Dengue incidence has grown dramatically in recent decades, with about half of the world’s population now at risk. Climate plays a significant role in the incidence of dengue. However, the climate-dengue association needs to be clearly understood at regional levels due to the high spatial variability in weather conditions and the non-linear relationship between climate and dengue. The current study evaluates the impacts of weather on dengue mortality in the Pune district of India, for a 15-year period, from 2001 to 2015. To effectively resolve the complexity involved in the weather-dengue association, a new dengue metric is defined that includes temperature, relative humidity, and rainfall-dependent variables such as intraseasonal variability of monsoon (wet and dry spells), wet-week counts, flushing events, and weekly cumulative rains. We find that high dengue mortality years in Pune are comparatively dry, with fewer monsoon rains and flush events (rainfall > 150 mm), but they have more wet weeks and optimal humid days (days with relative humidity between 60–78%) than low dengue mortality years. These years also do not have heavy rains during the early monsoon days of June, and the temperatures mostly range between 27–35°C during the summer monsoon season (June–September).  Further, our analysis shows that dengue mortality over Pune occurs with a 2-5 months lag following the occurrence of favourable climatic conditions. Based on these weather-dengue associations, an early warning prediction model is built using the machine learning algorithm random forest regression. It provides a reasonable forecast accuracy with root mean square error (RMSE) = 1.01. To assess the future of dengue mortality over Pune under a global warming scenario, the dengue model is used in conjunction with climate change simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6). Future projections show that dengue mortality over Pune will increase in the future by up to 86 percent (relative to the reference period 1980–2014) by the end of the 21st century under the high emission scenario SSP5-8.5, primarily due to an increase in mean temperature (3°C increase relative to the reference period). The projected increase in dengue mortality due to climate change is a serious concern that necessitates effective prevention strategies and policy-making to control the disease spread.

How to cite: Yacob, S. and Mathew Koll, R.: A climate based dengue early warning system for Pune, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-370, https://doi.org/10.5194/egusphere-egu23-370, 2023.

Vibrio spp. is typically found in salty waters and is indigenous to coastal environments.  V. vulnificus and V. parahaemolyticus frequently causes food-borne and non-food-borne infections in the United States. Vibrio spp. is sensitive to changes in environmental conditions and various studies have explored their relationship with the environment and have identified water temperature as the strongest environmental predictor with salinity also affecting the abundance in some cases. It is unclear how additional environmental factors will affect intra-seasonal variance as well as the seasonal cycle. This study investigated the intra-seasonal variations in total and pathogenic V. parahaemolyticus and V. vulnificus organisms in oysters and surrounding waters from 2009 to 2012 at a few locations in the Chesapeake Bay. V. Vulnificus is always pathogenic, but it has been observed that there was greater sample-to-sample variability in pathogenic V. parahaemolyticus than in total V. parahaemolyticus. To determine the increase in the likelihood of vibrio presence when the value of a certain environmental parameter has changed, the odds ratio is examined for various values of environmental factors. The odds ratio that we employed measures the likelihood that the desired outcome would occur in samples with the vibrio in comparison to the likelihood that the desired outcome will occur in samples without the vibrio. This technique will give us the threshold value of the environmental variable above which the likelihood of vibrio spp. presence has increased drastically. With changing climate and environmental conditions, vibrio is posing increasing risks to human health. The findings of this study will demonstrate the effectiveness of the odds ratio technique in estimating the likelihood that vibrio abundance would increase when environmental conditions change, which can then be incorporated into prediction models to reduce the danger to the public's health.

How to cite: Gangwar, M., Brumfield, K., Usmani, M., Jamal, Y., Jutla, A., Huq, A., and Colwell, R.: Variability and the odds of Total and Pathogenic Vibrio abundance in Chesapeake Bay, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-593, https://doi.org/10.5194/egusphere-egu23-593, 2023.

Mosquitoes are climate-sensitive disease vectors. They need an aquatic environment for the development of their immature stages (egg-larva-nymph). The presence and maintenance of these egg-laying sites depends on rainfall. The development period of mosquitoes is reduced when temperature increases, up to a lethal threshold. Global warming will impact vector’s distribution and the diseases they transmit. The last deglaciation taught us that the melting of the ice sheet is highly non-linear and can include acceleration phases corresponding to sea level rise of more than 4 m per century. In addition, glacial instabilities such as iceberg break-ups (Heinrich events) had significant impacts on the North Atlantic Ocean circulation, causing major global climate changes. These melting processes and their feedbacks on climate are not considered in current climate models and their detailed impacts on health have not yet been studied.

