Multiple Sclerosis Incidence Associated with the Soil Lead and Arsenic Concentrations in Taiwan

Background

Few studies in the world have assessed the incidence of multiple sclerosis (MS) with soil heavy metal concentrations. We explored the association of soil heavy metal factors and the MS incidence in Taiwan.

Methods

There were 1240 new MS cases from the National Health Insurance Research Database and were verified with serious disabling disease certificates, 1997–2008. Soil heavy metal factors records included arsenic, mercury, cadmium, chromium, copper, nickel, lead and zinc in Taiwan from 1986 to 2002. Spatial regression was used to reveal the association of soil heavy metals and age- and gender-standardized incidence ratios for townships by controlling sunlight exposure hours, smoking prevalence and spatial autocorrelation.

Results

The lead (Pb) concentration in the soil positively correlated with the township incidence; on the other hand, the arsenic (As) concentration in soil negatively correlated with the township incidence and when found together controlled each other. The positive correlation of lead (Pb) predominated in males, whereas the negative correlation of arsenic (As) in soil predominated in females.

Conclusions

We conclude that exposure to lead (Pb) in soil positive associated with incidence of MS in Taiwan, especially in males. Exposure to arsenic (As) in soil negative associated with MS in Taiwan, especially in females.     

Socio-Demographic Predictors and Distribution of Pulmonary Tuberculosis (TB) in Xinjiang,China: A Spatial Analysis

Objectives

Xinjiang is one of the high TB burden provinces of China. A spatial analysis was conducted using geographical information system (GIS) technology to improve the understanding of geographic variation of the pulmonary TB occurrence in Xinjiang, its predictors, and to search for targeted interventions.

Methods

Numbers of reported pulmonary TB cases were collected at county/district level from TB surveillance system database. Population data were extracted from Xinjiang Statistical Yearbook (2006~2014). Spatial autocorrelation (or dependency) was assessed using global Moran’s I statistic. Anselin’s local Moran’s I and local Getis-Ord statistics were used to detect local spatial clusters. Ordinary least squares (OLS) regression, spatial lag model (SLM) and geographically-weighted regression (GWR) models were used to explore the socio-demographic predictors of pulmonary TB incidence from global and local perspectives. SPSS17.0, ArcGIS10.2.2, and GeoDA software were used for data analysis.

Results

Incidence of sputum smear positive (SS+) TB and new SS+TB showed a declining trend from 2005 to 2013. Pulmonary TB incidence showed a declining trend from 2005 to 2010 and a rising trend since 2011 mainly caused by the rising trend of sputum smear negative (SS-) TB incidence (p<0.0001). Spatial autocorrelation analysis showed the presence of positive spatial autocorrelation for pulmonary TB incidence, SS+TB incidence and SS-TB incidence from 2005 to 2013 (P <0.0001). The Anselin’s Local Moran’s I identified the “hotspots” which were consistently located in the southwest regions composed of 20 to 28 districts, and the “coldspots” which were consistently located in the north central regions consisting of 21 to 27 districts. Analysis with the Getis-Ord Gi* statistic expanded the scope of “hotspots” and “coldspots” with different intensity; 30 county/districts clustered as “hotspots”, while 47 county/districts clustered as “coldspots”. OLS regression model included the “proportion of minorities” and the “per capita GDP” as explanatory variables that explained 64% the variation in pulmonary TB incidence (adjR² = 0.64). The SLM model improved the fit of the OLS model with a decrease in AIC value from 883 to 864, suggesting “proportion of minorities” to be the only statistically significant predictor. GWR model also improved the fitness of regression (adj R² = 0.68, AIC = 871), which revealed that “proportion of minorities” was a strong predictor in the south central regions while “per capita GDP” was a strong predictor for the southwest regions.

Conclusion

The SS+TB incidence of Xinjiang had a decreasing trend during 2005–2013, but it still remained higher than the national average in China. Spatial analysis showed significant spatial autocorrelation in pulmonary TB incidence. Cluster analysis detected two clusters—the “hotspots”, which were consistently located in the southwest regions, and the “coldspots”, which were consistently located in the north central regions. The exploration of socio-demographic predictors identified the “proportion of minorities” and the “per capita GDP” as predictors and may help to guide TB control programs and targeting intervention.     

