Tzai-Hung Wen

Using Geographically Weighted Regression (GWR) to Explore Spatial Varying Relationships of Immature Mosquitoes and Human Densities with the Incidence of Dengue

The only way for dengue to spread in the human population is through the human-mosquito-human cycle. Most research in this field discusses the dengue-mosquito or dengue-human relationships over a particular study area, but few have explored the local spatial variations of dengue-mosquito and dengue-human relationships within a study area. This study examined whether spatial heterogeneity exists in these relationships. We used Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to analyze spatial relationships and identify the geographical heterogeneities by using the information of entomology and dengue cases in the cities of Kaohsiung and Fengshan in 2002. Our findings indicate that dengue-mosquito and dengue-human relationships were significantly spatially non-stationary. This means that in some areas higher dengue incidences were associated with higher vector/host densities, but in some areas higher incidences were related to lower vector/host densities. We demonstrated that a GWR model can be used to geographically differentiate the relationships of dengue incidence with immature mosquito and human densities. This study provides more insights into spatial targeting of intervention and control programs against dengue outbreaks within the study areas.

The role of imported cases and favorable meteorological conditions in the onset of dengue epidemics.

Background Travelers who acquire dengue infection are often routes for virus transmission to other regions. Nevertheless, the interplay between infected travelers, climate, vectors, and indigenous dengue incidence remains unclear. The role of foreign-origin cases on local dengue epidemics thus has been largely neglected by research. This study investigated the effect of both imported dengue and local meteorological factors on the occurrence of indigenous dengue in Taiwan. Methods and Principal Findings Using logistic and Poisson regression models, we analyzed bi-weekly, laboratory-confirmed dengue cases at their onset dates of illness from 1998 to 2007 to identify correlations between indigenous dengue and imported dengue cases (in the context of local meteorological factors) across different time lags. Our results revealed that the occurrence of indigenous dengue was significantly correlated with temporally-lagged cases of imported dengue (2–14 weeks), higher temperatures (6–14 weeks), and lower relative humidity (6–20 weeks). In addition, imported and indigenous dengue cases had a significant quantitative relationship in the onset of local epidemics. However, this relationship became less significant once indigenous epidemics progressed past the initial stage. Conclusions These findings imply that imported dengue cases are able to initiate indigenous epidemics when appropriate weather conditions are present. Early detection and case management of imported cases through rapid diagnosis may avert large-scale epidemics of dengue/dengue hemorrhagic fever. The deployment of an early-warning surveillance system, with the capacity to integrate meteorological data, will be an invaluable tool for successful prevention and control of dengue, particularly in non-endemic countries.

Spatial-temporal patterns of dengue in areas at risk of dengue hemorrhagic fever in Kaohsiung, Taiwan, 2002.

OBJECTIVE This study aimed to examine whether spatial-temporal patterns of dengue can be used to identify areas at risk of dengue hemorrhagic fever (DHF). METHODS Three indices - probability of case-occurrence, mean duration per wave, and transmission intensity - were used to differentiate eight local spatial-temporal patterns of dengue during the 2002 epidemic in Kaohsiung, Taiwan. DHF densities (DHF cases/km(2) per 100 dengue cases) in each spatial-temporal typed area were compared. RESULTS Areas with three high indices correlated with the highest DHF density: (1) high transmission intensity only; (2) long duration of wave only, and (3) high transmission intensity plus long duration of wave. However, cumulative incidences of dengue cases were not correlated with DHF densities. CONCLUSION Three spatial-temporal indices of dengue could provide useful information to identify areas at high risk of DHF.

