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IntroductionCombining genomic and geospatial data can be useful for understanding Mycobacterium tuberculosis (Mtb) transmission in high tuberculosis burden settings.MethodsWe performed whole genome sequencing (WGS) on Mtb DNA extracted from sputum cultures from a population-based tuberculosis study conducted in 2012–2016 in Gaborone, Botswana. We used kernel density estimation, spatial K-functions, and created spatial distributions of phylogenetic trees. WGS-based clusters of isolates ≤5 single nucleotide polymorphisms were considered recent transmission, and large WGS-based clusters (≥10 members) were considered outbreaks.ResultsWe analyzed data from 1449 participants with culture-confirmed TB. Among these, 946 (65%) participants had both molecular and geospatial data. A total of 62 belonged to five large outbreaks (10–19 participants each). Geospatial clustering was detected in two of the five large outbreaks, suggesting heterogeneous spatial patterns within the community.ConclusionsIntegration of genomic and geospatial data identified distinct patterns of tuberculosis transmission in a high-tuberculosis burden setting. Targeted interventions in these smaller geographies may interrupt on-going transmission.

Zoonotic spillover of animal viruses into human populations is a continuous and increasing public health risk. SARS-CoV-2 highlights the global impact emergence events can have. Considering the history and diversity of coronaviruses (CoVs), especially in bats, SARS-CoV-2 will likely not be the last to spillover from animals into human populations.We sampled and tested wildlife in the central African country Cameroon to determine which CoVs are circulating and how they relate to previously detected human and animal CoVs. We collected animal and ecological data at sampling locations and used family-level consensus PCR combined with amplicon sequencing for virus detection.Between 2003 and 2018, samples were collected from 6,580 animals of several different orders. CoV RNA was detected in 175 bats, a civet, and a shrew. The CoV RNAs detected in the bats represented 17 different genetic clusters, coinciding with alpha (n=8) and beta (n=9) CoVs. Sequences resembling human CoV-229E (HCoV-229E) were found in 40 Hipposideridae bats. Phylogenetic analyses place the human derived HCoV-229E isolates closest to those from camels in terms of the S and N genes, but closest to isolates from bats for the E, M, and RdRp genes. The CoV RNA positivity rate in bats varied significantly (pEight of the suspected CoV species of which we detected RNA appear to be entirely novel CoV species, which suggests that CoV diversity in African wildlife is still rather poorly understood. The detection of multiple different variants of HCoV-229E-like viruses supports the bat reservoir hypothesis for this virus, with the phylogenetic results casting some doubt on camels as an intermediate host. The findings also support the previously proposed influence of ecological factors on CoV circulation, indicating a high level of underlying complexity to the viral ecology. These results indicate the importance of investing in surveillance activities among wild animals to detect all potential threats as well as sentinel surveillance among exposed humans to determine emerging threats.

Beginning April 8, 2020, we enrolled 1787 frontline heath workers who were asymptomatic for COVID-19 into a longitudinal surveillance study. During that time 4 healthcare workers and 6 first responders tested positive for SARS-CoV-2 by RT-PCR. Additionally, 43 healthcare workers and 55 first responders had detectable IgG antibodies to SARS-CoV-2.

