DengueME: A Tool for the Modeling and Simulation of Dengue Spatiotemporal Dynamics

A Coupled Statistical and Deterministic Model for Forecasting Climate-Driven Dengue Incidence in Selangor, Malaysia

The mosquito-borne dengue virus remains a major public health concern in Malaysia. Despite various control efforts and measures introduced by the Malaysian Government to combat dengue, the increasing trend of dengue cases persists and shows no sign of decreasing. Currently, early detection and vector control are the main methods employed to curb dengue outbreaks. In this study, a coupled model consisting of the statistical ARIMAX model and the deterministic SI-SIR model was developed and validated using the weekly reported dengue data from year 2014 to 2019 for Selangor, Malaysia. Previous studies have shown that climate variables, especially temperature, humidity, and precipitation, were able to influence dengue incidence and transmission dynamics through their effect on the vector. In this coupled model, climate is linked to dengue disease through mosquito biting rate, allowing real-time forecast of dengue cases using climate variables, namely temperature, rainfall and humidity. For the period chosen for model validation, the coupled model can forecast 1-2 weeks in advance with an average error of less than 6%, three weeks in advance with an average error of 7.06% and four weeks in advance with an average error of 8.01%. Further model simulation analysis suggests that the coupled model generally provides better forecast than the stand-alone ARIMAX model, especially at the onset of the outbreak. Moreover, the coupled model is more robust in the sense that it can be further adapted for investigating the effectiveness of various dengue mitigation measures subject to the changing climate.

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

Dengue Risk Forecast with Mosquito Vector: A Multicomponent Fusion Approach Based on Spatiotemporal Analysis

Dengue as an acute infectious disease threatens global public health and has sparked broad research interest. However, existing studies generally ignore the spatial dependencies involved in dengue forecast, and consideration of temporal periodicity is absent. In this work, we propose a spatiotemporal component fusion model (STCFM) to solve the dengue risk forecast issue. Considering that mosquitoes are an important vector of dengue transmission, we introduce feature factors involving mosquito abundance and spatiotemporal lags to model temporal trends and spatial distributions separately on the basis of statistical properties. Specifically, we conduct multiscale modeling of temporal dependencies to enhance the forecast capability of relevant periods by capturing the historical variation patterns of the data across different segments in the temporal dimension. In the spatial dimension, we quantify the multivariate spatial correlation analysis as additional features to strengthen the spatial feature representation and adopt the ConvLSTM model to learn spatial dependencies adequately. The final forecast results are obtained by stacking strategy fusion in ensemble learning. We conduct experiments on real dengue datasets. The results indicate that STCFM improves prediction accuracy through effective spatiotemporal feature representations and outperforms candidate models with a reasonable component construction strategy.

Human factors applications in the design of decision support systems for population health: a scoping review

INTRODUCTION

Public health professionals engage in complex cognitive tasks, often using evidence-based decision support tools to bolster their decision-making. Human factors methods take a user-centred approach to improve the design of systems, processes, and interfaces to better support planning and decision-making. While human factors methods have been applied to the design of clinical health tools, these methods are limited in the design of tools for population health. The objective of this scoping review is to develop a comprehensive understanding of how human factors techniques have been applied in the design of population health decision support tools.

METHODS AND ANALYSIS

The scoping review will follow the methodology and framework proposed by Arksey and O'Malley. We include English-language documents between January 1990 and August 2021 describing the development, validation or application of human factors principles to decision support tools in population health. The search will include Ovid MEDLINE: Epub Ahead of Print, In-Process and Other Non-Indexed Citations, Ovid MEDLINE Daily and Ovid MEDLINE 1946-present; EMBASE, Scopus, PsycINFO, Compendex, IEEE Xplore and Inspec. The results will be integrated into Covidence. First, the abstract of all identified articles will be screened independently by two reviewers with disagreements being resolved by a third reviewer. Next, the full text for articles identified as include or inconclusive will be reviewed by two independent reviewers, leading to a final decision regarding inclusion. Reference lists of included articles will be manually screened to identify additional studies. Data will be extracted by one reviewer, verified by a second, and presented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews.

ETHICS AND DISSEMINATION

Ethics approval is not required for this work as human participants are not involved. The completed review will be published in a peer-reviewed, interdisciplinary journal.

