COVID-19 observations and accompanying dataset of non-pharmaceutical interventions across U.S. universities, March 2020.

BackgroundThe Centers for Disease Control and Prevention (CDC) publishes COVID-19 non-pharmaceutical intervention (NPI) guidance for specific institutional audiences to limit community spread. Audiences include: business, clinical, public health, education, community, and state/local government. The swift, severe, and global nature of COVID-19 offers an opportunity to systematically obtain a national view of how larger institutions of higher education adopted NPI guidance at the onset of the pandemic.MethodAn original database of COVID-19-related university NPI policy changes was compiled. Survey team members manually combed university websites and official statements capturing implementation decisions and dates for five NPI variables from 575 U.S. universities, across 50 states and the District of Columbia, during March of 2020. The universities included in this study were selected from the Department of Education Integrated Postsecondary Education Data System (IPEDS), which provides a set of university explanatory variables. Using IPEDS as the basis for the organizational data allows consistent mapping to event-time and institutional characteristic variables including public health announcements, geospatial, census, and political affiliation.ResultsThe dataset enables event-time analysis and offers a variety of variables to support institutional level study and identification of underlying biases like educational attainment. A descriptive analysis of the dataset reveals that there was substantial heterogeneity in the decisions that were made and the timing of these decisions as they temporally related to key state, national, and global emergency announcements. The WHO pandemic declaration coincided with the largest number of university decisions to implement NPIs.ConclusionThis study provides descriptive observations and produced an original dataset that will be useful for future research focused on drivers and trends of COVID-19 NPIs for U.S. Universities. This preliminary analysis suggests COVID-19 university decisions appeared to be made largely at the university level, leading to major variations in the nature and timing of the responses both between and within states, which requires further study

Applying next generation sequencing to detect tick-pathogens in Dermacentor nuttalli, Ixodes persulcatus, and Hyalomma asiaticum collected from Mongolia

Ticks and tick-borne diseases represent major threats to the public health of the Mongolian population, of which an estimated 26% live a traditional nomadic pastoralist lifestyle that puts them at increased risk for exposure. Ticks were collected by dragging and removal from livestock in Khentii, Selenge, Tuv, and Umnugovi aimags (provinces) during March-May 2020. Using next-generation sequencing (NGS) with confirmatory PCR and DNA sequencing, we sought to characterize the microbial species present in Dermacentor nuttalli (n = 98), Hyalomma asiaticum (n = 38), and Ixodes persulcatus (n = 72) tick pools. Rickettsia spp. were detected in 90.4% of tick pools, with Khentii, Selenge, and Tuv tick pools all having 100% pool positivity. Coxiella spp. were detected at an overall pool positivity rate of 60%, while Francisella spp. were detected in 20% of pools and Borrelia spp. detected in 13% of pools. Additional confirmatory testing for Rickettsia-positive pools demonstrated Rickettsia raoultii (n = 105), Candidatus Rickettsia tarasevichiae (n = 65) and R. slovaca/R. sibirica (n = 2), as well as the first report of Candidatus Rickettsia jingxinensis (n = 1) in Mongolia. For Coxiella spp. reads, most samples were identified as a Coxiella endosymbiont (n = 117), although Coxiella burnetii was detected in eight pools collected in Umnugovi. Borrelia species that were identified include Borrelia burgdorferi sensu lato (n = 3), B. garinii (n = 2), B. miyamotoi (n = 16), and B. afzelii (n = 3). All Francisella spp. reads were identified as Francisella endosymbiont species. Our findings emphasize the utility of NGS to provide baseline data across multiple tick-borne pathogen groups, which in turn can be used to inform health policy, determine regions for expanded surveillance, and guide risk mitigation strategies

Data release: targeted systematic literature search for tick and tick-borne pathogen distributions in six countries in sub-Saharan Africa from 1901 to 2020

Abstract Background Surveillance data documenting tick and tick-borne disease (TBD) prevalence is needed to develop risk assessments and implement control strategies. Despite extensive research in Africa, there is no standardized, comprehensive review. Methods Here we tackle this knowledge gap, by producing a comprehensive review of research articles on ticks and TBD between 1901 and 2020 in Chad, Djibouti, Ethiopia, Kenya, Tanzania, and Uganda. Over 8356 English language articles were recovered. Our search strategy included 19 related MeSH terms. Articles were reviewed, and 331 met inclusion criteria. Articles containing mappable data were compiled into a standardized data schema, georeferenced, and uploaded to VectorMap. Results Tick and pathogen matrixes were created, providing information on vector distributions and tick–pathogen associations within the six selected African countries. Conclusions These results provide a digital, mappable database of current and historical tick and TBD distributions across six countries in Africa, which can inform specific risk modeling, determine surveillance gaps, and guide future surveillance priorities. Graphical Abstrac