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Rutherford, G.

Publications and source records attributed to Rutherford, G..

2 recordsLinked to original sources

A systematic review and meta-analysis of the potential non-human animal reservoirs and arthropod vectors of the Mayaro virus

Improving our understanding of Mayaro virus (MAYV) ecology is critical to guide surveillance and risk assessment. We conducted a PRISMA-adherent systematic review of the published and grey literature to identify potential arthropod vectors and non-human animal reservoirs of MAYV. We searched PubMed, Embase, Web of Science, SciELO and grey-literature sources including PAHO databases and dissertation repositories. Studies were included if they assessed MAYV virological/immunological measured occurrence in field-caught, domestic, or sentinel animals or in field-caught arthropods. We conducted an animal seroprevalence meta-analysis using a random effects model. We compiled granular georeferenced maps of non-human MAYV occurrence and graded the quality of the studies using a customized framework. Overall, 57 studies were eligible out of 1523 screened, published between the years 1961 and 2020. Seventeen studies reported MAYV positivity in wild mammals, birds, or reptiles and five studies reported MAYV positivity in domestic animals. MAYV positivity was reported in 12 orders of wild-caught vertebrates, most frequently in the orders Charadriiformes and Primate. Sixteen studies detected MAYV in wild-caught mosquito genera including Haemagogus, Aedes, Culex, Psorophora, Coquillettidia, and Sabethes. Vertebrate animals or arthropods with MAYV were detected in Brazil, Panama, Peru, French Guiana, Colombia, Trinidad, Venezuela, Argentina, and Paraguay. Among non-human vertebrates, the Primate order had the highest pooled prevalence (PP) at 13.1% (95% CI: 4.3-25.1%). From the three most studied primate genera we found the highest prevalence was in Alouatta (PP: 32.2%, 95% CI: 0.0-79.2%), followed by Callithrix (PP: 17.8%, 95% CI: 8.6-28.5%), and Cebus/Sapajus (PP: 3.7%, 95% CI: 0.0-11.1%). We further found that MAYV occurs in a wide range of vectors beyond Haemagogus spp. The quality of evidence behind these findings was variable and prompts calls for standardization of reporting of arbovirus occurrence. These findings support further risk emergence prediction, guide field surveillance efforts, and prompt further in-vivo studies to better define the ecological drivers of MAYV maintenance and potential for emergence. Author SummaryMayaro virus (MAYV) is an emerging tropical public health threat in the Americas. We conducted a georeferenced, quality-graded systematic review to evaluate the current evidence regarding MAYV occurrence in non-human vertebrates and arthropods. Overall, 57 studies were eligible out of 1523 screened, published between the years 1961 and 2020. Seventeen studies reported MAYV positivity in wild mammals, birds, or reptiles and five studies reported MAYV positivity in domestic animals. MAYV positivity was reported in 12 orders of wild-caught vertebrates, most frequently in the orders Charadriiformes and Primate. Our systematic review identified 12 orders of wild-caught vertebrates and seven mosquito genera with evidence of MAYV occurrence. Primates had the highest pooled MAYV prevalence according to a seroprevalence meta-analysis. The graded quality of evidence behind these findings was variable and prompts calls for standardization of reporting of MAYV and perhaps other emerging arbovirus occurrence in animals and vectors. This study provides important information for public health authorities and disease ecologists concerned with the growing threat of MAYV in Latin America. Our analysis provides a foundation for future laboratory and field studies focused on the MAYV transmission cycle.

ecology↗

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↗