Space-Time Clustering Characteristics of Tuberculosis in China, 2005-2011

Objectives

China is one of the 22 tuberculosis (TB) high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics.

Methods

Spatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+) notification rate and total TB notification rate. Global Moran’s I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011.

Results

Two indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01). Global Moran’s I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01). The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces.

Conclusion

The total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized.     

A Novel Tool Improves Existing Estimates of Recent Tuberculosis Transmission in Settings of Sparse Data Collection

In any setting, a proportion of incident active tuberculosis (TB) reflects recent transmission (“recent transmission proportion”), whereas the remainder represents reactivation. Appropriately estimating the recent transmission proportion has important implications for local TB control, but existing approaches have known biases, especially where data are incomplete. We constructed a stochastic individual-based model of a TB epidemic and designed a set of simulations (derivation set) to develop two regression-based tools for estimating the recent transmission proportion from five inputs: underlying TB incidence, sampling coverage, study duration, clustered proportion of observed cases, and proportion of observed clusters in the sample. We tested these tools on a set of unrelated simulations (validation set), and compared their performance against that of the traditional ‘n-1’ approach. In the validation set, the regression tools reduced the absolute estimation bias (difference between estimated and true recent transmission proportion) in the ‘n-1’ technique by a median [interquartile range] of 60% [9%, 82%] and 69% [30%, 87%]. The bias in the ‘n-1’ model was highly sensitive to underlying levels of study coverage and duration, and substantially underestimated the recent transmission proportion in settings of incomplete data coverage. By contrast, the regression models’ performance was more consistent across different epidemiological settings and study characteristics. We provide one of these regression models as a user-friendly, web-based tool. Novel tools can improve our ability to estimate the recent TB transmission proportion from data that are observable (or estimable) by public health practitioners with limited available molecular data.     

Tuberculosis in Healthcare Workers: A Matched Cohort Study in Taiwan

Background

Proportional mortality ratio data indicate that healthcare workers (HCWs) have an elevated tuberculosis (TB) mortality. Whether this is caused by an increased TB incidence, a worse TB treatment outcome, or a combination of effects, remains unclear. To elucidate the hazard components of occupational TB, we assessed TB incidence and TB treatment outcome among HCWs in Taiwan.

Methods

We compared the incidence of active TB among HCWs at a major medical center in Taiwan with that of Taiwan general population in 2004–2012. We also compared the TB treatment outcome of HCWs with that of age/sex-matched non-HCW patients treated at the same hospital, as well as that of nationally registered TB patients.

Results

The standardized TB incidence ratio of the HCWs was 1.9 (95% confidence interval [CI]: 1.2–2.9), compared with the general population. HCWs with pulmonary TB (n = 30) were less likely to have underlying diseases, delay in diagnosis, delay in treatment, or side effects of treatment, compared with age/sex-matched non-HCW TB patients (n = 120) (all Ps<0.05). The TB treatment outcome of HCWs was significantly better than that of non-HCW patients (TB-related mortality: 0.0% vs. 5.8%, P = 0.008, Mantel-Haenszel test). The standardized TB-related mortality rate was 1.08% [95% CI: 0.96% - 1.20%] for all of the nationally registered TB patients in Taiwan.

Conclusions

HCWs are at increased risk of active TB, compared with general population. To mitigate this occupational hazard, more efforts need to be directed towards the prevention of nosocomial TB transmission. Healthy worker effect, more rapid diagnosis, and less delay in treatment contribute to a lower TB-related mortality in HCWs.     

