Special Issue "Spatio-temporal Frameworks for Infectious Disease Epidemiology"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: closed (31 May 2016).

Special Issue Editor

Prof. Dr. Peter Congdon
E-Mail Website
Guest Editor
Department of Geography and Life Sciences Institute, Queen Mary University of London, Mile End Road, London, E1 4NS, UK
Tel. 44 207 882 8200
Interests: chronic disease epidemiology; urban environments and health; spatial epidemiology; health geography; mental ill health; suicidology
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Special Issue Information

Dear Colleagues,

An established tradition in epidemiological research has been to identify spatial and spatio-temporal regularities in the spread of infectious disease, which might allow prediction of the eventual disease extent, or permit early intervention to prevent spread of the disease. Spatio-temporal diffusion has been considered both for historic diseases (bubonic plague in Medieval Europe), and for recent infectious disease outbreaks, such as the H1N1 pandemic, recurrent epidemics of dengue fever, and the spread of Ebola.

Spatiotemporal regularities and high-risk areas can be detected using measures of spatial, temporal and space-time clustering, and techniques such as empirical mode decomposition. GIS methods in general have a particular utility in describing and analyzing infectious disease incidence. Sophisticated mathematical and statistical techniques, including stochastic differential equations and Bayesian spatio-temporal modeling, have also been developed.

Underlying and modulating space-time patterns in infectious diffusion are a variety of environmental, climatic and socio-economic factors. For example, the incidence of dengue fever in tropical countries (and the high level of spatial clustering in such incidence) has been related both to urbanization, and climatic factors such as rainfall, temperature, and humidity.

In a longer time perspective, factors such as changing transport patterns, climate change, ecosystem disruption and the loss of biodiversity are also important for understanding and controlling infectious disease outbreaks. For example, increased air travel has been related to dengue dispersal, while deforestation has been linked to changing patterns of malaria and schistosomiasis.

This Special Issue invites contributions on the above and related themes.

Prof. Dr. Peter Congdon
Guest Editor

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Keywords

  • spatiotemporal regularities
  • space-time clustering
  • spatio-temporal modeling
  • infectious disease

Published Papers (13 papers)

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Editorial

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Open AccessEditorial
Spatiotemporal Frameworks for Infectious Disease Diffusion and Epidemiology
Int. J. Environ. Res. Public Health 2016, 13(12), 1261; https://doi.org/10.3390/ijerph13121261 - 20 Dec 2016
Cited by 3
Abstract
Emerging infectious diseases, and the resurgence of previously controlled infectious disease (e.g., malaria, tuberculosis), are a major focus for public health concern, as well as providing challenges for establishing aetiology and transmission. [...]
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(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)

