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.

Hotspots in a cold land-reported cases of rabies in wildlife and livestock in Mongolia from 2012-2018.

The epidemiological profile of rabies virus within Mongolia remains poorly characterized despite 21,302 domestic animal cases being reported between 1970-2005. This lack of knowledge is particularly concerning given that roughly 26% of the population lives a pastoral herding lifestyle and livestock production contributes up to 18% of Mongolia's total gross domestic product (GDP). The gaps in knowledge of the rabies disease ecology within Mongolia combined with the lack of routine vaccination of domestic animals and wildlife poses a significant threat to the more than 60 million heads of livestock within Mongolia. Animal rabies case data from the General Authority for Veterinary Services and National Center for Zoonotic Diseases were used in this study. Each data point included year of report, an animal descriptor, geographic coordinates and the aimag (province) of origin. A total of 2,359 animal rabies cases were reported between 2012-2018. Cattle were the most commonly reported animal overall (861 cases), followed by goats (268), sheep (251) and dogs (221) within the domestic animal category. Red foxes were responsible for most reported wildlife cases (317) followed by wolves (151). Most rabid animals were reported in the Khuvsgul, Uvurkhangai and Govi-Altai aimags, and a positive correlation was found between livestock numbers per soum and the number of rabies cases reported. Rabies poses a significant threat to the Mongolian economy and the health of human and animal populations within Mongolia. The close association of the nomadic pastoralists with both domestic animals and wildlife represents a significant threat for disease emergence and necessitates studies that describe the ecology of rabies, which may threaten these populations.

Survival analysis of factors affecting the timing of COVID-19 non-pharmaceutical interventions by U.S. universities.

BACKGROUND: During March of 2020 the Centers for Disease Control and Prevention (CDC) announced non-pharmaceutical intervention (NPI) guidance as the primary mitigation strategy against growing COVID-19 community spread due to the absence of a vaccine or effective treatment at that time. CDC guidance states that NPIs are most effective when instituted in an early, targeted, and layered fashion. NPIs are effective in slowing spread, and measures should be custom-tailored to each population. This study examines factors associated with implementation and timing of NPI interventions across large public and private U.S. universities at the onset of the COVID-19 pandemic. METHODS: NPI decisions of interest include when U.S. universities canceled international travel, shifted to online learning, moved faculty/staff to remote work, limited campus housing, and closed campus for all non-essential personnel. Cox proportional hazard analyses of retrospective data were conducted to assess the time to NPI events. Hazard ratios were calculated for university governance, campus setting, religious affiliation, health infrastructure, faculty diversity, and student demographics. The methods control for variance inflation factors, COVID case prevalence, and time varying covariates of spring break and states' state of emergency (SOE) orders. This study captures NPI decisions at 575 U.S. universities during spring of 2020 which affected the movement of seven million students and two million employees. RESULTS: Universities located in districts represented by Democratic party congressional members reported earlier NPI implementation than Republican (Cox proportional hazard ratio (HR) range 0.61-0.80). University religious affiliation was not associated with the timing any of the NPI decisions. Universities with more diverse faculty showed an association with earlier NPI implementation (HR range 0.65-0.76). The existence of university-affiliated health infrastructure was not associated with NPI timing. CONCLUSION: University NPI implementation was largely driven by local COVID-19 epidemiology, culture and political concerns. The timing of university NPI decisions varied by regional politics, faculty demographics, university governance, campus setting, and foreign student prevalence adjusting for COVID-19 state case prevalence and spring break timing. Religious affiliation and presence of university health infrastructure were not associated with timing.

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

BACKGROUND: The 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. METHOD: An 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. RESULTS: The 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. CONCLUSION: This 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.