A better understanding of emerging epidemics
overcoming reporting delays
Researchers at TU Delft have discovered a groundbreaking way to better understand epidemics during their early onset - a critical period when every ounce of knowledge matters and can save lives. This innovative statistical method, which is up to twice as accurate as currently used epidemic models, has been published in the prestigious journal Proceedings of the National Academy of Sciences (PNAS) Nexus.
One of the most significant challenges in a new epidemic outbreak - one that can cost many lives - is the delay in data reporting. A natural lag in data collection can skew the initial understanding of the epidemic's progression, leading to inadequate response measures. To address this issue, Maksim Kitsak and his team developed a statistical method to eliminate the delay in reporting data. "This is important because exponential growth, especially in early phases, can be very dangerous because you underestimate the growth of an epidemic," explains Kitsak. This method is applicable to tracking infections and monitoring disease progression and the effectiveness of cures.
You can read the publication here: https://academic.oup.com/pnasnexus/article/3/6/pgae204/7679825
The team of network scientists, with Associate Professor Maksim Kitsak, Professor Piet van Mieghem, and PhD researchers Zhihao Qiu and Long Ma, used the fact that epidemics follow a simple inherent pattern: the rates of growth of recovered and deceased populations at any moment in time are expected to be proportional to the number of infectious individuals, as long as the virus does not mutate over the observational period. By leveraging this stable pattern, the researchers can clean the data and significantly improve the accuracy of epidemic forecasts.
A mathematical model that can save lives
The benefits of this clean data are profound. The research shows that this method can improve the accuracy of predictions by up to two times. This improvement is particularly valuable at the onset of an epidemic when the virus is not well understood, and precise information is crucial.
And while the method was developed based on COVID-19 outbreak datasets, it is not limited to COVID-19: "this is a framework for all messy, new, and unknown epidemic data sets," explains Zhihao Qiu. By employing this method, the researchers believe it is possible to better manage and predict epidemics, ultimately leading to more effective responses and improved public health outcomes. The implications of their work extend beyond the immediate benefits of more accurate epidemic forecasting. This method represents a significant advancement in epidemiology, providing a robust tool for public health officials and researchers to combat the spread of infectious diseases more effectively.
Het team van netwerkwetenschappers, met universitair hoofddocent Maksim Kitsak, professor Piet van Mieghem, en promovendi Zhihao Qiu en Long Ma maakte gebruik van het feit dat epidemieën een eenvoudig, inherent patroon volgen: de groeisnelheden van herstelde en overleden populaties worden verwacht evenredig te zijn met het aantal besmettelijke personen, zolang het virus niet muteert gedurende de observatieperiode. Door gebruik te maken van dit stabiele patroon kunnen de onderzoekers de gegevens zuiveren en de nauwkeurigheid van epidemische voorspellingen aanzienlijk verbeteren.
Een wiskundig model dat levens kan redden
De voordelen van deze schone gegevens zijn verregaand. Onderzoek toont aan dat deze methode de nauwkeurigheid van voorspellingen tot wel twee keer kan verbeteren. Deze verbetering is met name waardevol bij het begin van een epidemie wanneer het virus nog niet goed begrepen is en nauwkeurige informatie cruciaal is.