Using the LIST model to Estimate the Effects of Contact Tracing on COVID-19 Endemic Equilibria in England and its Regions
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Abstract
Governments across Europe are preparing for the emergence from lockdown, in phases, to prevent a resurgence in cases of COVID-19. Along with social distancing (SD) measures, contact tracing – find, track, trace and isolate (FTTI) policies are also being implemented. Here, we investigate FTTI policies in terms of their impact on the endemic equilibrium. We used a generative model – the dynamic causal ‘Location’, ‘Infection’, ‘Symptom’ and ‘Testing’ (LIST) model to identify testing, tracing, and quarantine requirements. We optimised LIST model parameters based on time series of daily reported cases and deaths of COVID-19 in England— and based upon reported cases in the nine regions of England and in all 150 upper tier local authorities. Using these optimised parameters, we forecasted infection rates and the impact of FTTI for each area—national, regional, and local. Predicting data from early June 2020, we find that under conditions of medium-term immunity, a ‘40%’ FTTI policy (or greater), could reach a distinct endemic equilibrium that produces a significantly lower death rate and a decrease in ICU occupancy. Considering regions of England in isolation, some regions could substantially reduce death rates with 20% efficacy. We characterise the accompanying endemic equilibria in terms of dynamical stability, observing bifurcation patterns whereby relatively small increases in FTTI efficacy result in stable states with reduced overall morbidity and mortality. These analyses suggest that FTTI will not only save lives, even if only partially effective, and could underwrite the stability of any endemic steady-state we manage to attain.
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SciScore for 10.1101/2020.06.11.20128611: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: There are several limitations to the analysis. First, the data used to optimise the models would ideally consider death rates, rates of hospitalisations and rates of ICU occupancy as well as symptomatic and asymptomatic infections for all regions. Since the only data publicly available for regions and UTLAs were case reports – the model is fallible to incorrect or incomplete reporting as well as omitted data. Second, our results …
SciScore for 10.1101/2020.06.11.20128611: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Limitations: There are several limitations to the analysis. First, the data used to optimise the models would ideally consider death rates, rates of hospitalisations and rates of ICU occupancy as well as symptomatic and asymptomatic infections for all regions. Since the only data publicly available for regions and UTLAs were case reports – the model is fallible to incorrect or incomplete reporting as well as omitted data. Second, our results are predicated on a generative model of the epidemic course of COVID-19 (Daunizeau et al 2020) which should be refined based on biological assays of the community. For example, the overall rates of infection within UK’s UTLAs (Winter & Hegde 2020, Yong et al 2020) particularly given the low seroprevalence in global epicentres (To et al 2020). Most crucially, immunity after infection (where here, we assume a relatively long period of immunity) has yet to be established (Kissler et al 2020). Furthermore, the models account for a proportion of the population that are not susceptible to infection in the current outbreak, i.e. only a subset of the population are in the susceptible compartment at the beginning of the outbreak. Possible causes of ‘resistance’ as estimated by the model include: geographical effects, i.e. not involved in current outbreaks through lockdown measures and social sequestering from the mixing population (Flaxman et al 2020), innate host factors e.g., under expression of the angiotensin-converting enzyme 2 (ACE2) gene in...
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.
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