Modeling serological testing to inform relaxation of social distancing for COVID-19 control

SARS-CoV-2 Viral and Serological Testing When College Campuses Reopen: Some Practical Considerations

The coronavirus disease 2019 (COVID-19) pandemic prompted universities across the United States to close campuses in Spring 2020. Universities are deliberating whether, when, and how they should resume in-person instruction in Fall 2020. In this essay, we discuss some practical considerations for the use of 2 potentially useful control strategies based on testing: (1) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcriptase-polymerase chain reaction (RT-PCR) testing followed by case-patient isolation and quarantine of close contacts, and (2) serological testing followed by an "immune shield" approach, that is, low social distancing requirements for seropositive persons. The isolation of case-patients and quarantine of close contacts may be especially challenging, and perhaps prohibitively difficult, on many university campuses. The "immune shield" strategy might be hobbled by a low positive predictive value of the tests used in populations with low seroprevalence. Both strategies carry logistical, ethical, and financial implications. The main nonpharmaceutical interventions will remain methods based on social distancing (eg, capping class size) and personal protective behaviors (eg, universal facemask wearing in public space) until vaccines become available, or unless the issues discussed herein can be resolved in such a way that using mass testing as main control strategies becomes viable.

Serological determinants of COVID-19

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection spreaded rapidly worldwide, as far as it has become a global pandemic. Therefore, the introduction of serological tests for determination of IgM and IgG antibodies has become the main diagnostic tool, useful for tracking the spread of the virus and for consequently allowing its containment. In our study we compared point of care test (POCT) lateral flow immunoassay (FIA) vs automated chemiluminescent immunoassay (CLIA), in order to assess their specificity and sensibility for COVID-19 antibodies detection.

RESULTS

We find that different specificities and sensitivities for IgM and IgG tests. Notably IgM POCT FIA method vs CLIA method (gold standard) has a low sensitivity (0.526), while IgG POCT FIA method vs CLIA method (gold standard) test has a much higher sensitivity (0.937); further, with respect of IgG, FIA and CLIA could arguably provide equivalent information.

CONCLUSIONS

FIA method could be helpful in assessing in short time, the possible contagiousness of subjects that for work reasons cannot guarantee "social distancing".

Quantifying antibody kinetics and RNA detection during early-phase SARS-CoV-2 infection by time since symptom onset

Understanding and mitigating SARS-CoV-2 transmission hinges on antibody and viral RNA data that inform exposure and shedding, but extensive variation in assays, study group demographics and laboratory protocols across published studies confounds inference of true biological patterns. Our meta-analysis leverages 3214 datapoints from 516 individuals in 21 studies to reveal that seroconversion of both IgG and IgM occurs around 12 days post-symptom onset (range 1-40), with extensive individual variation that is not significantly associated with disease severity. IgG and IgM detection probabilities increase from roughly 10% at symptom onset to 98-100% by day 22, after which IgM wanes while IgG remains reliably detectable. RNA detection probability decreases from roughly 90% to zero by day 30, and is highest in feces and lower respiratory tract samples. Our findings provide a coherent evidence base for interpreting clinical diagnostics, and for the mathematical models and serological surveys that underpin public health policies.

Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic

Lockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, the indiscriminate nature of mitigation - applying to all individuals irrespective of disease status - has come with substantial socioeconomic costs. Here, we explore how to leverage the increasing reliability and scale of both molecular and serological tests to balance transmission risks with economic costs involved in responding to Covid-19 epidemics. First, we introduce an optimal control approach that identifies personalized interaction rates according to an individual's test status; such that infected individuals isolate, recovered individuals can elevate their interactions, and activity of susceptible individuals varies over time. Critically, the extent to which susceptible individuals can return to work depends strongly on isolation efficiency. As we show, optimal control policies can yield mitigation policies with similar infection rates to total shutdown but lower socioeconomic costs. However, optimal control policies can be fragile given mis-specification of parameters or mis-estimation of the current disease state. Hence, we leverage insights from the optimal control solutions and propose a feedback control approach based on monitoring of the epidemic state. We utilize genetic algorithms to identify a 'switching' policy such that susceptible individuals (both PCR and serological test negative) return to work after lockdowns insofar as recovered fraction is much higher than the circulating infected prevalence. This feedback control policy exhibits similar performance results to optimal control, but with greater robustness to uncertainty. Overall, our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.