Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA (preprint)

Public health indicators typically used for COVID-19 surveillance can be biased or lag changing community transmission patterns. The United States city of Chicago opportunistically investigated whether sentinel surveillance of recently symptomatic individuals receiving outpatient diagnostic testing for SARS-CoV-2 could accurately assess the instantaneous reproductive number R(t) and provide early warning of changes in transmission. Patients tested at community-based diagnostic testing sites between September 2020 and June 2021, and reporting symptom onset within four days preceding their test, formed the sentinel population. R(t) calculated from sentinel cases agreed well with R(t) from other indicators. Retrospectively, trends in sentinel cases did not precede trends in COVID-19 hospital admissions by any identifiable lead time. In deployment, sentinel surveillance held an operational recency advantage of nine days over hospital admissions. The promising performance of opportunistic sentinel surveillance suggests that deliberately designed outpatient sentinel surveillance would provide robust early warning of increasing transmission.

Time-varying transmission heterogeneity of SARS and COVID-19 in Hong Kong (preprint)

Transmission heterogeneity is a notable feature of the severe acute respiratory syndrome (SARS) and coronavirus disease 2019 (COVID-19) epidemics, though previous efforts to estimate how heterogeneity changes over time are limited. Using contact tracing data, we compared the epidemiology of SARS and COVID-19 infection in Hong Kong in 2003 and 2020-21 and estimated time-varying transmission heterogeneity (kt) by fitting negative binomial models to offspring distributions generated across variable observation windows. kt fluctuated over time for both COVID-19 and SARS on a continuous scale though SARS exhibited significantly greater (p < 0.001) heterogeneity compared to COVID-19 overall and in-time. For COVID-19, kt declined over time and was significantly associated with increasingly stringent non-pharmaceutical interventions though similar evidence for SARS was inconclusive. Underdetection of sporadic COVID-19 cases led to a moderate overestimation of kt, indicating COVID-19 heterogeneity of could be greater than observed. Time-varying or real-time estimates of transmission heterogeneity could become a critical indicator for epidemic intelligence in the future.

Comparative cost-effectiveness of SARS-CoV-2 vaccine dose fractionation in India: a modelling study (preprint)

Given constrained vaccine supplies globally, fractionation of vaccine doses may be an effective strategy for reducing disease and healthcare burdens, even with the emergence of COVID-19 variants. Using a multi-scale model that incorporates population-level transmission and individual-level vaccination, we estimate the costs associated with hospitalization, vaccine costs, and the economic benefit of reducing COVID-19 deaths associated with dose-fractionation strategies. Assuming a willingness-to-pay of US$10,517 per averted year of life lost (YLL) and a price of $12 per vaccine, under various transmission scenarios, with effective reproduction numbers ranging from 1.1 to 5.0 and with vaccine efficacy against transmission from 52% to 91%, the optimal vaccination strategy would always involve fractional doses of vaccines. Vaccine dose fractionation is a cost-effective strategy for mitigating the COVID-19 pandemic and could save a large number of lives, even after the emergence of variants with higher transmissibility.

Incorporating temporal distribution of population-level viral load enables real-time estimation of COVID-19 transmissibility (preprint)

Many locations around the world have used real-time estimates of the time-varying effective reproductive number (\({R}_{t}\)) of COVID-19 to provide evidence of transmission intensity to inform control strategies. Estimates of \({R}_{t}\) are typically based on statistical models applied to case counts and typically suffer lags of more than a week because of the incubation period and reporting delays. Noting that viral loads tend to decline over time since illness onset, analysis of the distribution of viral loads among confirmed cases can provide insights into epidemic trajectory. Here, we analyzed viral load data on confirmed cases during two local epidemics in Hong Kong, identifying a strong correlation between temporal changes in the distribution of viral loads (measured by cycle threshold values) and estimates of \({R}_{t}\) based on case counts. We demonstrate that cycle threshold values could be used to improve real-time \({R}_{t}\) estimation, enabling more timely tracking of epidemic dynamics.

Geographic and demographic heterogeneity of SARS-CoV-2 diagnostic testing in Illinois, USA, March to December 2020 (preprint)

Background Availability of SARS-CoV-2 testing in the United States (U.S.) has fluctuated through the course of the COVID-19 pandemic, including in the U.S. state of Illinois. Despite substantial ramp-up in test volume, access to SARS-CoV-2 testing remains limited, heterogeneous, and insufficient to control spread. Methods We compared SARS-CoV-2 testing rates across geographic regions, over time, and by demographic characteristics (i.e., age and racial/ethnic groups) in Illinois during March through December 2020. We compared age-matched case fatality ratios and infection fatality ratios through time to estimate the fraction of SARS-CoV-2 infections that have been detected through diagnostic testing. Results By the end of 2020, initial geographic differences in testing rates had closed substantially. Case fatality ratios were higher in non-Hispanic Black and Hispanic/Latino populations in Illinois relative to non-Hispanic White populations, suggesting that tests were insufficient to accurately capture the true burden of COVID-19 disease in the minority populations during the initial epidemic wave. While testing disparities decreased during 2020, Hispanic/Latino populations consistently remained the least tested at 1.87 tests per 1000 population per day compared with 2.58 and 2.87 for non-Hispanic Black and non-Hispanic White populations, respectively, at the end of 2020. Despite a large expansion in testing since the beginning of the first wave of the epidemic, we estimated that over half (50-80%) of all SARS-CoV-2 infections were not detected by diagnostic testing and continued to evade surveillance. Conclusions Systematic methods for identifying relatively under-tested geographic regions and demographic groups may enable policymakers to regularly monitor and evaluate the shifting landscape of diagnostic testing, allowing officials to prioritize allocation of testing resources to reduce disparities in COVID-19 burden and eventually reduce SARS-CoV-2 transmission.

Model-informed COVID-19 vaccine prioritization strategies by age and serostatus (preprint)

When a vaccine for COVID-19 becomes available, limited initial supply will raise the question of how to prioritize the available doses and thus underscores the need for transparent, evidence-based strategies that relate knowledge of, and uncertainty in, disease transmission, risk, vaccine efficacy, and existing population immunity. Here, we employ a model-informed approach to vaccine prioritization that evaluates the impact of prioritization strategies on cumulative incidence and mortality and accounts for population factors such as age, contact structure, and seroprevalence, and vaccine factors including imperfect and age-varying efficacy. This framework can be used to evaluate and compare existing strategies, and it can also be used to derive an optimal prioritization strategy to minimize mortality or incidence. We find that a transmission-blocking vaccine should be prioritized to adults ages 20-49y to minimize cumulative incidence and to adults over 60y to minimize mortality. Direct vaccination of adults over 60y minimizes mortality for vaccines that do not block transmission. We also estimate the potential benefit of using individual-level serological tests to redirect doses to only seronegative individuals, improving the marginal impact of each dose. We argue that this serology-informed vaccination approach may improve the efficiency of vaccination efforts while partially addressing existing inequities in COVID-19 burden and impact.

Practical considerations for measuring the effective reproductive number, Rt (preprint)

Estimation of the effective reproductive number, Rt, is important for detecting changes in disease transmission over time. During the COVID-19 pandemic, policymakers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make methodological recommendations. For near real-time estimation of Rt, we recommend the approach of Cori et al. (2013), which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis (2004), are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for some retrospective analyses. We advise against using methods derived from Bettencourt and Ribeiro (2008), as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. A challenge common to all approaches is reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation.