Forecasting Outbreaks of Hantaviral Disease: Future Directions in Geospatial Modeling.

Hantaviral diseases have been recognized as 'place diseases' from their earliest identification and, epidemiologically, are tied to single host species with transmission occurring from infectious hosts to humans. As such, human populations are most at risk when they are in physical proximity to suitable habitats for reservoir populations, when numbers of infectious hosts are greatest. Because of the lags between improving habitat conditions and increasing infectious host abundance and spillover to humans, it should be possible to anticipate (forecast) where and when outbreaks will most likely occur. Most mammalian hosts are associated with specific habitat requirements, so identifying these habitats and the ecological drivers that impact population growth and the dispersal of viral hosts should be markers of the increased risk for disease outbreaks. These regions could be targeted for public health and medical education. This paper outlines the rationale for forecasting zoonotic outbreaks, and the information that needs to be clarified at various levels of biological organization to make the forecasting of orthohantaviruses successful. Major challenges reflect the transdisciplinary nature of forecasting zoonoses, with needs to better understand the implications of the data collected, how collections are designed, and how chosen methods impact the interpretation of results.

Two Decades of Wildlife Pathogen Surveillance: Case Study of Choclo orthohantavirus and Its Wild Reservoir Oligoryzomys costaricensis.

The Costa Rican pygmy rice rat (Oligoryzomys costaricensis) is the primary reservoir of Choclo orthohantavirus (CHOV), the causal agent of hantavirus disease, pulmonary syndrome, and fever in humans in Panama. Since the emergence of CHOV in early 2000, we have systematically sampled and archived rodents from >150 sites across Panama to establish a baseline understanding of the host and virus, producing a permanent archive of holistic specimens that we are now probing in greater detail. We summarize these collections and explore preliminary habitat/virus associations to guide future wildlife surveillance and public health efforts related to CHOV and other zoonotic pathogens. Host sequences of the mitochondrial cytochrome b gene form a single monophyletic clade in Panama, despite wide distribution across Panama. Seropositive samples were concentrated in the central region of western Panama, consistent with the ecology of this agricultural commensal and the higher incidence of CHOV in humans in that region. Hantavirus seroprevalence in the pygmy rice rat was >15% overall, with the highest prevalence in agricultural areas (21%) and the lowest prevalence in shrublands (11%). Host-pathogen distribution, transmission dynamics, genomic evolution, and habitat affinities can be derived from the preserved samples, which include frozen tissues, and now provide a foundation for expanded investigations of orthohantaviruses in Panama.

Temporal and Spatial Differences between Symptomatic and Asymptomatic Malaria Infections in the Chittagong Hill Districts, Bangladesh.

Mapping asymptomatic malaria infections, which contribute to the transmission reservoir, is important for elimination programs. This analysis compared the spatiotemporal patterns of symptomatic and asymptomatic Plasmodium falciparum malaria infections in a cohort study of ∼25,000 people living in a rural hypoendemic area of about 179 km2 in a small area of the Chittagong Hill Districts of Bangladesh. Asymptomatic infections were identified by active surveillance; symptomatic clinical cases presented for care. Infections were identified by a positive rapid diagnostic test and/or microscopy. Fifty-three subjects with asymptomatic P. falciparum infection were compared with 572 subjects with symptomatic P. falciparum between mid-October 2009 and mid-October 2012 with regard to seasonality, household location, and extent of spatial clustering. We found increased spatial clustering of symptomatic compared with asymptomatic infections, and the areas of high intensity were only sometimes overlapping. Symptomatic cases had a distinct seasonality, unlike asymptomatic infections, which were detected year-round. In a comparison of 42 symptomatic Plasmodium vivax and 777 symptomatic P. falciparum cases from mid-October 2009 through mid-March 2015, we found substantial spatial overlap in areas with high infection rates, but the areas with the greatest concentration of infection differed. Detection of both symptomatic P. falciparum and symptomatic P. vivax infections was greater during the May-to-October high season, although a greater proportion of P. falciparum cases occurred during the high season compared with P. vivax. These findings reinforce that passive malaria surveillance and treatment of symptomatic cases will not eliminate the asymptomatic reservoirs that occur distinctly in time and space.

Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples.

Ensembles of Species Distribution Models (SDMs) represent the geographic ranges of pathogen vectors by combining alternative analytical approaches and merging information on vector occurrences with more extensive environmental data. Biased collection data impact SDMs, regardless of the target species, but no studies have compared the differences in the distributions predicted by the ensemble models when different sampling frameworks are used for the same species. We compared Ensemble SDMs for two important Ixodid tick vectors, Amblyomma americanum and Ixodes scapularis in mainland Florida, USA, when inputs were either convenience samples of ticks, or collections obtained using the standard protocols promulgated by the U.S. Centers for Disease Control and Prevention. The Ensemble SDMs for the convenience samples and standard surveys showed only a slight agreement (Kappa = 0.060, A. americanum; 0.053, I. scapularis). Convenience sample SDMs indicated A. americanum and I. scapularis should be absent from nearly one third (34.5% and 30.9%, respectively) of the state where standard surveys predicted the highest likelihood of occurrence. Ensemble models from standard surveys predicted 81.4% and 72.5% (A. americanum and I. scapularis) of convenience sample sites. Omission errors by standard survey SDMs of the convenience collections were associated almost exclusively with either adjacency to at least one SDM, or errors in geocoding algorithms that failed to correctly locate geographic locations of convenience samples. These errors emphasize commonly overlooked needs to explicitly evaluate and improve data quality for arthropod survey data that are applied to spatial models.

Prevalence of Hantaviruses Harbored by Murid Rodents in Northwestern Ukraine and Discovery of a Novel Puumala Virus Strain.

In Europe, two species of hantaviruses, Puumala orthohantavirus (PUUV) and Dobrava orthohantavirus (DOBV), cause hemorrhagic fever with renal syndrome in humans. The rodent reservoirs for these viruses are common throughout Ukraine, and hence, the goal of this study was to identify the species and strains of hantaviruses circulating in this region. We conducted surveillance of small rodent populations in a rural region in northwestern Ukraine approximately 30 km from Poland. From the 424 small mammals captured, we identified nine species, of which the most abundant were Myodes glareolus, the bank vole (45%); Apodemus flavicollis, the yellow-necked mouse (29%); and Apodemus agrarius, the striped field mouse (14.6%) Using an indirect immunofluorescence assay, 15.7%, 20.5%, and 33.9% of the sera from M. glareolus, A. glareolus, and A. flavicollis were positive for hantaviral antibodies, respectively. Additionally, we detected antibodies to the hantaviral antigen in one Microtus arvalis, one Mus musculus, and one Sorex minutus. We screened the lung tissue for hantaviral RNA using next-generation sequencing and identified PUUV sequences in 25 small mammals, including 23 M. glareolus, 1 M. musculus, and 1 A. flavicollis, but we were unable to detect DOBV sequences in any of our A. agrarius specimens. The percent identity matrix and Bayesian phylogenetic analyses of the S-segment of PUUV from 14 M. glareolus lungs suggest the highest similarity (92-95% nucleotide or 99-100% amino acid) with the Latvian lineage. This new genetic information will contribute to future molecular surveillance of human cases in Ukraine.

Modelling distributions of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competition.

Florida faces the challenge of repeated introduction and autochthonous transmission of arboviruses transmitted by Aedes aegypti and Aedes albopictus. Empirically-based predictive models of the spatial distribution of these species would aid surveillance and vector control efforts. To predict the occurrence and abundance of these species, we fit a mixed-effects zero-inflated negative binomial regression to a mosquito surveillance dataset with records from more than 200,000 trap days, representative of 53% of the land area and ranging from 2004 to 2018 in Florida. We found an asymmetrical competitive interaction between adult populations of Aedes aegypti and Aedes albopictus for the sampled sites. Wind speed was negatively associated with the occurrence and abundance of both vectors. Our model predictions show high accuracy (72.9% to 94.5%) in validation tests leaving out a random 10% subset of sites and data since 2017, suggesting a potential for predicting the distribution of the two Aedes vectors.

