Early warning systems (EWSs) for chikungunya, dengue, malaria, yellow fever, and Zika outbreaks: What is the evidence? A scoping review

Impacts of climate change on water-related mosquito-borne diseases in temperate regions: A systematic review of literature and meta-analysis

Mosquito-borne diseases are a known tropical phenomenon. This review was conducted to assess the mecha-nisms through which climate change impacts mosquito-borne diseases in temperate regions. Articles were searched from PubMed, Scopus, Web of Science, and Embase databases. Identification criteria were scope (climate change and mosquito-borne diseases), region (temperate), article type (peer-reviewed), publication language (English), and publication years (since 2015). The WWH (who, what, how) framework was applied to develop the research question and thematic analyses identified the mechanisms through which climate change affects mosquito-borne diseases. While temperature ranges for disease transmission vary per mosquito species, all are viable for temperate regions, particularly given projected temperature increases. Zika, chikungunya, and dengue transmission occurs between 18-34 °C (peak at 26-29 °C). West Nile virus establishment occurs at monthly average temperatures between 14-34.3 °C (peak at 23.7-25 °C). Malaria establishment occurs when the consecutive average daily temperatures are above 16 °C until the sum is above 210 °C. The identified mechanisms through which climate change affects the transmission of mosquito-borne diseases in temperate regions include: changes in the development of vectors and pathogens; changes in mosquito habitats; extended transmission seasons; changes in geographic spread; changes in abundance and behaviors of hosts; reduced abundance of mosquito predators; interruptions to control operations; and influence on other non-climate factors. Process and stochastic approaches as well as dynamic and spatial models exist to predict mosquito population dynamics, disease transmission, and climate favorability. Future projections based on the observed relations between climate factors and mosquito-borne diseases suggest that mosquito-borne disease expansion is likely to occur in temperate regions due to climate change. While West Nile virus is already established in some temperate regions, Zika, dengue, chikungunya, and malaria are also likely to become established over time. Moving forward, more research is required to model future risks by incorporating climate, environmental, sociodemographic, and mosquito-related factors under changing climates.

Optimising the surveillance of Aedes aegypti in Brazil by selecting smaller representative areas within an endemic city

OBJECTIVES

Arboviruses, such as dengue (DENV), zika (ZIKV), and chikungunya (CHIKV), constitute a growing urban public health threat. Focusing on Aedes aegypti mosquitoes, their primary vectors, is crucial for mitigation. While traditional immature-stage mosquito surveillance has limitations, capturing adult mosquitoes through traps yields more accurate data on disease transmission. However, deploying traps presents logistical and financial challenges, demonstrating effective temporal predictions but lacking spatial accuracy. Our goal is to identify smaller representative areas within cities to enhance the early warning system for DENV outbreaks.

METHODS

We created Sentinel Geographic Units (SGUs), smaller areas of 1 km2 within each stratum, larger areas, with the aim of aligning the Trap Positivity Index (TPI) and Adult Density Index (ADI) with their respective strata. We conducted a two-step evaluation of SGUs. First, we examined the equivalence of TPI and ADI between SGUs and strata from January 2017 to July 2022. Second, we assessed the ability of SGU's TPI and ADI to predict DENV outbreaks in comparison to Foz do Iguaçu's Early-Warning System, which forecasts outbreaks up to 4 weeks ahead. Spatial and temporal analyses were carried out, including data interpolation and model selection based on Akaike information criteria (AIC).

RESULTS

Entomological indicators produced in small SGUs can effectively replace larger sentinel areas to access dengue outbreaks. Based on historical data, the best predictive capability is achieved 2 weeks after infestation verification. Implementing the SGU strategy with more frequent sampling can provide more precise space-time estimates and enhance dengue control.

CONCLUSIONS

The implementation of SGUs offers an efficient way to monitor mosquito populations, reducing the need for extensive resources. This approach has the potential to improve dengue transmission management and enhance the public health response in endemic cities.

