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Al-Mamun, M. A.

Publications and source records attributed to Al-Mamun, M. A..

2 recordsLinked to original sources

Correlates of non-random patterns of capsule switching in pneumococcus

BackgroundPneumococcus is a diverse pathogen, with >90 serotypes, each of which has a distinct polysaccharide capsule. Pneumococci can switch capsules, evading vaccine pressure. Certain serotype pairs are more likely to switch, but the drivers of these patterns are not well understood.\n\nMethodsWe used the PubMLST and Global Pneumococcal Sequencing (GPS) databases to quantify the number of genetic lineages on which different serotype pairs co-occur. We also quantified the genetic diversity of each serotype. Regression models evaluated the relationship between shared polysaccharide structural components and the frequency of serotype switching and diversity.\n\nResultsA number of serotype pairs co-occurred on the same genetic lineage more commonly than expected. Co-occurrence of between-serogroup pairs was more common when both serotypes had glucose as a component of the capsule (and, potentially, glucuronic acid). Diversity also varied markedly by serotype and was lower for serotypes with glucuronic acid in the capsule and higher for those with galactose in the capsule.\n\nConclusionsCertain pairs of serotypes are more likely to occur on the same genetic background, and these patterns were correlated with shared polysaccharide components. This might indicate adaptation of strains to produce capsules with particular characteristics.

microbiology↗

A Data-driven Individual-based Model of Infectious Disease in Livestock Operation: A Validation Study for Paratuberculosis

Chronic livestock diseases cause large financial loss and affect the animal health and welfare. Controlling these diseases mostly requires precise information on both individual animal and population dynamics to inform farmers decision. Mathematical models provide opportunities to test different control and elimination options rather implementing them in real herds, but these models require valid parameter estimation and validation. Fitting these models to data is a difficult task due to heterogeneities in livestock processes. In this paper, we develop an infectious disease modeling framework for a livestock disease (paratuberculosis) that is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Infection with MAP leads to reduced milk production, pregnancy rates, and slaughter value and increased culling rates in cattle and causes significant economic losses to the dairy industry in the US. These economic effects are particularly important motivations in the control and elimination of MAP. In this framework, an individual-based model (IBM) of a dairy herd was built and a MAP infection was integrated on top of it. Once the model produced realistic dynamics of MAP infection, we implemented an evaluation method by fitting it to data from three dairy herds from the Northeast region of the US. The model fitting exercises used least-squares and parameter space searching methods to obtain the best-fitted values of selected parameters. The best set of parameters were used to model the effect of interventions. The results show that the presented model can complement real herd statistics where the intervention strategies suggested a reduction in MAP but no elimination was observed. Overall, this research not only provides a complete model for MAP infection dynamics in a cattle herd, but also offers a method for estimating parameter by fitting IBM models.

epidemiology↗