Validating Species Distribution Models With Standardized Surveys for Ixodid Ticks in Mainland Florida.

Tick-borne pathogens are of growing concern. The U.S. Centers for Disease Control and Prevention (CDC) developed guidelines standardizing surveys of tick vectors to better monitor the changes in their occurrences. Unbiased surveillance data, from standardized surveys, are presumed critical to generate valid species distribution models (SDMs). We tested previously generated SDMs from standardized protocols for three medically important ticks [Amblyomma americanum (Linnaeus, Ixodida, Ixodidae), Ixodes scapularis (Say, Ixodida, Ixodidae), and Dermacentor variabilis (Say, Ixodida, Ixodidae)]. These previous models ruled out a quarter to half of the state as having these species, with consensus occurrence in about a quarter of the state. New surveys performed throughout 2019 on 250 transects at 43 sites indicated the rule-out functions were 100% accurate for I. scapularis and D. variabilis and 91.9% for A. americanum. As SDM concordance increased, the proportion of transects yielding ticks increased. Independent surveys of SDMs provide external validation-an aspect missing from many SDM studies.

Seroprevalence of Old World Hantaviruses and Crimean Congo Hemorrhagic Fever Viruses in Human Populations in Northwestern Ukraine.

In Ukraine, a retrospective review of clinical case reports by public health officials suggest that human cases of febrile illnesses associated with hemorrhage may be due to infections of Crimean-Congo hemorrhagic fever virus (CCHFV) and Old World hantaviruses. In a serosurvey of 966 healthy individuals in the Lviv Oblast, Ukraine, bordering Poland, we found that 1.6% showed cross-reactivity to hantaviral antigens by an immunofluorescence assay (IFA) and 1.7% of the study participants had antibodies cross-reactive to CCHFV by enzyme-linked immunosorbent assay (ELISA). Demographic variables and history of exposures obtained through questionnaires were assessed by logistic regression models for association with seroprevalence for both viruses with no significant risk factors found. Analysis of spatial distribution identified two clusters of samples positive for antibodies to both hantaviruses and CCHFV, which, however, were not statistically significant (p > 0.05). In general, the study results suggest that the population of the study area is exposed to hantaviruses and CCHFV. Further surveillance for respective pathogens in Ukraine is warranted and prospective surveillance of febrile patients with unidentified febrile illness.

The interplay of movement and spatiotemporal variation in transmission degrades pandemic control.

Successful public health regimes for COVID-19 push below unity long-term regional Rt -the average number of secondary cases caused by an infectious individual. We use a susceptible-infectious-recovered (SIR) model for two coupled populations to make the conceptual point that asynchronous, variable local control, together with movement between populations, elevates long-term regional Rt , and cumulative cases, and may even prevent disease eradication that is otherwise possible. For effective pandemic mitigation strategies, it is critical that models encompass both spatiotemporal heterogeneity in transmission and movement.

Cocirculation of Two Orthohantavirus Species in Small Mammals of the Northwestern Ukraine.

Rodent-borne hantaviruses have been reported in many of the countries surrounding Ukraine; however, to date we have no knowledge of the viral strains circulating in Ukraine within reservoirs such as the striped field mouse (Apodemus agrarius), the yellow-necked field mouse (Apodemus flavicollis), and the bank vole (Myodes glareolus). To determine the prevalence of hantaviruses in Ukraine, we captured 1,261 mammals, of which 1,109 were rodents, in 58 field sites within the province of Volyn in western Ukraine. Foci of the striped field mouse tended to occur in the eastern and southern parts of the province, whereas the bank vole were clustered in western and northern regions. The striped field mouse and bank vole had detectable serum antibodies to Puumala virus (PUUV) or Dobrava virus (DOBV) antigens at 7% or 2%, respectively, using an indirect immunofluorescence assay. Antibody prevalence among the bank vole males and females was equivalent, whereas for the striped field mouse, the prevalence among males was 5% versus 1% for females. In two bank vole specimens, we were able to detect partial nucleotide sequences that showed identity to PUUV. In summary, this study suggests that two human pathogens, PUUV and DOBV, cocirculate in the bank vole and the striped field mouse, respectively, in Ukraine. Future studies will focus on new rodent collections that will enable obtaining the complete genome sequences of the PUUV and DOBV strains circulating in Ukraine to provide guidance on the design of optimal molecular diagnostics that can enable insight into the potential contribution of hantaviruses to human disease in Ukraine.

