated with 8-month lagged temperature, suggesting environmental conditions during overwintering might be key for WNV amplification during summer outbreaks. Finally, model validation showed that seasonal autoregressive models successfully predicted mosquito WNV infection rates up to 2 months ahead, but did rather poorly at predicting mosquito abundance, a result that might reflect impacts of vector control for mosquito population reduction, geographic scale, and other artifacts generated by operational constraints of mosquito surveillance systems.

Forest cover and climate as potential drivers for dengue fever in Sumatra and Kalimantan 2006-2016: a spatiotemporal analysis

OBJECTIVES

To describe and quantify spatiotemporal trends of dengue fever at district level in Sumatra and Kalimantan, Indonesia in relation to forest cover and climatic factors.

METHODS

A spatial ecological study design was used to analyse monthly surveillance data of notified dengue fever cases from January 2006 to December 2016 in the 154 districts of Sumatra and 56 districts of Kalimantan. A multivariate, zero-inflated Poisson regression model was developed with a conditional autoregressive prior structure with posterior parameters estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling.

RESULTS

There were 230 745 cases in Sumatra and 132 186 cases in Kalimantan during the study period. In Sumatra, the risk of dengue fever decreased by 9% (95% credible interval [CrI] 8.5-9.5%) for a 1% increase in forest cover and by 12.2% (95% CrI 11.9-12.6%) for a 1% increase in relative humidity. In Kalimantan, dengue fever risk fell by 17.6% (95% CrI 17.1-18.1%) for a 1% increase in relative humidity and rose by 7.6% (95% CrI 6.9-8.4%) for a 1 °C increase in minimum temperature. There was no significant residual spatial clustering in Sumatra after accounting for climate and demographic variables. In Kalimantan, high residual risk areas were primarily centred in North and East of the island.

CONCLUSIONS

Dengue fever in Sumatra and Kalimantan was highly seasonal and associated with climate factors and deforestation. Incorporation of climate indicators into risk-based surveillance might be warranted for dengue fever in Indonesia.

Effect of meteorological factors on scarlet fever incidence in Guangzhou City, Southern China, 2006-2017

OBJECTIVE

To explore the relationship between meteorological factors and scarlet fever incidence from 2006 to 2017 in Guangzhou, the largest subtropical city of Southern China, and assist public health prevention and control measures.

METHODS

Data for weekly scarlet fever incidence and meteorological variables from 2006 to 2017 in Guangzhou were collected from the National Notifiable Disease Report System (NNDRS) and the Guangzhou Meteorological Bureau (GZMB). Distributed lag nonlinear models (DLNMs) were conducted to estimate the effect of meteorological factors on weekly scarlet fever incidence in Guangzhou.

RESULTS

We observed nonlinear effects of temperature, relative humidity, and wind velocity. The risk was the highest when the weekly mean temperature was 31 °C during lag week 14, yielding a relative risk (RR) of 1.48 (95% CI: 1.01-2.17). When relative humidity was 43.5% during lag week 0, the RR was 1.49 (95% CI: 1.04-2.12); the highest RR (1.55, 95% CI: 1.20-1.99) was reached when relative humidity was 93.5% during lag week 20. When wind velocity was 4.4 m/s during lag week 13, the RR was highest at 3.41 (95% CI: 1.57-7.44). Positive correlations were observed among weekly temperature ranges and atmospheric pressure with scarlet fever incidence, while a negative correlation was detected with aggregate rainfall. The cumulative extreme effect of meteorological variables on scarlet fever incidence was statistically significant, except for the high effect of wind velocity.

CONCLUSION

Weekly mean temperature, relative humidity, and wind velocity had double-trough effects on scarlet fever incidence; high weekly temperature range, high atmospheric pressure, and low aggregate rainfall were risk factors for scarlet fever morbidity. Our findings provided preliminary, but fundamental, information that may be useful for a better understanding of epidemic trends of scarlet fever and for developing an early warning system. Laboratory surveillance for scarlet fever should be strengthened in the future.

Do we know how mosquito disease vectors will respond to climate change?

