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Establishing communicable disease surveillance systems.

Thomas Falconer HallDavid Ross
Published in: BMJ military health (2021)
Humanitarian emergencies can result in an increase of communicable diseases, leading to a rise in mortality and/or morbidity in vulnerable populations. This requires a public health approach to re-establish control of communicable disease. Communicable disease surveillance systems play a key role, providing the information required for disease control measures, through systematic data collection, analysis, interpretation and dissemination. In humanitarian emergencies, they use the principles, practices and processes of wider surveillance systems, while being more focused on urgent priorities. However, communicable disease surveillance systems in humanitarian emergencies are constrained by multiple environmental, epidemiological and sociopolitical factors. Basic data collection, the bedrock of surveillance systems, can be extremely challenging and may require additional methods to estimate population size and prioritise diseases. Surveillance systems may be operating in conditions of weak state capacity with little physical or institutional infrastructure to support their operation. However, there are examples of successful self-sustaining disease surveillance systems in these circumstances, such as the deployment of WHO's Early Warning Alert and Response System in a Box. Individuals and organisations charged with establishing communicable disease surveillance systems in emergencies would be well advised to learn from recent examples of success, use the sources of planning guidance outlined in this article and seek advice from organisations with recent experience. This is a paper commissioned as a part of the Humanitarian and Disaster Relief Operations special issue of BMJ Military Health.
Keyphrases
  • public health
  • healthcare
  • mental health
  • primary care
  • type diabetes
  • physical activity
  • cardiovascular disease
  • risk factors
  • machine learning
  • cardiovascular events
  • health information
  • big data
  • data analysis