Background: In light of the media coverage concerning recent infectious disease outbreaks such as the ongoing influenza pandemic as well as previous ones involving Severe Acute Respiratory Syndrome (SARS), the public domain has become more conscience of infectious diseases as well as the effect of travel aiding their spread. Meningococcal meningitis has been a disease which among other factors such as age, climate and life style, is facilitated by travel. The causative bacterial agent, Neisseria meningitidis, being spread through aerosol respiratory droplets can cause Invasive Meningococcal Disease (IMD), and is a widely distributed, complex human disease affecting all age categories. Forecasting models exist for diseases based on vector-borne or wind related movement, as well as climate derived assessments, and recently travel oriented detection. Hence a combination of several factors ranging from meteorological to molecular level information should aid in determining likely occurrences of meningococcal meningitis epidemics in spatial and temporal means.
Methods: A systematic review was conducted in order to identify the different risks and models available in terms of IMD spread. Varying risk stratification was used to classify risk factors such as: travel related spread, respiratory co-infections, the effect of new meningococcal clones into a susceptible population, low humidity, high temperatures and lifestyle based aspects. These data will be used to create an early warning system generated through various technologies and transmission models in order to highlight risk areas and periods of ongoing IMD.
Results: As other studies have shown, travel patterns such as destination, duration and timing during the year affect risk of IMD development and spread among travelers, whilst climate, geography and factors such as virulence of circulating strain are more important in determining the severity of an outbreak in terms of local occurrence and potential epidemic developments.
Conclusion: Meningococcal meningitis is not only a well established threat in terms of epidemic or endemic occurrences, but also for travelers. Hence the development of more accurate and timely detection and forecasting methods are required to help in decision making processes involving prevention and early warning purposes.