Data science approaches to confronting the COVID-19 pandemic: a narrative review.

During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale 'big data' generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development.

Sustainable targeted interventions to mitigate the COVID-19 pandemic: A big data-driven modeling study in Hong Kong.

Nonpharmaceutical interventions (NPIs) for contact suppression have been widely used worldwide, which impose harmful burdens on the well-being of populations and the local economy. The evaluation of alternative NPIs is needed to confront the pandemic with less disruption. By harnessing human mobility data, we develop an agent-based model that can evaluate the efficacies of NPIs with individualized mobility simulations.

Mining e-cigarette adverse events in social media using Bi-LSTM recurrent neural network with word embedding representation.

Objective: Recent years have seen increased worldwide popularity of e-cigarette use. However, the risks of e-cigarettes are underexamined. Most e-cigarette adverse event studies have achieved low detection rates due to limited subject sample sizes in the experiments and surveys.

Transmission characteristics of different students during a school outbreak of (H1N1) pdm09 influenza in China, 2009.

Many outbreaks of A(H1N1)pdm09 influenza have occurred in schools with a high population density. Containment of school outbreaks is predicted to help mitigate pandemic influenza. Understanding disease transmission characteristics within the school setting is critical to implementing effective control measures.

[Quantitative evaluation on the effectiveness of prevention and control measures against pandemic influenza A (H1N1) in Beijing, 2009].

Objective: To quantitatively evaluate the effectiveness of prevention and control measures against pandemic influenza A (H1N1) in Beijing, 2009 and to provide evidence for developing and adjusting strategies for prevention and control of the disease.

Methods: Considering the seasonality and the number of vaccination on pandemic influenza A (H1N1), data regarding pandemic influenza A (H1N1) in Beijing were collected and analyzed. Based on the dynamics of infectious disease transmission, a quantitative model for evaluation of prevention and control measures was developed.

Spatio-temporal evolution of Beijing 2003 SARS epidemic.

Studying spatio-temporal evolution of epidemics can uncover important aspects of interaction among people, infectious diseases, and the environment, providing useful insights and modeling support to facilitate public health response and possibly prevention measures. This paper presents an empirical spatio-temporal analysis of epidemiological data concerning 2321 SARS-infected patients in Beijing in 2003. We mapped the SARS morbidity data with the spatial data resolution at the level of street and township.

[Comparison between early outbreak detection models and simulated outbreaks of measles in Beijing].

Objective: Using simulated outbreaks to choose the optimal model and its related parameters on measles so as to provide technical support for developing an Auto Warning System (AWS).

Methods: AEGIS-Cluster Creation Tool was applied to simulate a range of unique outbreak signals. Then these simulations were added to the actual daily counts of measles from the National Disease Surveillance System, between 2005 and 2007.