Present and future climatic suitability for dengue fever in Africa

A State-of-the-Science Review of Long-Term Predictions of Climate Change Impacts on Dengue Transmission Risk

BACKGROUND

Climate change is predicted to profoundly impact dengue transmission risk, yet a thorough review of evidence is necessary to refine understanding of climate scenarios, projection periods, spatial resolutions, and modeling approaches.

OBJECTIVES

We conducted a state-of-the-science review to comprehensively understand long-term dengue risk predictions under climate change, identify research gaps, and provide evidence-based guidelines for future studies.

METHODS

We searched three medical databases (PubMed, Embase, and Web of Science) up to 5 December 2024 to extract relevant modeling studies. An a priori search strategy, predefined eligibility criteria, and systematic data extraction procedures were implemented to identify and evaluate studies.

RESULTS

Of 5,035 studies retrieved, 57 met inclusion criteria. Prediction for dengue risk ranged from 1950 to 2115, and 52.63% (n = 30 ) of all studies used Representative Concentration Pathways (RCPs). Specifically, RCP 8.5 (34.94%; n = 29 ), Shared Socioeconomic Pathways (SSPs) 2 (32.35%; n = 11 ), and the Special Report on Emissions Scenarios (SRES) A1 (58.33%; n = 7 ) were utilized the most among all the RCPs, SSPs, and SRES climate change scenarios. Most studies (57.89%; n = 33 ) used only climatic variables for the prediction, and 21.05% (n = 12 ) of studies employed fine spatial resolution (≈ 1 km ) for the climate data. We identified that correlative approach was used mostly across the studies for modeling the future risk (61.40%; n = 35 ). Among mechanistic models, 35% (n = 7 ) lacked outcome validation, and 75% (n = 15 ) did not report model evaluation metrics.

DISCUSSION

We identified the urgent need to strengthen dengue databases, use finer spatial resolutions to integrate big data, and incorporate potential socioenvironmental factors such as human movement, vegetation, microclimate, and vector control efficacy in modeling. Utilizing appropriate spatiotemporal models and validation techniques will be crucial for developing functional climate-driven early warning systems for dengue fever. https://doi.org/10.1289/EHP14463.

Dengue virus transmission during non-outbreak period in Dar Es Salaam, Tanzania: a cross-sectional survey

BACKGROUND

Tanzania has experienced multiple dengue outbreaks between 2010 and 2019, caused by various dengue virus (DENV) strains. In 2019, there were 6917 confirmed dengue cases and 13 deaths in Tanzania. Routine diagnosis of dengue fever is unfortunately excluded, particularly during non-outbreak periods, resulting in delayed outbreak detection and control. The aim of this study was to improve early detection and control measures for DENV by investigating its circulation in human and Aedes aegypti (A.aegypti) mosquitoes during the non-outbreak periods in Dar es Salaam, Tanzania, which is an area frequently affected by dengue outbreaks.

METHODS

Four hundred and fifteen (415) blood samples were collected from patients attending randomly selected health facilities in five wards; Azimio, Keko, Mtoni, Mbagala and Chamazi within Temeke district. The samples were tested for DENV NS1 antigen and anti-dengue IgM and IgG antibodies by rapid test. Then, 150 out of 415 blood samples were tested for the DENV by conventional Reverse Transcriptase Polymerase Chain Reaction (RT-PCR). Two thousand two hundred and fifty (2,250) adult female A.aegypti mosquitoes were collected using a Prokopack aspirator and BG sentinel trap or obtained after rearing immature stages and tested, in pools of 15 for DENV by RT-PCR. Statistical Software, SPSS version 23, was used for data analysis.

RESULTS

Of the tested blood samples, 17% (71/415) were positive by NS1 antigen, 0.5% (2/415) by IgM, 0.5% (2/415) by IgG antibodies, and 0.5% (2/415) by IgM and IgG. None of the samples tested positive by DENV RT-PCR. Moreover, 3.3% (5/150) of tested mosquito pools had DENV by RT-PCR. Individuals aged between 21 and 40 years of age had increased risk of testing positive for DENV NS1 antigen, followed by those aged 5-20 years old, particularly those residing from Azimio ward, Keko ward, Mtoni ward and Mbagala ward, p-value ≤ 0.05.

