A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic.

BACKGROUND: Testing is one of the most effective means to manage the COVID-19 pandemic. However, there is an upper bound on daily testing volume because of limited healthcare staff and working hours, as well as different testing methods, such as random testing and contact-tracking testing. In this study, a network-based epidemic transmission model combined with a testing mechanism was proposed to study the role of testing in epidemic control. The aim of this study was to determine how testing affects the spread of epidemics and the daily testing volume needed to control infectious diseases. METHODS: We simulated the epidemic spread process on complex networks and introduced testing preferences to describe different testing strategies. Different networks were generated to represent social contact between individuals. An extended susceptible-exposed-infected-recovered (SEIR) epidemic model was adopted to simulate the spread of epidemics in these networks. The model establishes a testing preference of between 0 and 1; the larger the testing preference, the higher the testing priority for people in close contact with confirmed cases. RESULTS: The numerical simulations revealed that the higher the priority for testing individuals in close contact with confirmed cases, the smaller the infection scale. In addition, the infection peak decreased with an increase in daily testing volume and increased as the testing start time was delayed. We also discovered that when testing and other measures were adopted, the daily testing volume required to keep the infection scale below 5% was reduced by more than 40% even if other measures only reduced individuals' infection probability by 10%. The proposed model was validated using COVID-19 testing data. CONCLUSIONS: Although testing could effectively inhibit the spread of infectious diseases and epidemics, our results indicated that it requires a huge daily testing volume. Thus, it is highly recommended that testing be adopted in combination with measures such as wearing masks and social distancing to better manage infectious diseases. Our research contributes to understanding the role of testing in epidemic control and provides useful suggestions for the government and individuals in responding to epidemics.

Scenario-Entity Analysis based on an entity-relationship model: Revisiting crime reconstruction.

For a crime case, the related physical evidence and information can be termed entities, and there exist different types of relationships between entities. Entity-relationship models connect numerous entities through different relationships, which is useful in crime reconstructions. However, two types of problems may occur that can mislead crime reconstructions in the real world. Specifically, important entities may not be collected and vital relationships may go undiscovered. In this paper, we used an approach based on an entity-relationship model to address these problems. We organized the related entities used to reconstruct crimes according to their physical properties and sorted the relationships between entities through temporal, spatial and logical dimensions. The proposed approach is called 'Scenario-Entity Analysis' (SEA), and it uses several steps for discovering entities and relationships. The SEA also provides a framework for associating events/scenarios with evidence, which is important for crime reconstructions. Using a combination of SEA and Bayesian networks, a three-layered Bayesian network was constructed for uncertainty reasoning. A knife-attack case is then presented to demonstrate the analytical process of SEA.

Impact of travel patterns on epidemic dynamics in heterogeneous spatial metapopulation networks.

We report the results of a series of simulations of a susceptible-infected-recovered epidemic model in heterogeneous spatial metapopulation networks with quantitative knowledge of human traveling statistics that human travel behavior obeys scaling laws in the sense of geographical distance and period of waiting time. By tuning the edge length distribution of the spatial metapopulation network, we can conveniently control the distribution of human travel distance. The simulation results show that the occurrence probability of global outbreaks is significantly dependent on the characteristic travel distance, the characteristic waiting time, and the memory effects of human travel. We also present some preliminary results on the effects of travel restrictions in epidemic control.