Spatiotemporal Clustering of Mycobacterium tuberculosis Complex Genotypes in Florida: Genetic Diversity Segregated by Country of Birth

BackgroundTuberculosis (TB) is caused by members of the Mycobacterium tuberculosis complex (MTBC). Although the MTBC is highly clonal, between-strain genetic diversity has been observed. In low TB incidence settings, immigration may facilitate the importation of MTBC strains with a potential to complicate TB control efforts.MethodsWe investigated the genetic diversity and spatiotemporal clustering of 2,510 MTBC strains isolated in Florida, United States, between 2009 and 2013 and genotyped using spoligotyping and 24-locus MIRU-VNTR. We mapped the genetic diversity to the centroid of patient residential zip codes using a geographic information system (GIS). We assessed transmission dynamics and the influence of immigration on genotype clustering using space-time permutation models adjusted for foreign-born population density and county-level HIV risk and multinomial models stratified by country of birth and timing of immigration in SaTScan.Conclusions/SignificanceAlmost five percent of TB cases reported in Florida during 2009–2013 were potentially due to recent transmission. Improvements to TB screening practices among the prison population and recent immigrants are likely to impact TB control. Due to the monomorphic nature of available markers, whole genome sequencing is needed to conclusively delineate recent transmission events between U.S. and foreign-born persons.     

A Geographic Analysis about the Spatiotemporal Pattern of Breast Cancer in Hangzhou from 2008 to 2012

Background

Breast cancer (BC) is the most common female malignant tumor. Previous studies have suggested a big incidence disparity among different cities in China. The present work selected a typical city, Hangzhou, to study BC incidence disparity within the city.

Methods

Totally, 8784 female breast cancer cases were obtained from the Hangzhou Center for Disease Control and Prevention during the period 2008–2012. Analysis of Variance and Poisson Regression were the statistical tools implemented to compare incidence disparity in the space-time domain (reference group: township residents during 2008, area: subdistrict, town, and township, time frame: 2008–2012), space-time scan statistics was employed to detect significant spatiotemporal clusters of BC compared to the null hypothesis that the probability of cases diagnosed at a particular location was equal to the probability of cases diagnosed in the whole study area. Geographical Information System (GIS) was used to generate BC spatial distribution and cluster maps at the township level.

Results

The subdistrict populations were found to have the highest and most stable BC incidence. Although town and township populations had a relatively low incidence, it displayed a significant increasing trend from 2008 to 2012. The BC incidence distribution was spatially heterogeneous and clustered with a trend-surface from the southwest low area to the northeast high area. High clusters were located in the northeastern Hangzhou area, whereas low clusters were observed in the southwestern area during the time considered.

Conclusions

Better healthcare service and lifestyle changes may be responsible for the increasing BC incidence observed in towns and townships. One high incidence cluster (Linping subdistrict) and two low incidence clusters (middle Hangzhou) were detected. The low clusters may be attributable mainly to developmental level disparity, whereas the high cluster could be associated with other risk factors, such as environmental pollution.     

Detection of Tuberculosis Infection Hotspots Using Activity Spaces Based Spatial Approach in an Urban Tokyo,from 2003 to 2011

Background

Identifying ongoing tuberculosis infection sites is crucial for breaking chains of transmission in tuberculosis-prevalent urban areas. Previous studies have pointed out that detection of local accumulation of tuberculosis patients based on their residential addresses may be limited by a lack of matching between residences and tuberculosis infection sites. This study aimed to identify possible tuberculosis hotspots using TB genotype clustering statuses and a concept of “activity space”, a place where patients spend most of their waking hours. We further compared the spatial distribution by different residential statuses and describe urban environmental features of the detected hotspots.

Methods

Culture-positive tuberculosis patients notified to Shinjuku city from 2003 to 2011 were enrolled in this case-based cross-sectional study, and their demographic and clinical information, TB genotype clustering statuses, and activity space were collected. Spatial statistics (Global Moran’s I and Getis-Ord Gi* statistics) identified significant hotspots in 152 census tracts, and urban environmental features and tuberculosis patients’ characteristics in these hotspots were assessed.

