Rae Wannier’s research while affiliated with University of California, San Francisco and other places

Prisons have been hotspots for COVID-19 and likely an important driver of racial disparity in disease burden. From the first COVID-19 case detected through March 25, 2022, 66,684 of 196,652 residents of California’s state prison system were infected, most of them in two large winter waves of outbreaks that reached all 35 of the state prisons. We used individual-level data on disease timing and nightly room assignments in these prisons to reconstruct locations and pathways of transmission statistically, and from that estimated reproduction numbers, locations of unobserved infection events, and the subsequent magnitude and distribution of long COVID prevalence. Where earlier work has recommended smaller cells over large dormitory housing to reduce transmission, recommended use of cells with solid doors over those with bars only, and cautioned against reliance on solid doors (e.g., in cold months when HVAC systems can circulate aerosols), we found evidence of substantial transmission in both dorms and cells regardless of the door and season. Effective reproduction numbers were found to range largely between 0 and 5, in both cells and dorms of all door types. Our estimates of excess case rates suggest that as a result of disparities in incarceration, prison outbreaks contributed to disproportionate disease burden on Black and Indigenous people in California. We estimated that 9,100–11,000 people have developed long COVID as a result of infection in these prison outbreaks, 1,700–2,000 of them with disabling consequences, and that this burden is disproportionately on Black and Indigenous people in comparison to the state as a whole. We urge high-quality medical care for prison residents affected by long COVID, and decarceration to reduce the risk of future outbreaks of both COVID-19 and other diseases.

Objectives To evaluate the safety of antimotility agents (AAs) in a population of patients with hematologic malignancies and concurrent Clostridioides difficile infection (CDI) and to describe the outcomes of AA use in a hospital setting. Patients and Methods We used the electronic health record to identify patients who were hospitalized in the adult malignant hematology service who had 1 or more toxin-positive C difficile stool assay between April 1, 2012, and September 21, 2017. We reviewed medical charts to obtain information on the use of AAs and any subsequent gastrointestinal complications. Results There were 339 patients who were stool toxin positive for CDI during the study period. Of those, 94 patients (27%) were prescribed AAs within 14 days of CDI diagnosis. All patients received CDI antimicrobial therapy within the first 24 hours. There were 2 adverse gastrointestinal events in the group that received AAs and 6 in the group that did not receive AAs. The risk of adverse events did not differ between patients who received AAs and those who did not (adjusted odds ratio, 0.36; 95% CI, 0.06 to 2.10). The mean age of the full cohort was 52.7±15.5 years, and the mean length of stay was 26.7±22.6 days. Early AA use (<48 hours of diagnosis) was not associated with increased adverse effects. Conclusion There was no increase in the incidence of gastrointestinal events in the arm that used AAs compared with the control arm. The evidence suggests that for patients with hematologic malignancies and CDI, the addition of AAs to appropriate antimicrobial therapy poses no additional risk.

The current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco's shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, -20.1%--81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.

We estimated time-varying reproduction numbers of COVID-19 transmission in counties and regions of California and in states of the United States, using the Wallinga-Teunis method of estimations applied to publicly available data. The serial interval distribution assumed incorporates wide uncertainty in delays from symptom onset to case reporting. This assumption contributes smoothing and a small but meaningful increase in numerical estimates of reproduction numbers due to the likely existence of secondary cases not yet reported. Transmission in many areas of the U.S. may not yet be controlled, including areas in which case counts appear to be stable or slowly declining.

A large measles outbreak in 2014-2015, linked to Disneyland theme parks, attracted international attention, and led to changes in California vaccine policy. We use dates of symptom onset and known epidemic links for California cases in this outbreak to estimate time-varying transmission in the outbreak, and to estimate generation membership of cases probabilistically. We find that transmission declined significantly during the course of the outbreak (p = 0.012), despite also finding that estimates of transmission rate by day or by generation can overestimate temporal decline. We additionally find that the outbreak size and duration alone are sufficient in this case to distinguish temporal decline from time-invariant transmission (p = 0.014). As use of a single large outbreak can lead to underestimates of immunity, however, we urge caution in interpretation of quantities estimated from this outbreak alone. Further research is needed to distinguish causes of temporal decline in transmission rates.

