日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

A large-scale machine learning study of sociodemographic factors contributing to COVID-19 severity

一项大规模机器学习研究,旨在探究影响新冠肺炎严重程度的社会人口因素。

Tumbas, Marko; Markovic, Sofija; Salom, Igor; Djordjevic, Marko

Analyzing the GHSI puzzle of whether highly developed countries fared worse in COVID-19

分析全球健康指数(GHSI)之谜:发达国家在新冠疫情中的表现是否更差

Markovic, Sofija; Salom, Igor; Rodic, Andjela; Djordjevic, Marko

PM(2.5) as a major predictor of COVID-19 basic reproduction number in the USA

PM2.5是美国新冠病毒基本再生数的重要预测指标

Milicevic, Ognjen; Salom, Igor; Rodic, Andjela; Markovic, Sofija; Tumbas, Marko; Zigic, Dusan; Djordjevic, Magdalena; Djordjevic, Marko

Understanding Infection Progression under Strong Control Measures through Universal COVID-19 Growth Signatures

通过通用的 COVID-19 增长特征了解强有力控制措施下的感染进展

Djordjevic, Magdalena; Djordjevic, Marko; Ilic, Bojana; Stojku, Stefan; Salom, Igor

COVID-19 severity determinants inferred through ecological and epidemiological modeling

通过生态学和流行病学模型推断 COVID-19 严重程度的决定因素

Markovic, Sofija; Rodic, Andjela; Salom, Igor; Milicevic, Ognjen; Djordjevic, Magdalena; Djordjevic, Marko

Inferring the Main Drivers of SARS-CoV-2 Global Transmissibility by Feature Selection Methods

利用特征选择方法推断SARS-CoV-2全球传播的主要驱动因素

Djordjevic, Marko; Salom, Igor; Markovic, Sofija; Rodic, Andjela; Milicevic, Ognjen; Djordjevic, Magdalena

A systems biology approach to COVID-19 progression in population.

运用系统生物学方法研究 COVID-19 在人群中的传播

Djordjevic Magdalena, Rodic Andjela, Salom Igor, Zigic Dusan, Milicevic Ognjen, Ilic Bojana, Djordjevic Marko