Michal Perry Markovich

Background: Neospora caninum is a protozoan parasite and a main cause of abortions in cattle worldwide. However, its role in abortions and decreased fertility in sheep is not completely understood, especially due to the complex, multifactorial etiology of abortions. This study aimed to perform a longitudinal field study to investigate the epidemiology of neosporosis and its effect on fertility in endemic sheep herds. Methods: Serological (IFAT) and clinical (outcome of pregnancy) data from 153 ewe-lambs was collected in four intensive management farms in Israel during three consecutive pregnancies. Results: The seroprevalence in ewe-lambs at different farms varied between 24% and 93%. The overall seroprevalence increased from 50% in ewe-lambs to 96.6% at the end of the third pregnancy. Horizontal infection was observed in all farms, with seroconversion in 59% of seronegative sheep. Abortion rates were lower (p = 0.004) in seropositive ewes in the first pregnancy and not significantly higher in seropositive sheep in consecutive pregnancies. Seropositivity or seroconversion were not associated with abortions or repeated abortions; however, many aborting ewes were removed from the flock. Conclusions: No direct short- or long-term association was found between Neopsora infection and abortions. The variations between flocks and pregnancies suggest a more complex etiology.

Cryptosporidium parvum is the second-most prevalent Cryptosporidium species that infects humans worldwide. In European countries, it is the most prevalent species in sheep, suggesting that these animals are a source of zoonotic infection. Preweaned lambs and goats are particularly susceptible to infection by the parasite and may suffer from severe diarrhea whilst excreting large quantities of infectious oocysts. Fifty fecal samples from preweaned lambs and goats with diarrhea from 35 farms across Israel, found to be Cryptosporidium-positive by microscopy, were tested by PCR and sequence analyses to determine the infective species and subtypes. Cryptosporidium parvum DNA was detected in most samples from both lambs and goats (46/50). Cryptosporidium xiaoi DNA was detected in three samples from kids, with co-infection detected in a single sample. Eleven different C. parvum subtypes were found, 10 in lambs and 5 in goats. All subtypes were from the IIa and IId subtype families, with subtypes IIdA20G1 and IIaA15G2R1 being the most prevalent and widespread. These subtypes were previously found in calves and humans in Israel and are considered the most prevalent C. parvum subtypes in small ruminants globally. These results underline the zoonotic potential of C. parvum from small ruminants and the high subtype diversity compared to previous reports from other Middle Eastern countries. In addition, this is the first report of C. xiaoi in Israel.

Background: The COVID-19 outbreak required prompt action by health authorities around the world in response to a novel threat. With enormous amounts of information originating in sources with uncertain degree of validation and accuracy, it is essential to provide executive-level decision-makers with the most actionable, pertinent, and updated data analysis to enable them to adapt their strategy swiftly and competently. Objective: We report here the origination of a COVID-19 dedicated response in the Israel Defense Forces with the assembly of an operational Data Center for the Campaign against Coronavirus. Methods: Spearheaded by directors with clinical, operational, and data analytics orientation, a multidisciplinary team utilized existing and newly developed platforms to collect and analyze large amounts of information on an individual level in the context of SARS-CoV-2 contraction and infection. Results: Nearly 300,000 responses to daily questionnaires were recorded and were merged with other data sets to form a unified data lake. By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. The developed tools aided in the in the identification of high-risk individuals for severe disease and resulted in a 30% decline in their attendance to their units. Moreover, the queue for laboratory examination for COVID-19 was optimized using a predictive model and resulted in a high true-positive rate of 20%, which is more than twice as high as the baseline rate (2.28%, 95% CI 1.63%-3.19%). Conclusions: In times of ambiguity and uncertainty, along with an unprecedented flux of information, health organizations may find multidisciplinary teams working to provide intelligence from diverse and rich data a key factor in providing executives relevant and actionable support for decision-making.

Response to and monitoring of viral outbreaks can be efficiently focused when rapid, quantitative, kinetic information provides the location and the number of infected individuals. Environmental surveillance traditionally provides information on location of populations with contagious, infected individuals since infectious poliovirus is excreted whether infections are asymptomatic or symptomatic. Here, we describe development of rapid (1 week turnaround time, TAT), quantitative RT-PCR of poliovirus RNA extracted directly from concentrated environmental surveillance samples to infer the number of infected individuals excreting poliovirus. The quantitation method was validated using data from vaccination with bivalent oral polio vaccine (bOPV). The method was then applied to infer the weekly number of excreters in a large, sustained, asymptomatic outbreak of wild type 1 poliovirus in Israel (2013) in a population where >90% of the individuals received three doses of inactivated polio vaccine (IPV). Evidence-based intervention strategies were based on the short TAT for direct quantitative detection. Furthermore, a TAT shorter than the duration of poliovirus excretion allowed resampling of infected individuals. Finally, the method documented absence of infections after successful intervention of the asymptomatic outbreak. The methodologies described here can be applied to outbreaks of other excreted viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), where there are (1) significant numbers of asymptomatic infections; (2) long incubation times during which infectious virus is excreted; and (3) limited resources, facilities, and manpower that restrict the number of individuals who can be tested and re-tested.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: The COVID-19 outbreak required prompt action by health authorities around the world in response to a novel threat. With enormous amounts of information originating in sources with uncertain degree of validation and accuracy, it is essential to provide executive-level decision-makers with the most actionable, pertinent, and updated data analysis to enable them to adapt their strategy swiftly and competently. Objective: We report here the origination of a COVID-19 dedicated response in the Israel Defense Forces with the assembly of an operational Data Center for the Campaign against Coronavirus. Methods: Spearheaded by directors with clinical, operational, and data analytics orientation, a multidisciplinary team utilized existing and newly developed platforms to collect and analyze large amounts of information on an individual level in the context of SARS-CoV-2 contraction and infection. Results: Nearly 300,000 responses to daily questionnaires were recorded and were merged with other data sets to form a unified data lake. By using basic as well as advanced analytic tools ranging from simple aggregation and display of trends to data science application, we provided commanders and clinicians with access to trusted, accurate, and personalized information and tools that were designed to foster operational changes and mitigate the propagation of the pandemic. The developed tools aided in the in the identification of high-risk individuals for severe disease and resulted in a 30% decline in their attendance to their units. Moreover, the queue for laboratory examination for COVID-19 was optimized using a predictive model and resulted in a high true-positive rate of 20%, which is more than twice as high as the baseline rate (2.28%, 95% CI 1.63%-3.19%). Conclusions: In times of ambiguity and uncertainty, along with an unprecedented flux of information, health organizations may find multidisciplinary teams working to provide intelligence from diverse and rich data a key factor in providing executives relevant and actionable support for decision-making.