Assessing the value and knowledge gains from an online tick identification and tick-borne disease management course for the Southeastern United States.

BACKGROUND: Tick-borne diseases are a growing public health threat in the United States. Despite the prevalence and rising burden of tick-borne diseases, there are major gaps in baseline knowledge and surveillance efforts for tick vectors, even among vector control districts and public health agencies. To address this issue, an online tick training course (OTTC) was developed through the Southeastern Center of Excellence in Vector-Borne Diseases (SECOEVBD) to provide a comprehensive knowledge base on ticks, tick-borne diseases, and their management. METHODS: The OTTC consisted of training modules covering topics including tick biology, tick identification, tick-borne diseases, and public health, personal tick safety, and tick surveillance. The course was largely promoted to vector control specialists and public health employees throughout the Southeastern US. We collected assessment and survey data on participants to gauge learning outcomes, perceptions of the utility of knowledge gained, and barriers and facilitators to applying the knowledge in the field. RESULTS: The OTTC was successful in increasing participants' baseline knowledge across all course subject areas, with the average score on assessment increasing from 62.6% (pre-course) to 86.7% (post-course). More than half of participants (63.6%) indicated that they would definitely use information from the course in their work. Barriers to using information identified in the delayed assessment included lack of opportunities to apply skills (18.5%) and the need for additional specialized training beyond what the OTTC currently offers (18.5%), while the main facilitator (70.4%) for applying knowledge was having opportunities at work, such as an existing tick surveillance program. CONCLUSIONS: Overall, this OTTC demonstrated capacity to improve knowledge in a necessary and underserved public health field, and more than half of participants use or plan to use the information in their work. The geographic reach of this online resource was much larger than simply for the Southeastern region for which it was designed, suggesting a much broader need for this resource. Understanding the utility and penetrance of training programs such as these is important for refining materials and assessing optimal targets for training.

Mapping current and future thermal limits to suitability for malaria transmission by the invasive mosquito Anopheles stephensi.

BACKGROUND: Anopheles stephensi is a malaria-transmitting mosquito that has recently expanded from its primary range in Asia and the Middle East, to locations in Africa. This species is a competent vector of both Plasmodium falciparum and Plasmodium vivax malaria. Perhaps most alarming, the characteristics of An. stephensi, such as container breeding and anthropophily, make it particularly adept at exploiting built environments in areas with no prior history of malaria risk. METHODS: In this paper, global maps of thermal transmission suitability and people at risk (PAR) for malaria transmission by An. stephensi were created, under current and future climate. Temperature-dependent transmission suitability thresholds derived from recently published species-specific thermal curves were used to threshold gridded, monthly mean temperatures under current and future climatic conditions. These temperature driven transmission models were coupled with gridded population data for 2020 and 2050, under climate-matched scenarios for future outcomes, to compare with baseline predictions for 2020 populations. RESULTS: Using the Global Burden of Disease regions approach revealed that heterogenous regional increases and decreases in risk did not mask the overall pattern of massive increases of PAR for malaria transmission suitability with An. stephensi presence. General patterns of poleward expansion for thermal suitability were seen for both P. falciparum and P. vivax transmission potential. CONCLUSIONS: Understanding the potential suitability for An. stephensi transmission in a changing climate provides a key tool for planning, given an ongoing invasion and expansion of the vector. Anticipating the potential impact of onward expansion to transmission suitable areas, and the size of population at risk under future climate scenarios, and where they occur, can serve as a large-scale call for attention, planning, and monitoring.

Shifting transmission risk for malaria in Africa with climate change: a framework for planning and intervention.

