The effect of explicit convection on simulated malaria transmission across Africa.

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI's enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Climate-influenced vector-borne diseases in Africa: a call to empower the next generation of African researchers for sustainable solutions.

We look at the link between climate change and vector-borne diseases in low- and middle-income countries in Africa. The large endemicity and escalating threat of diseases such as malaria and arboviral diseases, intensified by climate change, disproportionately affects vulnerable communities globally. We highlight the urgency of prioritizing research and development, advocating for robust scientific inquiry to promote adaptation strategies, and the vital role that the next generation of African research leaders will play in addressing these challenges. Despite significant challenges such as funding shortages within countries, various pan-African-oriented funding bodies such as the African Academy of Sciences, the Africa Research Excellence Fund, the Wellcome Trust, the U.S. National Institutes of Health, and the Bill and Melinda Gates Foundation as well as initiatives such as the African Research Initiative for Scientific Excellence and the Pan-African Mosquito Control Association, have empowered (or are empowering) these researchers by supporting capacity building activities, including continental and global networking, skill development, mentoring, and African-led research. This article underscores the urgency of increased national investment in research, proposing the establishment of research government agencies to drive evidence-based interventions. Collaboration between governments and scientific communities, sustained by pan-African funding bodies, is crucial. Through these efforts, African nations are likely to enhance the resilience and adaptive capacity of their systems and communities by navigating these challenges effectively, fostering scientific excellence and implementing transformative solutions against climate-sensitive vector-borne diseases.

Ability of a dynamical climate sensitive disease model to reproduce historical Rift Valley Fever outbreaks over Africa.

Rift Valley Fever (RVF) is a zoonosis transmitted by Aedes and Culex mosquitoes, and is considered a priority pathogen by the WHO. RVF epidemics mostly occur in Africa and can decimate livestock herds, causing significant economic losses and posing health risks for humans. RVF transmission is associated with the occurrence of El Niño events that cause floods in eastern Africa and favour the emergence of mosquitoes in wetlands. Different risk models have been developed to forecast RVF transmission risk but very few studies have validated models at pan-African scale. This study aims to validate the skill of the Liverpool Rift Valley Fever model (LRVF) in reproducing RVF epidemics over Africa and to explore the relationship between simulated climatic suitability for RVF transmission and large-scale climate modes of variability such as the El Niño Southern Oscillation (ENSO) and the Dipole Mode Index (DMI). Our results show that the LRVF model correctly simulates RVF transmission hotspots and reproduces large epidemics that affected African countries. LRVF was able to correctly reproduce major RVF epidemics in Somalia, Kenya, Zambia and to a lesser extent for Mauritania and Senegal. The positive phases of ENSO and DMI are associated with an increased risk of RVF over the Horn of Africa, with important time lags. Following research activities should focus on the development of predictive modelling systems at different time scales.

Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014-2021.

BACKGROUND: Ticks are an important driver of veterinary health care, causing irritation and sometimes infection to their hosts. We explored epidemiological and geo-referenced data from > 7 million electronic health records (EHRs) from cats and dogs collected by the Small Animal Veterinary Surveillance Network (SAVSNET) in Great Britain (GB) between 2014 and 2021 to assess the factors affecting tick attachment in an individual and at a spatiotemporal level. METHODS: EHRs in which ticks were mentioned were identified by text mining; domain experts confirmed those with ticks on the animal. Tick presence/absence records were overlaid with a spatiotemporal series of climate, environment, anthropogenic and host distribution factors to produce a spatiotemporal regression matrix. An ensemble machine learning spatiotemporal model was used to fine-tune hyperparameters for Random Forest, Gradient-boosted Trees and Generalized Linear Model regression algorithms, which were then used to produce a final ensemble meta-learner to predict the probability of tick attachment across GB at a monthly interval and averaged long-term through 2014-2021 at a spatial resolution of 1 km. Individual host factors associated with tick attachment were also assessed by conditional logistic regression on a matched case-control dataset. RESULTS: In total, 11,741 consultations were identified in which a tick was recorded. The frequency of tick records was low (0.16% EHRs), suggesting an underestimation of risk. That said, increased odds for tick attachment in cats and dogs were associated with younger adult ages, longer coat length, crossbreeds and unclassified breeds. In cats, males and entire animals had significantly increased odds of recorded tick attachment. The key variables controlling the spatiotemporal risk for tick attachment were climatic (precipitation and temperature) and vegetation type (Enhanced Vegetation Index). Suitable areas for tick attachment were predicted across GB, especially in forests and grassland areas, mainly during summer, particularly in June. CONCLUSIONS: Our results can inform targeted health messages to owners and veterinary practitioners, identifying those animals, seasons and areas of higher risk for tick attachment and allowing for more tailored prophylaxis to reduce tick burden, inappropriate parasiticide treatment and potentially TBDs in companion animals and humans. Sentinel networks like SAVSNET represent a novel complementary data source to improve our understanding of tick attachment risk for companion animals and as a proxy of risk to humans.

