ABSTRACT
Equine Piroplasmosis (EP) is a tick-borne disease caused by three apicomplexan protozoan parasites, Theileria equi (T. equi), Babesia caballi (B. caballi) and T. haneyi, which can cause similar clinical symptoms. There are five known 18S rRNA genotypes of T. equi group (including T. haneyi) and three of B. caballi. Real-time PCR methods for detecting EP based on 18S rRNA analysis have been developed, but these methods cannot detect all genotypes of EP in China, especially genotype A of T. equi. In this study, a duplex real-time PCR detection method was developed for the simultaneous detection and differentiation of T. equi and B. caballi. The primers and probes for this duplex real-time PCR assay were designed based on the conserved 18S rRNA gene sequences of all genotypes of T. equi and B. caballi including Chinese strain. Double-quenched probes were used in this method, which provide less background and more signal to decrease the number of false positives relative to single-quenched probes. The newly developed real-time PCR assays exhibited good specificity, sensitivity, repeatability and reproducibility. The real-time PCR assays were further validated by comparison with a nested PCR assay and a previous developed real-time PCR for EP and sequencing results in the analysis of 506 clinical samples collected from 2019 to 2020 in eleven provinces and regions of China. Based on clinical performance, the agreements between the duplex real-time PCR assay and the nPCR assay or the previous developed real-time PCR assay were 92.5% (T. equi) and 99.4% (B. caballi) or 87.4% (T. equi) and 97.2% (B. caballi). The detection results showed that the positivity rate of T. equi was 43.87% (222/506) (10 genotype A, 1 genotype B, 4 genotype C, 207 genotype E), while that of B. caballi was 5.10% (26/506) (26 genotype A), and the rate of T. equi and B. caballi co-infection was 2.40% (12/506). The established method could contribute to the accurate diagnosis, pathogenic surveillance and epidemiological investigation of T. equi and B. caballi infections in horses.
Subject(s)
Babesia , Babesiosis , Cattle Diseases , Horse Diseases , Theileria , Theileriasis , Animals , Babesia/genetics , Babesiosis/diagnosis , Babesiosis/epidemiology , Babesiosis/parasitology , Cattle , Horse Diseases/diagnosis , Horse Diseases/epidemiology , Horse Diseases/parasitology , Horses , RNA, Ribosomal, 18S/genetics , Real-Time Polymerase Chain Reaction/methods , Real-Time Polymerase Chain Reaction/veterinary , Reproducibility of Results , Theileria/genetics , Theileriasis/diagnosis , Theileriasis/epidemiology , Theileriasis/parasitologySubject(s)
Coronavirus Infections/epidemiology , Coronavirus , Pneumonia, Viral , Basic Reproduction Number , Betacoronavirus , COVID-19 , China , Humans , Pandemics , SARS-CoV-2ABSTRACT
BACKGROUND: In December 2019, an outbreak of respiratory illness caused by a novel coronavirus (2019-nCoV) emerged in Wuhan, China and has swiftly spread to other parts of China and a number of foreign countries. The 2019-nCoV cases might have been under-reported roughly from 1 to 15 January 2020, and thus we estimated the number of unreported cases and the basic reproduction number, R0, of 2019-nCoV. METHODS: We modelled the epidemic curve of 2019-nCoV cases, in mainland China from 1 December 2019 to 24 January 2020 through the exponential growth. The number of unreported cases was determined by the maximum likelihood estimation. We used the serial intervals (SI) of infection caused by two other well-known coronaviruses (CoV), Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) CoVs, as approximations of the unknown SI for 2019-nCoV to estimate R0. RESULTS: We confirmed that the initial growth phase followed an exponential growth pattern. The under-reporting was likely to have resulted in 469 (95% CI: 403-540) unreported cases from 1 to 15 January 2020. The reporting rate after 17 January 2020 was likely to have increased 21-fold (95% CI: 18-25) in comparison to the situation from 1 to 17 January 2020 on average. We estimated the R0 of 2019-nCoV at 2.56 (95% CI: 2.49-2.63). CONCLUSION: The under-reporting was likely to have occurred during the first half of January 2020 and should be considered in future investigation.
ABSTRACT
BACKGROUNDS: An ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city in China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and other countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak. METHODS: Accounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate (γ), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI. FINDINGS: The early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0. CONCLUSION: The mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and is significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.