To simulate an accelerated partial melting of the Greenland ice sheet, a freshwater flux corresponding to a sea level rise of +1 and +3 m over a 50-year period is superimposed on the standard RCP8.5 radiative forcing scenario. These scenarios are then used as inputs for the IPSL-CM5A climate model to simulate global climate change for the 21^st century. These simulations allow to explore the consequences of such melting on the distribution of two vector-borne diseases which affect the African continent: malaria and Rift Valley Fever (RVF).  Malaria is a parasitic disease that causes more than 200 million cases and more than 600,000 deaths annually worldwide. RVF causes deaths and high abortion rates in herds and poses health risks to humans through contact with infected blood. Former studies have already characterised the evolution of the global distribution of malaria according to standard RCPs. Using the same malaria mathematical models, we study the impact of an accelerated Greenland melting on simulated malaria transmission risk in Africa. Future malaria transmission risk decreases over the Sahel and increases over East African highlands. The decrease over the Sahel is stronger in our simulations with respect to the standard RCP8.5 scenario, while the increase over east Africa is more moderate. Malaria risk strongly increases over southern Africa due to a southern shift of the rain belt which is induced by Greenland ice sheet melting.,. For RVF, the disease model correctly simulates historical epidemics over Somalia, Kenya, Mauritania, Zambia and Senegal.  However, our results show the difficulty to validate continental scale models with available health data. It is essential to develop climate scenarios that consider climate tipping points. Assessing the impact of these tipping point scenarios and the associated uncertainties on critical sectors, such as public health, should be a future research priority.

How to cite: Chemison, A., Defrance, D., Ramstein, G., and Caminade, C.: Impact of global warming and Greenland ice sheet melting on malaria and Rift Valley Fever, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5923, https://doi.org/10.5194/egusphere-egu23-5923, 2023.

New climate regimes, variability and extreme events affect the livestock sector in many aspects, ranging from animal welfare, production, reproduction, diseases and their spread, feed quality and availability. Heat stress, especially when combined with excess or low humidity, exacerbates the perceived temperature or the drought conditions, respectively, increasing hazards for animals. Also, cold extremes, extraordinary windy conditions and altered radiation regimes are detrimental to both animals and fodder.

In this context, the EU-funded SEBASTIEN project aims to provide stakeholders with a Decision Support System (DSS) for more efficient and sustainable management, and consequent valuation, of the livestock sector in Italy. SEBASTIEN DSS will integrate GIS, environmental and biological variables to generate updated risk maps for livestock diseases and zoonoses and their spread, alerting about the expected occurrence of stressing conditions for animals due to abiotic and biotic factors.

The presence of parasites, vectors, and outbreaks will be combined with environmental data, gathered by spatially distributed meteorological and satellite monitoring, to detect conditions that can potentially favor or trigger the spread of related diseases. Sensor-based monitoring data will be integrated with the above information to determine ranges in animal parameters potentially associated with a higher risk of critical pathogen load or density of vectors potential carriers of diseases. Medium to long-term climate forecasts will support predicting possible shifts of favorable conditions that will open up new areas for parasites and pathogens. The vast amounts of data will be integrated and summarized into user-tailored information through a range of techniques, from empirical/statistical indicators to Machine Learning algorithms.

How to cite: Nassisi, P., D'anca, A., Mancini, M., Santini, M., Milanesi, M., Caroli, C., Aloisio, G., Chillemi, G., Valentini, R., Negrini, R., and Ajmone Marsan, P.: An early warning decision support system for disease outbreaks in the livestock sector, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6855, https://doi.org/10.5194/egusphere-egu23-6855, 2023.

The Asian tiger mosquito, Aedes albopictus, is an invasive vector species. It is capable of transmitting more than 20 arboviruses, and is responsible for chikungunya, dengue, and zika transmission. Urbanisation, globalisation, and climate change are expected to expand its habitable range and increase the global vector-borne disease burden in the coming decades. To plan effective control strategies, early-warning and decision support systems are urgently needed.

We developed a climate- and environment-driven population dynamics model of Aedes albopictus with extensive geospatial applicability. The foundation of the model is the age- and stage-structured population dynamics model of Erguler et al. (2016)¹. We replaced its rainfall- and human population density-dependent breeding site component with a large-scale mechanistic ecological model. The extension effectively created an ecological-dynamic model hybrid capable of representing niche dependence and response to changing environmental and meteorological conditions over time and under various land characteristics. To the best of our knowledge, this is the first spatiotemporal mechanistic model developed with a capacity to learn from both vector presence and longitudinal abundance data.

We calibrated the model with an extensive field surveillance dataset by combining the data collected through the AIMSurv project, the first pan-European harmonized surveillance of Aedes invasive mosquito species of relevance for human vector-borne diseases, and the global surveillance records available from VectorBase MapVEu. By deriving the model structure and environmental dependencies from the literature and allowing a complete re-configuration of the entire parameter set, we asserted the biological relevance and geospatial applicability, which extends over Europe and North America.

