Counter-intuitive COVID-19 Trajectories - Explanations, Early Warning Indicator and Mitigation Strategies

The COVID-19 trajectories worldwide have shown several surprising features which are outside the purview of classical epidemiological models. These include (a) almost constant and low daily case rates over extended periods of time, (b) sudden waves emerging from the above solution despite no or minimal change in the level of non-pharmaceutical interventions (NPI), and (c) reduction or flattening of case counts following relaxation of NPI. To explain these phenomena, we add contact tracing to our recently developed cluster seeding and transmission (CST) model. We find no fewer than four effects which make prediction of epidemic trajectories uncertain. These are (a) cryptogenic instability, where a small increase in population-averaged contact rate causes a large increase in cases, (b) critical mass effect, where a wave manifests after weeks of quiescence with no change in parameter values, (c) knife-edge effect, where a small change in parameter across a critical value causes a huge change in the response of the system, and (d) hysteresis effect, where the timing and not just the strength of a particular NPI determines the subsequent behaviour. Despite these effects however, some non-obvious conclusions regarding NPI appear to be robust. In particular, (a) narrowing the circle of one's social interactions can be as effective a measure as reducing interactions altogether, and (b) a good contact tracing program can effectively substitute for much more invasive measures. Finally, we propose the contact tracing capacity ratio - a metric of the load to which the tracers are subject - as a reliable early warning indicator of an imminent epidemic wave.

The Research of Coal and Gas Outburst Warning Based on Logistic Regression and Geographic Information System

Coal and gas outburst is one of the major disasters in the safety production of coal mine. According to the mechanism of coal and gas outburst, based on the comprehensive analysis of various influencing factors of coal and gas outburst, with the principles of selected early warning indicator, the basic information database of coal and gas outburst warning is constructed, and the information data query function is realized. The mathematical model of coal and gas outburst warning is established by the logistic regression analysis based on the gas concentration, the gas desorption index of drill cuttings, and the initial velocity of gas emission from the borehole. The multivariate information coupled warning was conducted according to the selected early warning indicator system, and the early warning level was divided with the result of early warning. The design and research of the coal and gas outburst warning system are carried out based on the geographic information system (GIS). The coal and gas outburst warning system was verified by taking the Tunliu mine of Lu’an Group as an example. The establishment of the early warning system is a new technical way to the early warning management of coal and gas outburst and can provide a guarantee for coal and gas outburst prevention.