A comparison of least squares regression and geographically weighted regression modeling of West Nile virus risk based on environmental parameters

Impact of climate change on the global circulation of West Nile virus and adaptation responses: a scoping review

BACKGROUND

West Nile virus (WNV), the most widely distributed flavivirus causing encephalitis globally, is a vector-borne pathogen of global importance. The changing climate is poised to reshape the landscape of various infectious diseases, particularly vector-borne ones like WNV. Understanding the anticipated geographical and range shifts in disease transmission due to climate change, alongside effective adaptation strategies, is critical for mitigating future public health impacts. This scoping review aims to consolidate evidence on the impact of climate change on WNV and to identify a spectrum of applicable adaptation strategies.

MAIN BODY

We systematically analyzed research articles from PubMed, Web of Science, Scopus, and EBSCOhost. Our criteria included English-language research articles published between 2007 and 2023, focusing on the impacts of climate change on WNV and related adaptation strategies. We extracted data concerning study objectives, populations, geographical focus, and specific findings. Literature was categorized into two primary themes: 1) climate-WNV associations, and 2) climate change impacts on WNV transmission, providing a clear understanding. Out of 2168 articles reviewed, 120 met our criteria. Most evidence originated from North America (59.2%) and Europe (28.3%), with a primary focus on human cases (31.7%). Studies on climate-WNV correlations (n = 83) highlighted temperature (67.5%) as a pivotal climate factor. In the analysis of climate change impacts on WNV (n = 37), most evidence suggested that climate change may affect the transmission and distribution of WNV, with the extent of the impact depending on local and regional conditions. Although few studies directly addressed the implementation of adaptation strategies for climate-induced disease transmission, the proposed strategies (n = 49) fell into six categories: 1) surveillance and monitoring (38.8%), 2) predictive modeling (18.4%), 3) cross-disciplinary collaboration (16.3%), 4) environmental management (12.2%), 5) public education (8.2%), and 6) health system readiness (6.1%). Additionally, we developed an accessible online platform to summarize the evidence on climate change impacts on WNV transmission ( https://2xzl2o-neaop.shinyapps.io/WNVScopingReview/ ).

CONCLUSIONS

This review reveals that climate change may affect the transmission and distribution of WNV, but the literature reflects only a small share of the global WNV dynamics. There is an urgent need for adaptive responses to anticipate and respond to the climate-driven spread of WNV. Nevertheless, studies focusing on these adaptation responses are sparse compared to those examining the impacts of climate change. Further research on the impacts of climate change and adaptation strategies for vector-borne diseases, along with more comprehensive evidence synthesis, is needed to inform effective policy responses tailored to local contexts.

Spatial distribution of mortality from colorectal cancer in the southern region of Brazil

Colorectal cancer (CRC) is the leading cause of death due to cancer worldwide. In Brazil, it is the second most frequent cancer in men and women, with a mortality reaching 9.4% of those diagnosed. The aim of this study was to analyze the spatial heterogeneity of CRC deaths among municipalities in south Brazil, from 2015 to 2019, in different age groups (50-59 years, 60-69 years, 70-79 years, and 80 years old or more) and identify the associated variables. Global Spatial Autocorrelation (Moran's I) and Local Spatial Autocorrelation (LISA) analyses were used to evaluate the spatial correlation between municipalities and CRC mortality. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) were applied to evaluate global and local correlations between CRC deaths, sociodemographic, and coverage of health care services. For all age groups, our results found areas with high CRC rates surrounded by areas with similarly high rates mainly in the Rio Grande do Sul state. Even as factors associated with CRC mortality varied according to age group, our results suggested that improved access to specialized health centers, the presence of family health strategy teams, and higher rates of colonoscopies are protective factors against colorectal cancer mortality in southern Brazil.