Results

Of the enrolled 643 culture-positive tuberculosis patients, 416 (64.2%) were general inhabitants, 42 (6.5%) were foreign-born people, and 184 were homeless people (28.6%). The percentage of overall genotype clustering was 43.7%. Genotype-clustered general inhabitants and homeless people formed significant hotspots around a major railway station, whereas the non-clustered general inhabitants formed no hotspots. This suggested the detected hotspots of activity spaces may reflect ongoing tuberculosis transmission sites and were characterized by smaller residential floor size and a higher proportion of non-working households.

Conclusions

Activity space-based spatial analysis suggested possible TB transmission sites around the major railway station and it can assist in further comprehension of TB transmission dynamics in an urban setting in Japan.     

A Probabilistic Spatial Dengue Fever Risk Assessment by a Threshold-Based-Quantile Regression Method

Understanding the spatial characteristics of dengue fever (DF) incidences is crucial for governmental agencies to implement effective disease control strategies. We investigated the associations between environmental and socioeconomic factors and DF geographic distribution, are proposed a probabilistic risk assessment approach that uses threshold-based quantile regression to identify the significant risk factors for DF transmission and estimate the spatial distribution of DF risk regarding full probability distributions. To interpret risk, return period was also included to characterize the frequency pattern of DF geographic occurrences. The study area included old Kaohsiung City and Fongshan District, two areas in Taiwan that have been affected by severe DF infections in recent decades. Results indicated that water-related facilities, including canals and ditches, and various types of residential area, as well as the interactions between them, were significant factors that elevated DF risk. By contrast, the increase of per capita income and its associated interactions with residential areas mitigated the DF risk in the study area. Nonlinear associations between these factors and DF risk were present in various quantiles, implying that water-related factors characterized the underlying spatial patterns of DF, and high-density residential areas indicated the potential for high DF incidence (e.g., clustered infections). The spatial distributions of DF risks were assessed in terms of three distinct map presentations: expected incidence rates, incidence rates in various return periods, and return periods at distinct incidence rates. These probability-based spatial risk maps exhibited distinct DF risks associated with environmental factors, expressed as various DF magnitudes and occurrence probabilities across Kaohsiung, and can serve as a reference for local governmental agencies.     

Estimating the burden of tuberculosis among foreign-born persons acquired prior to entering the U.S., 2005-2009

Background

The true burden of reactivation of remote latent tuberculosis infection (reactivation TB) among foreign-born persons with tuberculosis (TB) within the United States is not known. Our study objectives were to estimate the proportion of foreign-born persons with TB due reactivation TB and to describe characteristics of foreign-born persons with reactivation TB.

Methods

We conducted a cross-sectional study of patients with an M. tuberculosis isolate genotyped by the U.S. National TB Genotyping Service, 2005–2009. TB cases were attributed to reactivation TB if they were not a member of a localized cluster of cases. Localized clusters were determined by a spatial scan statistic of cases with isolates with matching TB genotype results. Crude odds ratios and 95% confidence intervals were used to assess relations between reactivation TB and select factors among foreign-born persons.

Main Results

Among the 36,860 cases with genotyping and surveillance data reported, 22,151 (60%) were foreign-born. Among foreign-born persons with TB, 18,540 (83.7%) were attributed to reactivation TB. Reactivation TB among foreign-born persons was associated with increasing age at arrival, incidence of TB in the country of origin, and decreased time in the U.S. at the time of TB diagnosis.

Conclusions

Four out of five TB cases among foreign-born persons can be attributed to reactivation TB and present the largest challenge to TB elimination in the U.S. TB control strategies among foreign-born persons should focus on finding and treating latent tuberculosis infection prior to or shortly after arrival to the United States and on reducing the burden of LTBI through improvements in global TB control.     

Spatio-Temporal Analysis of Smear-Positive Tuberculosis in the Sidama Zone,Southern Ethiopia

Background

Tuberculosis (TB) is a disease of public health concern, with a varying distribution across settings depending on socio-economic status, HIV burden, availability and performance of the health system. Ethiopia is a country with a high burden of TB, with regional variations in TB case notification rates (CNRs). However, TB program reports are often compiled and reported at higher administrative units that do not show the burden at lower units, so there is limited information about the spatial distribution of the disease. We therefore aim to assess the spatial distribution and presence of the spatio-temporal clustering of the disease in different geographic settings over 10 years in the Sidama Zone in southern Ethiopia.

