7 research outputs found
Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning
Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation
Host movement, transmission hot spots, and vector-borne disease dynamics on spatial networks
We examine how spatial heterogeneity combines with mobility network structure
to influence vector-borne disease dynamics. Specifically, we consider a
Ross-Macdonald-type disease model on spatial locations that are coupled by
host movement on a strongly connected, weighted, directed graph. We derive a
closed form approximation to the domain reproduction number using a Laurent
series expansion, and use this approximation to compute sensitivities of the
basic reproduction number to model parameters. To illustrate how these results
can be used to help inform mitigation strategies, as a case study we apply
these results to malaria dynamics in Namibia, using published cell phone data
and estimates for local disease transmission. Our analytical results are
particularly useful for understanding drivers of transmission when mobility
sinks and transmission hot spots do not coincide.Comment: A few minor notation typos. 1) Figure 1, N_{i} corrected to
N_{i}^{h}. 2) Typo in vector equations, system 2.1. N_{i} corrected to
N_{i}^{h} and I_{i} corrected to I_{i}^{h} 3) On page 10, \mu_{v,i} corrected
to \mu{i}^{v
Review of Importance of Weather and Environmental Variables in Agent-Based Arbovirus Models
The study sought to review the works of literature on agent-based modeling and the influence of climatic and environmental factors on disease outbreak, transmission, and surveillance. Thus, drawing the influence of environmental variables such as vegetation index, households, mosquito habitats, breeding sites, and climatic variables including precipitation or rainfall, temperature, wind speed, and relative humidity on dengue disease modeling using the agent-based model in an African context and globally was the aim of the study. A search strategy was developed and used to search for relevant articles from four databases, namely, PubMed, Scopus, Research4Life, and Google Scholar. Inclusion criteria were developed, and 20 articles met the criteria and have been included in the review. From the reviewed works of literature, the study observed that climatic and environmental factors may influence the arbovirus disease outbreak, transmission, and surveillance. Thus, there is a call for further research on the area. To benefit from arbovirus modeling, it is crucial to consider the influence of climatic and environmental factors, especially in Africa, where there are limited studies exploring this phenomenon
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A Holistic Approach to Dynamic Modelling of Malaria Transmission. An Investigation of Climate-Based Models used for Predicting Malaria Transmission
The uninterrupted spread of malaria, besides its seasonal uncertainty, is
due to the lack of suitable planning and intervention mechanisms and
tools. Several studies have been carried out to understand the factors
that affect the development and transmission of malaria, but these efforts
have been largely limited to piecemeal specific methods, hence they do
not offer comprehensive solutions to predict disease outbreaks. This thesis introduces a ’holistic’ approach to understand the relationship between
climate parameters and the occurrence of malaria using both mathematical and computational methods. In this respect, we develop new climate-based models using mathematical, agent-based and data-driven modelling
techniques. A malaria model is developed using mathematical modelling
to investigate the impact of temperature-dependent delays. Although this method is widely applicable, but it is limited to the study of homogeneous
populations. An agent-based technique is employed to address this limitation, where the spatial and temporal variability of agents involved in the transmission of malaria are taken into account. Moreover, whilst the mathematical and agent-based approaches allow for temperature and precipitation in the modelling process, they do not capture other dynamics that might potentially affect malaria. Hence, to accommodate the climatic predictors of malaria, an intelligent predictive model is developed using
machine-learning algorithms, which supports predictions of endemics in
certain geographical areas by monitoring the risk factors, e.g., temperature
and humidity. The thesis not only synthesises mathematical and computational methods to better understand the disease dynamics and its transmission, but also provides healthcare providers and policy makers with better planning and intervention tools