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Background: Mosquitoes and the diseases they transmit pose a significant public health threat worldwide, causing more fatalities than any other animal. To effectively combat this issue, there is a need for increased public awareness and mosquito control. However, traditional surveillance programs are time-consuming, expensive, and lack scalability. Fortunately, the widespread availability of mobile devices with high-resolution cameras presents a unique opportunity for mosquito surveillance. In response to this, the Global Mosquito Observations Dashboard (GMOD) was developed as a free, public platform to improve the detection and monitoring of invasive and vector mosquitoes through citizen science participation worldwide., Methods: GMOD is an interactive web interface that collects and displays mosquito observation and habitat data supplied by four datastreams with data generated by citizen scientists worldwide. By providing information on the locations and times of observations, the platform enables the visualization of mosquito population trends and ranges. It also serves as an educational resource, encouraging collaboration and data sharing. The data acquired and displayed on GMOD is freely available in multiple formats and can be accessed from any device with an internet connection., Results: Since its launch less than a year ago, GMOD has already proven its value. It has successfully integrated and processed large volumes of real-time data (~ 300,000 observations), offering valuable and actionable insights into mosquito species prevalence, abundance, and potential distributions, as well as engaging citizens in community-based surveillance programs., Conclusions: GMOD is a cloud-based platform that provides open access to mosquito vector data obtained from citizen science programs. Its user-friendly interface and data filters make it valuable for researchers, mosquito control personnel, and other stakeholders. With its expanding data resources and the potential for machine learning integration, GMOD is poised to support public health initiatives aimed at reducing the spread of mosquito-borne diseases in a cost-effective manner, particularly in regions where traditional surveillance methods are limited. GMOD is continually evolving, with ongoing development of powerful artificial intelligence algorithms to identify mosquito species and other features from submitted data. The future of citizen science holds great promise, and GMOD stands as an exciting initiative in this field., (© 2023. The Author(s).)

Within the contiguous USA, Florida is unique in having tropical and subtropical climates, a great abundance and diversity of mosquito vectors, and high rates of human travel. These factors contribute to the state being the national ground zero for exotic mosquito-borne diseases, as evidenced by local transmission of viruses spread by Aedes aegypti, including outbreaks of dengue in 2022 and Zika in 2016. Because of limited treatment options, integrated vector management is a key part of mitigating these arboviruses. Practical knowledge of when and where mosquito populations of interest exist is critical for surveillance and control efforts, and habitat predictions at various geographic scales typically rely on ecological niche modeling. However, most of these models, usually created in partnership with academic institutions, demand resources that otherwise may be too time-demanding or difficult for mosquito control programs to replicate and use effectively. Such resources may include intensive computational requirements, high spatiotemporal resolutions of data not regularly available, and/or expert knowledge of statistical analysis. Therefore, our study aims to partner with mosquito control agencies in generating operationally useful mosquito abundance models. Given the increasing threat of mosquito-borne disease transmission in Florida, our analytic approach targets recent Ae. aegypti abundance in the Tampa Bay area. We investigate explanatory variables that: 1) are publicly available, 2) require little to no preprocessing for use, and 3) are known factors associated with Ae. aegypti ecology. Out of our 4 final models, none required more than 5 out of the 36 predictors assessed (13.9%). Similar to previous literature, the strongest predictors were consistently 3- and 4-wk temperature and precipitation lags, followed closely by 1 of 2 environmental predictors: land use/land cover or normalized difference vegetation index. Surprisingly, 3 of our 4 final models included one or more socioeconomic or demographic predictors. In general, larger sample sizes of trap collections and/or citizen science observations should result in greater confidence in model predictions and validation. However, given disparities in trap collections across jurisdictions, individual county models rather than a multicounty conglomerate model would likely yield stronger model fits. Ultimately, we hope that the results of our assessment will enable more accurate and precise mosquito surveillance and control of Ae. aegypti in Florida and beyond., (Copyright © 2023 by The American Mosquito Control Association, Inc.)

