• Epidemics remain a major public health issue.
  • Data science enters when traditional modeling methods fail.
  • Occurrence analytics can handle complex data related to pandemic assessment.

Over the last two decades, more new communicable diseases have been identified that are of global concern than at any similar time in history. [1]. Ebola, influenza A (H1N1), SARS, MERS, Zika virus were all worrying, but it wasn’t until the COVID-19 pandemic that the world felt acutely inadequacy of the traditional outbreak control system. Despite the best efforts of officials to curb the outbreak of Covid-19, traditional methods — surveillance, response, and management — failed to address a large number of warnings, in part because of inadequate procedures used to process different data.

Successful investigation and control of communicable disease cases is based on the analysis of complex and diverse sources of information. The analysis of this data can only be achieved through a multidisciplinary approach, using a number of different, complementary approaches and tools, including: epidemic analysis. Clinical researchers and medical professionals around the world joined forces to find ways to process complex data, open source data, and work together on system improvements [2].

Review of outbreak analysis

Performance analytics was developed to focus technical and methodological aspects of the outbreak information tube from data collection to information on the control of outbreaks. It is at the crossroads of computer science and many areas of public health, including design, field epidemiology, and methodological development. Occurrence analytics is part of an overall prevention and control plan that includes several other core pillars of disease control, including case management, surveillance and contact tracing, logistics and testing. [3].

The multidisciplinary field uses data science methods from many perspectives report an outbreakincluding [4]:

  • Bayesian statistics,
  • Database design and mobile technology,
  • Evidence synthesis approaches
  • Frequentist statistics,
  • Geostatistics,
  • Graph theory,
  • Interactive data visualization,
  • Mathematical modeling,
  • Maximum likelihood estimation,
  • Genetic analysis.

It can help answer questions like [4]:

  • What are mortality and risk factors?
  • Could a rapid test help reduce incidence?
  • What is the optimal vaccination strategy?
  • Should international travel be restricted?
  • Has the delay between the onset of symptoms and hospitalization been reduced?

Despite outbreak analysis over the past 18 months, such as contact tracing, pandemic modeling, and risk assessment, the introduction of outbreak analytics has been at a snail’s pace. There is still a lack of a coherent platform for analyzing outbreaks [5].

The future of outbreak analysis

The emergence of occurrence analytics underscores the need for freely available, high-quality, and open source methods to combat infectious diseases. Although it is not yet fully recognized as an industry that deserves recognition and support [6], it is likely the development of case analysis continues. Many agencies, including the World Health Organization and UNICEF, have already introduced outbreak analysis in their programs. Earlier this year, a significant step towards recognition was taken when Whitehouse directed the Assistant to the President of National Security (APNSA), in collaboration with other coordinators and agencies, to develop a plan to establish an inter-agency national center. Epidemic prediction and analysis of outbreaks [7]. As globalization leads to an increasing risk of a pandemic, we can expect to hear much more in the future from this emerging field of computing.

References

Photo: Author

[1] To prevent future pandemics, the United States should invest in real-time …

[2] How data science helped combat the outbreak of the coronavirus

[3] Back to basics: Outbreak control poles

[4] Performance analytics: evolving data science res …

[5] OutbreakTools: A new platform for analyzing outbreaks t …

[6] Why the development of disease analysis tools should be appreciated, it is worth …

[7] National Security Memorandum on US Global Leadership …

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