El Niño Southern Oscillation as an early warning tool for dengue outbreak in India

Spatial and temporal trends of dengue infections in Curaçao: A 21-year analysis

Dengue viruses are a significant global health concern, causing millions of infections annually and putting approximately half of the world's population at risk, as reported by the World Health Organization (WHO). Understanding the spatial and temporal patterns of dengue virus spread is crucial for effective prevention of future outbreaks. By investigating these patterns, targeted dengue surveillance and control measures can be improved, aiding in the management of outbreaks in dengue-affected regions. Curaçao, where dengue is endemic, has experienced frequent outbreaks over the past 25 years. To examine the spatial and temporal trends of dengue outbreaks in Curaçao, this study employs an interdisciplinary and multi-method approach. Data on >6500 cases of dengue infections in Curaçao between the years 1995 and 2016 were used. Temporal and spatial statistics were applied. The Moran's I index identified the presence of spatial autocorrelation for incident locations, allowing us to reject the null hypothesis of spatial randomness. The majority of cases were recorded in highly populated areas and a relationship was observed between population density and dengue cases. Temporal analysis demonstrated that cases mostly occurred from October to January, during the rainy season. Lower average temperatures, higher precipitation and a lower sea surface temperature appear to be related to an increase in dengue cases. This effect has a direct link to La Niña episodes, which is the cooling phase of El Niño Southern Oscillation. The spatial and temporal analyses conducted in this study are fundamental to understanding the timing and locations of outbreaks, and ultimately improving dengue outbreak management.

Mosquito-borne diseases in India over the past 50 years and their Global Public Health Implications: A Systematic Review

Mosquito-borne diseases (MBDs) pose a significant public health concern globally, and India, with its unique eco-sociodemographic characteristics, is particularly vulnerable to these diseases. This comprehensive review aims to provide an in-depth overview of MBDs in India, emphasizing their impact and potential implications for global health. The article explores distribution, epidemiology, control or elimination, and economic burden of the prevalent diseases such as malaria, dengue, chikungunya, Japanese encephalitis, and lymphatic filariasis, which collectively contribute to millions of cases annually. It sheds light on their profound effects on morbidity, mortality, and socioeconomic burdens and the potential for international transmission through travel and trade. The challenges and perspectives associated with controlling mosquito populations are highlighted, underscoring the importance of effective public health communication for prevention and early detection. The potential for these diseases to spread beyond national borders is recognized, necessitating a holistic approach to address the challenge. A comprehensive literature search was conducted, covering the past five decades (1972-2022), utilizing databases such as Web of Science, PubMed, and Google Scholar, in addition to in-person library consultations. The literature review analyzed 4,082 articles initially identified through various databases. After screening and eligibility assessment, 252 articles were included for analysis. The review focused on malaria, dengue, chikungunya, Japanese encephalitis, and lymphatic filariasis. The included studies focused on MBDs occurrence in India, while those conducted outside India, lacking statistical analysis, or published before 1970 were excluded. This review provides valuable insights into the status of MBDs in India and underscores the need for concerted efforts to combat these diseases on both national and global scales through consilience.

Hydrogeochemistry and quality evaluation of groundwater and its impact on human health in North Tripura, India

Groundwater contamination becomes an alarming threat to the provision of ecosystem services and natural resources. A very high level of groundwater contamination has been observed in the northeastern states particularly in North Tripura district. Therefore, the present study considered the region as a case study to evaluate the hydrogeochemical facies, heavy metal pollution and irrigation indices, and their impact on human health. For the investigation, we have collected a total of 35 groundwater samples from North Tripura district. Hydrogeochemical facies through Piper plot reflect Ca²⁺-Mg²⁺-HCO₃^- and Na⁺-HCO₃^- as dominant water types. Gibbs plot identifies the dominance of rock-water interaction process in groundwater hydrochemistry. Geochemical plots indicate the dominance of silicate weathering, ion exchange and carbonate dissolution processes in groundwater mineralisation. The order of trace metal contaminations follows Fe > As > Zn > Mn > Cu > Pb. Results of heavy metal indices suggest above 80% samples are at high risk due to high Fe contamination. The risk of the heavy metal indices is associated with rising elevation in southern part of North Tripura. Findings of health risk assessment study imply that children face much carcinogenic and non-carcinogenic risks than adults because of unsafe levels of Fe and As. Multivariate statistical tools are applied to unravel interrelationships among all ions and trace metals as well as probable hydrogeochemical processes in groundwater. Results of Wilcox and USSL plots suggest 77% samples meet irrigation suitability criteria. Besides, the analysis suggests a better insight to identify hydrogeochemical processes controlling groundwater chemistry and the suitability of groundwater for irrigation and drinking purposes. The study also suggests treatment and sustainable management of groundwater resources is compulsory to reduce trace metal contaminations before public use.