Research

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Open AccessArticle
Spatially Correlated Time Series and Ecological Niche Analysis of Cutaneous Leishmaniasis in Afghanistan
Int. J. Environ. Res. Public Health 2017, 14(3), 309; https://doi.org/10.3390/ijerph14030309 - 17 Mar 2017
Cited by 5
Abstract
Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 1.2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate [...] Read more.
Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 1.2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to study the geographically suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, and then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows were observed related to the months of the year, which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence—January to March and September to December—which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Open AccessArticle
Geographical Patterns of HIV Sero-Discordancy in High HIV Prevalence Countries in Sub-Saharan Africa
Int. J. Environ. Res. Public Health 2016, 13(9), 865; https://doi.org/10.3390/ijerph13090865 - 31 Aug 2016
Cited by 6
Abstract
Introduction: Variation in the proportion of individuals living in a stable HIV sero-discordant partnership (SDP), and the potential drivers of such variability across sub Saharan Africa (SSA), are still not well-understood. This study aimed to examine the spatial clustering of HIV sero-discordancy, and [...] Read more.
Introduction: Variation in the proportion of individuals living in a stable HIV sero-discordant partnership (SDP), and the potential drivers of such variability across sub Saharan Africa (SSA), are still not well-understood. This study aimed to examine the spatial clustering of HIV sero-discordancy, and the impact of local variation in HIV prevalence on patterns of sero-discordancy in high HIV prevalence countries in SSA. Methods: We described the spatial patterns of sero-discordancy among stable couples by analyzing Demographic and Health Survey data from Cameroon, Kenya, Lesotho, Tanzania, Malawi, Zambia, and Zimbabwe. We identified spatial clusters of SDPs in each country through a Kulldorff spatial scan statistics analysis. After a geographical cluster was identified, epidemiologic measures of sero-discordancy were calculated and analyzed. Results: Spatial clusters with significantly high numbers of SDPs were identified and characterized in Kenya, Malawi, and Tanzania, and they largely overlapped with the clusters with high HIV prevalence. There was a positive correlation between HIV prevalence and the proportion of SDPs among all stable couples across within and outside clusters. Conversely, there was a negative, but weak and not significant, correlation between HIV prevalence and the proportion of SDPs among all stable couples with at least one HIV-infected individual in the partnership. Discussion: There does not appear to be distinct spatial patterns for HIV sero-discordancy that are independent of HIV prevalence patterns. The variation of the sero-discordancy measures with HIV prevalence across clusters and outside clusters demonstrated similar patterns to those observed at the national level. The spatial variable does not appear to be a fundamental nor independent determinant of the observed patterns of sero-discordancy in high HIV prevalence countries in SSA. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Open AccessArticle
City-Specific Spatiotemporal Infant and Neonatal Mortality Clusters: Links with Socioeconomic and Air Pollution Spatial Patterns in France
Int. J. Environ. Res. Public Health 2016, 13(6), 624; https://doi.org/10.3390/ijerph13060624 - 22 Jun 2016
Cited by 8
Abstract
Infant and neonatal mortality indicators are known to vary geographically, possibly as a result of socioeconomic and environmental inequalities. To better understand how these factors contribute to spatial and temporal patterns, we conducted a French ecological study comparing two time periods between 2002 [...] Read more.
Infant and neonatal mortality indicators are known to vary geographically, possibly as a result of socioeconomic and environmental inequalities. To better understand how these factors contribute to spatial and temporal patterns, we conducted a French ecological study comparing two time periods between 2002 and 2009 for three (purposefully distinct) Metropolitan Areas (MAs) and the city of Paris, using the French census block of parental residence as the geographic unit of analysis. We identified areas of excess risk and assessed the role of neighborhood deprivation and average nitrogen dioxide concentrations using generalized additive models to generate maps smoothed on longitude and latitude. Comparison of the two time periods indicated that statistically significant areas of elevated infant and neonatal mortality shifted northwards for the city of Paris, are present only in the earlier time period for Lille MA, only in the later time period for Lyon MA, and decrease over time for Marseille MA. These city-specific geographic patterns in neonatal and infant mortality are largely explained by socioeconomic and environmental inequalities. Spatial analysis can be a useful tool for understanding how risk factors contribute to disparities in health outcomes ranging from infant mortality to infectious disease—a leading cause of infant mortality. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Open AccessArticle
Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China
Int. J. Environ. Res. Public Health 2016, 13(6), 600; https://doi.org/10.3390/ijerph13060600 - 16 Jun 2016
Cited by 9
Abstract
China was attacked by a serious influenza A (H7N9) virus in 2013. The first human infection case was confirmed in Shanghai City and soon spread across most of eastern China. Using the methods of Geographic Information Systems (GIS) and ecological niche modeling (ENM), [...] Read more.
China was attacked by a serious influenza A (H7N9) virus in 2013. The first human infection case was confirmed in Shanghai City and soon spread across most of eastern China. Using the methods of Geographic Information Systems (GIS) and ecological niche modeling (ENM), this research quantitatively analyzed the relationships between the H7N9 occurrence and the main environmental factors, including meteorological variables, human population density, bird migratory routes, wetland distribution, and live poultry farms, markets, and processing factories. Based on these relationships the probability of the presence of H7N9 was predicted. Results indicated that the distribution of live poultry processing factories, farms, and human population density were the top three most important determinants of the H7N9 human infection. The relative contributions to the model of live poultry processing factories, farms and human population density were 39.9%, 17.7% and 17.7%, respectively, while the maximum temperature of the warmest month and mean relative humidity had nearly no contribution to the model. The paper has developed an ecological niche model (ENM) that predicts the spatial distribution of H7N9 cases in China using environmental variables. The area under the curve (AUC) values of the model were greater than 0.9 (0.992 for the training samples and 0.961 for the test data). The findings indicated that most of the high risk areas were distributed in the Yangtze River Delta. These findings have important significance for the Chinese government to enhance the environmental surveillance at multiple human poultry interfaces in the high risk area. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Open AccessArticle
Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory
Int. J. Environ. Res. Public Health 2016, 13(5), 469; https://doi.org/10.3390/ijerph13050469 - 05 May 2016
Cited by 17
Abstract
Objective: To explore the spatial-temporal interaction effect within a Bayesian framework and to probe the ecological influential factors for tuberculosis. Methods: Six different statistical models containing parameters of time, space, spatial-temporal interaction and their combination were constructed based on a Bayesian [...] Read more.
Objective: To explore the spatial-temporal interaction effect within a Bayesian framework and to probe the ecological influential factors for tuberculosis. Methods: Six different statistical models containing parameters of time, space, spatial-temporal interaction and their combination were constructed based on a Bayesian framework. The optimum model was selected according to the deviance information criterion (DIC) value. Coefficients of climate variables were then estimated using the best fitting model. Results: The model containing spatial-temporal interaction parameter was the best fitting one, with the smallest DIC value (−4,508,660). Ecological analysis results showed the relative risks (RRs) of average temperature, rainfall, wind speed, humidity, and air pressure were 1.00324 (95% CI, 1.00150–1.00550), 1.01010 (95% CI, 1.01007–1.01013), 0.83518 (95% CI, 0.93732–0.96138), 0.97496 (95% CI, 0.97181–1.01386), and 1.01007 (95% CI, 1.01003–1.01011), respectively. Conclusions: The spatial-temporal interaction was statistically meaningful and the prevalence of tuberculosis was influenced by the time and space interaction effect. Average temperature, rainfall, wind speed, and air pressure influenced tuberculosis. Average humidity had no influence on tuberculosis. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Open AccessArticle
Seasonal and Geographic Variation of Pediatric Malaria in Burundi: 2011 to 2012
Int. J. Environ. Res. Public Health 2016, 13(4), 425; https://doi.org/10.3390/ijerph13040425 - 15 Apr 2016
Cited by 6
Abstract
We analyzed hospitalization records from 2011 to 2012 to examine the spatial patterns of pediatric malaria in Burundi. Malaria case data for those below the age of five years were categorized according to the four principal seasons of Burundi, which are two rainy [...] Read more.
We analyzed hospitalization records from 2011 to 2012 to examine the spatial patterns of pediatric malaria in Burundi. Malaria case data for those below the age of five years were categorized according to the four principal seasons of Burundi, which are two rainy seasons (February to May; September to November) and two dry seasons (June to August; December to January). The Getis-Ord Gi* statistic was used to examine seasonal spatial patterns of pediatric malaria, whereas geographically weighted regression (GWR) were used to examine the potential role of environmental variables on the spatial patterns of cases. There were a total of 19,890 pediatric malaria cases reported during the study period. The incidence among males was higher than that among females; and it was higher in rural districts. The seasonal incidence peaks occurred in the northern half of the country during the wet season while during the dry season, incidence was higher in southern Burundi. Elevation played a greater role in explaining variance in the prevalence of pediatric malaria during seasonal peaks than rainfall. The counterintuitive finding in northern Burundi confirms previous findings and suggests other factors (e.g., land cover/land use) facilitate the persistence of the mosquito population in the highlands of Africa. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Open AccessArticle
Using a Hybrid Model to Forecast the Prevalence of Schistosomiasis in Humans
Int. J. Environ. Res. Public Health 2016, 13(4), 355; https://doi.org/10.3390/ijerph13040355 - 23 Mar 2016
Cited by 7
Abstract
Background: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in [...] Read more.
Background: We previously proposed a hybrid model combining both the autoregressive integrated moving average (ARIMA) and the nonlinear autoregressive neural network (NARNN) models in forecasting schistosomiasis. Our purpose in the current study was to forecast the annual prevalence of human schistosomiasis in Yangxin County, using our ARIMA-NARNN model, thereby further certifying the reliability of our hybrid model. Methods: We used the ARIMA, NARNN and ARIMA-NARNN models to fit and forecast the annual prevalence of schistosomiasis. The modeling time range included was the annual prevalence from 1956 to 2008 while the testing time range included was from 2009 to 2012. The mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the model performance. We reconstructed the hybrid model to forecast the annual prevalence from 2013 to 2016. Results: The modeling and testing errors generated by the ARIMA-NARNN model were lower than those obtained from either the single ARIMA or NARNN models. The predicted annual prevalence from 2013 to 2016 demonstrated an initial decreasing trend, followed by an increase. Conclusions: The ARIMA-NARNN model can be well applied to analyze surveillance data for early warning systems for the control and elimination of schistosomiasis. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Open AccessArticle
Spatio-Temporal Distribution Characteristics and Trajectory Similarity Analysis of Tuberculosis in Beijing, China
Int. J. Environ. Res. Public Health 2016, 13(3), 291; https://doi.org/10.3390/ijerph13030291 - 07 Mar 2016
Cited by 11
Abstract
Tuberculosis (TB) is an infectious disease with one of the highest reported incidences in China. The detection of the spatio-temporal distribution characteristics of TB is indicative of its prevention and control conditions. Trajectory similarity analysis detects variations and loopholes in prevention and provides [...] Read more.
Tuberculosis (TB) is an infectious disease with one of the highest reported incidences in China. The detection of the spatio-temporal distribution characteristics of TB is indicative of its prevention and control conditions. Trajectory similarity analysis detects variations and loopholes in prevention and provides urban public health officials and related decision makers more information for the allocation of public health resources and the formulation of prioritized health-related policies. This study analysed the spatio-temporal distribution characteristics of TB from 2009 to 2014 by utilizing spatial statistics, spatial autocorrelation analysis, and space-time scan statistics. Spatial statistics measured the TB incidence rate (TB patients per 100,000 residents) at the district level to determine its spatio-temporal distribution and to identify characteristics of change. Spatial autocorrelation analysis was used to detect global and local spatial autocorrelations across the study area. Purely spatial, purely temporal and space-time scan statistics were used to identify purely spatial, purely temporal and spatio-temporal clusters of TB at the district level. The other objective of this study was to compare the trajectory similarities between the incidence rates of TB and new smear-positive (NSP) TB patients in the resident population (NSPRP)/new smear-positive TB patients in the TB patient population (NSPTBP)/retreated smear-positive (RSP) TB patients in the resident population (RSPRP)/retreated smear-positive TB patients in the TB patient population (RSPTBP) to detect variations and loopholes in TB prevention and control among the districts in Beijing. The incidence rates in Beijing exhibited a gradual decrease from 2009 to 