Advancing the Science of Tick and Tick-Borne Disease Surveillance in the United States.

Globally, vector-borne diseases are an increasing public health burden; in the United States, tick-borne diseases have tripled in the last three years. The United States Centers for Disease Control and Prevention (CDC) recognizes the need for resilience to the increasing vector-borne disease burden and has called for increased partnerships and sustained networks to identify and respond to the most pressing challenges that face vector-borne disease management, including increased surveillance. To increase applied research, develop communities of practice, and enhance workforce development, the CDC has created five regional Centers of Excellence in Vector-borne Disease. These Centers are a partnership of public health agencies, vector control groups, academic institutions, and industries. This special issue on tick and tick-borne disease surveillance is a collection of research articles on multiple aspects of surveillance from authors that are affiliated with or funded by the CDC Centers of Excellence. This body of work illustrates a community-based system of research by which participants share common problems and use integrated methodologies to produce outputs and effect outcomes that benefit human, animal and environmental health.

A Survey of Tick-Borne Bacterial Pathogens in Florida.

Within the past three decades, new bacterial etiological agents of tick-borne disease have been discovered in the southeastern U.S., and the number of reported tick-borne pathogen infections has increased. In Florida, few systematic studies have been conducted to determine the presence of tick-borne bacterial pathogens. This investigation examined the distribution and presence of tick-borne bacterial pathogens in Florida. Ticks were collected by flagging at 41 field sites, spanning the climatic regions of mainland Florida. DNA was extracted individually from 1608 ticks and screened for Anaplasma, Borrelia, Ehrlichia and Rickettsia using conventional PCR and primers that amplified multiple species for each genus. PCR positive samples were Sanger sequenced. Four species of ticks were collected: Amblyomma americanum, Amblyomma maculatum, Dermacentor variabilis, and Ixodes scapularis. Within these ticks, six bacterial species were identified: Borrelia burgdorferi, Borrelia lonestari, Ehrlichia ewingii, Rickettsia amblyommatis, Rickettsia andeanae, Rickettsia parkeri, and Rickettsia endosymbionts. Pathogenic Borrelia, Ehrlichia, and Rickettsia species were all detected in the North and North-Central Florida counties; however, we found only moderate concordance between the distribution of ticks infected with pathogenic bacteria and human cases of tick-borne diseases in Florida. Given the diversity and numerous bacterial species detected in ticks in Florida, further investigations should be conducted to identify regional hotspots of tick-borne pathogens.

Standardized Ixodid Tick Survey in Mainland Florida.

A statewide survey of questing ixodid ticks in mainland Florida was developed consistent with U.S. CDC standards to maximize the amount of epidemiologic and environmental data gathered. Survey sites were stratified by climatic zones and proportional to recognized land cover categories. A total of 560 transects on 41 sites within the state were sampled repeatedly by flagging between 2015 and 2018. Four tick species were collected; Amblyomma americanum, Amblyomma maculatum, Ixodes scapularis and Dermacentor variabilis. All species were more commonly found in northern and central regions of the state than in southern and western regions. Adult I. scapularis were active from autumn through spring and complementary to adult A. americanum and D. variabilis. Standardized survey methods help reduce sampling biases and better characterize risk from the species surveyed. However, differences in the attractiveness of collection methods for different tick species makes cross-species comparisons a continuing challenge.

Modeling the Distribution of Medically Important Tick Species in Florida.

The lone star (Amblyomma americanum), black-legged (Ixodes scapularis) and American dog ticks (Dermacentor variabilis) are species of great public health importance as they are competent vectors of several notable pathogens. While the regional distributions of these species are well characterized, more localized distribution estimates are sparse. We used records of field collected ticks and an ensemble modeling approach to predict habitat suitability for each of these species in Florida. Environmental variables capturing climatic extremes were common contributors to habitat suitability. Most frequently, annual precipitation (Bio12), mean temperature of the driest quarter (Bio9), minimum temperature of the coldest month (Bio6), and mean Normalized Difference Vegetation Index (NDVI) were included in the final models for each species. Agreement between the modeling algorithms used in this study was high and indicated the distribution of suitable habitat for all three species was reduced at lower latitudes. These findings are important for raising awareness of the potential for tick-borne pathogens in Florida.

