S. M. Niaz Arifin

Examining the Role of Linguistic Knowledge Sources in the Automatic Identification and Classification of Reviews

This paper examines two problems in document-level sentiment analysis: (1) determining whether a given document is a review or not, and (2) classifying the polarity of a review as positive or negative. We first demonstrate that review identification can be performed with high accuracy using only unigrams as features. We then examine the role of four types of simple linguistic knowledge sources in a polarity classification system.

An agent-based model of the population dynamics of Anopheles gambiae

BackgroundAgent-based models (ABMs) have been used to model the behaviour of individual mosquitoes and other aspects of malaria. In this paper, a conceptual entomological model of the population dynamics of Anopheles gambiae and the agent-based implementations derived from it are described. Hypothetical vector control interventions (HVCIs) are implemented to target specific activities in the mosquito life cycle, and their impacts are evaluated.MethodsThe core model is described in terms of the complete An. gambiae mosquito life cycle. Primary features include the development and mortality rates in different aquatic and adult stages, the aquatic habitats and oviposition. The density- and age-dependent larval and adult mortality rates (vector senescence) allow the model to capture the age-dependent aspects of the mosquito biology. Details of hypothetical interventions are also described.ResultsResults show that with varying coverage and temperature ranges, the hypothetical interventions targeting the gonotrophic cycle stages produce higher impacts than the rest in reducing the potentially infectious female (PIF) mosquito populations, due to their multi-hour mortality impacts and their applicability at multiple gonotrophic cycles. Thus, these stages may be the most effective points of target for newly developed and novel interventions. A combined HVCI with low coverage can produce additive synergistic impacts and can be more effective than isolated HVCIs with comparatively higher coverages. It is emphasized that although the model described in this paper is designed specifically around the mosquito An. gambiae, it could effectively apply to many other major malaria vectors in the world (including the three most efficient nominal anopheline species An. gambiae, Anopheles coluzzii and Anopheles arabiensis) by incorporating a variety of factors (seasonality cycles, rainfall, humidity, etc.). Thus, the model can essentially be treated as a generic Anopheles model, offering an excellent framework for such extensions. The utility of the core model has also been demonstrated by several other applications, each of which investigates well-defined biological research questions across a variety of dimensions (including spatial models, insecticide resistance, and sterile insect techniques).

Examining the impact of larval source management and insecticide-treated nets using a spatial agent-based model of Anopheles gambiae and a landscape generator tool

BackgroundAgent-based models (ABMs) have been used to estimate the effects of malaria-control interventions. Early studies have shown the efficacy of larval source management (LSM) and insecticide-treated nets (ITNs) as vector-control interventions, applied both in isolation and in combination. However, the robustness of results can be affected by several important modelling assumptions, including the type of boundary used for landscapes, and the number of replicated simulation runs reported in results. Selection of the ITN coverage definition may also affect the predictive findings. Hence, by replication, independent verification of prior findings of published models bears special importance.MethodsA spatially-explicit entomological ABM of Anopheles gambiae is used to simulate the resource-seeking process of mosquitoes in grid-based landscapes. To explore LSM and replicate results of an earlier LSM study, the original landscapes and scenarios are replicated by using a landscape generator tool, and 1,800 replicated simulations are run using absorbing and non-absorbing boundaries. To explore ITNs and evaluate the relative impacts of the different ITN coverage schemes, the settings of an earlier ITN study are replicated, the coverage schemes are defined and simulated, and 9,000 replicated simulations for three ITN parameters (coverage, repellence and mortality) are run. To evaluate LSM and ITNs in combination, landscapes with varying densities of houses and human populations are generated, and 12,000 simulations are run.ResultsGeneral agreement with an earlier LSM study is observed when an absorbing boundary is used. However, using a non-absorbing boundary produces significantly different results, which may be attributed to the unrealistic killing effect of an absorbing boundary. Abundance cannot be completely suppressed by removing aquatic habitats within 300 m of houses. Also, with density-dependent oviposition, removal of insufficient number of aquatic habitats may prove counter-productive. The importance of performing large number of simulation runs is also demonstrated. For ITNs, the choice of coverage scheme has important implications, and too high repellence yields detrimental effects. When LSM and ITNs are applied in combination, ITNs’ mortality can play more important roles with higher densities of houses. With partial mortality, increasing ITN coverage is more effective than increasing LSM coverage, and integrating both interventions yields more synergy as the densities of houses increase.ConclusionsUsing a non-absorbing boundary and reporting average results from sufficiently large number of simulation runs are strongly recommended for malaria ABMs. Several guidelines (code and data sharing, relevant documentation, and standardized models) for future modellers are also recommended.

