Drought and immunity determine the intensity of West Nile virus epidemics and climate change impacts

Effects of climate change and human activities on vector-borne diseases

Vector-borne diseases are transmitted by haematophagous arthropods (for example, mosquitoes, ticks and sandflies) to humans and wild and domestic animals, with the largest burden on global public health disproportionately affecting people in tropical and subtropical areas. Because vectors are ectothermic, climate and weather alterations (for example, temperature, rainfall and humidity) can affect their reproduction, survival, geographic distribution and, consequently, ability to transmit pathogens. However, the effects of climate change on vector-borne diseases can be multifaceted and complex, sometimes with ambiguous consequences. In this Review, we discuss the potential effects of climate change, weather and other anthropogenic factors, including land use, human mobility and behaviour, as possible contributors to the redistribution of vectors and spread of vector-borne diseases worldwide.

Drivers and epidemiological patterns of West Nile virus in Serbia

Background

West Nile virus (WNV) is an emerging mosquito-borne pathogen in Serbia, where it has been detected as a cause of infection in humans since 2012. We analyzed and modelled WNV transmission patterns in the country between 2012 and 2023.

Methods

We applied a previously developed modelling approach to quantify epidemiological parameters of interest and to identify the most important environmental drivers of the force of infection (FOI) by means of statistical analysis in the human population in the country.

Results

During the study period, 1,387 human cases were recorded, with substantial heterogeneity across years. We found that spring temperature is of paramount importance for WNV transmission, as FOI magnitude and peak timing are positively associated with it. Furthermore, FOI is also estimated to be greater in regions with a larger fraction of older adult people, who are at higher risk to develop severe infections.

Conclusion

Our results highlight that temperature plays a key role in shaping WNV outbreak magnitude in Serbia, confirming the association between spring climatic conditions and WNV human transmission risk and thus pointing out the importance of this factor as a potential early warning predictor for timely application of preventive and control measures.

NPCC4: Climate change and New York City's health risk

This chapter of the New York City Panel on Climate Change 4 (NPCC4) report considers climate health risks, vulnerabilities, and resilience strategies in New York City's unique urban context. It updates evidence since the last health assessment in 2015 as part of NPCC2 and addresses climate health risks and vulnerabilities that have emerged as especially salient to NYC since 2015. Climate health risks from heat and flooding are emphasized. In addition, other climate-sensitive exposures harmful to human health are considered, including outdoor and indoor air pollution, including aeroallergens; insect vectors of human illness; waterborne infectious and chemical contaminants; and compounding of climate health risks with other public health emergencies, such as the COVID-19 pandemic. Evidence-informed strategies for reducing future climate risks to health are considered.

Multiple bouts of blood feeding in mosquitoes allow prolonged survival and are predicted to increase viral transmission during drought

Survival through periods of drought is critical for mosquitoes to reside in semi-arid regions with humans, but water sources may be limited. Previous studies have shown that dehydrated mosquitoes will increase blood feeding propensity, but how this would occur over extended dry periods is unknown. Following a bloodmeal, prolonged exposure to dry conditions increased secondary blood feeding in mosquitoes by nearly two-fold, and chronic blood feeding allowed mosquitoes to survive twenty days without access to water sources. This refeeding did not alter the number of eggs generated, suggesting this refeeding is for hydration and nutrient replenishment. Exposure to desiccating conditions following a bloodmeal resulted in increased activity, decreased sleep levels, and prompted a return of CO2 sensing before egg deposition. The increased blood feeding during the vitellogenic stage and higher survival during dry periods are predicted to increase pathogen transmission and explain the elevated levels of specific arbovirus cases during dry conditions. These results solidify our understanding of the role of dry periods on mosquito blood feeding and how mosquito dehydration contributes to vectorial capacity and disease transmission dynamics.

Population-specific thermal responses contribute to regional variability in arbovirus transmission with changing climates

Temperature is increasing globally, and vector-borne diseases are particularly responsive to such increases. While it is known that temperature influences mosquito life history traits, transmission models have not historically considered population-specific effects of temperature. We assessed the interaction between Culex pipiens population and temperature in New York State (NYS) and utilized novel empirical data to inform predictive models of West Nile virus (WNV) transmission. Genetically and regionally distinct populations from NYS were reared at various temperatures, and life history traits were monitored and used to inform trait-based models. Variation in Cx. pipiens life history traits and population-dependent thermal responses account for a predicted 2.9°C difference in peak transmission that is reflected in regional differences in WNV prevalence. We additionally identified genetic signatures that may contribute to distinct thermal responses. Together, these data demonstrate how population variation contributes to significant geographic variability in arbovirus transmission with changing climates.

Exploring Climate-Disease Connections in Geopolitical Versus Ecological Regions: The Case of West Nile Virus in the United States

Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector-borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large-scale analyses from geopolitical frameworks to more ecologically meaningful regions.

Commonly Reported Mosquito-Borne Viruses in the United States: A Primer for Pharmacists

Mosquito-borne diseases are a public health concern. Pharmacists are often a patient's first stop for health information and may be asked questions regarding transmission, symptoms, and treatment of mosquito borne viruses (MBVs). The objective of this paper is to review transmission, geographic location, symptoms, diagnosis and treatment of MBVs. We discuss the following viruses with cases in the US in recent years: Dengue, West Nile, Chikungunya, LaCrosse Encephalitis, Eastern Equine Encephalitis Virus, and Zika. Prevention, including vaccines, and the impact of climate change are also discussed.

Homogenized gridded dataset for drought and hydrometeorological modeling for the continental United States

We present a novel data set for drought in the continental US (CONUS) built to enable computationally efficient spatio-temporal statistical and probabilistic models of drought. We converted drought data obtained from the widely-used US Drought Monitor (USDM) from its native geo-referenced polygon format to a 0.5 degree regular grid. We merged known environmental drivers of drought, including those obtained from the North American Land Data Assimilation System (NLDAS-2), US Geological Survey (USGS) streamflow data, and National Oceanic and Atmospheric Administration (NOAA) teleconnections data. The resulting data set permits statistical and probabilistic modeling of drought with explicit spatial and/or temporal dependence. Such models could be used to forecast drought at short-range, seasonal to sub-seasonal, and inter-annual timescales with uncertainty, extending the reach and value of the current US Drought Outlook from the National Weather Service Climate Prediction Center. This novel data product provides the first common gridded dataset that includes critical variables used to inform hydrological and meteorological drought.

A Specific Pattern of Routine Cerebrospinal Fluid Parameters Might Help to Identify Cases of West Nile Virus Neuroinvasive Disease

BACKGROUND

Viral meningitis/encephalitis (ME) is a rare but potentially harmful disease. The prompt identification of the respective virus is important to guide not only treatment but also potential public health countermeasures. However, in about 40% of cases, no virus is identified despite an extensive diagnostic workup. The aim of the present study was to analyze demographic, seasonal, and routine cerebrospinal fluid (CSF) parameters in cases of viral ME and assess their utility for the prediction of the causative virus.