Prevalence of Human and Animal Fasciolosis in Butajira and Gilgel Gibe Health Demographic Surveillance System Sites in Ethiopia.

Fasciolosis is regarded as a major challenge to livestock productivity worldwide, but the burden of disease in humans has only started to receive some attention in the past three decades. The aim of this study was to determine the prevalence of human and animal fasciolosis and its determinant factors in the Gilgel Gibe and Butajira Health and Demographic Surveillance System (HDSS) sites in Ethiopia. A study was undertaken among 389 households across the two sites. Face-to-face interviews were conducted to investigate the knowledge, attitudes and practices of households with regard to fasciolosis. Stools from 377 children aged 7-15 years, and 775 animals (cattle, goats and sheep) were analyzed using a proprietary Fasciola hepatica (F. hepatica) coproantigen ELISA kit. The prevalence of fasciolosis in children was 0.5% and 1% in Butajira and Gilgel Gibe HDSS sites, respectively. The overall prevalence of animal fasciolosis was 29%, 29.2%, and 6% among cattle, sheep, and goats, respectively. More than half of the respondents from Gilgel Gibe (59%, n = 115) did not know that humans can be infected with F. hepatica. The majority of respondents in Gilgel Gibe (n = 124, 64%) and Butajira (n = 95, 50%) did not know the transmission route for fasciolosis. Grazing animals were 7 times more likely to be infected with fasciolosis than animals in cut-and-carry production systems (adjusted odds ratio [AOR] = 7.2; 95% confidence interval [CI]: 3.91-13.17). The findings indicated a lack of knowledge amongst local populations about fasciolosis. Thus, there is a need for public health awareness campaigns about fasciolosis in the study areas.

Is the Ollerenshaw fasciolosis forecasting model fit for the 21st century?

BACKGROUND: The Ollerenshaw forecasting model is based on rainfall and evapotranspiration and has been in use to predict losses from fasciolosis since 1959. We evaluated the performance of the model against observed data. METHODS: Weather data were used to calculate, map and plot fasciolosis risk values for each year from 1950 to 2019. We then compared the model's predictions with recorded acute fasciolosis losses in sheep from 2010 to 2019 and calculated the sensitivity and specificity of the model. RESULTS: The forecast risk has varied over time but has not markedly increased over the past 70 years. The model correctly forecasted the highest and lowest incidence years at both the regional and national (Great Britain) levels. However, the sensitivity of the model for predicting fasciolosis losses was poor. Modification to include the full May and October rainfall and evapotranspiration values made only a small improvement. LIMITATIONS: Reported acute fasciolosis losses are subject to bias and error due to unreported cases and variations in region size and livestock numbers. CONCLUSION: The Ollerenshaw forecasting model, in either its original or modified forms, is insufficiently sensitive to be relied upon as a standalone early warning system for farmers.

Effects of Land-Use and Environmental Factors on Snail Distribution and Trematode Infection in Ethiopia.

Freshwater snails are intermediate hosts for several snail-borne diseases affecting humans and animals. Understanding the distribution of snail intermediate hosts and their infection status is very important to plan and implement effective disease prevention and control interventions. In this study, we determined the abundance, distribution, and trematode infection status of freshwater snails in two agro-ecological zones of Ethiopia. We sampled snails from 13 observation sites and examined them for trematode infections using a natural cercarial shedding method. A redundancy analysis (RDA) was used to examine the relationship between snail abundance and environmental variables. Overall, a total of 615 snails belonging to three species were identified. Lymnea natalensis and Bulinus globosus were the dominant snail species, representing 41% and 40% of the total collection, respectively. About one-third of the total snail population (33%) shed cercariae. The cercariae species recorded were Xiphidiocercaria, Brevifurcate apharyngeate distome (BAD), Echinostome, and Fasciola. Snail species were found in high abundance in aquatic habitats located in the agricultural landscape. Therefore, land-use planning and protection of aquatic habitats from uncontrolled human activities and pollution can be considered as important strategies to prevent and control the spread of snail-borne diseases in the region.