Spatial analysis of dengue transmission in an endemic city in Brazil reveals high spatial structuring on local dengue transmission dynamics

In the last decades, dengue has become one of the most widespread mosquito-borne arboviruses in the world, with an increasing incidence in tropical and temperate regions. The mosquito Aedes aegypti is the dengue primary vector and is more abundant in highly urbanized areas. Traditional vector control methods have showing limited efficacy in sustaining mosquito population at low levels to prevent dengue virus outbreaks. Considering disease transmission is not evenly distributed in the territory, one perspective to enhance vector control efficacy relies on identifying the areas that concentrate arbovirus transmission within an endemic city, i.e., the hotspots. Herein, we used a 13-month timescale during the SARS-Cov-2 pandemic and its forced reduction in human mobility and social isolation to investigate the spatiotemporal association between dengue transmission in children and entomological indexes based on adult Ae. aegypti trapping. Dengue cases and the indexes Trap Positive Index (TPI) and Adult Density Index (ADI) varied seasonally, as expected: more than 51% of cases were notified on the first 2 months of the study, and higher infestation was observed in warmer months. The Moran's Eigenvector Maps (MEM) and Generalized Linear Models (GLM) revealed a strong large-scale spatial structuring in the positive dengue cases, with an unexpected negative correlation between dengue transmission and ADI. Overall, the global model and the purely spatial model presented a better fit to data. Our results show high spatial structure and low correlation between entomological and epidemiological data in Foz do Iguaçu dengue transmission dynamics, suggesting the role of human mobility might be overestimated and that other factors not evaluated herein could be playing a significant role in governing dengue transmission.

Defining Roles and Responsibilities of the Health Workforce to Respond to the Climate Crisis

Importance

The adverse effects of climate change are now apparent, disproportionately affecting marginalized and vulnerable populations and resulting in urgent worldwide calls to action. Health professionals occupy a critical position in the response to climate change, including in climate mitigation and adaptation, and their professional expertise and roles as health messengers are currently underused in the society-wide response to this crisis.

Observations

Clinical and public health professionals have important roles and responsibilities, some of which are shared, that they must fill for society to successfully mitigate the root causes of climate change and build a health system that can reduce morbidity and mortality impacts from climate-related hazards. When viewed through a preventive framework, the unique and synergizing roles and responsibilities provide a blueprint for investment in climate change-related prevention (primary, secondary, and tertiary), capacity building, education, and training of the health workforce. Substantial investment in increasing the competence and collaboration of health professionals is required, which must be undertaken in an urgent, coordinated, and deliberate manner.

Conclusions and Relevance

Exceptional collaboration, knowledge sharing, and workforce capacity building are essential to tackle the complex ways in which climate change threatens health. This framework serves as a guide for health system leaders, education institutions, policy planners, and others seeking to create a more resilient and just health system.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Early warning and response system for dengue outbreaks: Moving from research to operational implementation in Mexico

Dengue disease epidemics have increased in time and space due to climatic and non-climatic factors such as urbanization. In the absence of an effective vaccine, preventing dengue outbreak relies on vector control activities. Employing computerized tools to predict outbreaks and respond in advance has great potential for improving dengue disease control. Evidence of integrating or implementing such applications into control programs and their impact are scarce, and endemic countries demand for experience sharing and know-how transfer. Mexico has extensive experience of pre-validated EWARS (Early Warning And Response System), a tool that was developed in 2012 as part of a collaboration with the Special Program for Research and Training in Tropical Diseases Unit (TDR) at the World Health Organization and used at national level. The advancement of EWARS since 2014 and its stepwise integration into the national surveillance system has increased the appreciation of the need for integrated surveillance (including disease, vector and climate surveillance), and for linking inter-institutional and trans-sectoral information for holistic epidemiological intelligence. The integration of the EWARS software into the national surveillance platform in Mexico was a remarkable milestone and a successful experience. This manuscript describes the implementation process of EWARS in Mexico, which started in 2012 and further demonstrates benefits, threats, and opportunities of integrating EWARS into existing national surveillance programs.