METHODS

Demographic data, season, and routine CSF parameters (total leucocytes, CSF cell differentiation, age-adjusted CSF/serum albumin ratio, and total immunoglobulin ratios) were retrospectively assessed in cases of viral ME.

RESULTS

In total, 156 cases of acute viral ME (74 female, median age 40.0 years) were treated at a tertiary-care hospital in Germany. Specific viral infections were detected in 93 (59.6%) cases. Of these, 14 (9.0%) cases were caused by herpes simplex virus (HSV), 36 (23.1%) by varicella-zoster virus (VZV), 27 (17.3%) by enteroviruses, 9 (5.8%) by West Nile virus (WNV), and 7 (4.5%) by other specific viruses. Additionally, 64 (41.0%) cases of ME of unknown viral etiology were diagnosed. Cases of WNV ME were older, predominantly male, showed a severe disruption of the blood-CSF-barrier, a high proportion of neutrophils in CSF, and an intrathecal total immunoglobulin M synthesis in the first CSF sample. In a multinominal logistic regression analysis, the accuracy of these CSF parameters together with age and seasonality was best for the prediction of WNV (87.5%), followed by unknown viral etiology (66.7%), VZV (61.8%), and enteroviruses (51.9%).

CONCLUSIONS

Cases with WNV ME showed a specific pattern of routine CSF parameters and demographic data that allowed for their identification with good accuracy. These findings might help to guide the diagnostic workup in cases with viral ME, in particular allowing the timely identification of cases with ME due to WNV.

Transboundary determinants of avian zoonotic infectious diseases: challenges for strengthening research capacity and connecting surveillance networks

As the climate changes, global systems have become increasingly unstable and unpredictable. This is particularly true for many disease systems, including subtypes of highly pathogenic avian influenzas (HPAIs) that are circulating the world. Ecological patterns once thought stable are changing, bringing new populations and organisms into contact with one another. Wild birds continue to be hosts and reservoirs for numerous zoonotic pathogens, and strains of HPAI and other pathogens have been introduced into new regions via migrating birds and transboundary trade of wild birds. With these expanding environmental changes, it is even more crucial that regions or counties that previously did not have surveillance programs develop the appropriate skills to sample wild birds and add to the understanding of pathogens in migratory and breeding birds through research. For example, little is known about wild bird infectious diseases and migration along the Mediterranean and Black Sea Flyway (MBSF), which connects Europe, Asia, and Africa. Focusing on avian influenza and the microbiome in migratory wild birds along the MBSF, this project seeks to understand the determinants of transboundary disease propagation and coinfection in regions that are connected by this flyway. Through the creation of a threat reduction network for avian diseases (Avian Zoonotic Disease Network, AZDN) in three countries along the MBSF (Georgia, Ukraine, and Jordan), this project is strengthening capacities for disease diagnostics; microbiomes; ecoimmunology; field biosafety; proper wildlife capture and handling; experimental design; statistical analysis; and vector sampling and biology. Here, we cover what is required to build a wild bird infectious disease research and surveillance program, which includes learning skills in proper bird capture and handling; biosafety and biosecurity; permits; next generation sequencing; leading-edge bioinformatics and statistical analyses; and vector and environmental sampling. Creating connected networks for avian influenzas and other pathogen surveillance will increase coordination and strengthen biosurveillance globally in wild birds.

Conditioning factors in the spreading of Covid-19 - Does geography matter?

There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.

Temperature dependence of mosquitoes: comparing mechanistic and machine learning approaches

Mosquito vectors of pathogens (e.g., Aedes , Anopheles , and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically-plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.90). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming.

A Spatially Resolved and Environmentally Informed Forecast Model of West Nile Virus in Coachella Valley, California

West Nile virus (WNV) is the most significant arbovirus in the United States in terms of both morbidity and mortality. West Nile exists in a complex transmission cycle between avian hosts and the arthropod vector, Culex spp. mosquitoes. Human spillover events occur when humans are bitten by an infected mosquito and predicting these rates of infection and therefore the risk to humans may be associated with fluctuations in environmental conditions. In this study, we evaluate the hydrological and meteorological drivers associated with mosquito biology and viral development to determine if these associations can be used to forecast seasonal mosquito infection rates with WNV in the Coachella Valley of California. We developed and tested a spatially resolved ensemble forecast model of the WNV mosquito infection rate in the Coachella Valley using 17 years of mosquito surveillance data and North American Land Data Assimilation System-2 environmental data. Our multi-model inference system indicated that the combination of a cooler and dryer winter, followed by a wetter and warmer spring, and a cooler than normal summer was most predictive of the prevalence of West Nile positive mosquitoes in the Coachella Valley. The ability to make accurate early season predictions of West Nile risk has the potential to allow local abatement districts and public health entities to implement early season interventions such as targeted adulticiding and public health messaging before human transmission occurs. Such early and targeted interventions could better mitigate the risk of WNV to humans.

Multi-Model Prediction of West Nile Virus Neuroinvasive Disease With Machine Learning for Identification of Important Regional Climatic Drivers

West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental United States (CONUS). Spatial heterogeneity in historical incidence, environmental factors, and complex ecology make prediction of spatiotemporal variation in WNV transmission challenging. Machine learning provides promising tools for identification of important variables in such situations. To predict annual WNV neuroinvasive disease (WNND) cases in CONUS (2015-2021), we fitted 10 probabilistic models with variation in complexity from naïve to machine learning algorithm and an ensemble. We made predictions in each of nine climate regions on a hexagonal grid and evaluated each model's predictive accuracy. Using the machine learning models (random forest and neural network), we identified the relative importance and variation in ranking of predictors (historical WNND cases, climate anomalies, human demographics, and land use) across regions. We found that historical WNND cases and population density were among the most important factors while anomalies in temperature and precipitation often had relatively low importance. While the relative performance of each model varied across climatic regions, the magnitude of difference between models was small. All models except the naïve model had non-significant differences in performance relative to the baseline model (negative binomial model fit per hexagon). No model, including the ensemble or more complex machine learning models, outperformed models based on historical case counts on the hexagon or region level; these models are good forecasting benchmarks. Further work is needed to assess if predictive capacity can be improved beyond that of these historical baselines.