A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings

Dengue fever is an acute mosquito-borne disease that mostly spreads within urban or semi-urban areas in warm climate zones. The dengue-related risk map is one of the most practical tools for executing effective control policies, breaking the transmission chain, and preventing disease outbreaks. Mapping risk at a small scale, such as at an urban level, can demonstrate the spatial heterogeneities in complicated built environments. This review aims to summarize state-of-the-art modeling methods and influential factors in mapping dengue fever risk in urban settings. Data were manually extracted from five major academic search databases following a set of querying and selection criteria, and a total of 28 studies were analyzed. Twenty of the selected papers investigated the spatial pattern of dengue risk by epidemic data, whereas the remaining eight papers developed an entomological risk map as a proxy for potential dengue burden in cities or agglomerated urban regions. The key findings included: (1) Big data sources and emerging data-mining techniques are innovatively employed for detecting hot spots of dengue-related burden in the urban context; (2) Bayesian approaches and machine learning algorithms have become more popular as spatial modeling tools for predicting the distribution of dengue incidence and mosquito presence; (3) Climatic and built environmental variables are the most common factors in making predictions, though the effects of these factors vary with the mosquito species; (4) Socio-economic data may be a better representation of the huge heterogeneity of risk or vulnerability spatial distribution on an urban scale. In conclusion, for spatially assessing dengue-related risk in an urban context, data availability and the purpose for mapping determine the analytical approaches and modeling methods used. To enhance the reliabilities of predictive models, sufficient data about dengue serotyping, socio-economic status, and spatial connectivity may be more important for mapping dengue-related risk in urban settings for future studies.

Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore

Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5–1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) within sub-zones varied differently, especially those in the western and north-eastern regions of Singapore. Areas with both high residential population density and high levels of urbanisation are potential risk factors for COVID-19 transmission. These findings provide further insights for directing appropriate resources to enhance infection prevention and control strategies to contain COVID-19 transmission.

GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe

The COVID-19 epidemic has emerged as one of the most severe public health crises worldwide, especially in Europe. Until early July 2021, reported infected cases exceeded 180 million, with almost 4 million associated deaths worldwide, almost a third of which are in continental Europe. We analyzed the spatio-temporal distribution of the disease incidence and mortality rates considering specific periods in this continent. Further, we applied Global Moran's I to examine the spatio-temporal distribution patterns of COVID-19 incidence rates and Getis-Ord Gi* hotspot analysis to represent high-risk areas of the disease. Additionally, we compiled a set of 40 demographic, socioeconomic, environmental, transportation, health, and behavioral indicators as potential explanatory variables to investigate the spatial variations of COVID-19 cumulative incidence rates (CIRs). Ordinary Least Squares (OLS), Spatial Lag model (SLM), Spatial Error Model (SLM), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) regression models were implemented to examine the spatial dependence and non-stationary relationships. Based on our findings, the spatio-temporal distribution pattern of COVID-19 CIRs was highly clustered and the most high-risk clusters of the disease were situated in central and western Europe. Moreover, poverty and the elderly population were selected as the most influential variables due to their significant relationship with COVID-19 CIRs. Considering the non-stationary relationship between variables, MGWR could describe almost 69% of COVID-19 CIRs variations in Europe. Since this spatio-temporal research is conducted on a continental scale, spatial information obtained from the models could provide general insights to authorities for further targeted policies.

Spatial differentiation and determinants of COVID-19 in Indonesia

Background

The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors.

Methods

The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020.

Results

Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali.

Conclusion

Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.

Determining the effects of socioeconomic and environmental determinants on chronic obstructive pulmonary disease (COPD) mortality using geographically and temporally weighted regression model across Xi'an during 2014-2016

Numerous methods have been implemented to evaluate the relationship between environmental factors and respiratory mortality. However, the previous epidemiological studies seldom considered the spatial and temporal variation of the independent variables. The present study aims to detect the relations between respiratory mortality and related affecting factors across Xi'an during 2014-2016 based on a novel geographically and temporally weighted regression model (GTWR). Meanwhile, the ordinary least square (OLS) and the geographically weighted regression (GWR) models were developed for cross-comparison. Additionally, the spatial autocorrelation and Hot Spot analysis methods were conducted to detect the spatiotemporal dynamic of respiratory mortality. Some important outcomes were obtained. Socioeconomic and environmental determinants represented significant effects on respiratory diseases. The respiratory mortality exhibited an obvious spatial correlation feature, and the respiratory diseases tend to occur in winter and rural areas of the study area. The GTWR model outperformed OLS and GWR for determining the relations between respiratory mortality and socioeconomic as well as environmental determinants. The influence degree of anthropic factors on COPD mortality was higher than natural factors, and the effects of independent variables on COPD varied timely and locally. The results can supply a scientific basis for respiratory disease controlling and health facilities planning.