Population Movement and Vector-Borne Disease Transmission: Differentiating Spatial–Temporal Diffusion Patterns of Commuting and Noncommuting Dengue Cases

Commuters who acquire dengue infections could be an important route for the transmission of the virus from their homes to workplaces. Understanding the effects of routine human movement on dengue transmission can be helpful in identifying high-risk areas for effective intervention. This study investigated the effects of local environmental and demographic characteristics to clarify the role of the daily commute in dengue transmission. We analyzed the clustering patterns of space–time distances between commuting and noncommuting dengue cases from June 2007 to January 2008 in Tainan City, Taiwan. We also analyzed the network topology of space–time distances to identify possible key individuals and conducted time-to-event analysis for geographic diffusion through commuting versus noncommuting dengue cases. Our significant findings indicate that most of the space–time distances of noncommuting cases clustered within 100 m and one week, whereas commuting cases clustered within 2 to 4 km and one to five weeks. Analysis of the temporality of the geographical diffusion by villages showed that commuting cases diffuse more rapidly across villages than noncommuting cases in the late epidemic period. The role of commuting was identified as a significant risk factor contributing to epidemic diffusion (hazard ratio: 3.08, p value < 0.05). Local neighborhood characteristics (number of vacant grounds and empty houses) are independent facilitating factors for diffusion through both noncommuting cases and commuting cases (hazard ratio: 1.035 and 1.022, respectively, both p < 0.05). Higher population density is a significant risk factor only for diffusion through commuters (hazard ratio: 1.174). In summary, noncommuters, mostly elderly adults and housewives, might initiate local outbreaks, whereas commuters carrying the virus to geographically distant areas cause large-scale epidemics.

A Dengue Vector Surveillance by Human Population-Stratified Ovitrap Survey for Aedes (Diptera: Culicidae) Adult and Egg Collections in High Dengue-Risk Areas of Taiwan

ABSTRACT Aedes aegypti L. is the primary dengue vector in southern Taiwan. This article is the first report on a large-scale surveillance program to study the spatial-temporal distribution of the local Ae. aegytpi population using ovitraps stratified according to the human population in high dengue-risk areas. The sampling program was conducted for 1 yr and was based on weekly collections of eggs and adults in Kaohsiung City. In total, 10,380 ovitraps were placed in 5,190 households. Paired ovitraps, one indoors and one outdoors were used per 400 people. Three treatments in these ovitraps (paddleshaped wooden sticks, sticky plastic, or both) were assigned by stratified random sampling to two areas (i.e., metropolitan or rural, respectively). We found that the sticky plastic alone had a higher sensitivity for detecting the occurrence of indigenous dengue cases than other treatments with time lags of up to 14 wk. The wooden paddle alone detected the oviposition of Ae. aegypti throughout the year in this study area. Furthermore, significantly more Ae. aegypti females were collected indoors than outdoors. Therefore, our survey identified the whole year oviposition activity, spatial-temporal distribution of the local Ae. aegypti population and a 14 wk lag correlation with dengue incidence to plan an effectively proactive control.

Socio-spatial patterns of neighborhood effects on adult obesity in Taiwan: a multi-level model.

Obesity, one of the most significant health problems now facing developed countries, has been increasing steadily in Taiwan. This study addresses how neighborhood factors affect individual obesity by simultaneously examining individual-level socioeconomic status and neighborhood-level characteristics using a multi-level approach combined with a spatial analysis. The data are from Taiwans 2001 Social Development Survey on Health and Safety; a representative sample of 27,593 adults over 262 townships (i.e. neighborhoods). A spatial autocorrelation model is employed to investigate the spatial clustering of neighborhood affluence. A two-level Generalized Hierarchical Linear Model (GHLM) is used to combine neighborhood-level (level-2) characteristics (i.e., spatial patterns of neighborhood affluence and ethnic composition), and individual-level SES position (level-1) to examine the factors associated with adult obesity risk. Three principal findings were obtained. First, individual obesity risk is significantly higher in spatially clustered neighborhoods of economic affluence. Neighborhood factors associated with obesity are likely to operate over a wide geographical area and are not limited to conditions in the immediate residential neighborhood. Second, aboriginal people living adjacent to the most affluent cluster in northern Taiwan have elevated obesity risk, revealing possible spatial diffusion and ethnic acculturation. Third, adult obesity is, however, associated with socioeconomically disadvantaged groups in different neighborhood contexts. These findings suggest that accounting for spatial interdependencies among neighborhoods enhances the accuracy of estimated neighborhood effects on obesity.

