Time to lead the prevention and control of public health emergencies by informatics technologies in an information era

On March 11, 2020, the World Health Organization (WHO) announced that the novel coronavirus (COVID-19) outbreak can be characterized a pandemic.¹ Thereafter, we have witnessed the exponential growth of COVID-19 cases globally over the past few months (Fig. 1 ). This pandemic has comprehensively disrupted daily life and social-economic activity for everyone. As a result of the significant disruption caused by the COVID-19 pandemic, we are acutely aware that many activities face difficulties in meeting their normal process timelines. Although most of them have been slowed down, some must be accelerated, such as epidemic simulation and data sharing, and a variety of novel methods are being introduced and performed to provide evidence for disease control.

Fig. 1.

The growth of COVID-19 cases globally from December 2019 to May 2020.

Numerous models such as the classical dynamic model (SEIS) and artificial intelligence (AI) tools, are helping in the fight against the COVID-19 pandemic by predicting the spread of the disease, hospital demand, diagnosis, and death.2, 3, 4, 5, 6, 7 Epidemic forecasting is helping decision-makers design prompt and flexible policies that allow interventions to be implemented effectively and medical resources allocated reasonably.8, 9, 10, 11 These forecasts which use different types of data (e.g., COVID-19 data, demographic data, mobility data, etc.), methods (e.g., exponential and linear statistical models, SEIR model, Mechanistic Bayesian compartment model, Nonparametric spatiotemporal model, Nonlinear Bayesian hierarchical regression with a negative-binomial model, etc.) and estimates of the impact of interventions (e.g., social distancing, use of face coverings, etc.), help in understanding how they compare to each other and the level of uncertainty regarding what may happen in the upcoming months.¹² Furthermore, many advanced technologies are being used to identify the emerging risk from COVID-19 and help notify the government via visual platform.¹³ These technologies reveal COVID-19′s spread and deliver regular reporting to answer the most pressing questions.¹⁴ Meanwhile, many experts intend to present the first step towards building an artificial intelligence framework, with predictive analytics capabilities applied to real patient data, to provide rapid clinical decision-making support.15, 16 Thus, when a public health emergency occurs, there will be many potential options to block transmission. But which option is effective and feasible? This was unknown at the beginning of the epidemic; however, a variety of models which were developed to evaluate the interventions provide evidence for intervention selection to policymakers.17, 18, 19, 20, 21, 22, 23 This is valuable to controlling the spread.

Real-time data sharing makes spread forecasting for public health emergencies and the evaluation of various interventions available. The web-based platform makes a great contribution to data transparency in the epidemic. These platforms provide a prompt and uniform route for global researchers.24, 25, 26, 27, 28 Furthermore, the visual dashboard makes it easier for all people to understand the meaning behind the data.29, 30, 31, 32, 33, 34

The rapid spread of COVID-19, and the fact that healthcare facilities could be sources of contagion, has focused attention on new models of care that avoid face-to-face contact between clinician and patient.³⁵ With the first emergency COVID-19 authorization, the US government lifted provisions that limited telemedicine services to rural areas, allowing the use of telemedicine services for all beneficiaries of fee-for-service Medicare.³⁶ These video consultations had already been deployed in many countries as part of their national, digital health strategies. These changes will have a huge impact on our lives.

Consequently, the COVID-19 pandemic presents a great challenge to global health. The turning point, however, where informatics technologies lead the prevention and control of public health emergencies in an information era, has already arrived.

Declarations

Conflicts of interest: None of the authors have expressed any conflict of interest.

Author contributions: FZ/PZ conceived and designed the study and implemented quality control; FZ/YJZ/ZM contributed to data collection and analyses and the writing for the first and subsequent drafts; FZ/PZ contributed to refining the data design, interpreting the data, revising and editing the manuscript. All authors read and approved the final manuscript and agree to be accountable for it.

Funding: No.

Availability of data and material: The data used for this study can be freely downloaded from Data on COVID-19 by Our World in Data: https://github.com/owid/covid-19-data/tree/master/public/data/.