We corroborate that temperate northern territories are becoming increasingly suitable for Aedes albopictus establishment, while neighbouring southern territories become less suitable, as climate continues to change. We identify potential hotspots over Europe and North America by employing the combination of vector abundance and activity as a proxy to pathogen transmission risk.  By investigating routes of introduction to new territories, we demonstrate the significant role of dynamic environmental suitability in the highly efficient spread of this invasive mosquito.

The model is scheduled for integration into the "Climate-driven vector-borne disease risk assessment platform", to predict habitat suitability and dynamic abundance of important disease vectors and the risk of diseases transmitted by them at any location and time up to the end of the century. With the continental model of Aedes albopictus, the platform will reliably inform public health professionals and policy makers and contribute to the global strategies of integrated vector management.

¹ Erguler K, Smith-Unna SE, Waldock J, Proestos Y, Christophides GK, Lelieveld J, Parham PE. Large-scale modelling of the environmentally-driven population dynamics of temperate Aedes albopictus (Skuse). PloS one. 2016 Feb 12;11(2):e0149282.

How to cite: Erguler, K., Marsboom, C., Zittis, G., Proestos, Y., Christophides, G., Lelieveld, J., and Wint, W.: The first continental population dynamics model of the Asian tiger mosquito driven by climate and environment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9509, https://doi.org/10.5194/egusphere-egu23-9509, 2023.

Vesicular stomatitis (VS) is a multi-vector arboviral disease that affects livestock and has a significant impact on agriculture in both the US and Mexico. Biting midges (Culicoides species) are known vectors of VS. Presence-only species distribution models (SDMs) provide a powerful and versatile tool for estimating both the habitat suitability of biting midges and the distribution of VS, the disease they spread. Such models can improve our understanding of Culicoides ecology, provide opportunities for more efficient VS surveillance and mitigation, and help determine geographical areas where VS is endemic or vulnerable to potential future transmission.

Here, we discuss two case studies related to modeling the distribution of VS and its insect vector. The first focused on predicting the habitat suitability of biting midges, including C. sonorensis and its close relatives (C. variipennis, C. albertensis, and C. occidentalis), based on species presence records collected in the past hundred years from various sources. The second study involved directly estimating the distribution of VS in Mexico, where we used occurrence data in the form of confirmed VS cases in livestock from 2005-2020 in historically endemic regions of Mexico.

SDMs are typically generated using temporally static input data. However, we improved the accuracy of our predictions by applying the Maxent algorithm to time-specific input data, creating dynamic species distribution models and habitat suitability maps. For both case studies, a robust dynamic Maxent distribution modeling workflow was implemented using temporally matched occurrence and environmental data that were carefully selected in collaboration with domain experts.

How to cite: Veron, M.: Dynamic distribution modeling of arboviral vesicular stomatitis and its vector, the biting midge (Culicoides spp.): two case studies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10475, https://doi.org/10.5194/egusphere-egu23-10475, 2023.

Vibrio spp. are pathogenic bacteria native to warm and brackish water. Vibriosis- the disease caused by these pathogens in humans accounts for around 80000 illnesses and 100 deaths annually in the United States. Of all the species, V. vulnificus has the highest mortality rate of all seafood-borne pathogens in the United States. In this context, understanding the environmental conditions that lead to increased V. vulnificus growth and spread can aid in the development of early warning systems and targeted prevention strategies. Besides sea surface temperature (SST), biotic parameters like coastal chlorophyll are also determined to affect V. vulnificus incidence in humans locally. However, the precise role of coastal chlorophyll as a potential confounding variable is understudied. Moreover, the spatial scale to which the data for environmental variables could be obtained also poses characterization constraints for researchers since the commonly employed in-situ sampling-based methods usually work with discrete locations covering a small area. The present study uses the odds ratio analysis to determine SST and chlorophyll-a threshold values critical to V. vulnificus incidence. The analysis reveals a definite positive relationship between remotely derived environmental variables and the odds of V. vulnificus incidence, where a specific statistical value of SST and chlorophyll-a marks a clear distinction between low and high odds of V. vulnificus incidence. This finding translates into a consistent pattern when checked for counties of coastal Florida. We anticipate our methodology to help distinguish between high and low-risk conditions, enabling public health workers to take proactive measures to protect the health and well-being of the public.

How to cite: Jamal, Y., Usmani, M., Gangwar, M., and Jutla, A.: Identification of thresholds on Sea surface temperature and coastal chlorophyll for understanding environmental suitability of V. vulnificus incidence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17226, https://doi.org/10.5194/egusphere-egu23-17226, 2023.