BACKGROUND: Malaria continues to be a disease of massive burden in Africa, and the public health resources targeted at surveillance, prevention, control, and intervention comprise large outlays of expense. Malaria transmission is largely constrained by the suitability of the climate for Anopheles mosquitoes and Plasmodium parasite development. Thus, as climate changes, shifts in geographic locations suitable for transmission, and differing lengths of seasons of suitability will occur, which will require changes in the types and amounts of resources. METHODS: The shifting geographic risk of malaria transmission was mapped, in context of changing seasonality (i.e. endemic to epidemic, and vice versa), and the number of people affected. A published temperature-dependent model of malaria transmission suitability was applied to continental gridded climate data for multiple future AR5 climate model projections. The resulting outcomes were aligned with programmatic needs to provide summaries at national and regional scales for the African continent. Model outcomes were combined with population projections to estimate the population at risk at three points in the future, 2030, 2050, and 2080, under two scenarios of greenhouse gas emissions (RCP4.5 and RCP8.5). RESULTS: Estimated geographic shifts in endemic and seasonal suitability for malaria transmission were observed across all future scenarios of climate change. The worst-case regional scenario (RCP8.5) of climate change predicted an additional 75.9 million people at risk from endemic (10-12 months) exposure to malaria transmission in Eastern and Southern Africa by the year 2080, with the greatest population at risk in Eastern Africa. Despite a predominance of reduction in season length, a net gain of 51.3 million additional people is predicted be put at some level of risk in Western Africa by midcentury. CONCLUSIONS: This study provides an updated view of potential malaria geographic shifts in Africa under climate change for the more recent climate model projections (AR5), and a tool for aligning findings with programmatic needs at key scales for decision-makers. In describing shifting seasonality, it was possible to capture transitions between endemic and epidemic risk areas, to facilitate the planning for interventions aimed at year-round risk versus anticipatory surveillance and rapid response to potential outbreak locations.

A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador.

BACKGROUND: Vector-borne disease places a high health and economic burden in the American tropics. Comprehensive vector control programs remain the primary method of containing local outbreaks. With limited resources, many vector control operations struggle to serve all affected communities within their districts. In the coastal city of Machala, Ecuador, vector control services, such as application of larvicides and truck-mounted fogging, are delivered through two deployment facilities managed by the Ecuadorian Ministry of Health. Public health professionals in Machala face several logistical issues when delivering mosquito abatement services, namely applying limited resources in ways that will most effectively suppress vectors of malaria, dengue, and encephalitis viruses. METHODS: Using a transportation network analysis framework, we built models of service areas and optimized delivery routes based on distance costs associated with accessing neighborhoods throughout the city. Optimized routes were used to estimate the relative cost of accessing neighborhoods for mosquito control services in Machala, creating a visual tool to guide decision makers and maximize mosquito control program efficiency. Location-allocation analyses were performed to evaluate efficiency gains of moving service deployment to other available locations with respect to distance to service hub, neighborhood population, dengue incidence, and housing condition. RESULTS: Using this framework, we identified different locations for targeting mosquito control efforts, dependent upon management goals and specified risk factors of interest, including human population, housing condition, and reported dengue incidence. Our models indicate that neighborhoods on the periphery of Machala with the poorest housing conditions are the most costly to access. Optimal locations of facilities for deployment of control services change depending on pre-determined management priorities, increasing the population served via inexpensive routes up to 34.9%, and reducing overall cost of accessing neighborhoods up to 12.7%. CONCLUSIONS: Our transportation network models indicate that current locations of mosquito control facilities in Machala are not ideal for minimizing driving distances or maximizing populations served. Services may be optimized by moving vector control operations to other existing public health facilities in Machala. This work represents a first step in creating a spatial tool for planning and critically evaluating the systematic delivery of mosquito control services in Machala and elsewhere.

Thermal biology of mosquito-borne disease.

Mosquito-borne diseases cause a major burden of disease worldwide. The vital rates of these ectothermic vectors and parasites respond strongly and nonlinearly to temperature and therefore to climate change. Here, we review how trait-based approaches can synthesise and mechanistically predict the temperature dependence of transmission across vectors, pathogens, and environments. We present 11 pathogens transmitted by 15 different mosquito species - including globally important diseases like malaria, dengue, and Zika - synthesised from previously published studies. Transmission varied strongly and unimodally with temperature, peaking at 23-29ºC and declining to zero below 9-23ºC and above 32-38ºC. Different traits restricted transmission at low versus high temperatures, and temperature effects on transmission varied by both mosquito and parasite species. Temperate pathogens exhibit broader thermal ranges and cooler thermal minima and optima than tropical pathogens. Among tropical pathogens, malaria and Ross River virus had lower thermal optima (25-26ºC) while dengue and Zika viruses had the highest (29ºC) thermal optima. We expect warming to increase transmission below thermal optima but decrease transmission above optima. Key directions for future work include linking mechanistic models to field transmission, combining temperature effects with control measures, incorporating trait variation and temperature variation, and investigating climate adaptation and migration.