Exploring the socio-economic and environmental components of infectious diseases using multivariate geovisualization: West Nile Virus

Background

This study postulates that underlying environmental conditions and a susceptible population's socio-economic status should be explored simultaneously to adequately understand a vector borne disease infection risk. Here we focus on West Nile Virus (WNV), a mosquito borne pathogen, as a case study for spatial data visualization of environmental characteristics of a vector's habitat alongside human demographic composition for understanding potential public health risks of infectious disease. Multiple efforts have attempted to predict WNV environmental risk, while others have documented factors related to human vulnerability to the disease. However, analytical modeling that combines the two is difficult due to the number of potential explanatory variables, varying spatial resolutions of available data, and differing research questions that drove the initial data collection. We propose that the use of geovisualization may provide a glimpse into the large number of potential variables influencing the disease and help distill them into a smaller number that might reveal hidden and unknown patterns. This geovisual look at the data might then guide development of analytical models that can combine environmental and socio-economic data.

Methods

Geovisualization was used to integrate an environmental model of the disease vector's habitat alongside human risk factors derived from socio-economic variables. County level WNV incidence rates from California, USA, were used to define a geographically constrained study area where environmental and socio-economic data were extracted from 1,133 census tracts. A previously developed mosquito habitat model that was significantly related to WNV infected dead birds was used to describe the environmental components of the study area. Self-organizing maps found 49 clusters, each of which contained census tracts that were more similar to each other in terms of WNV environmental and socio-economic data. Parallel coordinate plots permitted visualization of each cluster's data, uncovering patterns that allowed final census tract mapping exposing complex spatial patterns contained within the clusters.

Results

Our results suggest that simultaneously visualizing environmental and socio-economic data supports a fuller understanding of the underlying spatial processes for risks to vector-borne disease. Unexpected patterns were revealed in our study that would be useful for developing future multilevel analytical models. For example, when the cluster that contained census tracts with the highest median age was examined, it was determined that those census tracts only contained moderate mosquito habitat risk. Likewise, the cluster that contained census tracts with the highest mosquito habitat risk had populations with moderate median age. Finally, the cluster that contained census tracts with the highest WNV human incidence rates had unexpectedly low mosquito habitat risk.

Spatio-temporal analysis of association between incidence of malaria and environmental predictors of malaria transmission in Nigeria

Malaria still poses a significant threat in Nigeria despite the various efforts to abate its transmission. Certain environmental factors have been implicated to increase the risk of malaria in Nigeria and other affected countries. The study aimed to evaluate the spatial and temporal association between the incidence of malaria and some environmental risk factors in Nigeria. The study used malaria incidence and environmental risk factors data emanating from 2015 Nigeria Malaria Indicator Survey accessed from the Demographic and Health Survey database. A total of 333 and 326 clusters throughout the country were used for malaria incidence study and environmental variables respectively. The spatial autocorrelation of malaria incidence and hotspot analysis was determined by the Moran’s diagram and local Moran’s I index, respectively. The relationships between the malaria incidence and the ecological predictors of transmission were analysed in all the six geopolitical zones of Nigeria from 2000–2015 using ordinary least square (OLS), spatial lag model (SLM), and spatial error model (SEM). Annual rainfall, precipitation and proximity to water showed significant positive relationship with the incidence rate of malaria in the OLS model (P < 0.01), whereas aridity was negatively related to malaria incidence (P < 0.001) in the same model. The rate of incidence of malaria increased significantly with increase in temperature, aridity, rainfall and proximity to water in the SEM whereas only temperature and proximity to water have significant positive effect on malaria incidence in the SLM. The modelling of the ecological predictors of malaria transmission and spatial maps provided in this study could aid in developing framework to mitigate malaria and identify its hotspots for urgent intervention in the endemic regions.