The human cost of global warming: Deadly landslides and their triggers (1995-2014).

Worldwide, landslides incur catastrophic and significant economic and human losses. Previous studies have characterized the patterns in landslides' fatalities, from all kinds of triggering causes, at a continental or global scale, but they were based on data from periods of <10 years. The research herein presented hypothesizes that climate change associated with extreme rainfall and population distribution is contributing to a higher number of deadly landslides worldwide. This study maps and identified deadly landslides in 128 countries and it encompasses their role, for a 20 years' period from January/1995 to December/2014, considered representative for establishing a relationship between landslides and their meteorological triggers. A database of georeferenced landslides, their date, and casualties' information, duly validated, was implemented. A hot spot analysis for the daily record of landslide locations was performed, as well as a percentile-based approach to evaluate the trend of extreme rainfall events for each occurrence. The relationship between casualty, population distribution, and rainfall was also evaluated. For 20 years, 3876 landslides caused a total of 163,658 deaths and 11,689 injuries globally. They occurred most frequently between June and December in the Northern Hemisphere, and between December and February in the Southern Hemisphere. A significant global rise in the number of deadly landslides and hotspots across the studied period was observed. Analysis of daily rainfall confirmed that more than half of the events were in areas exposed to the risk of extreme rainfall. The relationships established between extreme rainfall, population distribution, seasonality, and landslides provide a useful basis for efforts to model the adverse impacts of extreme rainfall due to climate change and human activities and thus contribute towards a more resilient society.

A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States.

Despite the usefulness of artificial neural networks (ANNs) in the study of various complex problems, ANNs have not been applied for modeling the geographic distribution of tuberculosis (TB) in the US. Likewise, ecological level researches on TB incidence rate at the national level are inadequate for epidemiologic inferences. We collected 278 exploratory variables including environmental and a broad range of socio-economic features for modeling the disease across the continental US. The spatial pattern of the disease distribution was statistically evaluated using the global Moran's I, Getis⁻Ord General G, and local Gi* statistics. Next, we investigated the applicability of multilayer perceptron (MLP) ANN for predicting the disease incidence. To avoid overfitting, L1 regularization was used before developing the models. Predictive performance of the MLP was compared with linear regression for test dataset using root mean square error, mean absolute error, and correlations between model output and ground truth. Results of clustering analysis showed that there is a significant spatial clustering of smoothed TB incidence rate (p < 0.05) and the hotspots were mainly located in the southern and southeastern parts of the country. Among the developed models, single hidden layer MLP had the best test accuracy. Sensitivity analysis of the MLP model showed that immigrant population (proportion), underserved segments of the population, and minimum temperature were among the factors with the strongest contributions. The findings of this study can provide useful insight to health authorities on prioritizing resource allocation to risk-prone areas.

Estimating the Geographic Distribution of Host-Seeking Adult Amblyomma americanum (Acari: Ixodidae) in Florida.

The lone star tick, Amblyomma americanum, is the most commonly reported human-biting tick in the southeastern United States and is a vector for several human and livestock pathogens. Although it is endemic to Florida, little is known about the ecological preferences and current spatial distribution within the state. Using occurrence records of adult A. americanum collected between August 2015 and September 2016, a logistic regression model was used to estimate environmental associations, as well as to predict the distribution of the tick at a one hectare resolution. Occurrence of adult lone star ticks was associated with land cover and bioclimatic variables, namely the presence of forested areas and precipitation seasonality. The estimated spatial distribution indicated that central and northern regions show greater suitability than the southern half of the state. Furthermore, areas predicted to be suitable for the species decreases from north to south with very little area deemed suitable in the far southern reaches of the state. High heterogeneity in the distribution of suitable habitat has implications for the distribution of tick-borne disease cases in the state. The subcounty resolution of the estimated distribution is an improvement over distributions currently published and may better inform the public and state or federal agencies of potential risk of exposure to A. americanum and its associated pathogens.

