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Late 20th century disease and transmission-risk models in the Indian peninsula (A) and South America (B). These models were calibrated according to human-dengue cases from the late 20th century (Fig 2A). The locations of dengue cases recorded in the late 20th and the early 21st centuries are shown in order to illustrate the predictive capacity of these models (see explanations and implications in the main text). See early 21st-century models and data for these areas in S8 Fig. Coast lines source: https://developers.google.com/earth-engine/datasets/catalog/FAO_GAUL_2015_level0.
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Dengue is a viral disease transmitted by mosquitoes. The rapid spread of dengue could lead to a global pandemic, and so the geographical extent of this spread needs to be assessed and predicted. There are also reasons to suggest that transmission of dengue from non-human primates in tropical forest cycles is being underestimated. We investigate the...
Citations
... A strategy for controlling dengue disease is spatial distribution analysis, which provides information about the spreading pattern across various areas [5]. The distribution models also benefit disease prevention and mitigation strategies, including vector control, large-scale vaccination programs, and healthcare advice for travelers [6]. Mala and Kumar Jat [7] have demonstrated the use of geostatistical techniques to detect and understand the spatial distribution of epidemics. ...
Background and Aim: Dengue fever is a recurring arboviral disease. The presence of livestock and domestic animals potentially increases the risk of dengue fever in an area due to the shared habitats of vectors and humans. Therefore, this study aimed to determine the vulnerability map of dengue disease and identify the influence of livestock and domestic animals on the number of cases in Bantul Regency. Materials and Methods: An observational study was conducted in 3 Kapanewon (subdistricts) in the Bantul regency, known as the dengue-endemic area. The locations of 302 cases were recorded using the Global Positioning System. Dengue case density was analyzed using Kernel Density Estimation, and vulnerability was assessed using an overlay in ArcGIS Desktop 10.8. Furthermore, buffer analysis was conducted to determine the relationship between case density and the presence of livestock and pet pens. Results: Banguntapan, Kasihan, and Sewon subdistricts had high vulnerability areas of 424.12 Ha (14.97%), 334.76 Ha (10.46%), and 196.12 Ha (7.05%), respectively. The villages with dengue hotspots were Banguntapan and Potorono (Banguntapan Subdistrict) and Tirtonirmolo (Kasihan Subdistrict). The highest number of patients (180 cases) occurred at a buffer distance of <100 m from houses to livestock pens, closely related to the flight distance of Aedes spp. mosquitoes, the dengue vector. Conclusion: The three subdistricts were predominantly characterized by low dengue vulnerability. However, livestock and domestic animal pens are significant risk factors. This information is crucial for effectively controlling and managing dengue disease in Bantul Regency. Keywords: dengue, domestic animals, livestock, vulnerability.
... albopictus in response to climate change [10][11][12]. Aedes mosquito vectors are currently expanding their distributions to more temperate climates across all continents where they now occur [13][14][15], also favored by the globalization of trade through increased international travel and shipping. Many ports and airports are easy entry points for mosquito species such as Ae. ...
... Areas prone to sylvatic arboviral transmission to humans from non-human primates may be larger than estimated [14,19]. Anthropogenic disturbance in forests often allows the interaction of different mosquito communities with varying habitat preferences at ecotonal forest edges [20]. ...
... In order to analyze the effect of climate change on dengue and yellow fever vector distributions, we took into account the favorability models for these species published for the period 2001-2017 [14,19], henceforth referred to as the baseline models. These models were based on the favorability concept, which permits model combination through fuzzy logic operations [22]. ...
