Contact tracing can explain counter-intuitive COVID-19 trajectories, mitigate disease transmission and provide an early warning indicator - a mathematical modeling study
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Abstract
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 even after relaxation of NPI. To explain these phenomena, we add contact tracing to our recently developed cluster seeding and transmission (CST) model, which is predicated on heterogeneous rather than homogeneous mixing of people in society. With this addition, 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 can manifest after weeks of quiescence with no change in parameter values, (c) knife-edge effect, where a small change in parameter across a critical value can cause 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 evolution of the epidemic. Despite these effects however, it is a robust conclusion that a good contact tracing program can effectively substitute for much more invasive measures. We further find that the contact tracing capacity ratio - a metric of the stress to which the tracers are subject - can act as a reliable early warning indicator of an imminent epidemic wave. Extensive simulations demonstrate that whenever there is a drop in capacity ratio during a period of low daily infections, there is a very high probability of the case counts rising significantly in the immediate future.
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SciScore for 10.1101/2021.09.27.21264174: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Model limitations and their consequences: Most of the limitations of the model have already been mentioned in Ref. [6]. They are The ways of circumventing these assumptions have also been discussed in Ref. [6] – in short, we have to convert the …
SciScore for 10.1101/2021.09.27.21264174: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Model limitations and their consequences: Most of the limitations of the model have already been mentioned in Ref. [6]. They are The ways of circumventing these assumptions have also been discussed in Ref. [6] – in short, we have to convert the deterministic model to a fully agent-based model. The additional part implementing contact tracing contains two assumptions which we have already discussed in §4. There is an approximation in the calculation of ρ since we assume that all the cluster members are being traced on the same day as the case is found. In reality, the most immediate contacts might be identified first and the more distant ones later, easing the tracers’ job. On the average however, the total number of contacts needing to be traced over say a week or ten days will remain unchanged. Another approximation arises in our counting of immune clusters wi and Δwi. In this case, since our scheme preserves the total susceptible and immune populations at any time, errors will tend to average out. Once again, converting from the deterministic CST to a fully agent-based model will obviate the need for most or all of these assumptions. A more interesting question is : how might our assumptions influence the model results ? As a first test, we have varied the parameter which might be the most restrictive – the cluster sequence. Instead of [1; 3; 6; 8; 5; 1], we have now considered the sequence [1; 4; 13; 6]. This describes much faster intra-cluster transmission, corresponding ...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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