The 10-year health impact, economic impact, and return on investment of the South African molecular diagnostics programme for HIV, Tuberculosis and SARS-CoV-2 (preprint)

To ensure there is adequate investment into diagnostics, an understanding of the magnitude of impact and return on investment is necessary. We therefore sought to understand the health and economic impacts of the molecular diagnostic programme in South Africa, to deepen the under-standing on the broad value of diagnostics and guide future healthcare investments. We calcu-lated the 10-year (where data were available) total cost and DALYs averted associated with molecular diagnosis of molecular TB testing (2013-2022), HIV viral load monitoring (2013-2022), early infant diagnosis of HIV infection (2013-2022), and SARS-CoV-2 testing (2020-2022). We then calculated the economic value associated with those health gains and subsequent return on investment. Since the inception of the molecular diagnostics programme in South Africa, 3,035,782 DALYs have been averted as a direct consequence of this pro-gramme. This has generated an estimated $20.5 billion in economic value due to these health gains. The return on investment varied by specific diagnostic test (19.0 for tuberculosis, 1.4 for HIV viral load testing, 64.8 for early infant diagnosis of HIV, and 2.5 for SARS-CoV-2), for an average of 9.9 for the entire molecular diagnostics programme between 2013 and 2022- or $9.9 of value for each $1 invested. The molecular diagnostics programme in South Africa gen-erated a significant amount of health gains and economic value associated with these health gains, and the return-on-investment rivals other high-impact public health interventions such as childhood vaccination. Consequently, the molecular diagnostics programme in South Africa is highly impactful, and will continue to be an excellent investment of South African public health expenditure.

In-person schooling and associated COVID-19 risk in the United States over Spring Semester 2021 (preprint)

Because of the importance of schools to childhood development, the relationship between in-person schooling and COVID-19 risk has been one of the most important questions of the COVID-19 pandemic. Previous work using data from the United States in winter 2020-21 showed that in-person schooling carried some risk for household members, and that mitigation measures reduced this risk. However, in-person schooling behavior and the COVID-19 landscape changed radically over the 2021 spring semester. Here we use data from a massive online survey to characterize changes in in-person schooling behavior and associated risks over that period. We find a significant increase in the frequency of in-person schooling and a reduction in mitigation, and that in-person schooling is associated with increased reporting of COVID-19 outcomes, even among vaccinated individuals (though the absolute risk among the vaccinated is greatly reduced). Moreover, vaccinated teachers working outside the home were less likely to report COVID-19-related outcomes than unvaccinated teachers reporting no work outside the home. Adequate mitigation measures appear to eliminate the excess risk associated with in person schooling.

Household COVID-19 risk and in-person schooling (preprint)

In-person schooling has proved contentious and difficult to study throughout the SARS-CoV-2 pandemic. Data from a massive online survey in the United States indicates an increased risk of COVID-19-related outcomes among respondents living with a child attending school in-person. School-based mitigation measures are associated with significant reductions in risk, particularly daily symptoms screens, teacher masking, and closure of extra-curricular activities. With seven or more mitigation measures, the association between in-person schooling and COVID-19- related outcomes all but disappears. Teachers working outside the home were more likely to report COVID-19-related outcomes, but this association is similar to other occupations (e.g., healthcare, office work). In-person schooling is associated with household COVID-19 risk, but this risk can likely be controlled with properly implemented school-based mitigation measures.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naive baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. f

Effect of specific non-pharmaceutical intervention policies on SARS-CoV-2 transmission in the counties of the United States (preprint)

Non-pharmaceutical interventions (NPIs) remain the only widely available tool for controlling the ongoing SARS-CoV-2 pandemic. We estimated weekly values of the effective basic reproductive number (Reff) using a mechanistic metapopulation model and associated these with county-level characteristics and NPIs in the United States (US). Interventions that included school and leisure activities closure and nursing home visiting bans were all associated with an Reff below 1 when combined with either stay at home orders (median Reff 0.97, 95% confidence interval (CI) 0.58-1.39)* or face masks (median Reff 0.97, 95% CI 0.58-1.39)*. While direct causal effects of interventions remain unclear, our results suggest that relaxation of some NPIs will need to be counterbalanced by continuation and/or implementation of others.

Maximizing and evaluating the impact of test-trace-isolate programs (preprint)

Background: Test-trace-isolate programs are an essential part of COVID-19 control that offer a more targeted approach than many other non-pharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. Methods and Findings: We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of case detection, speed of isolation, contact tracing completeness and speed of quarantine using parameters consistent with COVID-19 transmission (R0=2.5, generation time 6.5 days). We show that R is most sensitive to changes to the proportion of infections detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Formally framing the dynamical process also indicates that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of program performance are sensitive to assumptions about COVID-19 natural history, our qualitative findings are robust across numerous sensitivity analyses. Conclusions: Effective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic, and can alleviate the need for more restrictive social distancing measures.

Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys (preprint)

Comparisons of the utility and accuracy of methods for measuring social interactions relevant to disease transmission are rare. To increase the evidence base supporting specific methods to measure social interaction, we compared data from self-reported contact surveys and wearable proximity sensors from a cohort of schoolchildren in the Pittsburgh metropolitan area. Although the number and type of contacts recorded by each participant differed between the two methods, we found good correspondence between the two methods in aggregate measures of age-specific interactions. Fewer, but longer, contacts were reported in surveys, relative to the generally short proximal interactions captured by wearable sensors. When adjusted for expectations of proportionate mixing, though, the two methods produced highly similar, assortative age-mixing matrices. These aggregate mixing matrices, when used in simulation, resulted in similar estimates of risk of infection by age. While proximity sensors and survey methods may not be interchangeable for capturing individual contacts, they can generate highly correlated data on age-specific mixing patterns relevant to the dynamics of respiratory virus transmission.

A scenario modeling pipeline for COVID-19 emergency planning (preprint)

Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.

The incubation period of 2019-nCoV from publicly reported confirmed cases: estimation and application (preprint)

A novel human coronavirus (2019-nCoV) was identified in China in December, 2019. There is limited support for many of its key epidemiologic features, including the incubation period, which has important implications for surveillance and control activities. Here, we use data from public reports of 101 confirmed cases in 38 provinces, regions, and countries outside of Wuhan (Hubei province, China) with identifiable exposure windows and known dates of symptom onset to estimate the incubation period of 2019-nCoV. We estimate the median incubation period of 2019-nCoV to be 5.2 days (95% CI 4.4-6.0), and 97.5% of those who develop symptoms will do so within 10.5 days (95% CI: 7.3, 15.3) of infection. These estimates imply that, under conservative assumptions, 64 out of every 10,000 cases will develop symptoms after 14 days of active monitoring or quarantine. Whether this risk is acceptable depends on the underlying risk of infection and consequences of missed cases. The estimates presented here can be used to inform policy in multiple contexts based on these judgments.