Methods

A retrospective space–time and spatial analysis were carried out at the kebele level (the lowest administrative unit within a district) to identify spatial and space-time clusters of smear-positive pulmonary TB (PTB). Scan statistics, Global Moran’s I, and Getis and Ordi (Gi*) statistics were all used to help analyze the spatial distribution and clusters of the disease across settings.

Results

A total of 22,545 smear-positive PTB cases notified over 10 years were used for spatial analysis. In a purely spatial analysis, we identified the most likely cluster of smear-positive PTB in 192 kebeles in eight districts (RR= 2, p<0.001), with 12,155 observed and 8,668 expected cases. The Gi* statistic also identified the clusters in the same areas, and the spatial clusters showed stability in most areas in each year during the study period. The space-time analysis also detected the most likely cluster in 193 kebeles in the same eight districts (RR= 1.92, p<0.001), with 7,584 observed and 4,738 expected cases in 2003-2012.

Conclusion

The study found variations in CNRs and significant spatio-temporal clusters of smear-positive PTB in the Sidama Zone. The findings can be used to guide TB control programs to devise effective TB control strategies for the geographic areas characterized by the highest CNRs. Further studies are required to understand the factors associated with clustering based on individual level locations and investigation of cases.     

Projection of tuberculosis incidence with increasing immigration trends

Tuberculosis (TB) incidence rates vary substantially from regions to regions and from countries to countries. In countries such as Canada where TB incidence rate is low, increasing immigration trends may have significant impact on the TB transmission patterns in these countries. In this study we formulate a deterministic epidemiological model of TB transmission in two demographically distinct populations: Canadian born and foreign born populations, in order to investigate the effects of this demographic distinction on the short-term incidence and long-term transmission dynamics, and with special emphasis on the impact of immigration latent TB cases on the overall TB incidence rate in the whole population.     

Geographic dynamics of viral encephalitis in Thailand

Viral encephalitis (VE) continues to be a major disease in Asia, causing serious illness which may result in death or have neurological sequelae. This study involves an ecological analysis of the climatic, geographic and seasonal patterns of clinically reported VE in Thailand from 1993 to 1998 to investigate regional and seasonal differences in disease incidence. Three thousand eight hundred and twenty nine cases of VE were clinically diagnosed nationwide during the study period by the Thai Ministry of Public Health. Spearman rank correlations of temporal, spatial and geographic variables with disease incidence were performed. The monthly incidence of VE correlated significantly with seasonal changes in temperature, relative humidity and rainfall in the north-northeast region of Thailand (P < 0.001), whereas incidence in the south-central region correlated only with relative humidity (P = 0.003). Spatial analysis revealed a positive correlation of disease with elevation (P < 0.001), and negative correlations with rice-field cover (P < 0.001), agricultural land-use (P < 0.001) and temperature (P = 0.004) in the north-northeast region. No significant spatial correlation was identified in the south-central region. The spatial distribution of VE suggests that etiologic variations may be responsible, in part, for the geographic patterns of disease. Active etiologic surveillance is necessary in a variety of geographic settings in order to provide physicians with information necessary for disease prevention and clinical management.     

Environmental Transmission of Typhoid Fever in an Urban Slum

BackgroundEnteric fever due to Salmonella Typhi (typhoid fever) occurs in urban areas with poor sanitation. While direct fecal-oral transmission is thought to be the predominant mode of transmission, recent evidence suggests that indirect environmental transmission may also contribute to disease spread.MethodsData from a population-based infectious disease surveillance system (28,000 individuals followed biweekly) were used to map the spatial pattern of typhoid fever in Kibera, an urban informal settlement in Nairobi Kenya, between 2010–2011. Spatial modeling was used to test whether variations in topography and accumulation of surface water explain the geographic patterns of risk.ResultsAmong children less than ten years of age, risk of typhoid fever was geographically heterogeneous across the study area (p = 0.016) and was positively associated with lower elevation, OR = 1.87, 95% CI (1.36–2.57), p <0.001. In contrast, the risk of typhoid fever did not vary geographically or with elevation among individuals less than 6b ten years of age.ConclusionsOur results provide evidence of indirect, environmental transmission of typhoid fever among children, a group with high exposure to fecal pathogens in the environment. Spatially targeting sanitation interventions may decrease enteric fever transmission.     