Mosquito-borne diseases are on the rise globally. Besides invasion processes and the increasing connectivity between distant regions through the trade of goods and human mobility, climate change is seen as an important driver for changing the likelihood of occurrence of vectors and diseases, respectively. Ectothermic insects respond directly to thermal conditions and thus we can expect them to follow climatic trends. However, a variety of species and different stages in their life cycles need to be considered. Here, we review the current literature in this field and disentangle the state of knowledge and the challenges and open questions for future research. The integration of diurnal temperature ranges in prospective experimental studies will strongly improve the knowledge of mosquitoes’ ecology and mosquito-borne disease transmission for temperate regions in particular. In addition, invasive mosquitoes are known to rapidly adapt to the climatic conditions, but the underlying processes are not yet fully understood.

Climatic factors influencing dengue incidence in an epidemic area of Nepal

Objective

Geographic expansion of dengue incidence has drawn a global interest to identify the influential factors that instigate the spread of this disease. The objective of this study was to find the environmental factors linked to dengue incidence in a dengue epidemic area of Nepal by negative binomial models using climatic factors from 2010 to 2017.

Results

Minimum temperature at lag 2 months, maximum temperature and relative humidity without lag period significantly affected dengue incidence. Rainfall was not associated with dengue incidence in Chitwan district of Nepal. The incident rate ratio (IRR) of dengue case rise by more than 1% for every unit increase in minimum temperature at lag 2 months, maximum temperature and relative humidity, but decrease by .759% for maximum temperature at lag 3 months. Considering the effect of minimum temperature of previous months on dengue incidence, the vector control and dengue management program should be implemented at least 2 months ahead of dengue outbreak season.

Electronic supplementary material

The online version of this article (10.1186/s13104-019-4185-4) contains supplementary material, which is available to authorized users.

Assessing changing weather and the El Niño Southern Oscillation impacts on cattle rabies outbreaks and mortality in Costa Rica (1985-2016)

BACKGROUND

Rabies is a major zoonotic disease affecting humans, domestic and wildlife mammals. Cattle are the most important domestic animals impacted by rabies virus in the New World, leading to thousands of cattle deaths per year and eliciting large economic losses. In the New World, virus transmission in cattle is primarily associated with Desmodus rotundus, the common vampire bat. This study analyses the association of weather fluctuations and the El Niño Southern Oscillation (ENSO), with the occurrence and magnitude, in terms of associated mortality, of cattle rabies outbreaks. Data from the 100 cattle rabies outbreaks recorded between 1985 and 2016 in Costa Rica were analyzed. Periodograms for time series of rabies outbreaks and the El Niño 4 index were estimated. Seasonality was studied using a seasonal boxplot. The association between epidemiological and climatic time series was studied via cross wavelet coherence analysis. Retrospective space-time scan cluster analyses were also performed. Finally, seasonal autoregressive time series models were fitted to study linear associations between monthly number of outbreaks, monthly mortality rates and the El Niño 4 index, temperature, and rainfall.

RESULTS

Large rabies mortality occurred towards the Atlantic basin of the country. Outbreak occurrence and size were not directly associated with ENSO, but were sensitive to weather variables impacted by ENSO. Both, ENSO phases and rabies outbreaks, showed a similar 5 year period in their oscillations. Cattle rabies mortality and outbreak occurrence increased with temperature, whereas outbreak occurrence decreased with rainfall. These results suggest that special weather conditions might favor the occurrence of cattle rabies outbreaks.

CONCLUSIONS

Further efforts are necessary to articulate the mechanisms underpinning the association between weather changes and cattle rabies outbreaks. One hypothesis is that exacerbation of cattle rabies outbreaks might be mediated by impacts of weather conditions on common vampire bat movement and access to food resources on its natural habitats. Further eco-epidemiological field studies could help to understand rabies virus transmission ecology, and to propose sound interventions to control this major veterinary public health problem.

Infectious Diseases After Hydrologic Disasters

Hydrologic disasters, including hurricanes, tsunamis, and severe flooding, have been associated with infectious diseases, particularly among vulnerable and displaced populations in resource-poor settings. Skin and soft tissue infections, gastrointestinal infections, respiratory infections, zoonotic infections, and vector-borne diseases each present unique threats to human health in this setting. Increased emergency physician awareness of these infectious diseases and their diagnosis and management helps optimize medical care for survivors after a hydrologic disaster and safeguard the health of disaster responders.