Climate change may increase the risk of dengue and yellow fever transmission by urban and sylvatic mosquito vectors. Previous research primarily focused on Aedes aegypti and Aedes albopictus. However, dengue and yellow fever have a complex transmission cycle involving sylvatic vectors. Our aim was to analyze how the distribution of areas favorable to both urban and sylvatic vectors could be modified as a consequence of climate change. We projected, to future scenarios, baseline distribution models already published for these vectors based on the favorability function, and mapped the areas where mosquitoes' favorability could increase, decrease or remain stable in the near (2041-2060) and distant (2061-2080) future. Favorable areas for the presence of dengue and yellow fever vectors show little differences in the future compared to the baseline models, with changes being perceptible only at regional scales. The model projections predict dengue vectors expanding in West and Central Africa and in South-East Asia, reaching Borneo. Yellow fever vectors could spread in West and Central Africa and in the Amazon. In some locations of Europe, the models suggest a reestablishment of Ae. aegypti, while Ae. albopictus will continue to find new favorable areas. The results underline the need to focus more on vectors Ae. vittatus, Ae. luteocephalus and Ae. africanus in West and Central sub-Saharan Africa, especially Cameroon, Central Africa Republic, and northern Democratic Republic of Congo; and underscore the importance of enhancing entomological monitoring in areas where populations of often overlooked vectors may thrive as a result of climate changes.
... Integrating all the agents involved in the zoonotic cycles is important in order to get as close as possible to having reliable future projections. In this research, we aim to detect areas worldwide where changes in the risk for dengue and yellow fever transmission are expected to occur in the short and medium terms as a consequence of climate change, on the basis of the dengue and yellow fever transmission risk models published by Aliaga-Samanez et al. (2021. In this way, projections are built taking into account the most updated database of case reports to date, and considering both urban and sylvatic mosquito vectors, together with the distribution of non-human primates. ...
... Our forecasts consisted of projections to the future of the dengue and yellow fever transmission models published by Aliaga-Samanez et al. (2021, which were focused on the distribution of transmission risk in the period 2001-2017. These models were based on the favourability function. ...
... So, in contrast to probability, favourability values depend exclusively on the effect of environmental conditions in the distribution area under analysis (Acevedo and Real 2012). In Aliaga-Samanez et al. (2021, the degree of favourability for the transmission of a disease (i.e. the level of transmission risk) was considered to be a result of combining a vector model (which defined favourability values for the presence of mosquito vectors) with a disease model (which defined favourability values for the occurrence of disease cases in humans). This combination was made using the fuzzy intersection operator (Zadeh 1965) which assigns, to each geographic unit, the lowest favourability value provided by each model. ...
Dengue and yellow fever have complex cycles, involving urban and sylvatic mosquitoes, and non‐human primate hosts. To date, efforts to assess the effect of climate change on these diseases have neglected the combination of such crucial factors. Recent studies only considered urban vectors. This is the first study to include them together with sylvatic vectors and the distribution of primates to analyse the effect of climate change on these diseases. We used previously published models, based on machine learning algorithms and fuzzy logic, to identify areas where climatic favourability for the relevant transmission agents could change: 1) favourable areas for the circulation of the viruses due to the environment and to non‐human primate distributions; 2) the favourability for urban and sylvatic vectors. We obtained projections of future transmission risk for two future periods and for each disease, and implemented uncertainty analyses to test for predictions reliability. Areas currently favourable for both diseases could keep being climatically favourable, while global favourability could increase a 7% for yellow fever and a 10% increase for dengue. Areas likely to be more affected in the future for dengue include West Africa, South Asia, the Gulf of Mexico, Central America and the Amazon basin. A possible spread of dengue could take place into Europe, the Mediterranean basin, the UK and Portugal; and, in Asia, into northern China. For yellow fever, climate could become more favourable in Central and Southeast Africa; India; and in north and southeast South America, including Brazil, Paraguay, Bolivia, Peru, Colombia and Venezuela. In Brazil, favourability for yellow fever will probably increase in the south, the west and the east. Areas where the transmission risk spread is consistent to the dispersal of vectors are highlighted in respect of areas where the expected spread is directly attributable to environmental changes. Both scenarios could involve different prevention strategies.
... The favourability function is the membership function in the fuzzy set of OGUs with favourable conditions for the occurrence of WNF. Fuzzy logic has been used to detect favourable areas for species , and to assess the impact of climate change on species' distributions (Real et al. 2010) and for the study of zoonotic diseases (Olivero et al. 2017, Aliaga-Samanez et al. 2021 including WNV (García-Carrasco et al. 2021, 2023a. ...