Nonlinear and delayed impacts of climate on dengue risk in Barbados: A modelling study.

BACKGROUND: Over the last 5 years (2013-2017), the Caribbean region has faced an unprecedented crisis of co-occurring epidemics of febrile illness due to arboviruses transmitted by the Aedes sp. mosquito (dengue, chikungunya, and Zika). Since 2013, the Caribbean island of Barbados has experienced 3 dengue outbreaks, 1 chikungunya outbreak, and 1 Zika fever outbreak. Prior studies have demonstrated that climate variability influences arbovirus transmission and vector population dynamics in the region, indicating the potential to develop public health interventions using climate information. The aim of this study is to quantify the nonlinear and delayed effects of climate indicators, such as drought and extreme rainfall, on dengue risk in Barbados from 1999 to 2016. METHODS AND FINDINGS: Distributed lag nonlinear models (DLNMs) coupled with a hierarchal mixed-model framework were used to understand the exposure-lag-response association between dengue relative risk and key climate indicators, including the standardised precipitation index (SPI) and minimum temperature (Tmin). The model parameters were estimated in a Bayesian framework to produce probabilistic predictions of exceeding an island-specific outbreak threshold. The ability of the model to successfully detect outbreaks was assessed and compared to a baseline model, representative of standard dengue surveillance practice. Drought conditions were found to positively influence dengue relative risk at long lead times of up to 5 months, while excess rainfall increased the risk at shorter lead times between 1 and 2 months. The SPI averaged over a 6-month period (SPI-6), designed to monitor drought and extreme rainfall, better explained variations in dengue risk than monthly precipitation data measured in millimetres. Tmin was found to be a better predictor than mean and maximum temperature. Furthermore, including bidimensional exposure-lag-response functions of these indicators-rather than linear effects for individual lags-more appropriately described the climate-disease associations than traditional modelling approaches. In prediction mode, the model was successfully able to distinguish outbreaks from nonoutbreaks for most years, with an overall proportion of correct predictions (hits and correct rejections) of 86% (81%:91%) compared with 64% (58%:71%) for the baseline model. The ability of the model to predict dengue outbreaks in recent years was complicated by the lack of data on the emergence of new arboviruses, including chikungunya and Zika. CONCLUSION: We present a modelling approach to infer the risk of dengue outbreaks given the cumulative effect of climate variations in the months leading up to an outbreak. By combining the dengue prediction model with climate indicators, which are routinely monitored and forecasted by the Regional Climate Centre (RCC) at the Caribbean Institute for Meteorology and Hydrology (CIMH), probabilistic dengue outlooks could be included in the Caribbean Health-Climatic Bulletin, issued on a quarterly basis to provide climate-smart decision-making guidance for Caribbean health practitioners. This flexible modelling approach could be extended to model the risk of dengue and other arboviruses in the Caribbean region.

Quantifying seasonal and diel variation in Anopheline and Culex human biting rates in Southern Ecuador.

BACKGROUND: Quantifying mosquito biting rates for specific locations enables estimation of mosquito-borne disease risk, and can inform intervention efforts. Measuring biting itself is fraught with ethical concerns, so the landing rate of mosquitoes on humans is often used as a proxy measure. Southern coastal Ecuador was historically endemic for malaria (Plasmodium falciparum and Plasmodium vivax), although successful control efforts in the 2000s eliminated autochthonous transmission (since 2011). This study presents an analysis of data collected during the elimination period. METHODS: Human landing catch (HLC) data for three mosquito taxa: two malaria vectors, Anopheles albimanus and Anopheles punctimacula, and grouped Culex spp. were examined for this study. These data were collected by the National Vector Control Service of the Ministry of Health over a 5-year time span (2007-2012) in five cities in southern coastal Ecuador, at multiple households, in all months of the year, during dusk-dawn (18:00-6:00) hours, often at both indoor and outdoor locations. Hurdle models were used to determine if biting activity was fundamentally different for the three taxa, and to identify spatial and temporal factors influencing bite rate. Due to the many different approaches to studying and quantifying bite rates in the literature, a glossary of terms was created, to facilitate comparative studies in the future. RESULTS: Biting trends varied significantly with species and time. All taxa exhibited exophagic feeding behavior, and outdoor locations increased both the odds and incidence of bites across taxa. Anopheles albimanus was most frequently observed biting, with an average of 4.7 bites/h. The highest and lowest respective months for significant biting activity were March and July for An. albimanus, July and August for An. punctimacula, and February and July for Culex spp. CONCLUSIONS: Fine-scale differences in endophagy and exophagy, and temporal differences among months and hours exist in biting patterns among mosquito taxa in southern coastal Ecuador. This analysis provides detailed information for targeting vector control activities, and household level vector prevention strategies. These data were collected as part of routine vector surveillance conducted by the Ministry of Health, and such data have not been collected since. Reinstating such surveillance measures would provide important information to aid in preventing malaria re-emergence.

