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

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.

The need for larval source management accompanying urban development projects in malaria endemic areas: a case study on Bioko Island.

BACKGROUND: In 2017, several new housing districts were constructed on Bioko Island, Equatorial Guinea. This case study assessed the impact construction projects had on mosquito larval habitats and the effectiveness of larval source management in reducing malaria vector density within the surrounding area. METHODS: Anopheline larval presence was assessed at 11 new construction sites by the proportion of larval habitats containing Anopheline pupae and late instar larval stages. Bacillus thuringiensis israelensis (Bti) larvicide was applied weekly to nine locations for 30 weeks, while two locations received no larvicide and acted as controls. Adult mosquito density was monitored via human landing collections in adjacent communities of six construction sites, including the two control sites. RESULTS: The sites that received Bti had significantly lower observation rates of both pupae (3.2% vs. 18.0%; p < 0.001) and late instar Anopheles spp. mosquitoes (14.1 vs. 43.6%; p < 0.001) compared to the two untreated sites. Anopheles spp. accounted for 67% of mosquitoes collected with human landing collections and were captured at significantly lower levels in communities adjacent to treated construction sites compared to untreated sites (p < 0.001), with an estimated 38% reduction in human biting rate (IRR: 0.62, 95% CI IRR: 0.55, 0.69). Seven months after the start of the study, untreated sites were treated due to ethical concerns given results from treatment sties, necessitating immediate Bti application. The following week, the number of habitats, the proportion of larval sites with Anopheles spp. pupae, late instars, and adult biting rates in adjacent communities to these sites all decreased to comparable levels across all sites. CONCLUSION: Findings suggest larval source management represents an effective intervention to suppress mosquito populations during infrastructure development. Incorporating larval source management into ongoing and planned construction initiatives represents an opportunity to fine tune vector control in response to anthropogenetic changes. Ideally, this should become standard practice in malaria-endemic regions in order to reduce viable mosquito habitats that are common by-products of construction.

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.

Tracking tick-borne diseases in Mongolian livestock using next generation sequencing (NGS).

The livestock industry in Mongolia accounts for 24% of national revenue, with one third of the population maintaining a pastoral lifestyle. This close connection between Mongolian population and livestock is a major concern for pathogen transfer, especially given the increase in vector-borne diseases globally. This study examines blood samples from livestock to assess the prevalence of tick-borne bacterial infections across three provinces in Mongolia (Dornogovi, Selenge, Töv). Whole blood samples from 243 livestock (cattle=38, camel=11, goat=85, horse=22, sheep=87) were analyzed with 16S metagenomics next-generation sequencing (NGS) to screen for bacterial pathogens. Positive-NGS samples for Anaplasma, Bartonella, Ehrlichia, Francisella, Leptospira, and Rickettsia were confirmed by conventional PCR and DNA sequencing. Prevalence rates of Anaplasma, Bartonella, and Ehrlichia were 57.6%, 12.8%, and 0.4%, respectively. A significant difference in the prevalence of Anaplasma spp. in livestock by province was observed, with a higher prevalence in Selenge (74.2%, p<0.001) and Töv (64.2% p = 0.006) compared to the semi-arid region of Dornogovi (39.8%). In contrast, no association was observed in Bartonella prevalence by provinces. All Anaplasma sequences (N = 139) were characterized as A. ovis. For Bartonella species characterization, phylogenetic analyses of gltA and rpoB genes identified three Bartonella species including B. bovis, B. melophagi and Candidatus B. ovis. Bartonella bovis was detected in all 22-positive cattle, while B. melophagi and Candidatus B. ovis were found in four and three sheep, respectively. This study identifies a high prevalence of tick-borne pathogens within the livestock population and to our knowledge, is the first time NGS methods have been used to explore tick-borne diseases in Mongolia. Further research is needed in Mongolia to better understand the clinical and economic burdens associated with tick-borne diseases in both livestock and pastoral herder 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.