Infectious Disease Modeling and Epidemic Response Measures Analysis Considering Asymptomatic Infection.

Classical SIR dynamic model and its derivative improved model may not accurately describe the epidemic situation similar to COVID-19 with characteristics of relative long incubation period and a large number of asymptomatic infections. Based on the existing epidemic compartment model, a novel compartment dynamic model considering actual transmission path of the symptomatic and asymptomatic infected is presented. Theoretical analysis and numerical simulation are employed to conduct prediction of development of the epidemic. According to different epidemic response measures, i.e., mitigation measures, suppression measures, medical treatment, evolutionary trend of epidemic situation under the initial population distribution structure are discussed. Results show that the control effects of different response measures on the number of deaths depend on the timing of the implementation of the measures. For mitigation response measures, the timing of the implementation of the measures has no obvious effect on the final epidemic, while for suppression response measures, the effect of suppression response measures in the early stage of the epidemic is significantly better than that in the middle and late stage of the epidemic development. Furthermore, no matter which stage the epidemic is in, the improvement of medical treatment level will play an important role in effectively reducing mortality. This study provides useful enlightenment and decision-making reference for policy makers to choose appropriate epidemic prevention and response measures in practice.

A closer look at the spreaders of COVID-19 in Wisconsin and the US.

In this study, we design and use a mathematical model to primarily address the question of who are the main drivers of COVID-19 - the symptomatic infectious or the pre-symptomatic and asymptomatic infectious in the state of Wisconsin and the entire United States. To set the stage, we first briefly simulate and illustrate the benefit of lockdown. With these lockdown scenarios, and in general, the more dominant influence of the the pre-symptomatic and asymptomatic infectious over the symptomatic infectious, is shown in various ways. Numerical simulations for the U.S. show that an increase in testing and isolating for the pre-symptomatic and asymptomatic infectious group has up to 4 times more impact than an increase in testing for the symptomatic infectious in terms of cumulative deaths. An increase in testing for the pre-symptomatic and asymptomatic infectious group also has significantly more impact (on the order of twice as much) on reducing the control reproduction number than testing for symptomatic infectious. Lastly, we use our model to simulate an implementation of a natural herd immunity strategy for the entire U.S. and for the state of Wisconsin (once an epicenter for COVID-19). These simulations provide specific examples confirming that such a strategy requires a significant number of deaths before immunity is achieved, and as such, this strategy is certainly questionable in terms of success.