CONCLUSION

Findings from this study revealed evidence of DENV circulation during non-outbreak periods in Dar es Salaam, Tanzania. These findings underscore the importance of including testing for dengue infection in routine differential diagnoses of febrile cases, and also frequent dengue surveillance in mosquitos. This proactive approach will help early DENV outbreak detection and control in the country.

Mitigating the effects of climate change on human health with vaccines and vaccinations

Climate change represents an unprecedented threat to humanity and will be the ultimate challenge of the 21st century. As a public health consequence, the World Health Organization estimates an additional 250,000 deaths annually by 2030, with resource-poor countries being predominantly affected. Although climate change's direct and indirect consequences on human health are manifold and far from fully explored, a growing body of evidence demonstrates its potential to exacerbate the frequency and spread of transmissible infectious diseases. Effective, high-impact mitigation measures are critical in combating this global crisis. While vaccines and vaccination are among the most cost-effective public health interventions, they have yet to be established as a major strategy in climate change-related health effect mitigation. In this narrative review, we synthesize the available evidence on the effect of climate change on vaccine-preventable diseases. This review examines the direct effect of climate change on water-related diseases such as cholera and other enteropathogens, helminthic infections and leptospirosis. It also explores the effects of rising temperatures on vector-borne diseases like dengue, chikungunya, and malaria, as well as the impact of temperature and humidity on airborne diseases like influenza and respiratory syncytial virus infection. Recent advances in global vaccine development facilitate the use of vaccines and vaccination as a mitigation strategy in the agenda against climate change consequences. A focused evaluation of vaccine research and development, funding, and distribution related to climate change is required.

Trends in mosquito species distribution modeling: insights for vector surveillance and disease control

Species distribution modeling (SDM) has become an increasingly common approach to explore questions about ecology, geography, outbreak risk, and global change as they relate to infectious disease vectors. Here, we conducted a systematic review of the scientific literature, screening 563 abstracts and identifying 204 studies that used SDMs to produce distribution estimates for mosquito species. While the number of studies employing SDM methods has increased markedly over the past decade, the overwhelming majority used a single method (maximum entropy modeling; MaxEnt) and focused on human infectious disease vectors or their close relatives. The majority of regional models were developed for areas in Africa and Asia, while more localized modeling efforts were most common for North America and Europe. Findings from this study highlight gaps in taxonomic, geographic, and methodological foci of current SDM literature for mosquitoes that can guide future efforts to study the geography of mosquito-borne disease risk.

Predicting potential areas at risk of the Dengue Hemorrhagic Fever in Jakarta, Indonesia-analyzing the accuracy of predictive hot spot analysis in the absence of small geographical area data

Dengue Hemorrhagic Fever (DHF), a more severe form of dengue, is one of the most rapidly spreading mosquito-borne diseases in the world. This study is motivated by the rising DHF incidence in Jakarta, the capital city of Indonesia. We mainly utilized hot spot analysis, which employs spatial statistics to find at-risk areas for DHF outbreaks in Jakarta's five municipalities. However, producing informative results from hot spot analysis requires a complete set of data on each of Jakarta's 42 districts, which is not available. We thus propose the idea of using small area estimation (SAE) and machine learning to make up for the lack of data. To evaluate whether this proposed method is effective, we compare the hot spot results from the estimation with the actual data of each district. The results show that the estimated hot spot map is similar to the hot spot map from the actual data. This implies that it is possible to find potential at-risk areas of dengue fever without a complete dataset in every small geographic area. We expect that this research can increase the performance of DHF control measures at the district level, even in the absence of small area data.