Temporal and Spatial Synchronicity in West Nile Virus Cases Along the Central Flyway, USA

This study of West Nile virus (WNV) examined the possibility of avian transmission to explain synchronicity in the year-to-year variability of WNV case numbers from Texas northward to the Dakotas, and reasons for the large case numbers on the northern Great Plains. We determined correlation coefficients between annual disease incidence per 100,000 people among states within the Great Plains Region, as well as the Central Flyway. There was spatial and temporal synchronicity, as evidenced by Pearson "r," with values along the core of the Central Flyway (Oklahoma, Kansas, Nebraska, and South Dakota) varying between 0.69 and 0.79. Correlations for North Dakota (r = 0.6), however, were affected by local conditions. The concept of relative amplification is helpful in explaining why northerly states along the Central Flyway have larger annual case numbers per 100,000 than Texas but preserve the temporal signal. States differed in their capacity for amplifying the temporal signal in case numbers. For example, Nebraska, South Dakota, and North Dakota case numbers were commonly amplified relative to Texas, with Oklahoma and Kansas deamplified. Relative amplification factors for all states increased as a function of increasing case numbers in Texas. Thus, increased numbers of initially infected birds in Texas likely led to the rapid intensification of the zoonotic cycle as compared to more typical years. The study also confirmed the importance of winter weather in locally modulating disease cases. North Dakota appeared most impacted by these factors to the extent of reducing WNV case numbers in colder years and years with deep snow.

Assessing the Influence of Climate on the Spatial Pattern of West Nile Virus Incidence in the United States

BACKGROUND

West Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence.

OBJECTIVES

Our goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans.

METHODS

We developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R2=0.61.

RESULTS

Our model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels <23.3mm/month as having incidence levels over 11 times greater than those of counties that are wetter. Among the climate predictors, winter precipitation, fall precipitation, and winter temperature were the three most important predictive variables.

DISCUSSION

We consider which aspects of the WNV transmission cycle climate conditions may benefit the most and argued that dry and cold winters are climate conditions optimal for the mosquito species key to amplifying WNV transmission. Our statistical model may be useful in projecting shifts in WNV risk in response to climate change. https://doi.org/10.1289/EHP10986.

Diabetes and climate change: current evidence and implications for people with diabetes, clinicians and policy stakeholders

Climate change will be a major challenge for the world's health systems in the coming decades. Elevated temperatures and increasing frequencies of heat waves, wildfires, heavy precipitation and other weather extremes can affect health in many ways, especially if chronic diseases are already present. Impaired responses to heat stress, including compromised vasodilation and sweating, diabetes-related comorbidities, insulin resistance and chronic low-grade inflammation make people with diabetes particularly vulnerable to environmental risk factors, such as extreme weather events and air pollution. Additionally, multiple pathogens show an increased rate of transmission under conditions of climate change and people with diabetes have an altered immune system, which increases the risk for a worse course of infectious diseases. In this review, we summarise recent studies on the impact of climate-change-associated risk for people with diabetes and discuss which individuals may be specifically prone to these risk conditions due to their clinical features. Knowledge of such high-risk groups will help to develop and implement tailored prevention and management strategies to mitigate the detrimental effect of climate change on the health of people with diabetes.

Impacts of Climate Change and Air Pollution on Neurologic Health, Disease, and Practice: A Scoping Review

BACKGROUND AND OBJECTIVES

Although the international community collectively seeks to reduce global temperature rise to less than 1.5°C before 2100, irreversible environmental changes have already occurred, and as the planet warms, these changes will continue to occur. As we witness the effects of a warming planet on human health, it is imperative that neurologists anticipate how the epidemiology and incidence of neurologic disease may change. In this review, we organized our analysis around 3 key themes related to climate change and neurologic health: extreme weather events and temperature fluctuations, emerging neuroinfectious diseases, and pollutant impacts. Across each of these themes, we appraised and reviewed recent literature relevant to neurologic disease and practice.

METHODS

Studies were identified using search terms relating to climate change, pollutants, and neurologic disease in PubMed, OVID MEDLINE, EMBASE, PsycInfo, and gray literature. Studies published between 1990 and 2022 were included if they pertained to human incidence or prevalence of disease, were in English, and were relevant to neurologic disease.

RESULTS

We identified a total of 364 articles, grouped into the 3 key themes of our study: extreme weather events and temperature fluctuations (38 studies), emerging neuroinfectious diseases (37 studies), and pollutant impacts (289 studies). The included studies highlighted the relationships between neurologic symptom exacerbation and temperature variability, tick-borne infections and warming climates, and airborne pollutants and cerebrovascular disease incidence and severity.

DISCUSSION

Temperature extremes and variability both associated with stroke incidence and severity, migraine headaches, hospitalization in patients with dementia, and multiple sclerosis exacerbations. Exposure to airborne pollutants, especially PM2.5 and nitrates, associated with stroke incidence and severity, headaches, dementia risk, Parkinson disease, and MS exacerbation. Climate change has demonstrably expanded favorable conditions for zoonotic diseases beyond traditional borders and poses the risk of disease in new, susceptible populations. Articles were biased toward resource-rich regions, suggesting a discordance between where research occurs and where changes are most acute. As such, 3 key priorities emerged for further study: neuroinfectious disease risk mitigation, understanding the pathophysiology of airborne pollutants on the nervous system, and methods to improve delivery of neurologic care in the face of climate-related disruptions.

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction

BACKGROUND

West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement.

METHODS

We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill.

RESULTS

Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill.

CONCLUSIONS

Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).

An advanced sequence clustering and designation workflow reveals the enzootic maintenance of a dominant West Nile virus subclade in Germany

West Nile virus (WNV) is the most widespread arthropod-borne (arbo) virus and the primary cause of arboviral encephalitis globally. Members of WNV species genetically diverged and are classified into different hierarchical groups below species rank. However, the demarcation criteria for allocating WNV sequences into these groups remain individual and inconsistent, and the use of names for different levels of the hierarchical levels is unstructured. In order to have an objective and comprehensible grouping of WNV sequences, we developed an advanced grouping workflow using the 'affinity propagation clustering' algorithm and newly included the 'agglomerative hierarchical clustering' algorithm for the allocation of WNV sequences into different groups below species rank. In addition, we propose to use a fixed set of terms for the hierarchical naming of WNV below species level and a clear decimal numbering system to label the determined groups. For validation, we applied the refined workflow to WNV sequences that have been previously grouped into various lineages, clades, and clusters in other studies. Although our workflow regrouped some WNV sequences, overall, it generally corresponds with previous groupings. We employed our novel approach to the sequences from the WNV circulation in Germany 2020, primarily from WNV-infected birds and horses. Besides two newly defined minor (sub)clusters comprising only three sequences each, Subcluster 2.5.3.4.3c was the predominant WNV sequence group detected in Germany from 2018 to 2020. This predominant subcluster was also associated with at least five human WNV infections in 2019-20. In summary, our analyses imply that the genetic diversity of the WNV population in Germany is shaped by enzootic maintenance of the dominant WNV subcluster accompanied by sporadic incursions of other rare clusters and subclusters. Moreover, we show that our refined approach for sequence grouping yields meaningful results. Although we primarily aimed at a more detailed WNV classification, the presented workflow can also be applied to the objective genotyping of other virus species.