Spatio-temporal spillover risk of yellow fever in Brazil

BACKGROUND

Yellow fever virus is a mosquito-borne flavivirus that persists in an enzoonotic cycle in non-human primates (NHPs) in Brazil, causing disease in humans through spillover events. Yellow fever (YF) re-emerged in the early 2000s, spreading from the Amazon River basin towards the previously considered low-risk, southeastern region of the country. Previous methods mapping YF spillover risk do not incorporate the temporal dynamics and ecological context of the disease, and are therefore unable to predict seasonality in spatial risk across Brazil. We present the results of a bagged logistic regression predicting the propensity for YF spillover per municipality (administrative sub-district) in Brazil from environmental and demographic covariates aggregated by month. Ecological context was incorporated by creating National and Regional models of spillover dynamics, where the Regional model consisted of two separate models determined by the regions' NHP reservoir species richness (high vs low).

RESULTS

Of the 5560 municipalities, 82 reported YF cases from 2001 to 2013. Model accuracy was high for the National and low reservoir richness (LRR) models (AUC = 0.80), while the high reservoir richness (HRR) model accuracy was lower (AUC = 0.63). The National model predicted consistently high spillover risk in the Amazon, while the Regional model predicted strong seasonality in spillover risk. Within the Regional model, seasonality of spillover risk in the HRR region was asynchronous to the LRR region. However, the observed seasonality of spillover risk in the LRR Regional model mirrored the national model predictions.

CONCLUSIONS

The predicted risk of YF spillover varies with space and time. Seasonal trends differ between regions indicating, at times, spillover risk can be higher in the urban coastal regions than the Amazon River basin which is counterintuitive based on current YF risk maps. Understanding the spatio-temporal patterns of YF spillover risk could better inform allocation of public health services.

Increased Adult Aedes aegypti and Culex quinquefasciatus (Diptera: Culicidae) Abundance in a Dengue Transmission Hotspot, Compared to a Coldspot, within Kaohsiung City, Taiwan

The assumption that vector abundance differences might drive spatial and temporal heterogeneities in vector-borne disease transmission is common, though data supporting it is scarce. Here, we present data from two common mosquito species Aedes aegypti (Linnaeus) and Culex quinquefasciatus Say, biweekly sampled as adults, from March 2016 through December 2017, with BG-sentinel traps in two neighboring districts of Kaohsiung City (KC), Taiwan. One district has historically been a dengue transmission hotspot (Sanmin), and the other a coldspot (Nanzih). We collected a total 41,027 mosquitoes, and we found that average mosquito abundance (mean ± S.D.) was higher in Sanmin (Ae. aegypti: 9.03 ± 1.46; Cx. quinquefasciatus: 142.57 ± 14.38) than Nanzih (Ae. aegypti: 6.21 ± 0.47; Cx. quinquefasciatus: 63.37 ± 8.71) during the study period. In both districts, Ae. aegypti and Cx. quinquefasciatus population dynamics were sensitive to changes in temperature, the most platykurtic environmental variable at KC during the study period, a pattern predicted by Schmalhausen’s law, which states that organisms are more sensitive to small changes in environmental variables whose average value is more uncertain than its extremes. Our results also suggest that differences in Ae. aegypti abundance might be responsible for spatial differences in dengue transmission at KC. Our comparative approach, where we also observed a significant increment in the abundance of Cx. quinquefasciatus in the dengue transmission hotspot, suggests this area might be more likely to experience outbreaks of other vector borne diseases and should become a primary focus for vector surveillance and control.

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.

Investigating spatio-temporal distribution and diffusion patterns of the dengue outbreak in Swat, Pakistan

INTRODUCTION

Dengue has been endemic to Pakistan in the last two decades. There was a massive outbreak in the Swat valley in 2013. Here we demonstrate the spatio-temporal clustering and diffusion patterns of the dengue outbreak.

METHODS

Dengue case data were acquired from the hospital records in the Swat district of Pakistan. Ring maps visualize the distribution and diffusion of the number of cases and incidence of dengue at the level of the union council. We applied space-time scan statistics to identify spatio-temporal clusters. Ordinary least squares and geographically weighted regression models were used to evaluate the impact of elevation, population density, and distance to the river.

RESULTS

The results show that dengue distribution is not random, but clustered in space and time in the Swat district. Males constituted 68% of the cases while females accounted for about 32%. A majority of the cases (>55%) were younger than 40 years of age. The southern part was a major hotspot affected by the dengue outbreak in 2013. There are two space-time clusters in the spatio-temporal analysis. GWR and OLS show that population density is a significant explanatory variable for the dengue outbreak, while GWR exhibits better performance in terms of 'R²=0.49 and AICc=700'.

