arXiv subjects

Zhanshan Ma

Publications and source records attributed to Zhanshan Ma.

4 recordsLinked to original sources

arXiv2020

Spatiotemporal fluctuation scaling law and metapopulation modeling of the novel coronavirus (COVID-19) and SARS outbreaks

We comparatively analyzed the spatiotemporal fluctuations of the 2019-novel coronavirus (COVID-19) and SARS outbreaks to understand their epidemiological characteristics. Methodologically, we introduced TPL (Taylor power law) to characterize their spatiotemporal heterogeneity/stability and Hubbell (2001) unified neutral theory of biodiversity (UNTB) [specifically Harris et al. (2015) HDP-MSN model (hierarchical Dirichlet process multi-site neutral model)] to approximate the metapopulation of coronavirus infections. First, TPL analysis suggested that the coronaviruses appear to have a specific heterogeneity/stability scaling parameter (TPL-b) slightly exceeding 2 for cumulative infections or exceeding 1 for daily incremental infections, suggesting their potentially chaotic, unstable outbreaks. Another TPL parameter (M0) (i.e., infection critical threshold) depends on virus kinds (COVID-19/SARS), time (disease-stages), space (regions) and public-health interventions (e.g., quarantines and mobility control). M0 measures the infection level, at which infections are random (Poisson distribution) and below which infections follow uniform distribution and may die off if M0 coincides or below the level of Allee effects. It was found that COVID-19 outbreak seems nearly twice more risky than SARS, and the lower infection threshold may be due to its lower lethality than SARS since lower fatality rates can facilitate the survival and spread of pathogen. Second, metacommunity UNTB neutrality testing seems appropriate for approximating metapopulation of coronavirus infections. Specifically, two parameters θ and M, borrowed from neutral theory, may be used to assess the relative significance of infection through local contagion vs. infection through migration, both of which may depend on time, space, virus kinds, and particularly public-health interventions.

q-bio.PE↗

arXiv2017

Extending species-area relationships (SAR) to diversity-area relationships (DAR)

I extend the traditional SAR, which has achieved status of ecological law and plays a critical role in global biodiversity assessment, to the general (alpha- or beta-diversity in Hill numbers) diversity area relationship (DAR). The extension was motivated to remedy the limitation of traditional SAR that only address one aspect of biodiversity scaling, i.e., species richness scaling over space. The extension was made possible by the fact that all Hill numbers are in units of species (referred to as the effective number of species or as species equivalents), and I postulated that Hill numbers should follow the same or similar pattern of SAR. I selected three DAR models, the traditional power law (PL), PLEC (PL with exponential cutoff) and PLIEC (PL with inverse exponential cutoff). I defined three new concepts and derived their quantifications: (i)DAR profile: z-q series where z is the PL scaling parameter at different diversity order (q); (ii)PDO (pair-wise diversity overlap) profile: g-q series where g is the PDO corresponding to q; (iii) MAD (maximal accrual diversity) profile: Dmax-q series where Dmax is the MAD corresponding to q. Furthermore, the PDO-g is quantified based on the self-similarity property of the PL model, and Dmax can be estimated from the PLEC parameters. The three profiles constitute a novel DAR approach to biodiversity scaling. I verified the postulation with the American gut microbiome project (AGP) dataset of 1473 healthy North American individuals (the largest human dataset from a single project to date). The PL model was preferred due to its simplicity and established ecological properties such as self-similarity (necessary for establishing PDO profile), and PLEC has an advantage in establishing the MAD profile. All three profiles for the AGP dataset were successfully quantified and compared with existing SAR parameters in the literature whenever possible.

q-bio.PE↗

arXiv2017

A new dominance concept and its application to diversity-stability analysis

We introduce a new dominance concept consisting of three new dominance metrics based on Lloyd's (1967) mean crowding index. The new metrics link communities and species, whereas existing ones are applicable only to communities. Our community-level metric is a function of Simpson's diversity index. For species, our metric quantifies the difference between community dominance and the dominance of a virtual community whose mean population size (per species) equals the population size of the focal species. The new metrics have at least two immediate applications: (i) acting as proxies for diversity in diversity-stability modeling (ii) replacing population abundance in reconstructing species dominance networks. The first application is demonstrated here using data from a longitudinal study of the human vaginal microbiome, and provides new insights relevant for microbial community stability and disease etiology.

cs.CE↗

arXiv2014

Network analysis reveals a potentially 'evil' alliance of opportunistic pathogens inhibited by a cooperative network in human milk bacterial communities

The critical importance of human milk to infants and even human civilization has been well established. Although the human milk microbiome has received increasing attention with the expansion of research on the human microbiome, our understanding of the milk microbiome has been limited to cataloguing OTUs and computation of community diversity indexes. To the best of our knowledge, there has been no report on the bacterial interactions within the human milk microbiome. To bridge this gap, we reconstructed a milk bacterial community network with the data from Hunt et al (2011), which is the largest 16S-rRNA sequence data set of human milk microbiome available to date. Our analysis revealed that the milk microbiome network consists of two disconnected sub-networks. One sub-network is a fully connected complete graph consisting of seven genera as nodes and all of its pair-wise interactions among the bacteria are facilitative or cooperative. In contrast, the interactions in the other sub-network of 8 nodes are mixed but dominantly cooperative. Somewhat surprisingly, the only 'non-cooperative' nodes in the second sub-network are mutually cooperative Staphylococcus and Corynebacterium, genera that include some opportunistic pathogens. This potentially 'evil' alliance between Staphylococcus and Corynebacterium could be inhibited by the remaining nodes who cooperate with one another in the second sub-network. We postulate that the 'confrontation' between the 'evil' alliance and 'benign' alliance in human milk microbiome should have important health implications to lactating women and their infants and shifting the balance between the two alliances may be responsible for dysbiosis of the milk microbiome that permits mastitis. A related study focusing on ecological analysis was reported at (http://www.eurekalert.org/pub_releases/2014-09/scp-ahb090214.php).

q-bio.QM↗