A Spatial Agent-Based Model of Malaria: Model Verification and Effects of Spatial Heterogeneity

In agent-based modeling ABM, an explicit spatial representation may be required for certain aspects of the system to be modeled realistically. A spatial ABM includes landscapes in which agents seek resources necessary for their survival. The spatial heterogeneity of the underlying landscape plays a crucial role in the resource-seeking process. This study describes a previous agent-based model of malaria, and the modeling of its spatial extension. In both models, all mosquito agents are represented individually. In the new spatial model, the agents also possess explicit spatial information. Within a landscape, adult female mosquito agents search for two types of resources: aquatic habitats AHs and bloodmeal locations BMLs. These resources are specified within different spatial patterns, or landscapes. Model verification between the non-spatial and spatial models by means of docking is examined. Using different landscapes, the authors show that mosquito abundance remains unchanged. With the same overall system capacity, varying the density of resources in a landscape does not affect abundance. When the density of resources is constant, the overall capacity drives the system. For the spatial model, using landscapes with different resource densities of both resource-types, the authors show that spatial heterogeneity influences the mosquito population.

A test of agent-based models as a tool for predicting patterns of pathogen transmission in complex landscapes

BackgroundLandscape complexity can mitigate or facilitate host dispersal, influencing patterns of pathogen transmission. Spatial transmission of pathogens through landscapes, therefore, presents an important but not fully elucidated aspect of transmission dynamics. Using an agent-based model (LiNK) that incorporates GIS data, we examined the effects of landscape information on the spatial patterns of host movement and pathogen transmission in a system of long-tailed macaques and their gut parasites. We first examined the role of the landscape to identify any individual or additive effects on host movement. We then compared modeled dispersal distance to patterns of actual macaque gene flow to both confirm our model’s predictions and to understand the role of individual land uses on dispersal. Finally, we compared the rate and the spread of two gastrointestinal parasites, Entamoeba histolytica and E. dispar, to understand how landscape complexity influences spatial patterns of pathogen transmission.ResultsLiNK captured emergent properties of the landscape, finding that interaction effects between landscape layers could mitigate the rate of infection in a non-additive way. We also found that the inclusion of landscape information facilitated an accurate prediction of macaque dispersal patterns across a complex landscape, as confirmed by Mantel tests comparing genetic and simulated dispersed distances. Finally, we demonstrated that landscape heterogeneity proved a significant barrier for a highly virulent pathogen, limiting the dispersal ability of hosts and thus its own transmission into distant populations.ConclusionsLandscape complexity plays a significant role in determining the path of host dispersal and patterns of pathogen transmission. Incorporating landscape heterogeneity and host behavior into disease management decisions can be important in targeting response efforts, identifying cryptic transmission opportunities, and reducing or understanding potential for unintended ecological and evolutionary consequences. The inclusion of these data into models of pathogen transmission patterns improves our understanding of these dynamics, ultimately proving beneficial for sound public health policy.

Divide and conquer: a four-fold docking experience of agent-based models

Verification and validation (V&V) techniques are used in agent-based modeling (ABM) to determine whether the model is an accurate representation of the real system. Docking is a form of V&V that tries to align multiple simulation models. In a previous paper, we described the docking process of an ABM that simulates the life cycle of Anopheles gambiae. Results showed that the implementations were docked for adult but not for aquatic mosquito populations. In this paper, following the ‘Divide and Conquer’ paradigm, we compartmentalize the simulation world to prohibit the propagation of errors between compartments. Using four separate implementations that sprung from the same core model, we describe a series of docking experiments, analyze the results, and show how they lead to a successful dock. The complete four-fold docking encompasses verification between the four implementations, as well as validation against the core model with respect to these implementations.

Spatial Agent-Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology

Bridging the gap between agent-based modeling and simulation (ABMS) and geographic information systems (GIS), Spatial Agent-Based Simulation Modeling in Public Health: Design, Implementation, and Applications for Malaria Epidemiology provides a useful introduction to the development of agent-based models (ABMs) by following a conceptual and biological core model of Anopheles gambiae for malaria epidemiology. Using spatial ABMs, the book includes mosquito (vector) control interventions and GIS as two example applications of ABMs, as well as a brief description of epidemiology modeling. In addition, the authors discuss how to most effectively integrate spatial ABMs with a GIS. The book concludes with a combination of knowledge from entomological, epidemiological, simulation-based, and geo-spatial domains in order to identify and analyze relationships between various transmission variables of the disease.

A spatial agent-based model of Anopheles vagus for malaria epidemiology: examining the impact of vector control interventions

BackgroundMalaria, being a mosquito-borne infectious disease, is still one of the most devastating global health issues. The malaria vector Anopheles vagus is widely distributed in Asia and a dominant vector in Bandarban, Bangladesh. However, despite its wide distribution, no agent based model (ABM) of An. vagus has yet been developed. Additionally, its response to combined vector control interventions has not been examined.MethodsA spatial ABM, denoted as ABM

Verification, Validation, and Replication Methods for Agent-Based Modeling and Simulation: Lessons Learned the Hard Way!

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