Biology subjects

Rimoin, A. W.

Publications and source records attributed to Rimoin, A. W..

5 recordsLinked to original sources

bioRxiv2021

Wildlife in Cameroon harbor diverse coronaviruses including many isolates closely related to human coronavirus 229E

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 (p<0.001) between the wet (8.2%) and dry season (4.5%). Most sampled species accordingly had a wet season high and dry season low, while for some the opposite was found. Eight 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.

microbiology↗

bioRxiv2020

Coronavirus surveillance in Congo basin wildlife detects RNA of multiple species circulating in bats and rodents

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.

microbiology↗

bioRxiv2019

Quantifying transmission of emerging zoonoses: Using mathematical models to maximize the value of surveillance data

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 pathogens 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.\n\nAuthor 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.

epidemiology↗

bioRxiv2018

Real-time projections of epidemic transmission and estimation of vaccination impact during an Ebola virus disease outbreak in the Eastern region of the Democratic Republic of Congo

BackgroundAs 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.\n\nMethodsFor 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 Gotts 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.\n\nResultsDuring 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 Gotts 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.\n\nConclusionsBased 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.

epidemiology↗

bioRxiv2018

Early projections of Ebola outbreak size and duration with and without vaccine use in Equateur, Democratic Republic of Congo, as of May 21, 2018

BackgroundAs of May 27, 2018, 54 cases of Ebola virus disease (EVD) were reported in Equateur 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.\n\nMethodsWe 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.\n\nResultsWith 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).\n\nConclusionsThis 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.

epidemiology↗