Need for effective detection and early warnings for epidemic and pandemic preparedness planning in the context of multi-hazards: Lessons from the COVID-19 pandemic

The need for effective early detection and timely surveillance for a robust pandemic and epidemic early warning and preparedness has been widely discussed amidst the Covid-19 pandemic, which suddenly erupted worldwide. This need is further established by various other hazards reported in many countries amidst the COVID-19 pandemic. In addition, the failure of early detection of pathogens and their source of origin has been largely connected with global transmission and severe outbreaks in many contexts. Therefore, effective early detection , timely surveillance and early warning are key aspects of a successful response to an epidemic or pandemic. . Hence, this paper aims to identify key elements and stages of an effective epidemic and pandemic early warning (EW) and response system. Further, the paper analyses inter-connections of the elements of the early warning system, focusing on the COVID-19 and multi-hazard context. The systematic literature review method was used to collect data from electronic databases. Results suggest that epidemiological surveillance & detection, primary screening of raw data & information, risk and vulnerability assessments, prediction and decision-making, alerts & early warnings are critical components of epidemic and pandemic EW. In addition, response-control & mitigation, preparedness-preventive strategies, and reducing transmission , elimination and eradication of the disease are integrated components of the early warning and response ecosystem that largely depend on effective early warnings. The significance of integrating epidemic and pandemic EW with other EWs to operate as multi-hazard early warning systems is also analysed.

Differences in Longevity and Temperature-Driven Extrinsic Incubation Period Correlate with Varying Dengue Risk in the Arizona-Sonora Desert Region

Dengue transmission is determined by a complex set of interactions between the environment, Aedes aegypti mosquitoes, dengue viruses, and humans. Emergence in new geographic areas can be unpredictable, with some regions having established mosquito populations for decades without locally acquired transmission. Key factors such as mosquito longevity, temperature-driven extrinsic incubation period (EIP), and vector-human contact can strongly influence the potential for disease transmission. To assess how these factors interact at the edge of the geographical range of dengue virus transmission, we conducted mosquito sampling in multiple urban areas located throughout the Arizona-Sonora desert region during the summer rainy seasons from 2013 to 2015. Mosquito population age structure, reflecting mosquito survivorship, was measured using a combination of parity analysis and relative gene expression of an age-related gene, SCP-1. Bloodmeal analysis was conducted on field collected blood-fed mosquitoes. Site-specific temperature was used to estimate the EIP, and this predicted EIP combined with mosquito age were combined to estimate the abundance of "potential" vectors (i.e., mosquitoes old enough to survive the EIP). Comparisons were made across cities by month and year. The dengue endemic cities Hermosillo and Ciudad Obregon, both in the state of Sonora, Mexico, had higher abundance of potential vectors than non-endemic Nogales, Sonora, Mexico. Interestingly, Tucson, Arizona consistently had a higher estimated abundance of potential vectors than dengue endemic regions of Sonora, Mexico. There were no observed city-level differences in species composition of blood meals. Combined, these data offer insights into the critical factors required for dengue transmission at the ecological edge of the mosquito's range. However, further research is needed to integrate an understanding of how social and additional environmental factors constrain and enhance dengue transmission in emerging regions.

Re-Emerged Genotype IV of Japanese Encephalitis Virus Is the Youngest Virus in Evolution

An outbreak of viral encephalitis caused by a Japanese encephalitis virus (JEV) genotype IV infection occurred in Australia between 2021 and 2022. A total of 47 cases and seven deaths were reported as of November 2022. This is the first outbreak of human viral encephalitis caused by JEV GIV since it was first isolated in Indonesia in the late 1970s. Here, a comprehensive phylogenetic analysis based on the whole genome sequences of JEVs revealed it emerged 1037 years ago (95% HPD: 463 to 2100 years). The evolutionary order of JEV genotypes is as follows: GV, GIII, GII, GI, and GIV. The JEV GIV emerged 122 years ago (95% HPD: 57-233) and is the youngest viral lineage. The mean substitution rate of the JEV GIV lineage was 1.145 × 10-3 (95% HPD values, 9.55 × 10-4, 1.35 × 10-3), belonging to rapidly evolving viruses. A series of amino acid mutations with the changes of physico-chemical properties located in the functional important domains within the core and E proteins distinguished emerging GIV isolates from old ones. These results demonstrate the JEV GIV is the youngest JEV genotype at a rapid evolution stage and has good host/vector adaptability for introduction to non-endemic areas. Thus, surveillance of JEVs is highly recommended.