Predicting the current and future suitable habitats for endemic and endangered Ethiopian wolf using MaxEnt model

The Ethiopian wolf, endemic to Ethiopia, is the most endangered species in the world. As flagship species, a wide range of studies has been conducted on the Ethiopian wolf. However, there is scanty information about the impact of climate change on this globally important species. Thus, this study aimed to predict the current and future suitable habitats of the species based on four Representative Concentration Pathway scenarios of IPCC for the years 2050 and 2070 by using the MaxEnt model. A total of 479 species occurrence records were obtained from the field survey and Global Biodiversity Information Facility. The 19 bioclimatic variables and altitude were downloaded from worldclim and extracted for the study area using GIS software. The Pearson correlation analysis was employed to detect correlation among variables and maintained 10 variables. The prediction potential of the model was evaluated and found excellent to predict the distribution of the species. The result depicted that suitable habitats for Ethiopian wolves will be badly affected by climate change. Currently, about 9.4% of the total landmass of Ethiopia is suitable for wolves. However, it will be lost in the forthcoming couple of decade under all scenarios of global climate change. Consequently, the Ethiopian wolf is highly suspected to be extinct globally in the mid of 21st century, unless corrective measures are done in time. Therefore, enhancing the adaptive capacity of species as well as genetic resource preservation and captive breeding is advisable.

Integrating Spatial Modelling and Space-Time Pattern Mining Analytics for Vector Disease-Related Health Perspectives: A Case of Dengue Fever in Pakistan

The spatial–temporal assessment of vector diseases is imperative to design effective action plans and establish preventive strategies. Therefore, such assessments have potential public health planning-related implications. In this context, we here propose an integrated spatial disease evaluation (I-SpaDE) framework. The I-SpaDE integrates various techniques such as the Kernel Density Estimation, the Optimized Hot Spot Analysis, space–time assessment and prediction, and the Geographically Weighted Regression (GWR). It makes it possible to systematically assess the disease concentrations, patterns/trends, clustering, prediction dynamics, and spatially varying relationships between disease and different associated factors. To demonstrate the applicability and effectiveness of the I-SpaDE, we apply it in the second largest city of Pakistan, namely Lahore, using Dengue Fever (DF) during 2007–2016 as an example vector disease. The most significant clustering is evident during the years 2007–2008, 2010–2011, 2013, and 2016. Mostly, the clusters are found within the city’s central functional area. The prediction analysis shows an inclination of DF distribution from less to more urbanized areas. The results from the GWR show that among various socio-ecological factors, the temperature is the most significantly associated with the DF followed by vegetation and built-up area. While the results are important to understand the DF situation in the study area and have useful implications for public health planning, the proposed framework is flexible, replicable, and robust to be utilized in other similar regions, particularly in developing countries in the tropics and sub-tropics.

Predicting Spatial Patterns of Sindbis Virus (SINV) Infection Risk in Finland Using Vector, Host and Environmental Data

Pogosta disease is a mosquito-borne infection, caused by Sindbis virus (SINV), which causes epidemics of febrile rash and arthritis in Northern Europe and South Africa. Resident grouse and migratory birds play a significant role as amplifying hosts and various mosquito species, including Aedes cinereus, Culex pipiens, Cx. torrentium and Culiseta morsitans are documented vectors. As specific treatments are not available for SINV infections, and joint symptoms may persist, the public health burden is considerable in endemic areas. To predict the environmental suitability for SINV infections in Finland, we applied a suite of geospatial and statistical modeling techniques to disease occurrence data. Using an ensemble approach, we first produced environmental suitability maps for potential SINV vectors in Finland. These suitability maps were then combined with grouse densities and environmental data to identify the influential determinants for SINV infections and to predict the risk of Pogosta disease in Finnish municipalities. Our predictions suggest that both the environmental suitability for vectors and the high risk of Pogosta disease are focused in geographically restricted areas. This provides evidence that the presence of both SINV vector species and grouse densities can predict the occurrence of the disease. The results support material for public-health officials when determining area-specific recommendations and deliver information to health care personnel to raise awareness of the disease among physicians.