A computer virus spreading model based on resource limitations and interaction costs

Computer viruses are major threats to Internet security and privacy, therefore many researchers are addressing questions linked to virus propagation properties, spreading models, epidemic dynamics, tipping points, and control strategies. We believe that two important factors - resource limitations and costs - are being overlooked in this area due to an overemphasis on power-law connectivity distributions of scale-free networks affecting computer virus epidemic dynamics and tipping points. The study show (a) a significant epidemic tipping point does exists when resource limitations and costs are considered, with the tipping point exhibiting a lower bound; (b) when interaction costs increase or usable resources decrease, epidemic tipping points in scale-free networks grow linearly while density curves decrease linearly; (c) regardless of whether Internet user resources obey delta, uniform, or normal distributions, they retain the same epidemic dynamics and tipping points as long as the average value of those resources remains unchanged across different scale-free networks; (d) it is possible to control the spread of a computer virus in a scale-free network if resources are restricted and if costs associated with infection events are significantly increased through the use of a throttling strategy.

Spatial dependency of tuberculosis incidence in Taiwan.

Tuberculosis (TB) disease can be caused by either recent transmission from infectious patients or reactivation of remote latent infection. Spatial dependency (correlation between nearby geographic areas) in tuberculosis incidence is a signature for chains of recent transmission with geographic diffusion. To understand the contribution of recent transmission in the TB endemic in Taiwan, where reactivation has been assumed to be the predominant mode of pathogenesis, we used spatial regression analysis to examine whether there was spatial dependency between the TB incidence in each township and in its neighbors. A total of 90,661 TB cases from 349 townships in 2003–2008 were included in this analysis. After adjusting for the effects of confounding socioeconomic variables, including the percentages of aboriginals and average household income, the results show that the spatial lag parameter remains positively significant (0.43, p<0.001), which indicates that the TB incidences of neighboring townships had an effect on the TB incidence in each township. Townships with substantial spatial spillover effects were mainly located in the northern, western and eastern parts of Taiwan. Spatial dependency implies that recent transmission plays a significant role in the pathogenesis of TB in Taiwan. Therefore, in addition to the current focus on improving the cure rate under directly observed therapy programs, more resource need to be allocated to active case finding in order to break the chain of transmission.

Elucidating the changing socio-spatial dynamics of neighborhood effects on adult obesity risk in Taiwan from 2001 to 2005

Obesity poses a significant health threat in industrialized countries, with its incidence increasing steadily in Taiwan. This study addresses how neighborhood contexts influence individuals, using a multilevel spatial analysis of obesity risk from 2001 to 2005. A priority concern was whether contextual influences on health are limited to the immediate neighborhood or extend to a wider geographical area. The results led to the following conclusions. First, neighborhood factors related to obesity risk are likely to operate over a broad geographical area and are not limited to the focal neighborhood of residence. Second, a geographically based epidemiological change in the likelihood of obesity risk was observed from 2001 to 2005 in Taiwan. Third, the spatial lag model revealed significant spatial spillover of obesity risk in the study area in 2005. Policy interventions are recommended for the neighborhoods associated with the strong spillover effect. The results demonstrate that, in addition to enhancing the accuracy of prediction regarding the effects of neighborhood factors on obesity, incorporating spatial dynamics at the neighborhood level can encourage the development of contextually sensitive policy interventions.

Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network

A network approach, which simplifies geographic settings as a form of nodes and links, emphasizes the connectivity and relationships of spatial features. Topological networks of spatial features are used to explore geographical connectivity and structures. The PageRank algorithm, a network metric, is often used to help identify important locations where people or automobiles concentrate in the geographical literature. However, geographic considerations, including proximity and location attractiveness, are ignored in most network metrics. The objective of the present study is to propose two geographically modified PageRank algorithms—Distance-Decay PageRank (DDPR) and Geographical PageRank (GPR)—that incorporate geographic considerations into PageRank algorithms to identify the spatial concentration of human movement in a geospatial network. Our findings indicate that in both intercity and within-city settings the proposed algorithms more effectively capture the spatial locations where people reside than traditional commonly-used network metrics. In comparing location attractiveness and distance decay, we conclude that the concentration of human movement is largely determined by the distance decay. This implies that geographic proximity remains a key factor in human mobility.