Statistical Tools for West Nile Virus Disease Analysis

West Nile virus (WNV) is the most widespread arbovirus in the world and endemic to much of the United States. Its range continues to expand as land use patterns change, creating more habitable environments for the mosquito vector. Though WNV is endemic, the year-to-year risk is highly variable, thus making it difficult to understand the risk for human spillover events. Abatement districts monitor for infected mosquitoes to help understand these potential risks and to help guide our understanding of the risk posed by these observed infected mosquitoes. Creating optimal monitoring networks will provide more informed decision-making tools for abatement districts and policy makers. Investment in these monitoring networks that capture robust observations on mosquito infection rates will allow for environmentally informed inference systems to help guide decision-making and WNV risk. In turn, enhanced decision-making tools allow for faster response times of more targeted and economical surveillance and mosquito population reduction efforts and the overall reduction of WNV transmission. Here we discuss the data streams, their processing, and specifically three ways to calculate WNV infection rates in mosquitoes.

Investigating the impact of environmental factors on West Nile virus human case prediction in Ontario, Canada

West Nile virus is the most common mosquito borne disease in North America and the leading cause of viral encephalitis. West Nile virus is primarily transmitted between birds and mosquitoes while humans are incidental, dead-end hosts. Climate change may increase the risk of human infections as climatic variables have been shown to affect the mosquito life cycle, biting rate, incubation period of the disease in mosquitoes, and bird migration patterns. We develop a zero-inflated Poisson model to investigate how human West Nile virus case counts vary with respect to mosquito abundance and infection rates, bird abundance, and other environmental covariates. We use a Bayesian paradigm to fit our model to data from 2010-2019 in Ontario, Canada. Our results show mosquito infection rate, temperature, precipitation, and crow abundance are positively correlated with human cases while NDVI and robin abundance are negatively correlated with human cases. We find the inclusion of spatial random effects allows for more accurate predictions, particularly in years where cases are higher. Our model is able to accurately predict the magnitude and timing of yearly West Nile virus outbreaks and could be a valuable tool for public health officials to implement prevention strategies to mitigate these outbreaks.

Adaptive evolution of West Nile virus facilitated increased transmissibility and prevalence in New York State

West Nile virus (WNV; Flavivirus, Flaviviridae) was introduced to New York State (NYS) in 1999 and rapidly expanded its range through the continental United States (US). Apart from the displacement of the introductory NY99 genotype with the WN02 genotype, there has been little evidence of adaptive evolution of WNV in the US. WNV NY10, characterized by shared amino acid substitutions R1331K and I2513M, emerged in 2010 coincident with increased WNV cases in humans and prevalence in mosquitoes. Previous studies demonstrated an increase in frequency of NY10 strains in NYS and evidence of positive selection. Here, we present updated surveillance and sequencing data for WNV in NYS and investigate if NY10 genotype strains are associated with phenotypic change consistent with an adaptive advantage. Results confirm a significant increase in prevalence in mosquitoes though 2018, and updated sequencing demonstrates a continued dominance of NY10. We evaluated NY10 strains in Culex pipiens mosquitoes to assess vector competence and found that the NY10 genotype is associated with both increased infectivity and transmissibility. Experimental infection of American robins (Turdus migratorius) was additionally completed to assess viremia kinetics of NY10 relative to WN02. Modelling the increased infectivity and transmissibility of the NY10 strains together with strain-specific viremia demonstrates a mechanistic basis for selection that has likely contributed to the increased prevalence of WNV in NYS.

Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches

BACKGROUND

Tunisia has experienced several West Nile virus (WNV) outbreaks since 1997. Yet, there is limited information on the spatial distribution of the main WNV mosquito vector Culex pipiens suitability at the national level.

OBJECTIVES

In the present study, our aim was to predict and evaluate the potential and current distribution of Cx. pipiens in Tunisia.

METHODS

To this end, two species distribution models were used, i.e. MaxEnt and Random Forest. Occurrence records for Cx. pipiens were obtained from adult and larvae sampled in Tunisia from 2014 to 2017. Climatic and human factors were used as predictors to model the Cx. pipiens geographical distribution. Mean decrease accuracy and mean decrease Gini indices were calculated to evaluate the importance of the impact of different environmental and human variables on the probability distribution of Cx. pipiens.

RESULTS

Suitable habitats were mainly distributed next to oases, in the north and eastern part of the country. The most important predictor was the population density in both models. The study found out that the governorates of Monastir, Nabeul, Manouba, Ariana, Bizerte, Gabes, Medenine and Kairouan are at highest epidemic risk.

CONCLUSIONS

The potential distribution of Cx. pipiens coincides geographically with the observed distribution of the disease in humans in Tunisia. Our study has the potential for driving control effort in the fight against West Nile vector in Tunisia.

Stability of patch-turnover relationships under equilibrium and nonequilibrium metapopulation dynamics driven by biogeography

Two controversial tenets of metapopulation biology are whether patch quality and the surrounding matrix are more important to turnover (colonisation and extinction) than biogeography (patch area and isolation) and whether factors governing turnover during equilibrium also dominate nonequilibrium dynamics. We tested both tenets using 18 years of surveys for two secretive wetland birds, black and Virginia rails, during (1) a period of equilibrium with stable occupancy and (2) after drought and arrival of West Nile Virus (WNV), which resulted in WNV infections in rails, increased extinction and decreased colonisation probabilities modified by WNV, nonequilibrium dynamics for both species and occupancy decline for black rails. Area (primarily) and isolation (secondarily) drove turnover during both stable and unstable metapopulation dynamics, greatly exceeding the effects of patch quality and matrix conditions. Moreover, slopes between turnover and patch characteristics changed little between equilibrium and nonequilibrium, confirming the overriding influences of biogeographic factors on turnover.

Global Change and Emerging Infectious Diseases

Our world is undergoing rapid planetary changes driven by human activities, often mediated by economic incentives and resource management, affecting all life on Earth. Concurrently, many infectious diseases have recently emerged or spread into new populations. Mounting evidence suggests that global change-including climate change, land-use change, urbanization, and global movement of individuals, species, and goods-may be accelerating disease emergence by reshaping ecological systems in concert with socioeconomic factors. Here, we review insights, approaches, and mechanisms by which global change drives disease emergence from a disease ecology perspective. We aim to spur more interdisciplinary collaboration with economists and identification of more effective and sustainable interventions to prevent disease emergence. While almost all infectious diseases change in response to global change, the mechanisms and directions of these effects are system specific, requiring new, integrated approaches to disease control that recognize linkages between environmental and economic sustainability and human and planetary health.

