STEM: An Open Source Tool for Disease Modeling

Douglas, Judith V.; Bianco, Simone; Edlund, Stefan; Engelhardt, Tekla; Filter, Matthias; Günther, Taras; Hu, Kongxin; Nixon, Emily; Sevilla, Nereyda L; Swaid, Ahmad; Kaufman, James H.

doi:10.1089/hs.2019.0018

Cited by 21 publications

(11 citation statements)

References 41 publications

(45 reference statements)

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“…At present there are various modelling for the study of different disease with facilitating computational tools/ software. 31 Currently, compartmental models are widely used for the study of infectious disease. But large number of assumptions reduces the scope of such models.…”

Section: Methodsmentioning

confidence: 99%

Analysis of second wave of COVID-19 cases in Nepal with a logistic model

et al. 2021

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Background: COVID-19, caused by SARS-CoV-2, is a newly identified highly infectious disease. It has affected almost every country including Nepal causing a pandemic situation. Most of the properties of SARS-CoV-2 are not known and still under intense investigation. Due to high mutation rate, it reappears in many countries in the form of new variant. In Nepal, second wave impact of COVID-19 is mainly caused by newly found delta variant of SARS-CoV-2. In this case, the mathematical modelling is noted to play important role to understand control strategies for the spread of coronavirus. Aims and Objective: To analyze the second wave impact by modelling the data of COVID-19 cases in Nepal. Materials and Methods: We have analyzed COVID-19 daily cases and deaths reported by Ministry of Health and Population, Government of Nepal from April 1 to May 31, 2021. A logistic model has been used to present the trend line of COVID-19 infection in Nepal, based on the law of population growth developed by Verhulst. Results: The results show a good fit between observed and predicted data by logistic model as indicated by coefficient of determination having value near to unity. The point of inflection from the logistic model predicted a maximum of 9951 daily new cases. The maximum number of cumulative cases estimated at the end of second wave was found to be 307293 with 95% confidence interval. Conclusion: Logistic model properly describes the growth of COVID-19 cases with time. This type of data modelling and analysis will be very useful in predicting the upcoming trend of COVID-19 in Nepal as a basis for making health policy management by the government.

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Section: Methodsmentioning

confidence: 99%

Analysis of second wave of COVID-19 cases in Nepal with a logistic model

et al. 2021

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“…STEM software (version 1.3.8) ( Douglas et al, 2019 ) was applied to identify the specific gene expression clusters in PDLSCs treated with SDF-1, EX-4 and SDF-1+EX-4. The genes in the upregulated clusters in SDF-1+EX4-treated PDLSCs were selected, and the expression of all genes in these clusters was shown by a heatmap.…”

Section: Methodsmentioning

confidence: 99%

Transcriptome analysis reveals the mechanism of stromal cell-derived factor-1 and exendin-4 synergistically promoted periodontal ligament stem cells osteogenic differentiation

Kang

Liang

et al. 2021

PeerJ

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Stromal cell-derived factor-1 (SDF-1) and Exendin-4 (EX-4) play beneficial roles in promoting periodontal ligament stem cells (PDLSCs) osteogenic differentiation, while the detailed mechanism has not been clarified. In this study, we aimed to evaluate the biological mechanism of SDF-1 and EX-4 alone or synergistic application in regulating PDLSCs differentiation by RNA-sequencing (RNA-seq). A total of 110, 116 and 109 differentially expressed genes (DEGs) were generated in osteogenic medium induced PDLSCs treated by SDF-1, EX-4, and SDF-1+EX-4, respectively. The DEGs in SDF-1 group were enriched in signal transduction related signaling pathways; the DEGs in EX-4 group were enriched in metabolism and biosynthesis-related pathways; and the DEGs generated in SDF-1+EX-4 group were mainly enriched in RNA polymerase II transcription, cell differentiation, chromatin organization, protein phosphorylation pathways. Based on Venn analysis, a total of 37 specific DEGs were identified in SDF-1+EX-4 group, which were mainly enriched in negative regulation of autophagy and cellular component disassembly signaling pathways. Short time-series expression miner (STEM) analysis grouped all expressed genes of PDLSCs into 49 clusters according to the dynamic expression patterns and 25 genes, including NRSN2, CHD9, TUBA1A, distributed in 10 gene clusters in SDF-1+EX-4 treated PDLSCs were significantly up-regulated compared with the SDF-1 and EX-4 alone groups. The gene set enrichment analysis indicated that SDF-1 could amplify the role of EX-4 in regulating varied signaling pathways, such as type II diabetes mellitus and insulin signaling pathways; while EX-4 could aggravate the effect of SDF-1 on PDLSCs biological roles via regulating primary immunodeficiency, tight junction signaling pathways. In summary, our study confirmed that SDF-1 and EX-4 combined application could enhance PDLSCs biological activity and promote PDLSCs osteogenic differentiation by regulating the metabolism, biosynthesis and immune-related signaling pathways.

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“…For this study we chose a modeling framework, the SpatioTemporal Epidemiological Modeler (STEM) 13,14 , available through the Eclipse Foundation 15 . The framework and model are open source and available under the Eclipse Public License (EPL2) 16 .…”

Section: Methodsmentioning

confidence: 99%

Globally Local: Hyper-local Modeling for Accurate Forecast of COVID-19

Gopalakrishnan

Pethe

Kefayati

et al. 2020

Preprint

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Multiple efforts to model the epidemiology of SARS-CoV-2 have recently been launched in support of public health response at the national, state, and county levels. While the pandemic is global, the dynamics of this infectious disease varies with geography, local policies, and local variations in demographics. An underlying assumption of most infectious disease compartment modeling is that of a well mixed population at the resolution of the areas being modeled. The implicit need to model at fine spatial resolution is impeded by the quality of ground truth data for fine scale administrative subdivisions. To understand the trade-offs and benefits of such modeling as a function of scale, we compare the predictive performance of a SARS-CoV-2 modeling at the county, county cluster, and state level for the entire United States. Our results demonstrate that accurate prediction at the county level requires hyper-local modeling with county resolution. State level modeling does not accurately predict community spread in smaller sub-regions because state populations are not well mixed, resulting in large prediction errors. As an important use case, leveraging high resolution modeling with public health data and admissions data from Hillsborough County Florida, we performed weekly forecasts of both hospital admission and ICU bed demand for the county. The repeated forecasts between March and August 2020 were used to develop accurate resource allocation plans for Tampa General Hospital.2010 MSC92-D30, 91-C20

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STEM: An Open Source Tool for Disease Modeling

Cited by 21 publications

References 41 publications

Analysis of second wave of COVID-19 cases in Nepal with a logistic model

Analysis of second wave of COVID-19 cases in Nepal with a logistic model

Transcriptome analysis reveals the mechanism of stromal cell-derived factor-1 and exendin-4 synergistically promoted periodontal ligament stem cells osteogenic differentiation

Globally Local: Hyper-local Modeling for Accurate Forecast of COVID-19

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