Pliocene-Pleistocene lineage diversifications in the Eastern Indigo Snake (Drymarchon couperi) in the Southeastern United States.

Indigo Snakes (Drymarchon; with five currently recognized species) occur from northern Argentina, northward to the United States in southern Texas and eastward in disjunct populations in Florida and Georgia. Based on this known allopatry and a difference in supralabial morphology the two United States taxa previously considered as subspecies within D. corais (Boie 1827), the Western Indigo Snake, D. melanurus erebennus (Cope 1860), and Eastern Indigo Snake, D. couperi (Holbrook 1842), are currently recognized as separate species. Drymarchon couperi is a Federally-designated Threatened species by the United States Fish and Wildlife Service under the Endangered Species Act, and currently being incorporated into a translocation program. This, combined with its disjunct distribution makes it a prime candidate for studying speciation and genetic divergence. In this study, we (1) test the hypothesis that D. m. erebennus and D. couperi are distinct lineages by analyzing 2411 base pairs (bp) of two mitochondrial (mtDNA) loci and one single copy nuclear (scnDNA) locus; (2) estimate the timing of speciation using a relaxed phylogenetics method to determine if Milankovitch cycles during the Pleistocene might have had an influence on lineage diversifications; (3) examine historical population demography to determine if identified lineages have undergone population declines, expansions, or remained stable during the most recent Milankovitch cycles; and (4) use this information to assist in an effective and scientifically sound translocation program. Our molecular data support the initial hypothesis that D. melanurus and D. couperi should be recognized as distinct species, but further illustrate that D. couperi is split into two distinct genetic lineages that correspond to historical biogeography and sea level changes in peninsular Florida. These two well-supported genetic lineages (herein termed Atlantic and Gulf lineages) illustrate a common biogeographic distributional break previously identified for other plants and animals, suggesting that these organisms might have shared a common evolutionary history related to historic sea level changes caused by Milankovitch cycles. Our estimated divergence times suggest that the most recent common ancestor (MRCA) between D. melanurus and southeastern United States Drymarchon occurred ca. 5.9Ma (95% HPD=2.5-9.8Ma; during the late Blancan of the Pleistocene through the Hemphillian of the Miocene), whereas the MRCA between the Atlantic and Gulf lineages in the southeastern United States occurred ca. 2.0Ma (95% HPD=0.7-3.7Ma; during the Irvingtonian of the Pleistocene through the Blancan of the Pliocene). During one or more glacial intervals within these times, these two lineages must have become separated and evolved independently. Despite numerous Milankovitch cycles along with associated forming of physical barriers (i.e., sea level fluctuations, high elevation sand ridges, clayey soils, and/or insufficient habitats) since their initial lineage diversification, these two lineages have likely come in and out of contact with each other many times, yet today they still illustrate near discrete geographic distributions. Although the Atlantic and Gulf lineages appear to be cryptic, a thorough study examining morphological characters should be conducted. We believe that our molecular data is crucial and should be incorporated in making conscious decisions in the management of a translocation program. We suggest that source populations for translocations include maintaining the integrity of the known genetic lineages found herein, as well as those coming from the closest areas that currently support sizable Drymarchon populations.

Newer Surveillance Data Extends our Understanding of the Niche of Rickettsia montanensis (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae) in the United States.