An Agent-Based Simulation of the Spread of Dengue Fever

Vector-borne diseases (VBDs) account for more than 17% of all infectious diseases, causing more than 700,000 annual deaths. Lack of a robust infrastructure for timely collection, reporting, and analysis of epidemic data undermines necessary preparedness and thus posing serious health challenges to the general public. By developing a simulation framework that models population dynamics and the interactions of both humans and mosquitoes, we may enable epidemiologists to analyze and forecast the transmission and spread of an infectious disease in specific areas. We extend the traditional SEIR (Susceptible, Exposed, Infectious, Recovered) mathematical model and propose an Agent-based model to analyze the interactions between the host and the vector using: (i) our proposed algorithm to compute vector density, based on the reproductive behavior of the vector; and (ii) agent interactions to simulate transmission of virus in a spatio-temporal environment, and forecast the spread of the disease in a given area over a period of time. Our simulation results identify several expected dengue cases and their direction of spread, which can help in detecting epidemic outbreaks. Our proposed framework provides visualization and forecasting capabilities to study the epidemiology of a certain region and aid public health departments in emergency preparedness.

Smartphone geospatial apps for dengue control, prevention, prediction, and education: MOSapp, DISapp, and the mosquito perception index (MPI)

India has the largest number of dengue cases in the world, contributing approximately 34% of the global burden. The framework for a geospatially enabled early warning and adaptive response system (EWARS) was first proposed in 2008. It was meant to be a decision support system for enhancing traditional surveillance methods for preventing mosquito-borne diseases in India by utilizing remote sensing data and fuzzy logic-based mathematical predictive modeling. This conceptual paper presents a significant evolution of EWARS such that it synthesizes inputs from not only traditional surveillance and reporting systems for dengue but also from the public via participatory disease surveillance. Two smartphone-based applications have been developed to support EWARS. The first-MOSapp-allows field health workers to upload surveillance data and collect key data on environmental parameters by both direct observation and via portable microclimate stations. The second-DISapp-collects relevant information directly from the community to support participatory disease surveillance. It also gives the user a real-time estimate of the risk of exposure to dengue in proximity to their home and has an educational component that provides information on relevant preventive measures. Both applications utilize a new mosquito abundance measure-the mosquito perception index (MPI)-as reported by the user. These data streams will feed into the EWARS model to generate dynamic risk maps that can guide resource optimization and strengthen disease surveillance, prevention, and response. It is anticipated that such an approach can assist in addressing gaps in the current system of dengue surveillance and control in India.

Social Simulation Models at the Ethical Crossroads

Computational models of group opinion dynamics are one of the most active fields of sociophysics. In recent years, advances in model complexity and, in particular, the possibility to connect these models with detailed data describing individual behaviors, preferences and activities, have opened the way for the simulations to describe quantitatively selected, real world social systems. The simulations could be then used to study 'what-if' scenarios for opinion change campaigns, political, ideological or commercial. The possibility of the practical application of the attitude change models necessitates that the research community working in the field should consider more seriously the moral aspects of their efforts, in particular the potential for their use for unintended goals. The paper discusses these issues, and offers a suggestion for a new research direction: using the attitude models to increase the awareness and detection of social manipulation cases. Such research would offer a scientific challenge and meet the ethical criteria.

Using Multiple Scale Spatio-Temporal Patterns for Validating Spatially Explicit Agent-Based Models

Spatially explicit agent-based models (ABMs) have been widely utilized to simulate the dynamics of spatial processes that involve the interactions of individual agents. The assumptions embedded in the ABMs may be responsible for uncertainty in the model outcomes. To ensure the reliability of the outcomes in terms of their space-time patterns, model validation should be performed. In this paper, we propose the use of multiple scale spatio-temporal patterns for validating spatially explicit ABMs. We evaluated several specifications of vector-borne disease transmission models by comparing space-time patterns of model outcomes to observations at multiple scales via the sum of root mean square error (RMSE) measurement. The results indicate that specifications of the spatial configurations of residential area and immunity status of individual humans are of importance to reproduce observed patterns of dengue outbreaks at multiple space-time scales. Our approach to using multiple scale spatio-temporal patterns can help not only to understand the dynamic associations between model specifications and model outcomes, but also to validate spatially explicit ABMs.