Dehghan S, Seto J, Liu EB, Ismail AM, Madupu R, Heim A, Jones MS, Dyer DW, Chodosh J, Seto D.
A Zoonotic Adenoviral Human Pathogen Emerged through Genomic Recombination among Human and Nonhuman Simian Hosts. J Virol 2019;93(18)
AbstractGenomics analysis of a historically intriguing and predicted emergent human adenovirus (HAdV) pathogen, which caused pneumonia and death, provides insight into a novel molecular evolution pathway involving "ping-pong" zoonosis and anthroponosis. The genome of this promiscuous pathogen is embedded with evidence of unprecedented multiple, multidirectional, stable, and reciprocal cross-species infections of hosts from three species (human, chimpanzee, and bonobo). This recombinant genome, typed as HAdV-B76, is identical to two recently reported simian AdV (SAdV) genomes isolated from chimpanzees and bonobos. Additionally, the presence of a critical adenoviral replication element found in HAdV genomes, in addition to genes that are highly similar to counterparts in other HAdVs, reinforces its potential as a human pathogen. Reservoirs in nonhuman hosts may explain periods of apparent absence and then reemergence of human adenoviral pathogens, as well as present pathways for the genesis of those thought to be newly emergent. The nature of the HAdV-D76 genome has implications for the use of SAdVs as gene delivery vectors in human gene therapy and vaccines, selected to avoid preexisting and potentially fatal host immune responses to HAdV. An emergent adenoviral human pathogen, HAdV-B76, associated with a fatality in 1965, shows a remarkable degree of genome identity with two recently isolated simian adenoviruses that contain cross-species genome recombination events from three hosts: human, chimpanzee, and bonobo. Zoonosis (nonhuman-to-human transmission) and anthroponosis (human to nonhuman transmission) may play significant roles in the emergence of human adenoviral pathogens.
Deiner MS, McLeod SD, Wong J, Chodosh J, Lietman TM, Porco TC.
Google Searches and Detection of Conjunctivitis Epidemics Worldwide. Ophthalmology 2019;126(9):1219-1229.
AbstractPURPOSE: Epidemic and seasonal infectious conjunctivitis outbreaks can impact education, workforce, and economy adversely. Yet conjunctivitis typically is not a reportable disease, potentially delaying mitigating intervention. Our study objective was to determine if conjunctivitis epidemics could be identified using Google Trends search data. DESIGN: Search data for conjunctivitis-related and control search terms from 5 years and countries worldwide were obtained. Country and term were masked. Temporal scan statistics were applied to identify candidate epidemics. Candidates then were assessed for geotemporal concordance with an a priori defined collection of known reported conjunctivitis outbreaks, as a measure of sensitivity. PARTICIPANTS: Populations by country that searched Google's search engine using our study terms. MAIN OUTCOME MEASURES: Percent of known conjunctivitis outbreaks also found in the same country and period by our candidate epidemics, identified from conjunctivitis-related searches. RESULTS: We identified 135 candidate conjunctivitis epidemic periods from 77 countries. Compared with our a priori defined collection of known reported outbreaks, candidate conjunctivitis epidemics identified 18 of 26 (69% sensitivity) of the reported country-wide or island nationwide outbreaks, or both; 9 of 20 (45% sensitivity) of the reported region or district-wide outbreaks, or both; but far fewer nosocomial and reported smaller outbreaks. Similar overall and individual sensitivity, as well as specificity, were found on a country-level basis. We also found that 83% of our candidate epidemics had start dates before (of those, 20% were more than 12 weeks before) their concurrent reported outbreak's report issuance date. Permutation tests provided evidence that on average, conjunctivitis candidate epidemics occurred geotemporally closer to outbreak reports than chance alone suggests (P < 0.001) unlike control term candidates (P = 0.40). CONCLUSIONS: Conjunctivitis outbreaks can be detected using temporal scan analysis of Google search data alone, with more than 80% detected before an outbreak report's issuance date, some as early as the reported outbreak's start date. Future approaches using data from smaller regions, social media, and more search terms may improve sensitivity further and cross-validate detected candidates, allowing identification of candidate conjunctivitis epidemics from Internet search data potentially to complementarily benefit traditional reporting and detection systems to improve epidemic awareness.