Population-Level Impact of Shorter-Course Regimens for Tuberculosis: A Model-Based Analysis

Despite current control efforts, global tuberculosis (TB) incidence is decreasing slowly. New regimens that can shorten treatment hold promise for improving treatment completion and success, but their impact on population-level transmission remains unclear. Earlier models projected that a four-month regimen could reduce TB incidence by 10% but assumed that an entire course of therapy must be completed to derive any benefit. We constructed a dynamic transmission model of TB disease calibrated to global estimates of incidence, prevalence, mortality, and treatment success. To account for the efficacy of partial treatment, we used data from clinical trials of early short-course regimens to estimate relapse rates among TB patients who completed one-third, one-half, two-thirds, and all of their first-line treatment regimens. We projected population-level incidence and mortality over 10 years, comparing standard six-month therapy to hypothetical shorter-course regimens with equivalent treatment success but fewer defaults. The impact of hypothetical four-month regimens on TB incidence after 10 years was smaller than estimated in previous modeling analyses (1.9% [95% uncertainty range 0.6–3.1%] vs. 10%). Impact on TB mortality was larger (3.5% at 10 years) but still modest. Transmission impact was most sensitive to the proportion of patients completing therapy: four-month therapy led to greater incidence reductions in settings where 25% of patients leave care (“default”) over six months. Our findings remained robust under one-way variation of model parameters. These findings suggest that novel regimens that shorten treatment duration may have only a modest effect on TB transmission except in settings of very low treatment completion.     

Spatio-temporal clustering of Mountain-type Zoonotic Visceral Leishmaniasis in China between 2015 and 2019

With several decades of concerted control efforts, visceral leishmaniasis(VL) eradication had almost been achieved in China. However, VL cases continue to be detected in parts of western China recent years. Using data of reported cases, this study aimed to investigate the epidemiology and spatio⁃temporal distribution, of mountain-type zoonotic visceral leishmaniasis (MT-ZVL) in China between the years 2015 and 2019. Epidemiological data pertaining to patients with visceral leishmaniasis (VL) were collected in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei provinces between the years 2015 and 2019. Joinpoint regression analysis was performed to determine changes in the epidemic trend of MT-ZVL within the time period during which data was collected. Spatial autocorrelation of infection was examined using the Global Moran’s I statistic wand hotspot analysis was carried out using the Getis-Ord Gi* statistic. Spatio-temporal clustering analysis was conducted using the retrospective space-time permutation flexible spatial scanning statistics. A total of 529 cases of MT-ZVL were detected in the six provinces from which data were collected during the study time period, predominantly in Gansu (55.0%), Shanxi (21.7%), Shaanxi (12.5%) and Sichuan (8.9%) provinces. A decline in VL incidence in China was observed during the study period, whereas an increase in MT-ZVL incidence was observed in the six provinces from which data was obtained (t = 4.87, P < 0.05), with highest incidence in Shanxi province (t = 16.91, P < 0.05). Significant differences in the Moran’s I statistic were observed during study time period (P < 0.05), indicating spatial autocorrelation in the spatial distribution of MT-ZVL. Hotspot and spatial autocorrelation analysis revealed clustering of infection cases in the Shaanxi-Shanxi border areas and in east of Shanxi province, where transmission increased rapidly over the study duration, as well as in well know high transmission areas in the south of Gansu province and the north of the Sichuan province. It indicates resurgence of MT-ZVL transmission over the latter three years of the study. Spatial clustering of infection was observed in localized areas, as well as sporadic outbreaks of infection.     

