Emerging infectious diseases pose a growing threat to human populations. Many of the world’s epidemic diseases (particularly those transmitted by intermediate hosts) are known to be highly sensitive to long-term changes in climate and short-term fluctuations in the weather. The cases of worldwide spread of severe infectious diseases demonstrated the potential threat they pose in a closely interconnected and interdependent world. Real-time forecasts of infectious diseases can help public health planning, especially during outbreaks. If forecasts are generated from mechanistic models, they can be further used to target resources or to compare the impact of possible interventions.
However, there’s a limit to how accurate these forecasts can be, quotes Samuel Scarpino, an assistant professor in the Network Science Institute at the Northeastern University. In his study of 25 years of data of 10 infectious diseases he found that the diseases became harder to predict as more and more data was added. The problem is that outbreaks are inconsistent: People behave differently, a particular disease strain is unexpectedly virulent and vaccines are developed after. Rules change with every passing year. The researchers also found that certain characteristics could make a disease more difficult to forecast than others.
Accurate disease forecasting models would markedly improve epidemic prevention and control capabilities. Existing global systems for epidemic preparedness focus on disease surveillance using either expert knowledge or statistical modelling of disease activity and thresholds to identify times and areas of risk. Predictive health information systems would use monitored environmental variables, linked to a disease system, to be observed and provide prior information of outbreaks.