Background As of February 25, 2019, 875 cases of Ebola virus disease (EVD) were reported in North Kivu and Ituri Provinces, Democratic Republic of Congo. Since the beginning of October 2018, the outbreak has largely shifted into regions in which active armed conflict has occurred, and in which EVD cases and their contacts have been difficult for health workers to reach. We used available data on the current outbreak, with case-count time series from prior outbreaks, to project the short-term and long-term course of the outbreak. Methods For short- and long-term projections, we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission rates estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used two regression models to estimate similar projection periods. Short- and long-term projections were estimated using negative binomial autoregression and Theil-Sen regression, respectively. We also used Gott’s rule to estimate a baseline minimum-information projection. We then constructed an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20, 2018 to February 25, 2019, short-term model projections were validated against known case counts. Results During validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of February 25, the stochastic model projected a median case count of 933 cases by February 18 (95% prediction interval: 872–1054) and 955 cases by March 4 (95% prediction interval: 874–1105), while the auto-regression model projects median case counts of 889 (95% prediction interval: 876–933) and 898 (95% prediction interval: 877–983) cases for those dates, respectively. Projected median final counts range from 953 to 1,749. Although the outbreak is already larger than all past Ebola outbreaks other than the 2013–2016 outbreak of over 26,000 cases, our models do not project that it is likely to grow to that scale. The stochastic model estimates that vaccination coverage in this outbreak is lower than reported in its trial setting in Sierra Leone. Conclusions Our projections are concentrated in a range up to about 300 cases beyond those already reported. While a catastrophic outbreak is not projected, it is not ruled out, and prevention and vigilance are warranted. Prospective validation of our models in real time allowed us to generate more accurate short-term forecasts, and this process may prove useful for future real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges.

As of June 16, 2019, an Ebola virus disease (EVD) outbreak has led to 2136 reported cases in the northeastern region of the Democratic Republic of the Congo (DRC). As this outbreak continues to threaten the lives and livelihoods of people already suffering from civil strife and armed conflict, relatively simple mathematical models and their short-term predictions have the potential to inform Ebola response efforts in real time. We applied recently developed non-parametrically estimated Hawkes point processes to model the expected cumulative case count using daily case counts from May 3, 2018, to June 16, 2019, initially reported by the Ministry of Health of DRC and later confirmed in World Health Organization situation reports. We generated probabilistic estimates of the ongoing EVD outbreak in DRC extending both before and after June 16, 2019, and evaluated their accuracy by comparing forecasted vs. actual outbreak sizes, out-of-sample log-likelihood scores and the error per day in the median forecast. The median estimated outbreak sizes for the prospective thee-, six-, and nine-week projections made using data up to June 16, 2019, were, respectively, 2317 (95% PI: 2222, 2464); 2440 (95% PI: 2250, 2790); and 2544 (95% PI: 2273, 3205). The nine-week projection experienced some degradation with a daily error in the median forecast of 6.73 cases, while the six- and three-week projections were more reliable, with corresponding errors of 4.96 and 4.85 cases per day, respectively. Our findings suggest the Hawkes point process may serve as an easily-applied statistical model to predict EVD outbreak trajectories in near real-time to better inform decision-making and resource allocation during Ebola response efforts. Keywords: Ebola virus disease, Hawkes point process, Mathematical modeling, Democratic Republic of Congo, Compartmental models

Background Patients undergoing hematopoietic stem cell transplant often suffer from a predictable constellation of side effects related to therapy. Nonpharmacologic treatments for these side effects are attractive adjuncts to therapy due to a low side-effect profile. Objective To develop, implement, and evaluate a pilot program of massage therapy for symptom management in adult patients with hematologic malignancies admitted to the bone marrow transplant (BMT) service at a large academic medical center. Methods A single-arm feasibility study of massage therapy was conducted. Pre- and postintervention surveys were collected to assess the usefulness in management of 7 symptoms. Results Over an 11.5-month period, 109 patients received 142 massage treatments. one in five patients received more than one massage. We received surveys on 134 massage treatments. Patients reported significant reductions in anxiety, distress, fatigue, pain, and tension (P < .01) and improved sleep as a result of massage therapy. Conclusion Based on this pilot, massage therapy is a feasible and safe intervention to administer during BMT hospitalizations. It proved useful in managing a constellation of 5 side effects including, anxiety, distress, fatigue, pain, and tension.