Modelling the temperature suitability for the risk of West Nile Virus establishment in European Culex pipiens populations

Increases in temperature and extreme weather events due to global warming can create an environment that is beneficial to mosquito populations, changing and possibly increasing the suitable geographical range for many vector-borne diseases. West Nile Virus (WNV) is a flavivirus, maintained in a mosquito-avian host cycle that is usually asymptomatic but can cause primarily flu-like symptoms in human and equid accidental hosts. In rare circumstances, serious disease and death are possible outcomes for both humans and horses. The main European vector of WNV is the Culex pipiens mosquito. This study examines the effect of environmental temperature on WNV establishment in Europe via Culex pipiens populations through use of a basic reproduction number ( R 0 ${R_0}$ ) model. A metric of thermal suitability derived from R 0 ${R_0}$ was developed by collating thermal responses of different Culex pipiens traits and combining them through use of a next-generation matrix. WNV establishment was determined to be possible between 14°C and 34.3°C, with the optimal temperature at 23.7°C. The suitability measure was plotted against monthly average temperatures in 2020 and the number of months with high suitability mapped across Europe. The average number of suitable months for each year from 2013 to 2019 was also calculated and validated with reported equine West Nile fever cases from 2013 to 2019. The widespread thermal suitability for WNV establishment highlights the importance of European surveillance for this disease and the need for increased research into mosquito and bird distribution.

Integrated Forecasts Based on Public Health Surveillance and Meteorological Data Predict West Nile Virus in a High-Risk Region of North America

BACKGROUND

West Nile virus (WNV), a global arbovirus, is the most prevalent mosquito-transmitted infection in the United States. Forecasts of WNV risk during the upcoming transmission season could provide the basis for targeted mosquito control and disease prevention efforts. We developed the Arbovirus Mapping and Prediction (ArboMAP) WNV forecasting system and used it in South Dakota from 2016 to 2019. This study reports a post hoc forecast validation and model comparison.

OBJECTIVES

Our objective was to validate historical predictions of WNV cases with independent data that were not used for model calibration. We tested the hypothesis that predictive models based on mosquito surveillance data combined with meteorological variables were more accurate than models based on mosquito or meteorological data alone.

METHODS

The ArboMAP system incorporated models that predicted the weekly probability of observing one or more human WNV cases in each county. We compared alternative models with different predictors including a) a baseline model based only on historical WNV cases, b) mosquito models based on seasonal patterns of infection rates, c) environmental models based on lagged meteorological variables, including temperature and vapor pressure deficit, d) combined models with mosquito infection rates and lagged meteorological variables, and e) ensembles of two or more combined models. During the WNV season, models were calibrated using data from previous years and weekly predictions were made using data from the current year. Forecasts were compared with observed cases to calculate the area under the receiver operating characteristic curve (AUC) and other metrics of spatial and temporal prediction error.

RESULTS

Mosquito and environmental models outperformed the baseline model that included county-level averages and seasonal trends of WNV cases. Combined models were more accurate than models based only on meteorological or mosquito infection variables. The most accurate model was a simple ensemble mean of the two best combined models. Forecast accuracy increased rapidly from early June through early July and was stable thereafter, with a maximum AUC of 0.85. The model predictions captured the seasonal pattern of WNV as well as year-to-year variation in case numbers and the geographic pattern of cases.

DISCUSSION

The predictions reached maximum accuracy early enough in the WNV season to allow public health responses before the peak of human cases in August. This early warning is necessary because other indicators of WNV risk, including early reports of human cases and mosquito abundance, are poor predictors of case numbers later in the season. https://doi.org/10.1289/EHP10287.

Legal implications of the climate-health crisis: A case study analysis of the role of public health in climate litigation

Background

Strong scientific evidence affirms that climate change is now a public health emergency. Increasingly, climate litigation brought against governments and corporations utilizes international human rights, environmental and climate laws and policies to seek accountability for climate-destructive and health-harming actions. The health impacts of climate change make litigation an important means of pursuing justice and strategically challenging legal systems. Yet there is scant documentation in the literature of the role that public health has played in climate litigation and the legal weight public health narratives are given in such contexts. Therefore, we assessed to what extent courts of law have used public health harm in legal adjudication and sought to provide practical recommendations to address barriers to positioning legal arguments in public health-centric frames.

Methods

We reviewed legal databases to identify all publicly reported, documented, cases of climate litigation filed in any country or jurisdiction between 1990 and September 2020. For the 1641 cases identified, we quantified the frequency of cases where health concerns were explicitly or implicitly raised.

Findings

Case numbers are trending upwards, notably in high income countries. Resolution remains pending in over half of cases as the majority were initiated in the past three years. Cases were primarily based in climate and human rights law and brought by a wide range of groups and individuals predominantly against governments. About half of the decided cases found in favour for the plaintiffs. Based on this, we selected the 65 cases that were directly linked to public health. We found economic forces and pricing of health risks play a key role, as courts are challenged by litigants to adjudicate on the responsibility for health impacts.

Conclusions

While courts of law are receptive to public health science, significant legal reform is needed to enhance leveraging of public health evidence in legal judgements of climate litigation cases. The integration of a public health mandate into a new eco-centric legal paradigm will optimize its potential to promote human well-being—the core objective underpinning both international law, human rights, and public health. Existing legal doctrines and practices can be enhanced to increase the weight of public health arguments in climate legal action and consequently ensure legal rulings in climate litigation prioritize, protect and promote public health.

Periodic synchronisation of dengue epidemics in Thailand over the last 5 decades driven by temperature and immunity

The spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, less work has been done to understand how climate variability and change affects dengue in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatiotemporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last 30 years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronisation events. We show that although patterns in synchrony of dengue are similar to those observed in temperature, the relationship between the two is most consistent during synchronous periods, while during asynchronous periods, temperature plays a less prominent role. With simulations from temperature-driven models, we explore how dynamics of immunity interact with temperature to produce the observed patterns in synchrony. The simulations produced patterns in synchrony that were similar to observations, supporting an important role of immunity. We demonstrate that multiannual oscillations produced by immunity can lead to asynchronous dynamics and that synchrony in temperature can then synchronise these dengue dynamics. At higher mean temperatures, immune dynamics can be more predominant, and dengue dynamics more insensitive to multiannual fluctuations in temperature, suggesting that with rising mean temperatures, dengue dynamics may become increasingly asynchronous. These findings can help underpin predictions of disease patterns as global temperatures rise.

