RESUMO
In this paper we conduct a simulation study of the spread of an epidemic like COVID-19 with temporary immunity on finite spatial and non-spatial network models. In particular, we assume that an epidemic spreads stochastically on a scale-free network and that each infected individual in the network gains a temporary immunity after its infectious period is over. After the temporary immunity period is over, the individual becomes susceptible to the virus again. When the underlying contact network is embedded in Euclidean geometry, we model three different intervention strategies that aim to control the spread of the epidemic: social distancing, restrictions on travel, and restrictions on maximal number of social contacts per node. Our first finding is that on a finite network, a long enough average immunity period leads to extinction of the pandemic after the first peak, analogous to the concept of "herd immunity". For each model, there is a critical average immunity duration Lc above which this happens. Our second finding is that all three interventions manage to flatten the first peak (the travel restrictions most efficiently), as well as decrease the critical immunity duration Lc , but elongate the epidemic. However, when the average immunity duration L is shorter than Lc , the price for the flattened first peak is often a high second peak: for limiting the maximal number of contacts, the second peak can be as high as 1/3 of the first peak, and twice as high as it would be without intervention. Thirdly, interventions introduce oscillations into the system and the time to reach equilibrium is, for almost all scenarios, much longer. We conclude that network-based epidemic models can show a variety of behaviors that are not captured by the continuous compartmental models.
RESUMO
Motivation: Computational approaches to the functional characterization of the microbiome, such as the Microbiome Modelling Toolbox, require precise information on microbial composition and relative abundances. However, challenges arise from homosynonyms-different names referring to the same taxon, which can hinder the mapping process and lead to missed species mapping when using microbial metabolic reconstruction resources, such as AGORA and APOLLO. Results: We introduce the integrated MARS pipeline, a user-friendly Python-based solution that addresses these challenges. MARS automates the extraction of relative abundances from metagenomic reads, maps species and genera onto microbial metabolic reconstructions, and accounts for alternative taxonomic names. It normalizes microbial reads, provides an optional cut-off for low-abundance taxa, and produces relative abundance tables apt for integration with the Microbiome Modelling Toolbox. A sub-component of the pipeline automates the task of identifying homosynonyms, leveraging web scraping to find taxonomic IDs of given species, searching NCBI for alternative names, and cross-reference them with microbial reconstruction resources. Taken together, MARS streamlines the entire process from processed metagenomic reads to relative abundance, thereby significantly reducing time and effort when working with microbiome data. Availability and implementation: MARS is implemented in Python. It can be found as an interactive application here: https://mars-pipeline.streamlit.app/along with a detailed documentation here: https://github.com/ThieleLab/mars-pipeline.