A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings

Dengue fever is an acute mosquito-borne disease that mostly spreads within urban or semi-urban areas in warm climate zones. The dengue-related risk map is one of the most practical tools for executing effective control policies, breaking the transmission chain, and preventing disease outbreaks. Mapping risk at a small scale, such as at an urban level, can demonstrate the spatial heterogeneities in complicated built environments. This review aims to summarize state-of-the-art modeling methods and influential factors in mapping dengue fever risk in urban settings. Data were manually extracted from five major academic search databases following a set of querying and selection criteria, and a total of 28 studies were analyzed. Twenty of the selected papers investigated the spatial pattern of dengue risk by epidemic data, whereas the remaining eight papers developed an entomological risk map as a proxy for potential dengue burden in cities or agglomerated urban regions. The key findings included: (1) Big data sources and emerging data-mining techniques are innovatively employed for detecting hot spots of dengue-related burden in the urban context; (2) Bayesian approaches and machine learning algorithms have become more popular as spatial modeling tools for predicting the distribution of dengue incidence and mosquito presence; (3) Climatic and built environmental variables are the most common factors in making predictions, though the effects of these factors vary with the mosquito species; (4) Socio-economic data may be a better representation of the huge heterogeneity of risk or vulnerability spatial distribution on an urban scale. In conclusion, for spatially assessing dengue-related risk in an urban context, data availability and the purpose for mapping determine the analytical approaches and modeling methods used. To enhance the reliabilities of predictive models, sufficient data about dengue serotyping, socio-economic status, and spatial connectivity may be more important for mapping dengue-related risk in urban settings for future studies.

Development and user testing study of MozzHub: a bipartite network-based dengue hotspot detector

Traditionally, dengue is controlled by fogging, and the prime location for the control measure is at the patient's residence. However, when Malaysia was hit by the first wave of the Coronavirus disease (COVID-19), and the government-imposed movement control order, dengue cases have decreased by more than 30% from the previous year. This implies that residential areas may not be the prime locations for dengue-infected mosquitoes. The existing early warning system was focused on temporal prediction wherein the lack of consideration for spatial component at the microlevel and human mobility were not considered. Thus, we developed MozzHub, which is a web-based application system based on the bipartite network-based dengue model that is focused on identifying the source of dengue infection at a small spatial level (400 m) by integrating human mobility and environmental predictors. The model was earlier developed and validated; therefore, this study presents the design and implementation of the MozzHub system and the results of a preliminary pilot test and user acceptance of MozzHub in six district health offices in Malaysia. It was found that the MozzHub system is well received by the sample of end-users as it was demonstrated as a useful (77.4%), easy-to-operate system (80.6%), and has achieved adequate client satisfaction for its use (74.2%).

Distance to public transit predicts spatial distribution of dengue virus incidence in Medellín, Colombia

Dengue is a growing global threat in some of the world’s most rapidly growing landscapes. Research shows that urbanization and human movement affect the spatial dynamics and magnitude of dengue outbreaks; however, precise effects of urban growth on dengue are not well understood because of a lack of sufficiently fine-scaled data. We analyzed nine years of address-level dengue case data in Medellin, Colombia during a period of public transit expansion. We correlate changes in the spread and magnitude of localized outbreaks to changes in accessibility and usage of public transit. Locations closer to and with a greater utilization of public transit had greater dengue incidence. This relationship was modulated by socioeconomic status; lower socioeconomic status locations experienced stronger effects of public transit accessibility and usage on dengue incidence. Public transit is a vital urban resource, particularly among low socioeconomic populations. These results highlight the importance of public health services concurrent with urban growth.

The Early Warning and Response System (EWARS-TDR) for dengue outbreaks: can it also be applied to chikungunya and Zika outbreak warning?

BACKGROUND

In the Americas, endemic countries for Aedes-borne diseases such as dengue, chikungunya, and Zika face great challenges particularly since the recent outbreaks of CHIKV and ZIKV, all transmitted by the same insect vectors Aedes aegypti and Ae. albopictus. The Special Program for Research and Training in Tropical Diseases (TDR-WHO) has developed together with partners an Early Warning and Response System (EWARS) for dengue outbreaks based on a variety of alarm signals with a high sensitivity and positive predictive value (PPV). The question is if this tool can also be used for the prediction of Zika and chikungunya outbreaks.

