Predictive Modeling of Emerging Infections

Buczak, Anna L.; Babin, Steven M.; Feighner, Brian H.; Koshute, Phillip; Lewis, Sheri

doi:10.1002/9781118297469.ch13

Cited by 2 publications

(2 citation statements)

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“…After removal of duplicates, 248 records were screened at the title level, 146 at the abstract level, and 69 reports were fully read. Twenty-one reports were excluded during full-text reading: three were excluded due to incomplete model description [41][42][43], three modeled mosquito population only [44][45][46], ten were not mechanistic models [47][48][49][50][51][52][53][54][55][56], three were review papers [57][58][59] and two were theoretical without application to RVF [60,61]. Eventually, 49 studies were selected for the present review (Fig 1).…”

Section: Study Selectionmentioning

confidence: 99%

Mechanistic models of Rift Valley fever virus transmission: A systematic review

et al. 2022

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Rift Valley fever (RVF) is a zoonotic arbovirosis which has been reported across Africa including the northernmost edge, South West Indian Ocean islands, and the Arabian Peninsula. The virus is responsible for high abortion rates and mortality in young ruminants, with economic impacts in affected countries. To date, RVF epidemiological mechanisms are not fully understood, due to the multiplicity of implicated vertebrate hosts, vectors, and ecosystems. In this context, mathematical models are useful tools to develop our understanding of complex systems, and mechanistic models are particularly suited to data-scarce settings. Here, we performed a systematic review of mechanistic models studying RVF, to explore their diversity and their contribution to the understanding of this disease epidemiology. Researching Pubmed and Scopus databases (October 2021), we eventually selected 48 papers, presenting overall 49 different models with numerical application to RVF. We categorized models as theoretical, applied, or grey, depending on whether they represented a specific geographical context or not, and whether they relied on an extensive use of data. We discussed their contributions to the understanding of RVF epidemiology, and highlighted that theoretical and applied models are used differently yet meet common objectives. Through the examination of model features, we identified research questions left unexplored across scales, such as the role of animal mobility, as well as the relative contributions of host and vector species to transmission. Importantly, we noted a substantial lack of justification when choosing a functional form for the force of infection. Overall, we showed a great diversity in RVF models, leading to important progress in our comprehension of epidemiological mechanisms. To go further, data gaps must be filled, and modelers need to improve their code accessibility.

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Section: Study Selectionmentioning

confidence: 99%

Mechanistic models of Rift Valley fever virus transmission: A systematic review

et al. 2022

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“…To create sophisticated models for forecasting, the researchers must consider high-dimensional data on a large number of parameters: socioeconomic drivers (e.g., population growth and density, mixing patterns, migration, trade, agricultural practices, sanitation, age, diet, vaccination, drug and antibiotic use, cultural norms, occupational exposures, nutritional and immunological status), wildlife diversity, contact frequency, relatedness of host species, relatedness of microbial species present in host, evolvability of pathogens, host-pathogen coevolution, and several general factors such as capacities for reporting and response on the ground, geographical, and ecological conditions, and so on. An array of tools designed for predictive analytics, ranging from neural networks and tree models and regression models based on the observed parameters, to complex representations such as Bayesian network models that can capture their underlying dependence structure, are gaining in popularity (Buczak et al, 2013;Cooper et al, 2006).…”

Section: Disease Surveillancementioning

confidence: 99%