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

Abstract

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.

Keywords: COVID-19; Complex networks; Infectious disease control; Numerical simulation; Testing.

Fig. 1

A diagram illustrating the proposed model. a shows the epidemic transmission process and (b) shows the testing mechanism. The descriptions of parameters in the figure are described in Table 1

Fig. 2

The impact of testing volume V and testing start time T on epidemic transmission. a shows the impact on the infection peak. The red and blue color refer to high and low peak values, respectively. b shows the impact on the arrival time of infection peak. The blue color means that the epidemic breaks out very early, while the red color means the opposite. In region I of (a), the peak values were smaller than 0.005. In region I of (b), the peak times were larger than 130 time steps. Starting testing early and increasing daily testing volume could suppress the epidemic transmission

Fig. 3

The impact of changes in daily testing volume on infection scale. The red color means the large infection scale, while the blue color means the opposite. Breaking through the limitations of daily testing volume could greatly suppress the epidemic transmission, but promoting the increase speed of daily testing volume hardly changes the infection scale

Fig. 4

The impact of testing preference on epidemic transmission. Square, circle and triangle curves were obtained under T=30 (Group A) and solid, semi-solid and hollow triangle curves were obtained under V=0.06 (Group B). Priority testing for individuals in contacts with confirmed cases can suppress the epidemic transmission

Fig. 5

The effect of testing on epidemic transmission under different scenarios. S0 means that no other measures were taken except testing. S10 and S 30 indicate the scenarios where other measures were taken to reduce individuals’ infection probability by 10% and 30%, respectively. Combined with other measures such as wearing masks and social distancing, the daily testing volume could be significantly reduced while the epidemic will still be controlled

Fig. 6

The effect of basic reproductive number R₀ and testing on infection scale under different scenarios. The results of scenario S0 where only testing measure was adopted are shown in (a), and (b) describes the results of scenario S10 where other measures were implemented to reduce individuals’ infection rate by 10%. The solid line is the contour line where the infection scale is 5%. The daily testing volume required to control epidemics increased almost linearly as R₀, but when other measures were adopted, the required testing volume decreased

Fig. 7

The impact of network scale. The square, circle and triangle curves represent the simulation results on networks with 5000, 8000, and 10000 nodes, respectively. Even if the network scale was different, the trend of the infection scale with the daily testing volume was almost the same

Fig. 8

The simulation data versus real data. A hollow square point (real data) indicates one country, representing the average testing volume (x-axis) and the peak of the testing-positive rate curve (y-axis). The red circle points show the peak of positive rate curve under different testing volumes in the simulations. We set R₀=2.6 and α=1