Projecting the risk of mosquito-borne diseases in a warmer and more populated world: a multi-model, multi-scenario intercomparison modelling study.

BACKGROUND: Mosquito-borne diseases are expanding their range, and re-emerging in areas where they had subsided for decades. The extent to which climate change influences the transmission suitability and population at risk of mosquito-borne diseases across different altitudes and population densities has not been investigated. The aim of this study was to quantify the extent to which climate change will influence the length of the transmission season and estimate the population at risk of mosquito-borne diseases in the future, given different population densities across an altitudinal gradient. METHODS: Using a multi-model multi-scenario framework, we estimated changes in the length of the transmission season and global population at risk of malaria and dengue for different altitudes and population densities for the period 1951-99. We generated projections from six mosquito-borne disease models, driven by four global circulation models, using four representative concentration pathways, and three shared socioeconomic pathways. FINDINGS: We show that malaria suitability will increase by 1·6 additional months (mean 0·5, SE 0·03) in tropical highlands in the African region, the Eastern Mediterranean region, and the region of the Americas. Dengue suitability will increase in lowlands in the Western Pacific region and the Eastern Mediterranean region by 4·0 additional months (mean 1·7, SE 0·2). Increases in the climatic suitability of both diseases will be greater in rural areas than in urban areas. The epidemic belt for both diseases will expand towards temperate areas. The population at risk of both diseases might increase by up to 4·7 additional billion people by 2070 relative to 1970-99, particularly in lowlands and urban areas. INTERPRETATION: Rising global mean temperature will increase the climatic suitability of both diseases particularly in already endemic areas. The predicted expansion towards higher altitudes and temperate regions suggests that outbreaks can occur in areas where people might be immunologically naive and public health systems unprepared. The population at risk of malaria and dengue will be higher in densely populated urban areas in the WHO African region, South-East Asia region, and the region of the Americas, although we did not account for urban-heat island effects, which can further alter the risk of disease transmission. FUNDING: UK Space Agency, Royal Society, UK National Institute for Health Research, and Swedish Research Council.

Impact of an accelerated melting of Greenland on malaria distribution over Africa.

Studies about the impact of future climate change on diseases have mostly focused on standard Representative Concentration Pathway climate change scenarios. These scenarios do not account for the non-linear dynamics of the climate system. A rapid ice-sheet melting could occur, impacting climate and consequently societies. Here, we investigate the additional impact of a rapid ice-sheet melting of Greenland on climate and malaria transmission in Africa using several malaria models driven by Institute Pierre Simon Laplace climate simulations. Results reveal that our melting scenario could moderate the simulated increase in malaria risk over East Africa, due to cooling and drying effects, cause a largest decrease in malaria transmission risk over West Africa and drive malaria emergence in southern Africa associated with a significant southward shift of the African rain-belt. We argue that the effect of such ice-sheet melting should be investigated further in future public health and agriculture climate change risk assessments.

Assessing the suitability for Aedes albopictus and dengue transmission risk in China with a delay differential equation model.

Dengue is considered non-endemic to mainland China. However, travellers frequently import the virus from overseas and local mosquito species can then spread the disease in the population. As a consequence, mainland China still experiences large dengue outbreaks. Temperature plays a key role in these outbreaks: it affects the development and survival of the vector and the replication rate of the virus. To better understand its implication in the transmission risk of dengue, we developed a delay differential equation model that explicitly simulates temperature-dependent development periods and tested it with collected field data for the Asian tiger mosquito, Aedes albopictus. The model predicts mosquito occurrence locations with a high accuracy (Cohen's κ of 0.78) and realistically replicates mosquito population dynamics. Analysing the infection dynamics during the 2014 dengue outbreak that occurred in Guangzhou showed that the outbreak could have lasted for another four weeks if mosquito control interventions had not been undertaken. Finally, we analyse the dengue transmission risk in mainland China. We find that southern China, including Guangzhou, can have more than seven months of dengue transmission per year while even Beijing, in the temperate north, can have dengue transmission during hot summer months. The results demonstrate the importance of using detailed vector and infection ecology, especially when vector-borne disease transmission risk is modelled over a broad range of climatic zones.

Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide.

Approximately a year into the COVID-19 pandemic caused by the SARS-CoV-2 virus, many countries have seen additional "waves" of infections, especially in the temperate northern hemisphere. Other vulnerable regions, such as South Africa and several parts of South America have also seen cases rise, further impacting local economies and livelihoods. Despite substantial research efforts to date, it remains unresolved as to whether COVID-19 transmission has the same sensitivity to climate observed for other common respiratory viruses such as seasonal influenza. Here, we look for empirical evidence of seasonality using a robust estimation framework. For 359 large cities across the world, we estimated the basic reproduction number (R0) using logistic growth curves fitted to cumulative case data. We then assess evidence for association with climatic variables through ordinary least squares (OLS) regression. We find evidence of seasonality, with lower R0 within cities experiencing greater surface radiation (coefficient = -0.005, p < 0.001), after adjusting for city-level variation in demographic and disease control factors. Additionally, we find association between R0 and temperature during the early phase of the epidemic in China. However, climatic variables had much weaker explanatory power compared to socioeconomic and disease control factors. Rates of transmission and health burden of the continuing pandemic will be ultimately determined by population factors and disease control policies.

Modelling the impact of climate change on the distribution and abundance of tsetse in Northern Zimbabwe.

BACKGROUND: Climate change is predicted to impact the transmission dynamics of vector-borne diseases. Tsetse flies (Glossina) transmit species of Trypanosoma that cause human and animal African trypanosomiasis. A previous modelling study showed that temperature increases between 1990 and 2017 can explain the observed decline in abundance of tsetse at a single site in the Mana Pools National Park of Zimbabwe. Here, we apply a mechanistic model of tsetse population dynamics to predict how increases in temperature may have changed the distribution and relative abundance of Glossina pallidipes across northern Zimbabwe. METHODS: Local weather station temperature measurements were previously used to fit the mechanistic model to longitudinal G. pallidipes catch data. To extend the use of the model, we converted MODIS land surface temperature to air temperature, compared the converted temperatures with available weather station data to confirm they aligned, and then re-fitted the mechanistic model using G. pallidipes catch data and air temperature estimates. We projected this fitted model across northern Zimbabwe, using simulations at a 1 km × 1 km spatial resolution, between 2000 to 2016. RESULTS: We produced estimates of relative changes in G. pallidipes mortality, larviposition, emergence rates and abundance, for northern Zimbabwe. Our model predicts decreasing tsetse populations within low elevation areas in response to increasing temperature trends during 2000-2016. Conversely, we show that high elevation areas (> 1000 m above sea level), previously considered too cold to sustain tsetse, may now be climatically suitable. CONCLUSIONS: To our knowledge, the results of this research represent the first regional-scale assessment of temperature related tsetse population dynamics, and the first high spatial-resolution estimates of this metric for northern Zimbabwe. Our results suggest that tsetse abundance may have declined across much of the Zambezi Valley in response to changing climatic conditions during the study period. Future research including empirical studies is planned to improve model accuracy and validate predictions for other field sites in Zimbabwe.

Potential for Zika virus transmission by mosquitoes in temperate climates.

Mosquito-borne Zika virus (ZIKV) transmission has almost exclusively been detected in the tropics despite the distributions of its primary vectors extending farther into temperate regions. Therefore, it is unknown whether ZIKV's range has reached a temperature-dependent limit, or if it can spread into temperate climates. Using field-collected mosquitoes for biological relevance, we found that two common temperate mosquito species, Aedes albopictus and Ochlerotatus detritus, were competent for ZIKV. We orally exposed mosquitoes to ZIKV and held them at between 17 and 31°C, estimated the time required for mosquitoes to become infectious, and applied these data to a ZIKV spatial risk model. We identified a minimum temperature threshold for the transmission of ZIKV by mosquitoes between 17 and 19°C. Using these data, we generated standardized basic reproduction number R0-based risk maps and we derived estimates for the length of the transmission season for recent and future climate conditions. Our standardized R0-based risk maps show potential risk of ZIKV transmission beyond the current observed range in southern USA, southern China and southern European countries. Transmission risk is simulated to increase over southern and Eastern Europe, northern USA and temperate regions of Asia (northern China, southern Japan) in future climate scenarios.