CONCLUSION

Dengue fever is clustered in the southern part of the Swat district. This region is relatively urban in character, with most of the population of the district residing here. There is a need to strengthen the surveillance system for reporting dengue cases in order to respond to future outbreaks in a robust way.

Meteorological factors affecting dengue incidence in Davao, Philippines

Background

Dengue fever is a major public health concern in the Philippines, and has been a significant cause of hospitalizations and deaths among young children. Previous literature links climate change to dengue, and with increasingly unpredictable changing climate patterns, there is a need to understand how these meteorological variables affect dengue incidence in a highly endemic area.

Methods

Weekly dengue incidences (2011–2015) in Davao Region, Philippines were obtained from the Department of Health. Same period of weekly local meteorological variables were obtained from the National Climatic Data Center (NCDC) and the National Oceanic and Atmospheric Administration (NOAA). Wavelet coherence analysis was used to determine the presence of non-stationary relationships, while a quasi-Poisson regression combined with distributed lag nonlinear model (DLNM) was used to analyze the association between meteorological variables and dengue incidences.

Results

Significant periodicity was detected in the 7 to 14-week band between the year 2011–2012 and a 26-week periodicity from the year 2013–2014. Overall cumulative risks were particularly high for rainfall at 32 mm (RR: 1.67, 95% CI: 1.07–2.62), while risks were observed to increase with increasing dew point. On the other hand, lower average temperature of 26 °C has resulted to an increased RR of dengue (RR: 1.96, 95% CI: 0.47–8.15) while higher temperature from 27 °C to 31 °C has lower RR.

Conclusions

The observed possible threshold levels of these meteorological variables can be integrated into an early warning system to enhance dengue prediction for better vector control and management in the future.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5532-4) contains supplementary material, which is available to authorized users.

Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines

Background

Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting.

Methods

Dengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 – December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated.

Results

Among the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model.

Conclusion

The study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3066-0) contains supplementary material, which is available to authorized users.

The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

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A LASSO based forecast model for endemic infectious diseases is proposed.

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Predictions at 4 weeks achieve desirable accuracy.

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Models predict outbreaks but may struggle to predict outbreak size.

Introduction

Accurate and timely prediction for endemic infectious diseases is vital for public health agencies to plan and carry out any control methods at an early stage of disease outbreaks. Climatic variables has been identified as important predictors in models for infectious disease forecasts. Various approaches have been proposed in the literature to produce accurate and timely predictions and potentially improve public health response.

Methods

We assessed how the machine learning LASSO method may be useful in providing useful forecasts for different pathogens in countries with different climates. Separate LASSO models were constructed for different disease/country/forecast window with different model complexity by including different sets of predictors to assess the importance of different predictors under various conditions.

Results

There was a more apparent cyclicity for both climatic variables and incidence in regions further away from the equator. For most diseases, predictions made beyond 4 weeks ahead were increasingly discrepant from the actual scenario. Prediction models were more accurate in capturing the outbreak but less sensitive to predict the outbreak size. In different situations, climatic variables have different levels of importance in prediction accuracy.

Conclusions

For LASSO models used for prediction, including different sets of predictors has varying effect in different situations. Short term predictions generally perform better than longer term predictions, suggesting public health agencies may need the capacity to respond at short-notice to early warnings.

Epidemiological Characteristics and Space-Time Analysis of the 2015 Dengue Outbreak in the Metropolitan Region of Tainan City, Taiwan

The metropolitan region of Tainan City in southern Taiwan experienced a dengue outbreak in 2015. This manuscript describes basic epidemiological features of this outbreak and uses spatial and temporal analysis tools to understand the spread of dengue during the outbreak. The analysis found that, independently of gender, dengue incidence rate increased with age, and proportionally affected more males below the age of 40 years but females above the age of 40 years. A spatial scan statistic was applied to detect clusters of disease transmission. The scan statistic found that dengue spread in a north-south diffusion direction, which is across the North, West-Central and South districts of Tainan City. Spatial regression models were used to quantify factors associated with transmission. This analysis indicated that neighborhoods with high proportions of residential area (or low wetland cover) were associated with dengue transmission. However, these association patterns were non-linear. The findings presented here can help Taiwanese public health agencies to understand the fundamental epidemiological characteristics and diffusion patterns of the 2015 dengue outbreak in Tainan City. This type of information is fundamental for policy making to prevent future uncontrolled dengue outbreaks, given that results from this study suggest that control interventions should be emphasized in the North and West-Central districts of Tainan city, in areas with a moderate percentage of residential land cover.