... A high over-prediction is not usually adequate in distribution modelling. However, when the modelled entity is an infectious disease whose presence is underestimated in certain areas, this indicator could be pointing to the existence of potential risk in underreported locations (Aliaga-Samanez et al. 2021, García-Carrasco et al. 2023b). The aforementioned results exemplify the efficacy of our models, particularly in light of the dearth of disease data on which they were built. ...
West Nile virus (WNV) is a globally widespread arthropod‐borne virus that poses a significant public health concern. Mosquitoes transmit the virus in an enzootic cycle among birds, which act as reservoirs. Climate plays a crucial role in these outbreaks as mosquitoes are highly influenced by climatic conditions, and bird migrations are also affected by weather patterns. Consequently, changes in climate can potentially impact the occurrence of WNV outbreaks. We used biogeographic modelling based on machine learning algorithms and fuzzy logic to analyse and evaluate separately the risk of WNV outbreaks in two different biogeographic regions, the Afrotropical and the Western Palaearctic region. By employing fuzzy logic tools, we constructed a comprehensive risk model that integrates the Afro‐Palaearctic system as a unified operational unit for WNV spread. This innovative approach recognizes the Afro‐Palaearctic region as a pathogeographic system, characterized by biannual connections facilitated by billions of migratory bird reservoirs carrying the disease. Subsequently, we forecasted the effects of different climate change scenarios on the spread of WNV in the Afro‐Palaearctic system for the years 2040 and 2070. Our findings revealed an increasing epidemic and epizootic risk south of the Sahara. However, the area where an upsurge in risk was forecasted the most lies within Europe, with the anticipation of risk expansion into regions presently situated beyond the virus' distribution range, including central and northern Europe. Gaining insight into the risk within the Afro‐Palaearctic system is crucial for establishing coordinated and international One Health surveillance efforts. This becomes particularly relevant in the face of ongoing climate change, which disrupts the ecological equilibrium among vectors, reservoirs, and human populations. We show that the application of biogeographical tools to assess risk of infectious disease, i.e. pathogeography, is a promising approach for understanding distribution patterns of zoonotic diseases and for anticipating their future spread.
... Without the need for an incubation period ), this dengue virus has not only been rapidly transmitted in tropical urban areas, but also in rural ones (Man et al. 2023), which is influenced by multiple factors, such as mosquito population density, climatic conditions, and population movement patterns (Zeng et al. 2023). Today, this potential worldwide epidemic (Mukherji and Kaushik 2015) is spreading from the USA to China, from Europe to Australia, and even in Japan, Colombia, and Venezuela (Aliaga-Samanez et al. 2021). In a nutshell, South America, Africa, and Southeast Asia are mainly the hotspots of dengue outbreaks, with over 105 million worldwide estimated dengue infections every year (Cattarino et al. 2020). ...
Background:
Dengue fever, a viral illness spread mostly by Aedes mosquitoes, continues to pose a substantial public health issue in Dhaka city, Bangladesh. In Dhaka, climatic and socio-demographic factors like population density affect the spread of dengue. The dengue indexes are greatest in the residential mixed zone. Numerous environmental parameters, such as temperature, relative humidity, rainfall, and the air pollution index, have been linked to mosquito larvae, and dengue prevalence is correlated with urbanization, decreased vegetation, and population expansion.
Methods:
By using an extensive dataset that encompasses a range of years, we use spatial and temporal analytic methodologies to investigate the correlation between land use attributes, climatic variables, and the occurrence of dengue fever. To better understand the dynamics of dengue, the built environment and climatic factors are treated as independent variables in this study. ArcPy is a Python package that facilitates here for geographic data analysis and ArcMap 10.7 also used for visualizing spatial data.
Results:
The results of our study demonstrate that land use significantly influences the spatial patterns of Dengue incidence in Dhaka city. The dengue hotspot Thana are identified and these are Badda, Jatrabari, kadamtali, Mir-pur, Mohammadpur, Sobujbagh, Shyampur, Tejgoan, Dhanmondi and Uttara. All of these areas' population density and residential use as land use is higher than the other Thana of Dhaka city. There exists a significant correlation between climatic characteristics, such as temperature (0.25), rainfall (.803), specific humidity (0.74), relative humidity (0.76), wind speed (0.4) and Dengue incidence patterns. This research emphasizes the structural use and climatic relationship in Dengue epidemics, with climatic conditions playing a significant role as drivers of these variations.
