Digital Epidemiology & Connectivity
Updated: Dec 17, 2021
Epidemiology is concerned with the dynamics of health and disease in human populations. Research in epidemiology aims to identify the distribution, incidence, and etiology of human diseases to improve the understanding of the causes of diseases and to prevent their spread.
Traditionally, epidemiology has been based on data collected by public health agencies through health personnel in hospitals, doctors' offices, and out in the field. With the advent of mobile analytics, extracting meaningful information holds unparalleled potential for epidemiology. The observation of the spatio-temporal movements of millions of people during disease outbreaks, the rapid detection of an unusual respiratory illness in a remote village anywhere on the globe, the near real-time estimation of influenza activity levels, and the assessment of vaccination sentiments during pandemic preparedness efforts are examples of realizations of this potential.
The everyday movements of humans create the dynamic links that connect populations and enable geographic spread and sustained transmission of infectious diseases. Mobile phone data in the form of call data records provide one of today's most exciting opportunities to study human mobility and connectivity.
Recent studies have shown that one of the main reasons for some epidemic turning into pandemics is the connectivity among different regions of the world, which makes it easier to affect a wider geographical area, restricting the mobility from the top-10 percentile of connected locations can reduce the number of infected individuals substantially.
Researchers have shown that mathematical proof that the reproduction number R0 is directly depends upon social connectivity of individuals, number of connected locations and individuals mobility between locations.
After lifting prolonged lockdowns, governments may be gradually re-open their nations’ economy. Managing connectivity of various places with high mobility and movement may be a useful tool. Simulation reveals that the mobility based SIR model can be helpful to forecast the expected number of cases. Measures such as temporarily suspending certain key transportation hubs such as major interchanges in mass transit networks may be useful.