ImportanceHealthcare workers (HCW) are slated to be early recipients of SARS-CoV-2 vaccines due to increased risk of exposure to patients with COVID-19, and will be tasked with administering approved vaccines to the general population. As lynchpins of the vaccination effort, HCWs’ opinions of a vaccine’s safety and efficacy may affect both public perception and uptake of the vaccine. Therefore, it is crucial to understand and address potential hesitancy prior to vaccine administration.ObjectiveTo understand healthcare workers’ attitudes about vaccine safety, efficacy, and acceptability in the context of the COVID-19 pandemic, including acceptance of a novel coronavirus vaccine.Design, Setting, ParticipantsA cross-sectional survey was distributed to participants enrolled in a longitudinal cohort study surveilling SARS-CoV-2 infection among 1,093 volunteer sampled University of California, Los Angeles (UCLA) Health System employees. Surveys were completed online between September 24 and October 16, 2020. In total, 609 participants completed this supplemental survey.ResultsWe averaged a 9-statement Likert scale matrix scored from 1 (“strongly disagree”) to 5 (“strongly agree”) and found respondents overwhelmingly confident about vaccine safety (4.47); effectiveness (4.44); importance, self-protection, and community health (4.67). Notably, 47.3% of respondents reported unwillingness to participate in a coronavirus vaccine trial, and most (66.5%) intend to delay vaccination. The odds of reporting intent to delay coronavirus vaccine uptake were 4.15 times higher among nurses, 2.45 times higher among other personnel with patient contact roles, and 2.15 times higher among those without patient contact compared to doctors. Evolving SARS-CoV-2 science (76.0%), current political climate (57.6%), and fast-tracked vaccine development timeline (83.4%) were cited as primary variables impacting HCW decisions to undergo vaccination. Of note, these results were obtained prior to release of Phase III data from companies manufacturing vaccines in the U.S.Conclusions and RelevanceDespite overall confidence in vaccines, a majority of HCW expressed concerns over a novel coronavirus vaccine. A large proportion plan to delay vaccine uptake due to concerns about expedited development, emerging scientific discoveries, and the political climate. Forthcoming vaccination campaigns must address these unique points of coronavirus vaccine hesitancy in order to achieve adequate vaccine coverage.

Coronaviruses play an important role as pathogens of humans and animals, and the emergence of epidemics like SARS, MERS and COVID-19 is closely linked to zoonotic transmission events primarily from wild animals. Bats have been found to be an important source of coronaviruses with some of them having the potential to infect humans, with other animals serving as intermediate or alternate hosts or reservoirs. Host diversity may be an important contributor to viral diversity and thus the potential for zoonotic events. To date, limited research has been done in Africa on this topic, in particular in the Congo Basin despite frequent contact between humans and wildlife in this region. We sampled and, using consensus coronavirus PCR-primers, tested 3,561 wild animals for coronavirus RNA. The focus was on bats (38%), rodents (38%), and primates (23%) that posed an elevated risk for contact with people, and we found coronavirus RNA in 121 animals, of which all but two were bats. Depending on the taxonomic family, bats were significantly more likely to be coronavirus RNA-positive when sampled either in the wet (Pteropodidae and Rhinolophidae) or dry season (Hipposideridae, Miniopteridae, Molossidae, and Vespertilionidae). The detected RNA sequences correspond to 15 Alpha- and 6 Beta-coronaviruses, with some of them being very similar (>95% nucleotide identities) to known coronaviruses and others being more unique and potentially representing novel viruses. In seven of the bats, we detected RNA most closely related to sequences of the human common cold coronaviruses 229E or NL63 (>80% nucleotide identities). The findings highlight the potential for coronavirus spillover, especially in regions with a high diversity of bats and close human contact, and reinforces the need for ongoing surveillance.

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian nonlinear mixed model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectory. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for infections and deaths in U.S. states. We evaluate forecasting accuracy on a two-week holdout set, finding that the model predicts COVID-19 cases and deaths well, with a mean absolute scaled error of 0.40 for cases and 0.32 for deaths throughout the two-week evaluation period. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Ohio, and Mississippi. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course. Author summary COVID-19 models can be roughly classified as mathematical models that simulate disease within a population, including epidemiological compartmental models, or statistical curve-fitting models that fit a function to observed data and extrapolate forward into the future. Bridging this divide, we combine the strengths of curve-fitting statistical models and the structure of epidemiological models, by embedding a Bayesian nonlinear mixed model for case velocity and a machine learning algorithm (random forest) into the framework of a compartmental model. Fusing these models together exploits the particular strengths of each to glean as much information as possible from the currently available data. We also identify the velocity of log cumulative cases as an excellent target for modeling and extrapolating COVID-19 case trajectories. We empirically evaluate the predictive performance of the model and provide predicted trajectories with credible intervals for cumulative confirmed case count, active confirmed infections and COVID-19 deaths for each of the 50 U.S. states. Combining sophisticated data analytic methods with proven epidemiological models offers an empirically grounded strategy for making realistic predictions and quantifying their uncertainty. These predictions indicate substantial variation in the COVID-19 trajectories of U.S. states.