METHODOLOGY

We conducted in nine districts of Mexico and one large city in Colombia a retrospective analysis of epidemiological data (for the outbreak definition) and of climate and entomological data (as potential alarm indicators) produced by the national surveillance systems for dengue, chikungunya and Zika outbreak prediction covering the following outbreak years: for dengue 2012-2016, for Zika 2015-2017, for chikungunya 2014-2016. This period was divided into a "run in period" (to establish the "historical" pattern of the disease) and an "analysis period" (to identify sensitivity and PPV of outbreak prediction).

RESULTS

In Mexico, the sensitivity of alarm signals for correctly predicting an outbreak was 100% for dengue, and 97% for Zika (chikungunya data could not be obtained in Mexico); the PPV was 83% for dengue and 100% for Zika. The time period between alarm and start of the outbreak (i.e. the time available for early response activities) was for Zika 4-5 weeks. In Colombia the sensitivity of the outbreak prediction was 92% for dengue, 93% for chikungunya and 100% for Zika; the PPV was 68% for dengue, 92% for chikungunya and 54% for Zika; the prediction distance was for dengue 3-5 weeks, for chikungunya 10-13 weeks and for Zika 6-10 weeks.

CONCLUSION

EWARS demonstrated promising capability of timely disease outbreak prediction with an operational design likely to improve the coordination among stakeholders. However, the prediction validity varied substantially across different types of diseases and appeared less optimal in low endemic settings.

Improving Dengue Forecasts by Using Geospatial Big Data Analysis in Google Earth Engine and the Historical Dengue Information-Aided Long Short Term Memory Modeling

Simple Summary

Forecasting dengue cases often face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without historical dengue cases. With the advance of the geospatial big data cloud computing in Google Earth Engine and deep learning, this study proposed an efficient framework of dengue prediction at an epidemiological week basis using geospatial big data analysis in Google Earth Engine and Long Short Term Memory modeling. We focused on the dengue epidemics in the Federal District of Brazil during 2007–2019. Based on Google Earth Engine and epidemiological calendar, we computed the weekly composite for each dengue driving factor, and spatially aggregated the pixel values into dengue transmission areas to generate the time series of driving factors. A multi-step-ahead Long Short Term Memory modeling was used, and the time-differenced natural log-transformed dengue cases and the time series of driving factors were considered as outcomes and explantary factors, respectively, with two modeling scenarios (with and without historical cases). The performance is better when historical cases were used, and the 5-weeks-ahead forecast has the best performance.

Abstract

Timely and accurate forecasts of dengue cases are of great importance for guiding disease prevention strategies, but still face challenges from (1) time-effectiveness due to time-consuming satellite data downloading and processing, (2) weak spatial representation capability due to data dependence on administrative unit-based statistics or weather station-based observations, and (3) stagnant accuracy without the application of historical case information. Geospatial big data, cloud computing platforms (e.g., Google Earth Engine, GEE), and emerging deep learning algorithms (e.g., long short term memory, LSTM) provide new opportunities for advancing these efforts. Here, we focused on the dengue epidemics in the urban agglomeration of the Federal District of Brazil (FDB) during 2007–2019. A new framework was proposed using geospatial big data analysis in the Google Earth Engine (GEE) platform and long short term memory (LSTM) modeling for dengue case forecasts over an epidemiological week basis. We first defined a buffer zone around an impervious area as the main area of dengue transmission by considering the impervious area as a human-dominated area and used the maximum distance of the flight range of Aedes aegypti and Aedes albopictus as a buffer distance. Those zones were used as units for further attribution analyses of dengue epidemics by aggregating the pixel values into the zones. The near weekly composite of potential driving factors was generated in GEE using the epidemiological weeks during 2007–2019, from the relevant geospatial data with daily or sub-daily temporal resolution. A multi-step-ahead LSTM model was used, and the time-differenced natural log-transformed dengue cases were used as outcomes. Two modeling scenarios (with and without historical dengue cases) were set to examine the potential of historical information on dengue forecasts. The results indicate that the performance was better when historical dengue cases were used and the 5-weeks-ahead forecast had the best performance, and the peak of a large outbreak in 2019 was accurately forecasted. The proposed framework in this study suggests the potential of the GEE platform, the LSTM algorithm, as well as historical information for dengue risk forecasting, which can easily be extensively applied to other regions or globally for timely and practical dengue forecasts.