A cluster-based model of COVID-19 transmission dynamics
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Abstract
Many countries have manifested COVID-19 trajectories where extended periods of constant and low daily case rate suddenly transition to epidemic waves of considerable severity with no correspondingly drastic relaxation in preventive measures. Such solutions are outside the scope of classical epidemiological models. Here, we construct a deterministic, discrete-time, discrete-population mathematical model called cluster seeding and transmission model, which can explain these non-classical phenomena. Our key hypothesis is that with partial preventive measures in place, viral transmission occurs primarily within small, closed groups of family members and friends, which we label as clusters. Inter-cluster transmission is infrequent compared with intra-cluster transmission but it is the key to determining the course of the epidemic. If inter-cluster transmission is low enough, we see stable plateau solutions. Above a cutoff level, however, such transmission can destabilize a plateau into a huge wave even though its contribution to the population-averaged spreading rate still remains small. We call this the cryptogenic instability. We also find that stochastic effects when case counts are very low may result in a temporary and artificial suppression of an instability; we call this the critical mass effect. Both these phenomena are absent from conventional infectious disease models and militate against the successful management of the epidemic.
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SciScore for 10.1101/2021.06.02.21258243: (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:Limitations and classical limit: Here we discuss the assumptions used in the model. Some obvious assumptions are those of constant household and cluster size. A more advanced formulation of the model can incorporate a distribution of household and cluster …
SciScore for 10.1101/2021.06.02.21258243: (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:Limitations and classical limit: Here we discuss the assumptions used in the model. Some obvious assumptions are those of constant household and cluster size. A more advanced formulation of the model can incorporate a distribution of household and cluster sizes. It can also account for the fact that more than one household member is socially active. Another assumption here is the constant cluster sequence of [1; 3; 6; 7; 5; 1], which remains valid even if multiple members of the same cluster are infected simultaneously. This sequence is a representation of an R0 = 2·5 dynamics in a large population, transferred to a small group. Accounting for multiple seeding will make the cluster get infected faster and thus bring forward a few cases by a few days; it will not affect the total count however. The basic phenomena we see here are independent of the specific choice of cluster sequence, but for a more accurate modeling we must use actual data collected from contact tracing activities (together with the necessary permissions). A caveat in the expressions (7) and (8) has already been mentioned in § 4 while the introduction of the parameter kmax has been discussed in § 5; these do not generate significant error. Although the model incorporates a discrete population with heterogeneous interaction rates, it is eventually deterministic. This is necessary for computational tractability – a typical run with high caseload like one in Fig. 3 takes about five minutes on a laptop computer. ...
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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