Conclusions:
This research demonstrates the complex relationship between land use, meteorological factors, and the spread of Dengue fever in Dhaka city. The results of this study have significant significance for several domains, including urban planning, public health measures, and vector control tactics. A comprehensive understanding of the temporal and geographical patterns of dengue transmission might aid in the development of accurate and effective prevention measures intended to lessen the effects of dengue in cities, such as Dhaka.
... albopictus in response to climate change [9][10][11] . Aedes mosquito vectors could be currently expanding their distributions to more temperate climates across all continents where they now occur [12][13][14] , also favoured by globalisation through increased international travel and shipping. Many ports and airports are easy entry points for mosquito species such as Ae. ...
... Areas prone to sylvatic arboviral transmission to humans from non-human primates may be larger than estimated 13,17 . Anthropogenic disturbance in forests often allows the preprint (which was not certified by peer review) is the author/funder. ...
... In order to analyse the effect of climate change on dengue and yellow fever vector distributions, we took into account the favourability models for these species published by Aliaga-Samanez et al. 13,17 for the period 2001-2017, henceforth referred to as the baseline models. These models represent the degree of environmental favourability for vector species to occur on a grid of 18,874 hexagons of 7,774-km 2 covering the whole World. ...
Climate change may increase the risk of dengue and yellow fever transmission by urban and sylvatic mosquito vectors. Previous research primarily focused on Aedes aegypti and Aedes albopictus. However, these diseases involve a complex transmission cycle in which sylvatic vectors are also involved. Our aim was to analyse which mosquito species could contribute to the increased risk of transmission of these diseases due to climate change, and to identify where the risk increase could most likely occur. Using a biogeographical approach, we mapped areas where mosquito favourability could increase, decrease or remain stable in the near (2041-2060) and distant (2061-2080) future.
Models predict dengue vectors expanding in West and Central Africa and in South-East Asia, reaching Borneo. Yellow fever vectors could spread in West and Central Africa and in the Amazon. In Europe, the models suggest a re-establishment of Ae. aegypti, while Ae. albopictus will continue to find new favourable areas. The results underline the need to focus more on vectors Ae. vittatus, Ae. luteocephalus and Ae. africanus in West and Central sub-Saharan Africa, especially Cameroon, Central Africa Republic, and northern Democratic Republic of Congo; and suggest the need for a protocol to prevent dengue and yellow fever that include surveillance of neglected sylvatic vectors.
... Several areas were favorable for Ae. aegypti in North and South America in the late 20th century [29]. One or more serotypes can be endemic in the same population, causing outbreaks every 3 to 5 years [30]. ...
In an increasingly interconnected society, preventing epidemics has become a major challenge. Numerous infectious diseases spread between individuals by a vector, creating bipartite networks of infection with the characteristics of complex networks. In the case of dengue, a mosquito-borne disease, these infection networks include a vector—the Aedes aegypti mosquito—which has expanded its endemic area due to climate change. In this scenario, innovative approaches are essential to help public agents in the fight against the disease. Using an agent-based model, we investigated the network morphology of a dengue endemic region considering four different serotypes and a small population. The degree, betweenness, and closeness distributions are evaluated for the bipartite networks, considering the interactions up to the second order for each serotype. We observed scale-free features and heavy tails in the degree distribution and betweenness and quantified the decay of the degree distribution with a q–Gaussian fit function. The simulation results indicate that the spread of dengue is primarily driven by human-to-human and human-to-mosquito interaction, reinforcing the importance of controlling the vector to prevent episodes of epidemic outbreaks.
... While Messina does not nd Spain suitable for dengue occurrence in its 2020-2080 projections, Kraemer identi es the South-west and Mediterranean coast (from Alicante to the north) as yielding moderate risk, however with great uncertainty in the predictions. Other bioclimatic vector model (a different but similar approach), however, classi es practically all Spain geography as a 'highly favourable' zone for A. Albopictus in the 21st century (35). ...
