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Modelling to explore the potential impact of asymptomatic human infections on transmission and dynamics of African sleeping sickness
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Aliee, Maryam, Keeling, Matt J. and Rock, Kat S. (2021) Modelling to explore the potential impact of asymptomatic human infections on transmission and dynamics of African sleeping sickness. PLoS Computational Biology, 17 (9). e1009367. doi:10.1371/journal.pcbi.1009367
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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1009367
Abstract
We examine how firms leverage their resources, through FDI decisions into profits growth. Drawing on over 19,000 multinational firms, we employ a matching process and find that while investment in developed countries leads to productivity improvement, profits growth is not automatic, but requires continued productivity growth. Contrasting the emphasis placed on different firm-level resources by the resource-based view and the knowledge-based view, we show that a firm’s capability to invest in firm-specific assets accelerates the speed of reaping the rents from knowledge seeking FDI in developed countries. In addition, profits growth as a result from investing in developing countries is greater for firms who appoint foreign directors from the same global or regional cluster as their foreign subsidiaries. Moreover, developing country MNEs, if properly deploying their firm resources, can leverage the benefits of FDI location into performance better than developed country MNEs. Gambiense human African trypanosomiasis (gHAT, sleeping sickness) is one of several neglected tropical diseases (NTDs) where there is evidence of asymptomatic human infection but there is uncertainty of the role it plays in transmission and maintenance. To explore possible consequences of asymptomatic infections, particularly in the context of elimination of transmission—a goal set to be achieved by 2030—we propose a novel dynamic transmission model to account for the asymptomatic population. This extends an established framework, basing infection progression on a number of experimental and observation gHAT studies. Asymptomatic gHAT infections include those in people with blood-dwelling trypanosomes, but no discernible symptoms, or those with parasites only detectable in skin. Given current protocols, asymptomatic infection with blood parasites may be diagnosed and treated, based on observable parasitaemia, in contrast to many other diseases for which treatment (and/or diagnosis) may be based on symptomatic infection. We construct a model in which exposed people can either progress to either asymptomatic skin-only parasite infection, which would not be diagnosed through active screening algorithms, or blood-parasite infection, which is likely to be diagnosed if tested. We add extra parameters to the baseline model including different self-cure, recovery, transmission and detection rates for skin-only or blood infections. Performing sensitivity analysis suggests all the new parameters introduced in the asymptomatic model can impact the infection dynamics substantially. Among them, the proportion of exposures resulting in initial skin or blood infection appears the most influential parameter. For some plausible parameterisations, an initial fall in infection prevalence due to interventions could subsequently stagnate even under continued screening due to the formation of a new, lower endemic equilibrium. Excluding this scenario, our results still highlight the possibility for asymptomatic infection to slow down progress towards elimination of transmission. Location-specific model fitting will be needed to determine if and where this could pose a threat.
| Item Type: | Journal Article | |||||||||
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| Subjects: | R Medicine > RC Internal medicine | |||||||||
| Divisions: | Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics |
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| SWORD Depositor: | Library Publications Router | |||||||||
| Library of Congress Subject Headings (LCSH): | African trypanosomiasis, African trypanosomiasis -- Transmission -- Mathematical models | |||||||||
| Journal or Publication Title: | PLoS Computational Biology | |||||||||
| Publisher: | Public Library of Science | |||||||||
| ISSN: | 1553-7358 | |||||||||
| Official Date: | 13 September 2021 | |||||||||
| Dates: |
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| Volume: | 17 | |||||||||
| Number: | 9 | |||||||||
| Article Number: | e1009367 | |||||||||
| DOI: | 10.1371/journal.pcbi.1009367 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| RIOXX Funder/Project Grant: |
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