Epidemiological dynamics with fine temporal resolution

Abstract: To better predict the dynamics of spread of COVID-19 epidemics, it is important not only to investigate the network of local and long-range contagious contacts, but also to understand the temporal dynamics of infectiousness and detectable symptoms. Here we present a model of infection spread in a well-mixed group of individuals, which usually corresponds to a node in large-scale epidemiological networks. The model uses delay equations that take into account the duration of infection and is based on experimentally-derived time courses of viral load, virus shedding, severity and detectability of symptoms. We show that because of an early onset of infectiousness, which is reported to be synchronous or even precede the onset of detectable symptoms, the tracing and immediate testing of everyone who came in contact with the detected infected individual reduces the spread of epidemics, hospital load, and fatality rate. We hope that this more precise node dynamics could be incorporated into complex large-scale epidemiological models to improve the accuracy and credibility of predictions.