STEM: An Open Source Tool for Disease Modeling

Sex and age as determinants of the seroprevalence of anti-measles IgG among European healthcare workers: A systematic review and meta-analysis

INTRODUCTION

The international literature shows good evidence of a significant rate of measles susceptibility among healthcare workers (HCWs). As such, they are an important public health issue.

METHODS

We conducted a systematic review and meta-analysis to estimate the prevalence of susceptible HCWs in EU/EEA countries and in the UK and to explore the characteristics (sex and age differences) and management of those found to be susceptible.

RESULTS

Nineteen studies were included in the meta-analysis. The prevalence of measles-susceptible HCWs was 13.3% (95 %CI: 10.0-17.0%). In a comparison of serosusceptible female vs. male HCWs, the RR was 0.92 (95 %CI = 0.83-1.03), and in a comparison of age classes (born after vs. before 1980) the RR was 2.78 (95 %CI = 2.20-3.50). The most recent studies proposed the mandatory vaccination of HCWs.

DISCUSSION

According to our meta-analysis, the prevalence of serosusceptible European HCWs is 13%; HCWs born in the post-vaccination era seem to be at higher risk. Healthcare professionals susceptible to measles are a serious epidemiological concern. Greater efforts should therefore be made to identify those who have yet to be vaccinated and actively encourage their vaccination.

Predictive Model of Lyme Disease Epidemic Process Using Machine Learning Approach

Lyme disease is the most prevalent tick-borne disease in Eastern Europe. This study focuses on the development of a machine learning model based on a neural network for predicting the dynamics of the Lyme disease epidemic process. A retrospective analysis of the Lyme disease cases reported in the Kharkiv region, East Ukraine, between 2010 and 2017 was performed. To develop the neural network model of the Lyme disease epidemic process, a multilayered neural network was used, and the backpropagation algorithm or the generalized delta rule was used for its learning. The adequacy of the constructed forecast was tested on real statistical data on the incidence of Lyme disease. The learning of the model took 22.14 s, and the mean absolute percentage error is 3.79%. A software package for prediction of the Lyme disease incidence on the basis of machine learning has been developed. Results of the simulation have shown an unstable epidemiological situation of Lyme disease, which requires preventive measures at both the population level and individual protection. Forecasting is of particular importance in the conditions of hostilities that are currently taking place in Ukraine, including endemic territories.

Globally local: Hyper-local modeling for accurate forecast of COVID-19

Importance:

Assumption of a well-mixed population during modeling is often erroneously made without due analysis of its validity. Ignoring the importance of the geo-spatial granularity at which the data is collected could have significant implications on the quality of forecasts and the actionable clinical recommendations that are based on it.

Objective:

This paper’s primary objective is to test the hypothesis that the characteristic dynamics defining the trajectory of the pandemic in a region is lost when the data is aggregated and modeled at higher geo-spatial levels.

Design:

We use publicly available confirmed SARS-CoV-2 cases and deaths from January 1st, 2020 to August 3rd, 2020 in the United States at different geo-spatial granularities to conduct our experiments. To understand the impact of this hypothesis, the output of this study was implemented in Tampa General Hospital (TGH) to provide resource demand forecast.

Results:

The Mean Absolute Percentage Error (MAPE) in the forecast confirmed cases can be 30% higher for modeling at the state-level than aggregating model results at the scale of counties or clusters of counties. Similarly, modeling at a state-level and crafting policy decisions based on them may not be effective — county-level forecasts made by partitioning state-level forecasts are 3x worse for confirmed cases and 20x worse for deaths relative to the same model at the county level. By leveraging these results, TGH was able to accurately allocate clinical resources to tackle COVID-19 cases, continue elective surgical procedures largely uninterrupted and avoid costly construction of overflow capacity in the first two epidemic waves.

Conclusions and Relevance:

Accurate forecasting 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. Actionable decisions such as deciding whether to cancel planned surgeries or construct overflow capacity require models with local specificity.

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

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.

Leveraging Informatics and Technology to Support Public Health Response: Framework and Illustrations using COVID-19

Objective

To develop a conceptual model and novel, comprehensive framework that encompass the myriad ways informatics and technology can support public health response to a pandemic.

Method

The conceptual model and framework categorize informatics solutions that could be used by stakeholders (e.g., government, academic institutions, healthcare providers and payers, life science companies, employers, citizens) to address public health challenges across the prepare, respond, and recover phases of a pandemic, building on existing models for public health operations and response.

Results

Mapping existing solutions, technology assets, and ideas to the framework helped identify public health informatics solution requirements and gaps in responding to COVID-19 in areas such as applied science, epidemiology, communications, and business continuity. Two examples of technologies used in COVID-19 illustrate novel applications of informatics encompassed by the framework. First, we examine a hub from The Weather Channel, which provides COVID-19 data via interactive maps, trend graphs, and details on case data to individuals and businesses. Second, we examine IBM Watson Assistant for Citizens, an AI-powered virtual agent implemented by healthcare providers and payers, government agencies, and employers to provide information about COVID-19 via digital and telephone-based interaction.

Discussion

Early results from these novel informatics solutions have been positive, showing high levels of engagement and added value across stakeholders.

Conclusion

The framework supports development, application, and evaluation of informatics approaches and technologies in support of public health preparedness, response, and recovery during a pandemic. Effective solutions are critical to success in recovery from COVID-19 and future pandemics.