Understanding and quantifying the transmission of zoonotic pathogens is essential for directing public health responses, especially for pathogens capable of transmission between humans. However, determining a pathogen’s transmission dynamics is complicated by challenges often encountered in zoonotic disease surveillance, including unobserved sources of transmission (both human and zoonotic), limited spatial information, and unknown scope of surveillance. In this work, we present a model-based inference method that addresses these challenges for subcritical zoonotic pathogens using a spatial model with two levels of mixing. After demonstrating the robustness of the method using simulation studies, we apply the new method to a dataset of human monkeypox cases detected during an active surveillance program from 1982-1986 in the Democratic Republic of the Congo (DRC). Our results provide estimates of the reproductive number and spillover rate of monkeypox during this surveillance period and suggest that most human-to-human transmission events occur over distances of 30km or less. Taking advantage of contact-tracing data available for a subset of monkeypox cases, we find that around 80% of contact-traced links could be correctly recovered from transmission trees inferred using only date and location. Our results highlight the importance of identifying the appropriate spatial scale of transmission, and show how even imperfect spatiotemporal data can be incorporated into models to obtain reliable estimates of human-to-human transmission patterns.Author SummarySurveillance datasets are often the only sources of information about the ecology and epidemiology of zoonotic infectious diseases. Methods that can extract as much information as possible from these datasets therefore provide a key advantage for informing our understanding of the disease dynamics and improving our ability to choose the optimal intervention strategy. We developed and tested a likelihood-based inference method based on a mechanistic model of the spillover and human-to-human transmission processes. We first used simulated datasets to explore which information about the disease dynamics of a subcritical zoonotic pathogen could be successfully extracted from a line-list surveillance dataset with non-localized spatial information and unknown geographic coverage. We then applied the method to a dataset of human monkeypox cases detected during an active surveillance program in the Democratic Republic of the Congo between 1982 and 1986 to obtain estimates of the reproductive number, spillover rate, and spatial dispersal of monkeypox in humans.

BackgroundAs 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, 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.MethodsFor 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.ResultsDuring 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.ConclusionsOur 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.Author summaryAs 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 been reported, and in which EVD cases and their contacts have been difficult for health workers to reach. We used an ensemble of models to estimate EVD transmission rates and to forecast the short- and long-term course of the outbreak. Our models project that a final size of roughly up to 300 additional cases is most likely, and estimate that transmission rates are higher than would be seen under optimal levels of contact tracing and vaccination. While a catastrophic outbreak is not projected, is it not ruled out, and prevention and vigilance are warranted.

BackgroundAs of May 27, 2018, 54 cases of Ebola virus disease (EVD) were reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the current outbreak size and duration with and without vaccine use.MethodsWe modeled Ebola virus transmission using a stochastic branching process model with a negative binomial distribution, using both estimates of reproduction number R declining from supercritical to subcritical derived from past Ebola outbreaks, as well as a particle filtering method to generate a probabilistic projection of the future course of the outbreak conditioned on its reported trajectory to date; modeled using 0%, 44%, and 62% estimates of vaccination coverage. Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize a regression model predicting the outbreak size from the number of observed cases from April 4 to May 27.ResultsWith the stochastic transmission model, we projected a median outbreak size of 78 EVD cases (95% credible interval: 52, 125.4), 86 cases (95% credible interval: 53, 174.3), and 91 cases (95% credible interval: 52, 843.5), using 62%, 44%, and 0% estimates of vaccination coverage. With the regression model, we estimated a median size of 85.0 cases (95% prediction interval: 53.5, 216.6).ConclusionsThis outbreak has the potential to be the largest outbreak in DRC since 2007. Vaccines are projected to limit outbreak size and duration but are only part of prevention, control, and care strategies.