Models and Surveillance Systems to Detect and Predict West Nile Virus Outbreaks

Over the past 20 yr, many models have been developed to predict risk for West Nile virus (WNV; Flaviviridae: Flavivirus) disease in the human population. These models have aided our understanding of the meteorological and land-use variables that drive spatial and temporal patterns of human disease risk. During the same period, electronic data systems have been adopted by surveillance programs across much of the United States, including a growing interest in integrated data services that preserve the autonomy and attribution of credit to originating agencies but facilitate data sharing, analysis, and visualization at local, state, and national scales. At present, nearly all predictive models have been limited to the scientific literature, with few having been implemented for use by public-health and vector-control decision makers. The current article considers the development of models for spatial patterns, early warning, and early detection of WNV over the last 20 yr and considers some possible paths toward increasing the utility of these models for guiding interventions.

Geostatistical predictive modeling for asthma and chronic obstructive pulmonary disease using socioeconomic and environmental determinants

The spatial distribution of the prevalence of asthma and chronic obstructive pulmonary disease (COPD) remains under the influence of a wide array of environmental, climatic, and socioeconomic determinants. However, a large proportion of these influences remain unexplained. In completion, this study examined the spatial associations between asthma/COPD morbidity and their determinants using ordinary least squares (OLS) and geographically weighted regressions (GWR). Inpatient records collected from the secondary and tertiary care hospitals in Kandy from 2010 to 2014 were considered as the dependent variable. Potential risk factors (explanatory variables) were identified in four distinguished classes: 1) meteorological factors, (2) direct and indirect factors of air pollution, (3) socioeconomic factors, and (4) characteristics of the physical environment. All possible combinations of candidate explanatory variables were evaluated through an exploratory regression. A comparison between the regression models was also explored. The best OLS regression models revealed about 55% of asthma variation and 62% of COPD variation while GWR models yielded 78% and 74% of the variation of asthma and COPD occurrences respectively. Relative humidity, proximity to roads (0-200 m), road density, use of firewood as a source of fuel, and elevation play a vital role in predicting morbidity from asthma and COPD. Both local and global regression models are important in assessing spatial relationships of asthma and COPD. However, the local models exhibit a better prediction capability for assessing non-stationary relationships of asthma and COPD than global models. The geostatistical aspects used in this study may also provide insights for evaluating heterogeneous environmental risk factors in other epidemiological studies across different spatial settings.

Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams-Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior

BACKGROUND

West Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus.

OBJECTIVE

This study aimed at exploring the potential predictive power of West Nile virus-related Web searches.

METHODS

Different novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis.

RESULTS

In the regression analysis, an association between Web searches and "real-world" epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus-related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus-related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus-related Wikipedia pages.

CONCLUSIONS

Our study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings.

Spatiotemporal Bayesian modeling of West Nile virus: Identifying risk of infection in mosquitoes with local-scale predictors

Monitoring and control of West Nile virus (WNV) presents a challenge to state and local vector control managers. Models of mosquito presence and viral incidence have revealed that variations in mosquito autecology and land use patterns introduce unique dynamics of disease at the scale of a county or city, and that effective prediction requires locally parameterized models. We applied Bayesian spatiotemporal modeling to West Nile surveillance data from 49 mosquito trap sites in Nassau County, New York, from 2001 to 2015 and evaluated environmental and sociological predictors of West Nile virus incidence in Culex pipiens-restuans. A Bayesian spike-and-slab variable selection algorithm was used to help select influential independent variables. This method can be used to identify locally-important predictors. The best model predicted West Nile positives well, with an Area Under Curve (AUC) of 0.83 on holdout data. The temporal trend was nonlinear and increased throughout the year. The spatial component identified increased West Nile incidence odds in the northwestern portion of the county, with lower odds in wetlands on the south shore of Long Island. High Normalized Difference Vegetation Index (NDVI) areas, wetlands, and areas of high urban development had negative associations with WNV incidence. In this study we demonstrate a method for improving spatiotemporal models of West Nile virus incidence for decision making at the county and community scale, which empowers disease and vector control organizations to prioritize and evaluate prevention efforts.