Landscape features predict the current and forecast the future geographic spread of Lyme disease

Lyme disease, the most prevalent vector-borne disease in North America, is increasing in incidence and geographic distribution as the tick vector, Ixodes scapularis, spreads to new regions. We re-construct the spatial-temporal invasion of the tick and human disease in the Midwestern US, a major focus of Lyme disease transmission, from 1967 to 2018, to analyse the influence of spatial factors on the geographic spread. A regression model indicates that three spatial factors—proximity to a previously invaded county, forest cover and adjacency to a river—collectively predict tick occurrence. Validation of the predictive capability of this model correctly predicts counties invaded or uninvaded with 90.6% and 98.5% accuracy, respectively. Reported incidence increases in counties after the first report of the tick; based on this modelled relationship, we identify 31 counties where we suspect I. scapularis already occurs yet remains undetected. Finally, we apply the model to forecast tick establishment by 2021 and predict 42 additional counties where I. scapularis will probably be detected based upon historical drivers of geographic spread. Our findings leverage resources dedicated to tick and human disease reporting and provide the opportunity to take proactive steps (e.g. educational efforts) to prevent and limit transmission in areas of future geographic spread.     

Unbiased estimation of greenhouse whitefly, Trialeurodes vaporariorum, mean density using yellow sticky trap in cherry tomato greenhouses

The spatial distribution of the count of adult greenhouse whiteflies, Trialeurodes vaporariorum (Westwood), on yellow sticky traps was analyzed using Taylor's power law and spatial autocorrelation statistics in the cherry tomato greenhouses from 1998–1999. Samples were collected weekly using a cylindrically shaped yellow sticky trap placed in a 5 by 8 grid covering 0.10–0.15 ha in each of five cherry tomato greenhouses. Taylor's (1961) power law indicated that counts of T. vaporariorum on traps were aggregated within greenhouses. Spatial autocorrelation analysis showed that trap catches were similar (positively autocorrelated) to a distance of 12.5 m, and then dissimilar (negatively autocorrelated) at >12.5 m. Autocorrelation-lag plots showed a globally significant spatial relation in 34 of 57 sample-weeks according to Bonferroni's approximation. The presence of this spatial relation was not related to the changes of mean density. Trap counts at the second lag distance (12.5–25 m) showed little spatial autocorrelation and tended to be the most spatially independent. A fixed-precision-level sequential sampling plan was developed using the parameters from Taylor's power law. The presence of spatial dependency in data sets degraded the sampling plan's precision relative to performance in data sets lacking significant spatial autocorrelation. Therefore, to obtain an unbiased mean density of T. vaporariorum per greenhouse, sticky traps should be placed at least >12.5 m apart to ensure that they are spatially independent.     

福建省安溪县土地利用时空演变规律及其关键驱动因素

以福建省安溪县为研究边界,选取了1999—2019年该县及所属乡镇的耕地、林地、草地、园地、水域、建设用地和未利用地等7个土地利用类型作为物种变量,与其相关性较大的社会经济指标为环境变量,采用典范对应分析和空间自相关等方法进行分析,以明确安溪县和所属乡镇的土地利用时空演变特征及其关键驱动因素。研究结果表明:安溪县1999—2019年间土地利用类型变化总体上呈"三减四增"态势,并在全局空间分布格局上呈明显的聚焦状态。全县耕地、草地和水域面积分别减少36.82%、22.91%和8.18%,而林地、园地(主要是茶园)、建设用地和未利用地面积则分别增加了10.37%、56.39%、206.08%和90.14%。就林地面积而言,近10年来安溪县24个乡镇中有1/3的乡镇林地呈下降趋势。其中,祥华、大坪、虎邱、参内、福田和城厢6个乡镇林地面积减少明显。研究表明:在县域水平上,主要土地利用类型变化,除了受地理因素的制约外,主要受社会经济指标和宏观政策的驱动因素调控。排位最大的前3个因素分别是社会消费品零售总额、地区生产总值和茶叶产量。在镇域水平上,不同乡镇由于地理条件,特别是城镇化水平和经济发展状况...