Background: Understanding the geographic distribution of Rickettsia montanensis infections in Dermacentor variabilis is important for tick-borne disease management in the United States, as both a tick-borne agent of interest and a potential confounder in surveillance of other rickettsial diseases. Two previous studies modeled niche suitability for D. variabilis with and without R. montanensis , from 2002-2012, indicating that the D. variabilis niche overestimates the infected niche. This study updates these, adding data since 2012. Methods: Newer surveillance and testing data were used to update Species Distribution Models (SDMs) of D. variabilis , and R. montanensis infected D. variabilis , in the United States. Using random forest (RF) models, found to perform best in previous work, we updated the SDMs and compared them with prior results. Warren's I niche overlap metric was used to compare between predicted suitability for all ticks and 'pathogen positive niche' models across datasets. Results: Warren's I indicated <2% change in predicted niche, and there was no change in order of importance of environmental predictors, for D. variabilis or R. montanensis positive niche. The updated D. variabilis niche model overpredicted suitability compared to the updated R. montanensis positive niche in key peripheral parts of the range, but slightly underpredicted through the northern and midwestern parts of the range. This reinforces previous findings of a more constrained pathogen-positive niche than predicted by D. variabilis records alone. Conclusions: The consistency of predicted niche suitability for D. variabilis in the United States, with the addition of nearly a decade of new data, corroborates this is a species with generalist habitat requirements. Yet a slight shift in updated niche distribution, even of low suitability, included more southern areas, pointing to a need for continued and extended monitoring and surveillance. This further underscores the importance of revisiting vector and vector-borne disease distribution maps.

Characterizing the Vector Data Ecosystem.

A growing body of information on vector-borne diseases has arisen as increasing research focus has been directed towards the need for anticipating risk, optimizing surveillance, and understanding the fundamental biology of vector-borne diseases to direct control and mitigation efforts. The scope and scale of this information, in the form of data, comprising database efforts, data storage, and serving approaches, means that it is distributed across many formats and data types. Data ranges from collections records to molecular characterization, geospatial data to interactions of vectors and traits, infection experiments to field trials. New initiatives arise, often spanning the effort traditionally siloed in specific research disciplines, and other efforts wane, perhaps in response to funding declines, different research directions, or lack of sustained interest. Thusly, the world of vector data - the Vector Data Ecosystem - can become unclear in scope, and the flows of data through these various efforts can become stymied by obsolescence, or simply by gaps in access and interoperability. As increasing attention is paid to creating FAIR (Findable Accessible Interoperable, and Reusable) data, simply characterizing what is 'out there', and how these existing data aggregation and collection efforts interact, or interoperate with each other, is a useful exercise. This study presents a snapshot of current vector data efforts, reporting on level of accessibility, and commenting on interoperability using an illustration to track a specimen through the data ecosystem to understand where it occurs for the database efforts anticipated to describe it (or parts of its extended specimen data).

Newer Surveillance Data Extends our Understanding of the Niche of Rickettsia montanensis (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae) in the United States.

Background: Understanding the geographic distribution of Rickettsia montanensis infections in Dermacentor variabilis is important for tick-borne disease management in the United States, as both a tick-borne agent of interest and a potential confounder in surveillance of other rickettsial diseases. Two previous studies modeled niche suitability for D. variabilis with and without R. montanensis, from 2002 to 2012, indicating that the D. variabilis niche overestimates the infected niche. This study updates these, adding data since 2012. Methods: Newer surveillance and testing data were used to update Species Distribution Models (SDMs) of D. variabilis, and R. montanensis-infected D. variabilis, in the United States. Using random forest models, found to perform best in previous work, we updated the SDMs and compared them with prior results. Warren's I niche overlap metric was used to compare between predicted suitability for all ticks and "R. montanensis-positive niche" models across datasets. Results: Warren's I indicated <2% change in predicted niche, and there was no change in order of importance of environmental predictors, for D. variabilis or R. montanensis-positive niche. The updated D. variabilis niche model overpredicted suitability compared with the updated R. montanensis-positive niche in key peripheral parts of the range, but slightly underpredicted through the northern and midwestern parts of the range. This reinforces previous findings of a more constrained R. montanensis-positive niche than predicted by D. variabilis records alone. Conclusions: The consistency of predicted niche suitability for D. variabilis in the United States, with the addition of nearly a decade of new data, corroborates this is a species with generalist habitat requirements. Yet a slight shift in updated niche distribution, even of low suitability, included more southern areas, pointing to a need for continued and extended monitoring and surveillance. This further underscores the importance of revisiting vector and vector-borne disease distribution maps.