The Maluku Province is an underdeveloped region in Indonesia with over 1,340 scattered islands. Due to the limited health facilities and transportation infrastructure, access to healthcare is very challenging. Here, we combined data from various sources to locate the population clusters, health facilities, roads, and ports/docks, and then utilize geographic information systems (GIS) to estimate distances from residents to health facilities. Health workforce distribution data was then integrated to elucidate overall healthcare equity among districts in the province. The average distances to puskesmas (primary health clinics) were 8.89 km (by land) and 18.43 km (by land and water) respectively, and the average distances to hospitals were 56.19 km (by land) and 73.09 km (by land and water), with large disparities within and among districts. Analysis of health workforce data shows that 65% of 207 puskesmas lack physicians, while 49% lack midwives. Ambon, Tual, and Southeast Maluku have the highest health equity, while East Ceram, Buru, and South Buru have the lowest. In general, this study demonstrates the utility of GIS and spatial analyses, which can help identify problem areas in healthcare accessibility and equity in archipelago settings, and provide recommendations to stakeholders such as public health officials and district administrators., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Leosari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The ongoing COVID-19 pandemic is among the worst in recent history, resulting in excess of 520,000,000 cases and 6,200,000 deaths worldwide. The United States (U.S.) has recently surpassed 1,000,000 deaths. Individuals who are elderly and/or immunocompromised are the most susceptible to serious sequelae. Rising sentiment often implicates younger, less-vulnerable populations as primary introducers of COVID-19 to communities, particularly around colleges and universities. Adjusting for more than 32 key socio-demographic, economic, and epidemiologic variables, we (1) implemented regressions to determine the overall community-level, age-adjusted COVID-19 case and mortality rate within each American county, and (2) performed a subgroup analysis among a sample of U.S. colleges and universities to identify any significant preliminary mitigation measures implemented during the fall 2020 semester. From January 1, 2020 through March 31, 2021, a total of 22,385,335 cases and 374,130 deaths were reported to the CDC. Overall, counties with increasing numbers of university enrollment showed significantly lower case rates and marginal decreases in mortality rates. County-level population demographics, and not university level mitigation measures, were the most significant predictor of adjusted COVID-19 case rates. Contrary to common sentiment, our findings demonstrate that counties with high university enrollments may be more adherent to public safety measures and vaccinations, likely contributing to safer communities., (© 2023. The Author(s).)

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)., (© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: West Nile virus (WNV), primarily vectored by mosquitoes of the genus Culex, is the most important mosquito-borne pathogen in North America, having infected thousands of humans and countless wildlife since its arrival in the USA in 1999. In locations with dedicated mosquito control programs, surveillance methods often rely on frequent testing of mosquitoes collected in a network of gravid traps (GTs) and CO 2 -baited light traps (LTs). Traps specifically targeting oviposition-seeking (e.g. GTs) and host-seeking (e.g. LTs) mosquitoes are vulnerable to trap bias, and captured specimens are often damaged, making morphological identification difficult., Methods: This study leverages an alternative mosquito collection method, the human landing catch (HLC), as a means to compare sampling of potential WNV vectors to traditional trapping methods. Human collectors exposed one limb for 15 min at crepuscular periods (5:00-8:30 am and 6:00-9:30 pm daily, the time when Culex species are most actively host-seeking) at each of 55 study sites in suburban Chicago, Illinois, for two summers (2018 and 2019)., Results: A total of 223 human-seeking mosquitoes were caught by HLC, of which 46 (20.6%) were mosquitoes of genus Culex. Of these 46 collected Culex specimens, 34 (73.9%) were Cx. salinarius, a potential WNV vector species not thought to be highly abundant in upper Midwest USA. Per trapping effort, GTs and LTs collected > 7.5-fold the number of individual Culex specimens than HLC efforts., Conclusions: The less commonly used HLC method provides important insight into the complement of human-biting mosquitoes in a region with consistent WNV epidemics. This study underscores the value of the HLC collection method as a complementary tool for surveillance to aid in WNV vector species characterization. However, given the added risk to the collector, novel mitigation methods or alternative approaches must be explored to incorporate HLC collections safely and strategically into control programs., (© 2022. The Author(s).)

Even as novel technologies emerge and medicines advance, pathogen-transmitting mosquitoes pose a deadly and accelerating public health threat. Detecting and mitigating the spread of Anopheles stephensi in Africa is now critical to the fight against malaria, as this invasive mosquito poses urgent and unprecedented risks to the continent. Unlike typical African vectors of malaria, An. stephensi breeds in both natural and artificial water reservoirs, and flourishes in urban environments. With An. stephensi beginning to take hold in heavily populated settings, citizen science surveillance supported by novel artificial intelligence (AI) technologies may offer impactful opportunities to guide public health decisions and community-based interventions. Coalitions like the Global Mosquito Alert Consortium (GMAC) and our freely available digital products can be incorporated into enhanced surveillance of An. stephensi and other vector-borne public health threats. By connecting local citizen science networks with global databases that are findable, accessible, interoperable, and reusable (FAIR), we are leveraging a powerful suite of tools and infrastructure for the early detection of, and rapid response to, (re)emerging vectors and diseases., Competing Interests: COMPETING INTERESTS The authors have no competing interests to declare.

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., Competing Interests: The authors have declared that no competing interests exist.

Background: Since 1999, West Nile virus (WNV) has moved rapidly across the United States, resulting in tens of thousands of human cases. Both the number of human cases and the minimum infection rate (MIR) in vector mosquitoes vary across time and space and are driven by numerous abiotic and biotic forces, ranging from differences in microclimates to socio-demographic factors. Because the interactions among these multiple factors affect the locally variable risk of WNV illness, it has been especially difficult to model human disease risk across varying spatial and temporal scales. Cook and DuPage Counties, comprising the city of Chicago and surrounding suburbs, experience some of the highest numbers of human neuroinvasive cases of WNV in the United States. Despite active mosquito control efforts, there is consistent annual WNV presence, resulting in more than 285 confirmed WNV human cases and 20 deaths from the years 2014-2018 in Cook County alone., Methods: A previous Chicago-area WNV model identified the fifty-five most high and low risk locations in the Northwest Mosquito Abatement District (NWMAD), an enclave ¼ the size of the combined Cook and DuPage county area. In these locations, human WNV risk was stratified by model performance, as indicated by differences in studentized residuals. Within these areas, an additional two-years of field collections and data processing was added to a 12-year WNV dataset that includes human cases, MIR, vector abundance, and land-use, historical climate, and socio-economic and demographic variables, and was assessed by an ultra-fine-scale (1 km spatial x 1 week temporal resolution) multivariate logistic regression model., Results: Multivariate statistical methods applied to the ultra-fine-scale model identified fewer explanatory variables while improving upon the fit of the previous model. Beyond MIR and climatic factors, efforts to acquire additional covariates only slightly improved model predictive performance., Conclusions: These results suggest human WNV illness in the Chicago area may be associated with fewer, but increasingly critical, key variables at finer scales. Given limited resources, these findings suggest large variations in model performance occur, depending on covariate availability, and provide guidance in variable selection for optimal WNV human illness modeling., Competing Interests: The authors have declared that no competing interests exist.

Modeling vector-borne diseases is best conducted when heterogeneity among interacting biotic and abiotic processes is captured. However, the successful integration of these complex processes is difficult, hindered by a lack of understanding of how these relationships influence disease transmission across varying scales. West Nile virus (WNV) is the most important mosquito-borne disease in the United States. Vectored by Culex mosquitoes and maintained in the environment by avian hosts, the virus can spill over into humans and horses, sometimes causing severe neuroinvasive illness. Several modeling studies have evaluated drivers of WNV disease risk, but nearly all have done so at broad scales and have reported mixed results of the effects of common explanatory variables. As a result, fine-scale relationships with common explanatory variables, particularly climatic, socioeconomic, and human demographic, remain uncertain across varying spatial extents. Using an interdisciplinary approach and an ongoing 12-year study of the Chicago region, this study evaluated the factors explaining WNV disease risk at high spatiotemporal resolution, comparing the human WNV model and covariate performance across three increasing spatial extents: ultrafine, local, and county scales. Our results demonstrate that as spatial extent increased, model performance increased. In addition, only six of the 23 assessed covariates were included in best-fit models of at least two scales. These results suggest that the mechanisms driving WNV ecology are scale-dependent and covariate importance increases as extent decreases. These tools may be particularly helpful for public health, mosquito, and disease control personnel in predicting and preventing disease within local and fine-scale jurisdictions, before spillover occurs.

West Nile virus (WNV) is the most important and widespread mosquito-borne virus in the United States (U.S.). WNV has the ability to spread rapidly and effectively, infecting more than 320 bird and mammalian species. An examination of environmental conditions and the health of keystone species may help predict the susceptibility of various habitats to WNV and reveal key risk factors, annual trends, and vulnerable regions. Since 2002, WNV outbreaks in Wisconsin varied by species, place, and time, significantly affected by unique climatic, environmental, and geographical factors. During a 15 year period, WNV was detected in 71 of 72 counties, resulting in 239 human and 1397 wildlife cases. Controlling for population and sampling efforts in Wisconsin, rates of WNV are highest in the western and northwestern rural regions of the state. WNV incidence rates were highest in counties with low human population densities, predominantly wetland, and at elevations greater than 1000 feet. Resources for surveillance, prevention, and detection of WNV were lowest in rural counties, likely resulting in underestimation of cases. Overall, increasing mean temperature and decreasing precipitation showed positive influence on WNV transmission in Wisconsin. This study incorporates the first statewide assessment of WNV in Wisconsin.