ABSTRACT
Wastewater surveillance is a useful complement to clinical testing for managing COVID-19. While good agreement has been found between community-scale wastewater and clinical data, little is known about sub-community relationships between the two data types. Moreover, effects of non-detects in qPCR wastewater data have been largely overlooked. We used data collected from September 2020-June 2021 in Davis, California (USA) to address these gaps. By applying a predictive probability model to spatially disaggregate clinical results, we compared wastewater and clinical data at the community scale, in 16 sampling zones isolating city sub-regions, and in seven zones isolating high-priority building complexes or neighborhoods. We found reasonable agreement between wastewater and clinical data at all scales. Greater activity (i.e., more frequent detections) in clinical data tended to be mirrored in wastewater data. Small, isolated clinical-data spikes were often matched as well. We also developed a method for handling such non-detects using multiple imputation and compared results to (i) single imputation using half the qPCR limit of detection, (ii) single imputation using maximum qPCR cycle number, and (iii) non-detect censoring. Apparent wastewater trends were significantly influenced by non-detect handling. Multiple imputation improved correlation relative to single imputation, though not necessarily relative to censoring. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=78 SRC="FIGDIR/small/22269911v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@5a57f5org.highwire.dtl.DTLVardef@144cf73org.highwire.dtl.DTLVardef@8fa56borg.highwire.dtl.DTLVardef@b52587_HPS_FORMAT_FIGEXP M_FIG C_FIG
ABSTRACT
The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from de-identified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data is available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.
ABSTRACT
Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the U.S. This paper studies the utility of five such indicators--derived from de-identified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity--from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that (a) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; (b) predictive gains are in general most pronounced during times in which COVID cases are trending in "flat" or "down" directions; (c) one indicator, based on Google searches, seems to be particularly helpful during "up" trends.
ABSTRACT
BackgroundBy March 2021, California had one of the least equitable COVID-19 vaccine distribution programs in the US. To rectify this, Governor Newsom ordered 4 million vaccine doses be reserved for the census tracts in the lowest quartile of the Healthy Places Index (HPI). California plans to lift state-wide restrictions on June 15th, 2021, as long as test positivity and vaccine equity thresholds are met in the states most vulnerable neighborhoods. This short investigation examines current vaccine equity and forecasts where California can expect to be when the economy fully reopens. MethodsCurrent vaccine equity was investigated with simple linear regression between the county mean HPI and both single and full-dose vaccination rate. Future vaccination coverage per county were predicted using a compartmental mathematical model based on the average rate over the previous 30 days with four different rate-change scenarios. ResultsCounty mean HPI had a strong positive association with both single and full dose vaccination rates (R2: 0.716 and 0.737, respectively). We predict the overall state rate will exceed 50% fully vaccinated by June 15th if the current rates are maintained; however, the bulk of this coverage comes from the top 18 counties while the remaining 40 counties lag behind. DiscussionThe clear association between county HPI and current vaccination rates shows that California is not initiating opening plans from an equitable foundation, despite previous equity programs. If nothing changes, many of the most vulnerable counties will not be prepared to open without consequences come June 15th.