Climate Variability and Malaria over West Africa.

Malaria is a major public health problem in West Africa. Previous studies have shown that climate variability significantly affects malaria transmission. The lack of continuous observed weather station data and the absence of surveillance data for malaria over long periods have led to the use of reanalysis data to drive malaria models. In this study, we use the Liverpool Malaria Model (LMM) to simulate spatiotemporal variability of malaria in West Africa using daily rainfall and temperature from the following: Twentieth Century Reanalysis (20th CR), National Center for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Twentieth Century (ERA20C), and interim ECMWF Re-Analysis (ERA-Interim). Malaria case data from the national surveillance program in Senegal are used for model validation between 2001 and 2016. The warm temperatures found over the Sahelian fringe of West Africa can lead to high malaria transmission during wet years. The rainfall season peaks in July to September over West Africa and Senegal, and the malaria season lasts from September to November, about 1-2 months after the rainfall peak. The long-term trends exhibit interannual and decadal variabilities. The LMM shows acceptable performance in simulating the spatial distribution of malaria incidence. However, some discrepancies are found. These results are useful for decision-makers who plan public health and control measures in affected West African countries. The study would have substantial implications for directing malaria surveillance activities and health policy. In addition, this malaria modeling framework could lead to the development of an early warning system for malaria in West Africa.

Modeling Potential Habitat for Amblyomma Tick Species in California.

The Amblyomma genus of ticks comprises species that are aggressive human biters and vectors of pathogens. Numerous species in the genus are undergoing rapid range expansion. Amblyomma ticks have occasionally been introduced into California, but as yet, no established populations have been reported in the state. Because California has high ecological diversity and is a transport hub for potentially parasitized humans and animals, the risk of future Amblyomma establishment may be high. We used ecological niche modeling to predict areas in California suitable for four tick species that pose high risk to humans: Amblyomma americanum, Amblyomma maculatum, Amblyomma cajennense and Amblyomma mixtum. We collected presence data in the Americas for each species from the published literature and online databases. Twenty-three climatic and ecological variables were used in a MaxEnt algorithm to predict the distribution of each species. The minimum temperature of the coldest month was an important predictor for all four species due to high mortality of Amblyomma at low temperatures. Areas in California appear to be ecologically suitable for A. americanum, A. maculatum, and A. cajennense, but not A. mixtum. These findings could inform targeted surveillance prior to an invasion event, to allow mitigation actions to be quickly implemented.

Lag effect of climatic variables on dengue burden in India.

Dengue is a widespread vector-borne disease believed to affect between 100 and 390 million people every year. The interaction between vector, host and pathogen is influenced by various climatic factors and the relationship between dengue and climatic conditions has been poorly explored in India. This study explores the relationship between El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD) and dengue cases in India. Additionally, distributed lag non-linear model was used to assess the delayed effects of climatic factors on dengue cases. The weekly dengue cases reported by the Integrated Disease Surveillance Program (IDSP) over India during the period 2010-2017 were analysed. The study shows that dengue cases usually follow a seasonal pattern, with most cases reported in August and September. Both temperature and rainfall were positively associated with the number of dengue cases. The precipitation shows the higher transmission risk of dengue was observed between 8 and 15 weeks of lag. The highest relative risk (RR) of dengue was observed at 60 mm rainfall with a 12-week lag period when compared with 40 and 80 mm rainfall. The RR of dengue tends to increase with increasing mean temperature above 24 °C. The largest transmission risk of dengue was observed at 30 °C with a 0-3 weeks of lag. Similarly, the transmission risk increases more than twofold when the minimum temperature reaches 26 °C with a 2-week lag period. The dengue cases and El Niño were positively correlated with a 3-6 months lag period. The significant correlation observed between the IOD and dengue cases was shown for a 0-2 months lag period.

Impact of recent and future climate change on vector-borne diseases.