Global climate change and human health: Pathways and possible solutions

Global warming has been changing the planet's climate pattern, leading to increasing frequency, intensity and duration of extreme weather events and natural disasters. These climate-changing events affect various health outcomes adversely through complicated pathways. This paper reviews the main signs of climate change so far, e.g., suboptimal ambient temperature, sea-level rise and other conditions, and depicts the interactive pathways between different climate-changing events such as suboptimal temperature, wildfires, and floods with a broad range of health outcomes. Meanwhile, the modifying effect of socioeconomic, demographic and environmental factors on the pathways is summarised, such that the youth, elderly, females, poor and those living in coastal regions are particularly susceptible to climate change. Although Earth as a whole is expected to suffer from climate change, this review article discusses some potential benefits for certain regions, e.g., a more liveable environment and sufficient food supply. Finally, we summarise certain mitigation and adaptation strategies against climate change and how these strategies may benefit human health in other ways. This review article provides a comprehensive and concise introduction of the pathways between climate change and human health and possible solutions, which may map directions for future research.

Climate Anomalies and Spillover of Bat-Borne Viral Diseases in the Asia-Pacific Region and the Arabian Peninsula

Climate variability and anomalies are known drivers of the emergence and outbreaks of infectious diseases. In this study, we investigated the potential association between climate factors and anomalies, including El Niño Southern Oscillation (ENSO) and land surface temperature anomalies, as well as the emergence and spillover events of bat-borne viral diseases in humans and livestock in the Asia-Pacific region and the Arabian Peninsula. Our findings from time series analyses, logistic regression models, and structural equation modelling revealed that the spillover patterns of the Nipah virus in Bangladesh and the Hendra virus in Australia were differently impacted by climate variability and with different time lags. We also used event coincidence analysis to show that the emergence events of most bat-borne viral diseases in the Asia-Pacific region and the Arabian Peninsula were statistically associated with ENSO climate anomalies. Spillover patterns of the Nipah virus in Bangladesh and the Hendra virus in Australia were also significantly associated with these events, although the pattern and co-influence of other climate factors differed. Our results suggest that climate factors and anomalies may create opportunities for virus spillover from bats to livestock and humans. Ongoing climate change and the future intensification of El Niño events will therefore potentially increase the emergence and spillover of bat-borne viral diseases in the Asia-Pacific region and the Arabian Peninsula.

Prediction of dengue fever outbreaks using climate variability and Markov chain Monte Carlo techniques in a stochastic susceptible-infected-removed model

The recent increase in the global incidence of dengue fever resulted in over 2.7 million cases in Latin America and many cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic outbreak prediction. EWS pertaining to dengue outbreaks is imperative; given the fact that dengue is linked to environmental factors owing to its dominance in the tropics. Prediction is an integral part of EWS, which is dependent on several factors, in particular, climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in developing novel predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high dengue incidence in 2019 and 2020, respectively. A random-sampling-based susceptible-infected-removed (SIR) model was used to obtain estimates of the susceptible fraction for modeling the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique that was used to fit the model to Singapore and Honduras case report data from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal model accounted for 98.5% and 92.8% of the variance in case count in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance in Singapore and Honduras outbreaks respectively, besides accounting for 75.4% of the variance in the major 2013 Singapore outbreak, 71.5% of the variance in the 2019 Singapore outbreak, and over 70% of the variance in 2015 and 2016 Honduras outbreaks. The seasonal model accounted for 14.2% and 83.1% of the variance in the 2013 and 2019 Singapore outbreaks, respectively, in addition to 91% and 59.5% of the variance in the 2015 and 2016 Honduras outbreaks, respectively. Autocorrelation lag tests showed that the climate model exhibited better prediction dynamics for Singapore outbreaks during the dry season from May to August and in the rainy season from June to October in Honduras. After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019–2020 dengue epidemic, in comparison to the climate model, which was more accurate in outbreaks of smaller magnitude. Such modeling studies could be further performed in various outbreaks, such as the ongoing COVID-19 pandemic to understand the outbreak dynamics and predict the occurrence of future outbreaks.