This study shows that spatially large-scale shifts in temperature can synchronize dengue dynamics across Thailand; however, as average temperatures rise, dengue dynamics may increasingly be dictated by dynamics of immunity, which may in turn mean fewer synchronous outbreaks in the future.

Bloodmeal regulation in mosquitoes curtails dehydration-induced mortality, altering vectorial capacity

Mosquitoes readily lose water when exposed to any humidity less than that of near saturated air unless mitigated, leading to shifts in behavior, survival, distribution, and reproduction. In this study, we conducted a series of physiological experiments on two prominent species in the Culicinae subfamily: Culex pipiens, a vector of West Nile virus, and Aedes aegypti, a vector of yellow fever and Zika to examine the effects of dehydration. We exposed C. pipiens and A. aegypti to non-dehydrating conditions (saturated air), dehydrating conditions (air at a 0.89 kPa saturation vapor pressure deficit), several recovery conditions, as well as to bloodfeeding opportunities. We show that dehydrated mosquitoes increase bloodfeeding propensity, improve retention, and decrease excretion of a post-dehydration bloodmeal. In addition, mosquitoes that take a bloodmeal prior to dehydration exposure show increased survival over non-bloodfed counterparts. Dehydration-induced alterations in survival, reproduction, and bloodfeeding propensity of C. pipiens and A. aegypti resulted in marked changes to vectorial capacity. Ultimately, these results become increasingly important as drought intensifies in association with climate change and mosquitoes become more likely to experience arid periods.

Both consumptive and non-consumptive effects of predators impact mosquito populations and have implications for disease transmission

Predator-prey interactions influence prey traits through both consumptive and non-consumptive effects, and variation in these traits can shape vector-borne disease dynamics. Meta-analysis methods were employed to generate predation effect sizes by different categories of predators and mosquito prey. This analysis showed that multiple families of aquatic predators are effective in consumptively reducing mosquito survival, and that the survival of Aedes, Anopheles, and Culex mosquitoes is negatively impacted by consumptive effects of predators. Mosquito larval size was found to play a more important role in explaining the heterogeneity of consumptive effects from predators than mosquito genus. Mosquito survival and body size were reduced by non-consumptive effects of predators, but development time was not significantly impacted. In addition, Culex vectors demonstrated predator avoidance behavior during oviposition. The results of this meta-analysis suggest that predators limit disease transmission by reducing both vector survival and vector size, and that associations between drought and human West Nile virus cases could be driven by the vector behavior of predator avoidance during oviposition. These findings are likely to be useful to infectious disease modelers who rely on vector traits as predictors of transmission.

West Nile virus is predicted to be more geographically widespread in New York State and Connecticut under future climate change

The effects of climate change on infectious diseases are a topic of considerable interest and discussion. We studied West Nile virus (WNV) in New York (NY) and Connecticut (CT) using a Weather Research and Forecasting (WRF) model climate change scenario, which allows us to examine the effects of climate change and variability on WNV risk at county level. We chose WNV because it is well studied, has caused over 50,000 reported human cases, and over 2200 deaths in the United States. The ecological impacts have been substantial (e.g., millions of avian deaths), and economic impacts include livestock deaths, morbidity, and healthcare-related expenses. We trained two Random Forest models with observational climate data and human cases to predict future levels of WNV based on future weather conditions. The Regional Model used present-day data from NY and CT, whereas the Analog Model was fit for states most closely matching the predicted future conditions in the region. Separately, we predicted changes to mosquito-based WNV risk using a trait-based thermal biology approach (Mosquito Model). The WRF model produced control simulations (present day) and pseudo-global warming simulations (future). The Regional and Analog Models predicted an overall increase in human cases of WNV under future warming. However, the Analog Model did not predict as strong of an increase in the number of human cases as the Regional Model, and predicted a decrease in cases in some counties that currently experience high numbers of WNV cases. The Mosquito Model also predicted a decrease in risk in current high-risk areas, with an overall reduction in the population-weighted relative risk (but an increase in area-weighted risk). The Mosquito Model supports the Analog Model as making more realistic predictions than the Regional Model. All three models predicted a geographic increase in WNV cases across NY and CT.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Does temperature matter for COVID-19 transmissibility? Evidence across Pakistani provinces

The outbreak of novel coronavirus (COVID-19) has become a global concern that is deteriorating environmental quality and damaging human health. Though some researchers have investigated the linkage between temperature and COVID-19 transmissibility across different geographical locations and over time, yet these studies are scarce. This study aims to bridge this gap using daily temperature and COVID-19 cases (transmissibility) by employing grey incidence analysis (GIA) models (i.e., Deng's grey incidence analysis (DGIA), the absolute degree GIA (ADGIA), the second synthetic degree GIA (SSDGIA), the conservative (maximin) model) and correlation analysis. Data on temperature are accessed from the NASA database, while the data on COVID-19 cases are collected from the official website of the government of Pakistan. Empirical results reveal the existence of linkages between temperature and COVID-19 in all Pakistani provinces. These linkages vary from a relatively stronger to a relatively weaker linkage. Based on calculated weights, the strength of linkages is ranked across provinces as follows: Gilgit Baltistan (0.715301) > Baluchistan (0.675091) > Khyber Pakhtunkhwa (0.619893) > Punjab (0.619286) > Sindh (0.601736). The disparity in the strength of linkage among provinces is explained by the discrepancy in the intensity of temperature. Besides, the diagrammatic correlation analysis shows that temperature is inversely linked to COVID-19 cases (per million persons) over time, implying that low temperatures are associated with high COVID-19 transmissibility and vice versa. This study is among the first of its kind to consider the linkages between temperature and COVID-19 transmissibility for a tropical climate country (Pakistan) using the advanced GIA models. Research findings provide an up-to-date glimpse of the outbreak and emphasize the need to raise public awareness about the devastating impacts of the COVID-19. The educational syllabus should provide information on the causes, signs, and precautions of the pandemic. Additionally, individuals should practice handwashing, social distancing, personal hygiene, mask-wearing, and the use of hand sanitizers to ensure a secure and supportive atmosphere for preventing and controlling the current pandemic.