Climate change is one of the greatest threats to human health in the 21st century. Climate directly impacts health through climatic extremes, air quality, sea-level rise, and multifaceted influences on food production systems and water resources. Climate also affects infectious diseases, which have played a significant role in human history, impacting the rise and fall of civilizations and facilitating the conquest of new territories. Our review highlights significant regional changes in vector and pathogen distribution reported in temperate, peri-Arctic, Arctic, and tropical highland regions during recent decades, changes that have been anticipated by scientists worldwide. Further future changes are likely if we fail to mitigate and adapt to climate change. Many key factors affect the spread and severity of human diseases, including mobility of people, animals, and goods; control measures in place; availability of effective drugs; quality of public health services; human behavior; and political stability and conflicts. With drug and insecticide resistance on the rise, significant funding and research efforts must to be maintained to continue the battle against existing and emerging diseases, particularly those that are vector borne.

Predicting the distribution of Phortica variegata and potential for Thelazia callipaeda transmission in Europe and the United Kingdom.

BACKGROUND: Male fruitflies Phortica variegata (Drosophilidae, Steganinae) are the intermediate host of the zoonotic nematode Thelazia callipaeda (Spirurida, Thelaziidae). More than 10 years ago, when T. callipaeda was confined to remote regions of southern Italy, ecological niche models were used to predict the potential distribution of P. variegata across Europe and the likely risk of the nematode spreading through infected dogs travelling to/from endemic regions. As predicted, over the last 10 years T. callipaeda has spread rapidly across Europe. Recently, we identified the potential for its introduction to the UK through infected dogs travelling to/from endemic regions of mainland Europe. METHODS: Here updated information is used to re-evaluate the model-predicted European, and specifically, UK distribution to determine the likelihood of T. callipaeda becoming established. Additionally, the UK distribution of P. variegata was further investigated through snapshot fly trapping at model-predicted locations. RESULTS: Ecological niche modelling using Genetic Algorithm for Rule-set Prediction (GARP) analysis suggests a European range similar to that described previously, with some indication of potential spread further eastward. Finer scale UK mapping suggested that P. variegata presence was limited mostly to southern England, but highlighted regions where P. variegata has not been documented previously. The arbitrary fly trapping identified activity of P. variegata at two locations where the species has been found previously late in the season. No specimens were collected at model-predicted locations, although habitat suitable for the species was identified. CONCLUSIONS: GARP-model prediction of P. variegata distribution suggests presence of suitable conditions in previously undocumented regions of the UK and Europe and highlight the possibility for further spread of T. callipaeda across Europe, including the UK. Further work to validate the P. variegata UK model with field data will help improve its accuracy in predicting suitable areas, whilst surveillance of sylvatic definitive host species in such locations is advised to monitor for evidence of autochthonous T. callipaeda transmission.

Mosquito-Borne Diseases: Advances in Modelling Climate-Change Impacts.

Vector-borne diseases are on the rise globally. As the consequences of climate change are becoming evident, climate-based models of disease risk are of growing importance. Here, we review the current state-of-the-art in both mechanistic and correlative disease modelling, the data driving these models, the vectors and diseases covered, and climate models applied to assess future risk. We find that modelling techniques have advanced considerably, especially in terms of using ensembles of climate models and scenarios. Effects of extreme events, precipitation regimes, and seasonality on diseases are still poorly studied. Thorough validation of models is still a challenge and is complicated by a lack of field and laboratory data. On a larger scale, the main challenges today lie in cross-disciplinary and cross-sectoral transfer of data and methods.

Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal.

The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal. The findings confirm that the risk of malaria transmission is mainly linked to climate variables such as rainfall and temperature as well as specific landscape characteristics. For the whole of Senegal, a lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November, corresponding to the second peak of temperature occurring in October. Observed malaria data from the Programme National de Lutte contre le Paludisme (PNLP, National Malaria control Programme in Senegal) and outputs from the meteorological data used in this study were compared. The malaria model outputs present some consistencies with observed malaria dynamics over Senegal, and further allow the exploration of simulations performed with reanalysis data sets over a longer time period. The simulated malaria risk significantly decreased during the 1970s and 1980s over Senegal. This result is consistent with the observed decrease of malaria vectors and malaria cases reported by field entomologists and clinicians in the literature. The main differences between model outputs and observations regard amplitude, but can be related not only to reanalysis deficiencies but also to other environmental and socio-economic factors that are not included in this mechanistic malaria model framework. The present study can be considered as a validation of the reliability of reanalysis to be used as inputs for the calculation of malaria parameters in the Sahel using dynamical malaria models.