Background: As of October 12, 2018, 211 cases of Ebola virus disease (EVD) were reported in North Kivu Province, Democratic Republic of Congo. Since the beginning of October the outbreak has largely shifted into regions in which active armed conflict is occurring, and in which EVD cases and their contacts are difficult for health workers to reach. We used available data on the current outbreak with case-count time series from prior outbreaks to project the short-term and long-term course of the outbreak. Methods: For short and long term projections we modeled Ebola virus transmission using a stochastic branching process that assumes gradually quenching transmission estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used a negative binomial autoregression for short-term projections, a Theil-Sen regression model for final sizes, and a baseline minimum-information projection using Gott's law to construct an ensemble of forecasts to be compared and recorded for future evaluation against final outcomes. From August 20 to October 13, short-term model projections were validated against actual case counts. Results: During validation of short-term projections, from one week to four weeks, we found models consistently scored higher on shorter-term forecasts. Based on case counts as of October 13, the stochastic model projected a median case count of 226 cases by October 27 (95% prediction interval: 205-268) and 245 cases by November 10 (95% prediction interval: 208-315), while the auto-regression model projects median case counts of 240 (95% prediction interval: 215-307) and 259 (95% prediction interval: 216-395) cases for those dates, respectively. Projected median final counts range from 274 to 421. Except for Gott's law, the projected probability of an outbreak comparable to 2013-2016 is exceedingly small. The stochastic model estimates that vaccine coverage in this outbreak is lower than reported in its trial setting in Sierra Leone. Conclusions: Based on our projections we believe that the epidemic had not yet peaked at the time of these estimates, though a trajectory on the scale of the West African outbreak is exceedingly improbable. Validating our models in real time allowed us to generate more accurate short-term forecasts, and this process may provide a useful roadmap for real-time short-term forecasting. We estimate that transmission rates are higher than would be seen under target levels of 62% coverage due to contact tracing and vaccination, and this model estimate may offer a surrogate indicator for the outbreak response challenges.

As of October 12, 2018, 211 cases of Ebola virus disease (EVD) were reported in North Kivu Province, Democratic Republic of Congo. Since the beginning of October the outbreak has largely shifted into regions in which active armed conflict is occurring, and in which EVD cases and their contacts are difficult for health workers to reach. We modeled EVD transmission using a branching process with gradually quenching transmission estimated from past EVD outbreaks, with outbreak trajectories conditioned on agreement with the course of the current outbreak, and with multiple levels of vaccination coverage. We used an autoregression for short-term projections, a regression model for final sizes, and a simple Gott's law rule as an ensemble of forecasts. Short-term model projections were validated against actual case counts. During validation of short-term projections, models consistently scored higher on shorter-term forecasts. Based on case counts as of October 13, the stochastic model projected a median case count of 226 by October 27 (95% prediction interval: 205-268) and 245 by November 10 (95% PI: 208-315), while the auto-regression model projected median case counts of 240 (95% PI: 215-307) and 259 (95% PI: 216-395) for those dates, respectively. Projected median final counts range from 274 to 421. Except for Gott's law, the projected probability of an outbreak surpassing 2013-2016 is exceedingly small. The stochastic model estimates that vaccine coverage in this outbreak is lower than reported in its trial. Based on our projections we believe that the epidemic had not yet peaked at the time of these estimates, though an outbreak like 2013-2016 is not likely. We estimate that transmission rates are higher than under target levels of vaccine coverage, and this model estimate may offer a surrogate indicator for the outbreak response challenges.