Unexplored Opportunities: Use of Climate- and Weather-Driven Early Warning Systems to Reduce the Burden of Infectious Diseases.

PURPOSE OF REVIEW: Weather and climate influence multiple aspects of infectious disease ecology. Creating and applying early warning systems based on temperature, precipitation, and other environmental data can identify where and when outbreaks of climate-sensitive infectious diseases could occur and can be used by decision makers to allocate resources. Whether an outbreak actually occurs depends heavily on other social, political, and institutional factors. RECENT FINDINGS: Improving the timing and confidence of seasonal climate forecasting, coupled with knowledge of exposure-response relationships, can identify prior conditions conducive to disease outbreaks weeks to months in advance of outbreaks. This information could then be used by public health professionals to improve surveillance in the most likely areas for threats. Early warning systems are well established for drought and famine. And while weather- and climate-driven early warning systems for certain diseases, such as dengue fever and cholera, are employed in some regions, this area of research is underdeveloped. Early warning systems based on temperature, precipitation, and other environmental data provide an opportunity for early detection leading to early action and response to potential pathogen threats, thereby reducing the burden of disease when compared with passive health indicator-based surveillance systems.

Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran.

The distribution and abundance of Phlebotomus papatasi, the primary vector of zoonotic cutaneous leishmaniasis in most semi-/arid countries, is a major public health challenge. This study compares several approaches to model the spatial distribution of the species in an endemic region of the disease in Golestan province, northeast of Iran. The intent is to assist decision makers for targeted interventions. We developed a geo-database of the collected Phlebotominae sand flies from different parts of the study region. Sticky paper traps coated with castor oil were used to collect sand flies. In 44 out of 142 sampling sites, Ph. papatasi was present. We also gathered and prepared data on related environmental factors including topography, weather variables, distance to main rivers and remotely sensed data such as normalized difference vegetation cover and land surface temperature (LST) in a GIS framework. Applicability of three classifiers: (vanilla) logistic regression, random forest and support vector machine (SVM) were compared for predicting presence/absence of the vector. Predictive performances were compared using an independent dataset to generate area under the ROC curve (AUC) and Kappa statistics. All three models successfully predicted the presence/absence of the vector, however, the SVM classifier (Accuracy = 0.906, AUC = 0.974, Kappa = 0.876) outperformed the other classifiers on predicting accuracy. Moreover, this classifier was the most sensitive (85%), and the most specific (93%) model. Sensitivity analysis of the most accurate model (i.e. SVM) revealed that slope, nighttime LST in October and mean temperature of the wettest quarter were among the most important predictors. The findings suggest that machine learning techniques, especially the SVM classifier, when coupled with GIS and remote sensing data can be a useful and cost-effective way for identifying habitat suitability of the species.

A 24-year exploratory spatial data analysis of Lyme disease incidence rate in Connecticut, USA.

Despite efforts to control Lyme disease in Connecticut, USA, it remains endemic in many towns, posing a heavy burden. We examined changes in the spatial distribution of significant spatial clusters of Lyme disease incidence rates at the town level from 1991 to 2014 as an approach for targeted interventions. Lyme disease data were grouped into four discrete time periods and incidence rates were smoothed with Empirical Bayes estimation in GeoDa. Local clustering was measured using a local indicator of spatial autocorrelation (LISA). Elliptic spatial scan statistics (SSS) in different shapes and directions were also performed in SaTScan. The accuracy of these two cluster detection methods was assessed and compared for sensitivity, specificity, and overall accuracy. There was significant clustering during each period and significant clusters persisted predominantly in western and eastern parts of the state. Generally, the SSS method was more sensitive, while LISA was more specific with higher overall accuracy in identifying clusters. Even though the location of clusters changed over time, some towns were persistently (across all four periods) identified as clusters in LISA and their neighbouring towns (three of four periods) in SSS suggesting these regions should be prioritized for targeted interventions.