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

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

The current landscape of software tools for the climate-sensitive infectious disease modelling community.

Climate-sensitive infectious disease modelling is crucial for public health planning and is underpinned by a complex network of software tools. We identified only 37 tools that incorporated both climate inputs and epidemiological information to produce an output of disease risk in one package, were transparently described and validated, were named (for future searching and versioning), and were accessible (ie, the code was published during the past 10 years or was available on a repository, web platform, or other user interface). We noted disproportionate representation of developers based at North American and European institutions. Most tools (n=30 [81%]) focused on vector-borne diseases, and more than half (n=16 [53%]) of these tools focused on malaria. Few tools (n=4 [11%]) focused on food-borne, respiratory, or water-borne diseases. The under-representation of tools for estimating outbreaks of directly transmitted diseases represents a major knowledge gap. Just over half (n=20 [54%]) of the tools assessed were described as operationalised, with many freely available online.

Co-learning during the co-creation of a dengue early warning system for the health sector in Barbados.

Over the past decade, the Caribbean region has been challenged by compound climate and health hazards, including tropical storms, extreme heat and droughts and overlapping epidemics of mosquito-borne diseases, including dengue, chikungunya and Zika. Early warning systems (EWS) are a key climate change adaptation strategy for the health sector. An EWS can integrate climate information in forecasting models to predict the risk of disease outbreaks several weeks or months in advance. In this article, we share our experiences of co-learning during the process of co-creating a dengue EWS for the health sector in Barbados, and we discuss barriers to implementation as well as key opportunities. This process has involved bringing together health and climate practitioners with transdisciplinary researchers to jointly identify needs and priorities, assess available data, co-create an early warning tool, gather feedback via national and regional consultations and conduct trainings. Implementation is ongoing and our team continues to be committed to a long-term process of collaboration. Developing strong partnerships, particularly between the climate and health sectors in Barbados, has been a critical part of the research and development. In many countries, the national climate and health sectors have not worked together in a sustained or formal manner. This collaborative process has purposefully pushed us out of our comfort zone, challenging us to venture beyond our institutional and disciplinary silos. Through the co-creation of the EWS, we anticipate that the Barbados health system will be better able to mainstream climate information into decision-making processes using tailored tools, such as epidemic forecast reports, risk maps and climate-health bulletins, ultimately increasing the resilience of the health system.

Correction: Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models.

[This corrects the article DOI: 10.1371/journal.pntd.0005568.].

A molecular surveillance-guided vector control response to concurrent dengue and West Nile virus outbreaks in a COVID-19 hotspot of Florida.

Background: Simultaneous dengue virus (DENV) and West Nile virus (WNV) outbreaks in Florida, USA, in 2020 resulted in 71 dengue virus serotype 1 and 86 WNV human cases. We hypothesized that we would find a number of DENV-1 positive mosquito pools, and that the distribution of these arbovirus-positive mosquito pools would be associated with those neighborhoods for which imported DENV cases have been recently reported in 2019 and 2020. Methods: We collected and screened Aedes aegypti, Ae. albopictus, Anopheles crucians, Culex coronator, Cx. nigripalpus, and Cx. quinquefasciatus mosquitoes from Miami-Dade County (Florida) for DENV and WNV by rRT-qPCR. Spatial statistical analyses were performed to capture positive mosquito pool distribution in relation to land use, human demography, environmental variables, mosquito trap placement and reported human travel associated DENV cases to guide future mosquito control outbreak responses. Findings: A rapid screen of 7,668 mosquitoes detected four DENV serotype 2 (DENV-2), nine DENV-4 and nine WNV-positive mosquito pools, which enabled swift and targeted abatement of trap sites by mosquito control. As expected, DENV-positive pools were in urban areas; however, we found WNV-positive mosquito pools in agricultural and recreational areas with no historical reports of WNV transmission. Interpretation: These findings demonstrate the importance of proactive arbovirus surveillance in mosquito populations to prevent and control outbreaks, particularly when other illnesses (e.g., COVID-19), which present with similar symptoms, are circulating concurrently. Growing evidence for substantial infection prevalence of dengue in mosquitoes in the absence of local index cases suggests a higher level of dengue endemicity in Florida than previously thought. Funding: This research was supported in part by U.S. Centers for Disease Control and Prevention (CDC) grant 1U01CK000510-03, Southeastern Regional Center of Excellence in Vector Borne Diseases Gateway Program.