Spatiotemporal dynamics of dengue in Colombia in relation to the combined effects of local climate and ENSO

Dengue virus (DENV) is an endemic disease in the hot and humid low-lands of Colombia. We characterize the association of monthly series of dengue cases with indices of El Niño/Southern Oscillation (ENSO) at the tropical Pacific and local climatic variables in Colombia during the period 2007-2017 at different temporal and spatial scales. For estimation purposes, we use lagged cross-correlations (Pearson test), cross-wavelet analysis (wavelet cross spectrum, and wavelet coherence), as well as a novel nonlinear causality method, PCMCI, that allows identifying common causal drivers and links among high dimensional simultaneous and time-lagged variables. Our results evidence the strong association of DENV cases in Colombia with ENSO indices and with local temperature and rainfall. El Niño (La Niña) phenomenon is related to an increase (decrease) of dengue cases nationally and in most regions and departments, with maximum correlations occurring at shorter time lags in the Pacific and Andes regions, closer to the Pacific Ocean. This association is mainly explained by the ENSO-driven increase in temperature and decrease in rainfall, especially in the Andes and Pacific regions. The influence of ENSO is not stationary, given the reduction of DENV cases since 2005, and that local climate variables vary in space and time, which prevents to extrapolate results from one region to another. The association between DENV and ENSO varies at national and regional scales when data are disaggregated by seasons, being stronger in DJF and weaker in SON. Overall, the Pacific and Andes regions control the relationship between dengue dynamics and ENSO at national scale. Cross-wavelet analysis indicates that the ENSO-DENV relation in Colombia exhibits a strong coherence in the 12 to 16-months frequency band, which implies the frequency locking between the annual cycle and the interannual (ENSO) timescales. Results of nonlinear causality metrics reveal the complex concomitant effects of ENSO and local climate variables, while offering new insights to develop early warning systems for DENV in Colombia.

The Complex Epidemiological Relationship between Flooding Events and Human Outbreaks of Mosquito-Borne Diseases: A Scoping Review

BACKGROUND

Climate change is expected to increase the frequency of flooding events. Although rainfall is highly correlated with mosquito-borne diseases (MBD) in humans, less research focuses on understanding the impact of flooding events on disease incidence. This lack of research presents a significant gap in climate change-driven disease forecasting.

OBJECTIVES

We conducted a scoping review to assess the strength of evidence regarding the potential relationship between flooding and MBD and to determine knowledge gaps.

METHODS

PubMed, Embase, and Web of Science were searched through 31 December 2020 and supplemented with review of citations in relevant publications. Studies on rainfall were included only if the operationalization allowed for distinction of unusually heavy rainfall events. Data were abstracted by disease (dengue, malaria, or other) and stratified by post-event timing of disease assessment. Studies that conducted statistical testing were summarized in detail.

RESULTS

From 3,008 initial results, we included 131 relevant studies (dengue n = 45 , malaria n = 61 , other MBD n = 49 ). Dengue studies indicated short-term (< 1  month ) decreases and subsequent (1-4 month) increases in incidence. Malaria studies indicated post-event incidence increases, but the results were mixed, and the temporal pattern was less clear. Statistical evidence was limited for other MBD, though findings suggest that human outbreaks of Murray Valley encephalitis, Ross River virus, Barmah Forest virus, Rift Valley fever, and Japanese encephalitis may follow flooding.

DISCUSSION

Flooding is generally associated with increased incidence of MBD, potentially following a brief decrease in incidence for some diseases. Methodological inconsistencies significantly limit direct comparison and generalizability of study results. Regions with established MBD and weather surveillance should be leveraged to conduct multisite research to a) standardize the quantification of relevant flooding, b) study nonlinear relationships between rainfall and disease, c) report outcomes at multiple lag periods, and d) investigate interacting factors that modify the likelihood and severity of outbreaks across different settings. https://doi.org/10.1289/EHP8887.

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.