Estimating the Instantaneous Reproduction Number With Imperfect Data: A Method to Account for Case-Reporting Variation and Serial Interval Uncertainty (preprint)

During an infectious disease outbreak, public health decision-makers require real-time monitoring of disease transmission to respond quickly and intelligently. In these settings, a key measure of transmission is the instantaneous time-varying reproduction number, $R_t$. Estimation of this number using a Time-Since-Infection model relies on case-notification data and the distribution of the serial interval on the target population. However, in practice, case-notification data may contain measurement error due to variation in case reporting while available serial interval estimates may come from studies on non-representative populations. We propose a new data-driven method that accounts for particular forms of case-reporting measurement error and can incorporate multiple partially representative serial interval estimates into the transmission estimation process. In addition, we provide practical tools for automatically identifying measurement error patterns and determining when measurement error may not be adequately accounted for. We illustrate the potential bias undertaken by methods that ignore these practical concerns through a variety of simulated outbreaks. We then demonstrate the use of our method on data from the COVID-19 pandemic to estimate transmission and explore the relationships between social distancing, temperature, and transmission.

The Association of Social Distancing, Population Density, and Temperature with the SARS-CoV-2 Instantaneous Reproduction Number in Counties Across the United States (preprint)

ImportanceThe Covid-19 pandemic has been marked by considerable heterogeneity in outbreaks across the United States. Local factors that may be associated with variation in SARS-CoV-2 transmission have not been well studied. ObjectiveTo examine the association of county-level factors with variation in the SARS-CoV-2 reproduction number over time. DesignObservational study Setting211 counties in 46 states and the District of Columbia between February 25, 2020 and April 23, 2020. ParticipantsResidents within the counties (55% of the US population) ExposuresSocial distancing as measured by percent change in visits to non-essential businesses, population density, lagged daily wet bulb temperatures. Main Outcomes and MeasuresThe instantaneous reproduction number (Rt) which is the estimated number of cases generated by one case at a given time during the pandemic. ResultsMedian case incidence was 1185 cases and fatality rate was 43.7 deaths per 100,000 people for the top decile of 21 counties, nearly ten times the incidence and fatality rate in the lowest density quartile. Average Rt in the first two weeks was 5.7 (SD 2.5) in the top decile, compared to 3.1 (SD 1.2) in the lowest quartile. In multivariable analysis, a 50% decrease in visits to non-essential businesses was associated with a 57% decrease in Rt (95% confidence interval, 56% to 58%). Cumulative temperature effects over 4 to 10 days prior to case incidence were nonlinear; relative Rt decreased as temperatures warmed above 32{degrees}F to 53{degrees}F, which was the point of minimum Rt, then increased between 53{degrees}F and 66{degrees}F, at which point Rt began to decrease. At 55{degrees}F, and with a 70% reduction in visits to non-essential business, 96% of counties were estimated to fall below a threshold Rt of 1.0, including 86% of counties among the top density decile and 98% of counties in the lowest density quartile. Conclusions and RelevanceSocial distancing, lower population density, and temperate weather change were associated with a decreased SARS-Co-V-2 Rt in counties across the United States. These relationships can inform selective public policy planning in communities during the SARS-CoV-2 pandemic. Key PointsO_ST_ABSQuestionC_ST_ABSHow is the instantaneous reproduction number (Rt) of SARS-CoV-2 influenced by local area effects of social distancing, wet bulb temperature, and population density in counties across the United States? FindingsSocial distancing, temperate weather, and lower population density were associated with a decrease in Rt. Of these county-specific factors, social distancing appeared to be the most significant in reducing SARS-CoV-2 transmission. MeaningRt varies significantly across counties. The relationship between Rt and county-specific factors can inform policies to reduce SARS-CoV-2 transmission in selective and heterogeneous communities.