Trends and Opportunities in Tick-Borne Disease Geography.

Tick-borne diseases are a growing problem in many parts of the world, and their surveillance and control touch on challenging issues in medical entomology, agricultural health, veterinary medicine, and biosecurity. Spatial approaches can be used to synthesize the data generated by integrative One Health surveillance systems, and help stakeholders, managers, and medical geographers understand the current and future distribution of risk. Here, we performed a systematic review of over 8,000 studies and identified a total of 303 scientific publications that map tick-borne diseases using data on vectors, pathogens, and hosts (including wildlife, livestock, and human cases). We find that the field is growing rapidly, with the major Ixodes-borne diseases (Lyme disease and tick-borne encephalitis in particular) giving way to monitoring efforts that encompass a broader range of threats. We find a tremendous diversity of methods used to map tick-borne disease, but also find major gaps: data on the enzootic cycle of tick-borne pathogens is severely underutilized, and mapping efforts are mostly limited to Europe and North America. We suggest that future work can readily apply available methods to track the distributions of tick-borne diseases in Africa and Asia, following a One Health approach that combines medical and veterinary surveillance for maximum impact.

Scoping review of distribution models for selected Amblyomma ticks and rickettsial group pathogens.

The rising prevalence of tick-borne diseases in humans in recent decades has called attention to the need for more information on geographic risk for public health planning. Species distribution models (SDMs) are an increasingly utilized method of constructing potential geographic ranges. There are many knowledge gaps in our understanding of risk of exposure to tick-borne pathogens, particularly for those in the rickettsial group. Here, we conducted a systematic scoping review of the SDM literature for rickettsial pathogens and tick vectors in the genus Amblyomma. Of the 174 reviewed articles, only 24 studies used SDMs to estimate the potential extent of vector and/or pathogen ranges. The majority of studies (79%) estimated only tick distributions using vector presence as a proxy for pathogen exposure. Studies were conducted at different scales and across multiple continents. Few studies undertook original data collection, and SDMs were mostly built with presence-only datasets from public database or surveillance sources. The reliance on existing data sources, using ticks as a proxy for disease risk, may simply reflect a lag in new data acquisition and a thorough understanding of the tick-pathogen ecology involved.

Exploring the Niche of Rickettsia montanensis (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae), Using Multiple Species Distribution Model Approaches.

The American dog tick, Dermacentor variabilis (Say) (Acari: Ixodidae), is a vector for several human disease-causing pathogens such as tularemia, Rocky Mountain spotted fever, and the understudied spotted fever group rickettsiae (SFGR) infection caused by Rickettsia montanensis. It is important for public health planning and intervention to understand the distribution of this tick and pathogen encounter risk. Risk is often described in terms of vector distribution, but greatest risk may be concentrated where more vectors are positive for a given pathogen. When assessing species distributions, the choice of modeling framework and spatial layers used to make predictions are important. We first updated the modeled distribution of D. variabilis and R. montanensis using maximum entropy (MaxEnt), refining bioclimatic data inputs, and including soil variables. We then compared geospatial predictions from five species distribution modeling frameworks. In contrast to previous work, we additionally assessed whether the R. montanensis positive D. variabilis distribution is nested within a larger overall D. variabilis distribution, representing a fitness cost hypothesis. We found that 1) adding soil layers improved the accuracy of the MaxEnt model; 2) the predicted 'infected niche' was smaller than the overall predicted niche across all models; and 3) each model predicted different sizes of suitable niche, at different levels of probability. Importantly, the models were not directly comparable in output style, which could create confusion in interpretation when developing planning tools. The random forest (RF) model had the best measured validity and fit, suggesting it may be most appropriate to these data.