2014. Although global spatial autocorrelation was not detected overall across all of the districts of Beijing, individual districts did show evidence of local spatial autocorrelation: Chaoyang and Daxing were Low-Low districts over the six-year period. The purely spatial scan statistics analysis showed significant spatial clusters of high and low incidence rates; the purely temporal scan statistics showed the temporal cluster with a three-year period from 2009 to 2011 characterized by a high incidence rate; and the space-time scan statistics analysis showed significant spatio-temporal clusters. The distribution of the mean centres (MCs) showed that the general distributions of the NSPRP MCs and NSPTBP MCs were to the east of the incidence rate MCs. Conversely, the general distributions of the RSPRP MCs and the RSPTBP MCs were to the south of the incidence rate MCs. Based on the combined analysis of MC distribution characteristics and trajectory similarities, the NSP trajectory was most similar to the incidence rate trajectory. Thus, more attention should be focused on the discovery of NSP patients in the western part of Beijing, whereas the northern part of Beijing needs intensive treatment for RSP patients. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Open AccessCommunication
Spatial Modelling Tools to Integrate Public Health and Environmental Science, Illustrated with Infectious Cryptosporidiosis
Int. J. Environ. Res. Public Health 2016, 13(2), 186; https://doi.org/10.3390/ijerph13020186 - 02 Feb 2016
Cited by 4
Abstract
Contemporary spatial modelling tools can help examine how environmental exposures such as climate and land use together with socio-economic factors sustain infectious disease transmission in humans. Spatial methods can account for interactions across global and local scales, geographic clustering and continuity of the [...] Read more.
Contemporary spatial modelling tools can help examine how environmental exposures such as climate and land use together with socio-economic factors sustain infectious disease transmission in humans. Spatial methods can account for interactions across global and local scales, geographic clustering and continuity of the exposure surface, key characteristics of many environmental influences. Using cryptosporidiosis as an example, this review illustrates how, in resource rich settings, spatial tools have been used to inform targeted intervention strategies and forecast future disease risk with scenarios of environmental change. When used in conjunction with molecular studies, they have helped determine location-specific infection sources and environmental transmission pathways. There is considerable scope for such methods to be used to identify data/infrastructure gaps and establish a baseline of disease burden in resource-limited settings. Spatial methods can help integrate public health and environmental science by identifying the linkages between the physical and socio-economic environment and health outcomes. Understanding the environmental and social context for disease spread is important for assessing the public health implications of projected environmental change. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
Open AccessArticle
Patterns of Bacillary Dysentery in China, 2005–2010
Int. J. Environ. Res. Public Health 2016, 13(2), 164; https://doi.org/10.3390/ijerph13020164 - 27 Jan 2016
Cited by 14
Abstract
Although the incidence of bacillary dysentery in China has been declining progressively, a considerable disease burden still exists. Few studies have analyzed bacillary dysentery across China and knowledge gaps still exist in the aspects of geographic distribution and ecological drivers, seasonality and its [...] Read more.
Although the incidence of bacillary dysentery in China has been declining progressively, a considerable disease burden still exists. Few studies have analyzed bacillary dysentery across China and knowledge gaps still exist in the aspects of geographic distribution and ecological drivers, seasonality and its association with meteorological factors, urban-rural disparity, prevalence and distribution of Shigella species. Here, we performed nationwide analyses to fill the above gaps. Geographically, we found that incidence increased along an east-west gradient which was inversely related to the economic conditions of China. Two large endemically high-risk regions in western China and their ecological drivers were identified for the first time. We characterized seasonality of bacillary dysentery incidence and assessed its association with meteorological factors, and saw that it exhibits north-south differences in peak duration, relative amplitude and key meteorological factors. Urban and rural incidences among China’s cities were compared, and disparity associated with urbanization level was invariant in most cities. Balanced decrease of urban and rural incidence was observed for all provinces except Hunan. S. flexneri and S. sonnei were identified as major causative species. Increasing prevalence of S. sonnei and geographic distribution of Shigella species were associated with economic status. Findings and inferences from this study draw broader pictures of bacillary dysentery in mainland China and could provide useful information for better interventions and public health planning. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Open AccessArticle
A Predictive Risk Model for A(H7N9) Human Infections Based on Spatial-Temporal Autocorrelation and Risk Factors: China, 2013–2014
Int. J. Environ. Res. Public Health 2015, 12(12), 15204-15221; https://doi.org/10.3390/ijerph121214981 - 01 Dec 2015
Cited by 5
Abstract
This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to [...] Read more.
This study investigated the spatial distribution, spatial autocorrelation, temporal cluster, spatial-temporal autocorrelation and probable risk factors of H7N9 outbreaks in humans from March 2013 to December 2014 in China. The results showed that the epidemic spread with significant spatial-temporal autocorrelation. In order to describe the spatial-temporal autocorrelation of H7N9, an improved model was developed by introducing a spatial-temporal factor in this paper. Logistic regression analyses were utilized to investigate the risk factors associated with their distribution, and nine risk factors were significantly associated with the occurrence of A(H7N9) human infections: the spatial-temporal factor φ (OR = 2546669.382, p < 0.001), migration route (OR = 0.993, p < 0.01), river (OR = 0.861, p < 0.001), lake(OR = 0.992, p < 0.001), road (OR = 0.906, p < 0.001), railway (OR = 0.980, p < 0.001), temperature (OR = 1.170, p < 0.01), precipitation (OR = 0.615, p < 0.001) and relative humidity (OR = 1.337, p < 0.001). The improved model obtained a better prediction performance and a higher fitting accuracy than the traditional model: in the improved model 90.1% (91/101) of the cases during February 2014 occurred in the high risk areas (the predictive risk > 0.70) of the predictive risk map, whereas 44.6% (45/101) of which overlaid on the high risk areas (the predictive risk > 0.70) for the traditional model, and the fitting accuracy of the improved model was 91.6% which was superior to the traditional model (86.1%). The predictive risk map generated based on the improved model revealed that the east and southeast of China were the high risk areas of A(H7N9) human infections in February 2014. These results provided baseline data for the control and prevention of future human infections. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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Review