Mathematical models have been used to understand complex, multi-faceted dengue transmission dynamics, but a gap persists between research and actionable public health tools for decision-making. We developed a plug-and-play system dynamics framework combining temperature-dependent vector population, transmission parameters, and epidemiological interactions that allows for testing multiple hypotheses and data availability scenarios. Spain is an at-risk country of imported dengue outbreaks, so we explored vector population and outbreak risk maps for the 26 provinces with known presence of A. Albopictus . Under our assumptions, only 15 provinces can sustain annual vector population cycles, mainly along the Mediterranean coast. Málaga and Almería, in the south, face the highest risk with outbreak beginning in spring with low vector density. Risk shifts to the summer in east coastal provinces, while some inner regions experience residual risks. Together, our framework depicts spatiotemporal risk profiles and can effectively assist evidence-based public health planning in various settings and contexts.
... Currently there is a moderate level of evidence of the presence dengue virus (DENV) in Guinea 60 and low-intermediate transmission risk. 61 DENV serotype 2 was reported in 1981 62 and in 1996, 63 and a case was identified in Faranah in 2006. 64 Aedes aegypti mosquitoes, the primary vector for dengue transmission is present in Guinea. ...
Neglected tropical diseases (NTDs) predominantly affect vulnerable and marginalized populations in tropical and subtropical areas and globally affect more than one billion people. In Guinea, the burden of NTDs is estimated to be >7.5 disability-adjusted life years per million inhabitants. Currently the Guinea NTDs master plan (2017-2020) has identified eight diseases as public health problems: onchocerciasis, lymphatic filariasis, trachoma, schistosomiasis and soil-transmitted helminthiasis, leprosy, human African trypanosomiasis and Buruli ulcer. In this review we discuss the past and the current case burden of the priority NTDs in Guinea, highlight the major milestones and discuss current and future areas of focus for achieving the 2030 target outlined by the World Health Organization.
... An increasing number of studies have confrmed the use of species distribution models as an efective tool in the prediction of diseases afecting human health [14,15], further establishing a new scientifc discipline called pathogeography [16] as an essential framework for the geographical analysis of zoonotic diseases afecting humans [17,18]. Assessment and analysis of the potential distribution of rodent reservoir hosts and the infuence of environmental factors on hantavirus transmission can be useful in understanding the spatial patterns of disease transmission risk [19,20]. ...
Hantaviruses are the causative agents of hantavirus pulmonary syndrome (HPS) in the Americas. In Central and South America, 28 hantavirus lineages were associated with different Sigmodontinae rodents. Of these, Lechiguanas hantavirus was initially described as a lineage associated with HPS cases in the central region of Argentina. Initial studies on the rodent hosts and viral lineages performed between 1999 and 2005 showed that HPS cases in Uruguay were distributed mostly in the southern region of the country, and that the Lechiguanas hantavirus (LECV) and the closely related Andes Central Plata hantaviruses were the viral lineages most frequently associated with HPS cases, both carried by the yellow pygmy rice rat (Oligoryzomys flavescens). Although these rodents are present all across the Uruguayan territory, determining the extent of the risk areas for hantavirus transmission based on the distribution of the infected rodents may be a useful tool for disease control and prevention. Distribution models are positioned as an effective instrument in the prediction of diseases affecting human health. Assessment of the potential distribution of rodent reservoir hosts and analysis of the influence of environmental factors on hantavirus transmission can help to understand the spatial patterns of disease transmission risk. In the present study, virological studies and species distribution models were integrated to understand the hantavirus infection risk pattern in Uruguay. Virological analyses confirmed that in Uruguay, the primary hantavirus reservoir host for both viral lineages is the yellow pygmy rice rat. Additionally, we report an Azara’s grass mouse (Akodon azarae) infected with the Andes Central Plata viral lineage. Based on the seropositive and nonseropositive yellow pygmy rice rats tested, the distribution models emphasized that favorable environmental conditions for the infected rodents are mainly related to the availability of human-disturbed rural environments with high humidity. We conclude that the innovative application of the methodologies reported herein allowed for the assessment of the current risk territory for HPS in Uruguay.