Integrating Spatiotemporal Epidemiology, Eco-Phylogenetics, and Distributional Ecology to Assess West Nile Disease Risk in Horses

Mosquito-borne West Nile virus (WNV) is the causative agent of West Nile disease in humans, horses, and some bird species. Since the initial introduction of WNV to the United States (US), approximately 30,000 horses have been impacted by West Nile neurologic disease and hundreds of additional horses are infected each year. Research describing the drivers of West Nile disease in horses is greatly needed to better anticipate the spatial and temporal extent of disease risk, improve disease surveillance, and alleviate future economic impacts to the equine industry and private horse owners. To help meet this need, we integrated techniques from spatiotemporal epidemiology, eco-phylogenetics, and distributional ecology to assess West Nile disease risk in horses throughout the contiguous US. Our integrated approach considered horse abundance and virus exposure, vector and host distributions, and a variety of extrinsic climatic, socio-economic, and environmental risk factors. Birds are WNV reservoir hosts, and therefore we quantified avian host community dynamics across the continental US to show intra-annual variability in host phylogenetic structure and demonstrate host phylodiversity as a mechanism for virus amplification in time and virus dilution in space. We identified drought as a potential amplifier of virus transmission and demonstrated the importance of accounting for spatial non-stationarity when quantifying interaction between disease risk and meteorological influences such as temperature and precipitation. Our results delineated the timing and location of several areas at high risk of West Nile disease and can be used to prioritize vaccination programs and optimize virus surveillance and monitoring.

The rise of West Nile Virus in Southern and Southeastern Europe: A spatial-temporal analysis investigating the combined effects of climate, land use and economic changes

West Nile Virus (WNV) has recently emerged as a major public health concern in Europe; its recent expansion also coincided with some remarkable socio-economic and environmental changes, including an economic crisis and some of the warmest temperatures on record. Here we empirically investigate the drivers of this phenomenon at a European wide scale by constructing and analyzing a unique spatial–temporal data-set, that includes data on climate, land-use, the economy, and government spending on environmental related sectors. Drivers and risk factors of WNV were identified by building a conceptual framework, and relationships were tested using a Generalized Additive Model (GAM), which could capture complex non-linear relationships and also account for spatial and temporal auto-correlation. Some of the key risk factors identified in our conceptual framework, such as a higher percentage of wetlands and arable land, climate factors (higher summer rainfall and higher summer temperatures) were positive predictors of WNV infections. Interestingly, winter temperatures of between 2 °C and 6 °C were among some of the strongest predictors of annual WNV infections; one possible explanation for this result is that successful overwintering of infected adult mosquitoes (likely Culex pipiens) is key to the intensity of outbreaks for a given year. Furthermore, lower surface water extent over the summer is also associated with more intense outbreaks, suggesting that drought, which is known to induce positive changes in WNV prevalence in mosquitoes, is also contributing to the upward trend in WNV cases in affected regions. Our indicators representing the economic crisis were also strong predictors of WNV infections, suggesting there is an association between austerity and cuts to key sectors, which could have benefited vector species and the virus during this crucial period. These results, taken in the context of recent winter warming due to climate change, and more frequent droughts, may offer an explanation of why the virus has become so prevalent in Europe.

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m-km, days-weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

Projected losses of ecosystem services in the US disproportionately affect non-white and lower-income populations

Addressing how ecosystem services (ES) are distributed among groups of people is critical for making conservation and environmental policy-making more equitable. Here, we evaluate the distribution and equity of changes in ES benefits across demographic and socioeconomic groups in the United States (US) between 2020 and 2100. Specifically, we use land cover and population projections to model potential shifts in the supply, demand, and benefits of the following ES: provision of clean air, protection against a vector-borne disease (West Nile virus), and crop pollination. Across the US, changes in ES benefits are unevenly distributed among socioeconomic and demographic groups and among rural and urban communities, but are relatively uniform across geographic regions. In general, non-white, lower-income, and urban populations disproportionately bear the burden of declines in ES benefits. This is largely driven by the conversion of forests and wetlands to cropland and urban land cover in counties where these populations are expected to grow. In these locations, targeted land use policy interventions are required to avoid exacerbating inequalities already present in the US.

Toward an Integrated System of Climate Change and Human Health Indicators: A Conceptual Framework

Environmental health indicators are helpful for tracking and communicating complex health trends, informing science and policy decisions, and evaluating public health actions. When provided on a national scale, they can help inform the general public, policy makers, and public health professionals about important trends in exposures and how well public health systems are preventing those exposures from causing adverse health outcomes. There is a growing need to understand national trends in exposures and health outcomes associated with climate change and the effectiveness of climate adaptation strategies for health. To date, most indicators for health implications of climate change have been designed as independent, individual metrics. This approach fails to take into account how exposure-outcome pathways for climate-attributable health outcomes involve multiple, interconnected components. We propose reframing climate change and health indicators as a linked system of indicators, which can be described as follows: upstream climate drivers affect environmental states, which then determine human exposures, which ultimately lead to health outcomes; these climate-related risks are modified by population vulnerabilities and adaptation strategies. We apply this new conceptual framework to three illustrative climate-sensitive health outcomes and associated exposure-outcome pathways: pollen allergies and asthma, West Nile Virus infection, and vibriosis.

Vector Surveillance, Host Species Richness, and Demographic Factors as West Nile Disease Risk Indicators

West Nile virus (WNV) is the most common arthropod-borne virus (arbovirus) in the United States (US) and is the leading cause of viral encephalitis in the country. The virus has affected tens of thousands of US persons total since its 1999 North America introduction, with thousands of new infections reported annually. Approximately 1% of humans infected with WNV acquire neuroinvasive West Nile Disease (WND) with severe encephalitis and risk of death. Research describing WNV ecology is needed to improve public health surveillance, monitoring, and risk assessment. We applied Bayesian joint-spatiotemporal modeling to assess the association of vector surveillance data, host species richness, and a variety of other environmental and socioeconomic disease risk factors with neuroinvasive WND throughout the conterminous US. Our research revealed that an aging human population was the strongest disease indicator, but climatic and vector-host biotic interactions were also significant in determining risk of neuroinvasive WND. Our analysis also identified a geographic region of disproportionately high neuroinvasive WND disease risk that parallels the Continental Divide, and extends southward from the US-Canada border in the states of Montana, North Dakota, and Wisconsin to the US-Mexico border in western Texas. Our results aid in unraveling complex WNV ecology and can be applied to prioritize disease surveillance locations and risk assessment.

A novel approach for predicting risk of vector-borne disease establishment in marginal temperate environments under climate change: West Nile virus in the UK

Vector-borne diseases (VBDs), such as dengue, Zika, West Nile virus (WNV) and tick-borne encephalitis, account for substantial human morbidity worldwide and have expanded their range into temperate regions in recent decades. Climate change has been proposed as a likely driver of past and future expansion, however, the complex ecology of host and vector populations and their interactions with each other, environmental variables and land-use changes makes understanding the likely impacts of climate change on VBDs challenging. We present an environmentally driven, stage-structured, host-vector mathematical modelling framework to address this challenge. We apply our framework to predict the risk of WNV outbreaks in current and future UK climates. WNV is a mosquito-borne arbovirus which has expanded its range in mainland Europe in recent years. We predict that, while risks will remain low in the coming two to three decades, the risk of WNV outbreaks in the UK will increase with projected temperature rises and outbreaks appear plausible in the latter half of this century. This risk will increase substantially if increased temperatures lead to increases in the length of the mosquito biting season or if European strains show higher replication at lower temperatures than North American strains.