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Open AccessReview
DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics
Int. J. Environ. Res. Public Health 2016, 13(9), 920; https://doi.org/10.3390/ijerph13090920 - 15 Sep 2016
Cited by 8
Abstract
The prevention and control of dengue are great public health challenges for many countries, particularly since 2015, as other arboviruses have been observed to interact significantly with dengue virus. Different approaches and methodologies have been proposed and discussed by the research community. An [...] Read more.
The prevention and control of dengue are great public health challenges for many countries, particularly since 2015, as other arboviruses have been observed to interact significantly with dengue virus. Different approaches and methodologies have been proposed and discussed by the research community. An important tool widely used is modeling and simulation, which help us to understand epidemic dynamics and create scenarios to support planning and decision making processes. With this aim, we proposed and developed DengueME, a collaborative open source platform to simulate dengue disease and its vector’s dynamics. It supports compartmental and individual-based models, implemented over a GIS database, that represent Aedes aegypti population dynamics, human demography, human mobility, urban landscape and dengue transmission mediated by human and mosquito encounters. A user-friendly graphical interface was developed to facilitate model configuration and data input, and a library of models was developed to support teaching-learning activities. DengueME was applied in study cases and evaluated by specialists. Other improvements will be made in future work, to enhance its extensibility and usability. Full article
(This article belongs to the Special Issue Spatio-temporal Frameworks for Infectious Disease Epidemiology)
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