Light pollution affects West Nile virus exposure risk across Florida

Emerging infectious diseases (EIDs) present global health threats, and their emergences are often linked to anthropogenic change. Artificial light at night (ALAN) is one form of anthropogenic change that spans beyond urban boundaries and may be relevant to EIDs through its influence on the behaviour and physiology of hosts and/or vectors. Although West Nile virus (WNV) emergence has been described as peri-urban, we hypothesized that exposure risk could also be influenced by ALAN in particular, which is testable by comparing the effects of ALAN on prevalence while controlling for other aspects of urbanization. By modelling WNV exposure among sentinel chickens in Florida, we found strong support for a nonlinear relationship between ALAN and WNV exposure risk in chickens with peak WNV risk occurring at low ALAN levels. Although our goal was not to discern how ALAN affected WNV relative to other factors, effects of ALAN on WNV exposure were stronger than other known drivers of risk (i.e. impervious surface, human population density). Ambient temperature in the month prior to sampling, but no other considered variables, strongly influenced WNV risk. These results indicate that ALAN may contribute to spatio-temporal changes in WNV risk, justifying future investigations of ALAN on other vector-borne parasites.

Linkages between environmental issues and zoonotic diseases: with reference to COVID-19 pandemic

Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) has taken mankind by surprise with various unprecedented impacts on human life around the globe. This zoonotic pandemic is proving to be the most destructive disease outbreaks since decades. The increasing human population and anthropogenic activities have impacted the environment and have direct linkages with the current and other recent outbreaks of zoonotic diseases. Despite having a difference in their origin, major reasons behind the emergence and spread of zoonotic pandemics are related to activities such as habitat fragmentation, deforestation, biodiversity loss, intensive agriculture and livestock farming, uncontrolled urbanization, pollution, climate change and bushmeat hunting and trading. It is important to focus on environmental and climatic factors that are involved in the emergence of such pandemics involving novel human pathogens and viruses in particular. Research and data analysis, particularly in relation to COVID-19, has shown that meteorological factors along with population density and living conditions (particularly in the urban and semi-urban areas) play a crucial role in the intensity, evolution and spread of SARS-CoV-2. This particular virus is novel but coronaviruses have a long history and are known to cause disease outbreaks earlier as well. COVID-19 pandemic provides learning for the future, in particular the importance of environmental sustainability for controlling such outbreaks. A strategic plan can be developed involving policy-makers, organizations, and governments to control the onset and spread of the novel pathogens. This review-based study recommends that prevention of COVID-19 like pandemics from re-occurring is through tackling the issues related to the environment by controlling anthropogenic activities. It will also be important to track the lineage and future evolution course of such human pathogens so as to determine the nexus of environmental and biological factors in the development and spread of novel strains.

Autochthonous West Nile virus infection outbreak in humans, Leipzig, Germany, August to September 2020

Following a distinct summer heat wave, nine autochthonous cases of West Nile fever and West Nile neuroinvasive disease, including one fatality, were observed in Leipzig, Germany, in August and September 2020. Phylogenetic analysis demonstrated close relationships in viruses from humans, animals and mosquitos in eastern Germany, obtained during the preceding 2 years. The described large cluster of autochthonous West Nile virus infections in Germany indicates endemic seasonal circulation of lineage 2 viruses in the area.

Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents

Climate drives population dynamics through multiple mechanisms, which can lead to seemingly context-dependent effects of climate on natural populations. For climate-sensitive diseases, such as dengue, chikungunya, and Zika, climate appears to have opposing effects in different contexts. Here we show that a model, parameterized with laboratory measured climate-driven mosquito physiology, captures three key epidemic characteristics across ecologically and culturally distinct settings in Ecuador and Kenya: the number, timing, and duration of outbreaks. The model generates a range of disease dynamics consistent with observed Aedes aegypti abundances and laboratory-confirmed arboviral incidence with variable accuracy (28–85% for vectors, 44–88% for incidence). The model predicted vector dynamics better in sites with a smaller proportion of young children in the population, lower mean temperature, and homes with piped water and made of cement. Models with limited calibration that robustly capture climate-virus relationships can help guide intervention efforts and climate change disease projections.

The effects of climate on vector-borne disease systems are highly context-dependent. Here, the authors incorporate laboratory-measured physiological traits of the mosquito Aedes aegypti into climate-driven mechanistic models to predict number, timing, and duration of outbreaks in Ecuador and Kenya.

Understanding national barriers to climate change adaptation for public health: a mixed-methods survey of national public health representatives

Purpose

To uncover the major government constraints to enactment and implementation of public health-targeted climate change adaptation (CCA) strategies in order to equip public health stakeholders and health advocates with the knowledge resources necessary to more effectively mobilize and support CCA for public health responses at the national level.

Design/methodology/approach

A mixed-methods online survey was distributed to the representatives of national public health associations and societies of 82 countries. The survey comprised 15 questions assessing national progress on CCA for public health and the effects of various institutional, economic/financial, technical and sociopolitical barriers on national adaptive capacity.

Findings

Survey responses from 11 countries indicated that national commitments to CCA for public health have increased markedly since prior assessments but significant shortcomings remain. The largest apparent barriers to progress in this domain were poor government coordination, lack of political will and inadequate adaptation finances.

Originality/value

This study is unique in relation to the prior literature on the topic in that it effectively captures an array of country-specific yet cross-cutting adaptation constraints across diverse national contexts. With a deepened understanding of the major determinants of national adaptive capacity, international actors can devise more effective, evidence-informed strategies to support national governments in responding to the health impacts of climate change.

Transmission of West Nile and five other temperate mosquito-borne viruses peaks at temperatures between 23°C and 26°C

The temperature-dependence of many important mosquito-borne diseases has never been quantified. These relationships are critical for understanding current distributions and predicting future shifts from climate change. We used trait-based models to characterize temperature-dependent transmission of 10 vector-pathogen pairs of mosquitoes (Culex pipiens, Cx. quinquefascsiatus, Cx. tarsalis, and others) and viruses (West Nile, Eastern and Western Equine Encephalitis, St. Louis Encephalitis, Sindbis, and Rift Valley Fever viruses), most with substantial transmission in temperate regions. Transmission is optimized at intermediate temperatures (23-26°C) and often has wider thermal breadths (due to cooler lower thermal limits) compared to pathogens with predominately tropical distributions (in previous studies). The incidence of human West Nile virus cases across US counties responded unimodally to average summer temperature and peaked at 24°C, matching model-predicted optima (24-25°C). Climate warming will likely shift transmission of these diseases, increasing it in cooler locations while decreasing it in warmer locations.