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ELISA is a commonly used analytical biochemistry assay, first described in 1971 (Engvall and Perlmann, 1971). This method and its improved versions have been adapted for the detection of a number of honey bee diseases. EFB may have been the first disease to be detected and quantified using a polyclonal antibody; bacteria can be detected at 10⁵ cells/mL, which is 100 times higher sensitivity than microscopy-based detection (Pinnock and Featherstone, 1984). Olsen reported that indirect ELISA with monoclonal antibodies was more sensitive and specific for the detection of AFB since it recognized Bacillus larvae (now P. larvae) but not six other Bacillus species (Olsen et al., 1990). Moreover, Anderson and colleagues performed a comparison among serological techniques to detect and identify some honey bee viruses, and confirmed that ELISA was the best method for sensitive and specific detection of BQCV, CBPV, KBV, SBV and honeybee related viruses (Anderson et al., 1984). Recently, this technique was also applied to identify other honey bee viruses such as SBV and Thai SBV (Lee et al., 2010). ELISA is simple and fast, and allows the quantification of an antigen present in a certain sample. However, the procedures still have critical disadvantages such as expensive antibodies, sophisticated techniques, high possibility of false results, and antibody instability, which need to be improved for its application in field study (Aubert et al., 2008).

The basis for AGID is concurrent migration of an antigen and antibody toward each other through agarose gel. When the antigen comes into contact with the complementary antibody, a precipitate is formed, and is trapped in the gel matrix, producing a visible line (Crowle et al., 1975). AGID is generally used to detect honey bee viruses (Anderson and Gibbs, 1989; Ribière et al., 2000; Todd et al., 2007) rather than bacteria or fungi. KBV and SBV were the first viruses to be detected by this method (Anderson and Gibbs, 1989), followed by most other honey bee viruses, including BQCV, SBPV (Slow bee paralysis virus), CBPV, ABPV, DWV, CSBV (Chinese sacbrood virus), and CWV (Cloudy wing virus) (Todd et al., 2007; Hu et al., 2016). The advantage of AGID is that it is a very simple, fast, and robust method that may be suitable for large-scale screening of viral infections. However, cross-contamination among viruses is an important problem that restricts the application of the AGID method. To avoid cross reactivity, monoclonal antibodies should be used, and this unfortunately raises the cost of the analysis. Moreover, the sensitivity of this technique is relatively low compared with those of molecular techniques (De Miranda et al., 2013).

Although ELISA and AGID have drawbacks, their immunological principle has successfully been applied to make some useful kits for quick screening of infections. Lateral flow devices (LFDs) are the best tools for field detection of several honey bee diseases. They were first reported to successfully detect M. plutonius, which is a causative agent of EFB, using antibodies, without cross reactivity with other bee pathogens (Tomkies et al., 2009; Mikušová et al., 2019a). To date, several types of LFDs have been commercialized to diagnose AFB or EFB, allowing more efficient detection and control of these diseases.

PCR is a versatile method that allows for easy gene amplification (Mullis et al., 1986). It is also applied in genetic diagnostics, site-directed mutagenesis, and gene therapy, making it a popular and flexible technology. Many PCR tests and their variations, including reverse transcription (RT)-PCR, multiplex PCR, nested PCR, and real-time PCR, are widely used as quick and easy methods for the detection and identification of the causative agents of infectious diseases (Chen et al., 2004; Chantawannakul et al., 2006; Kajobe et al., 2010; Yoo et al., 2012a; Rivière et al., 2013).

KBV is potentially lethal to bees, and is associated with honey bee colony collapse disorder (Cox-Foster et al., 2007); it was the first virus of honey bees to be detected by an RT-PCR-based method with high specificity and sensitivity, as low as 1 pg of total RNA (Stoltz et al., 1995). Subsequently, a number of honey bee viruses were detected using RT-PCR, especially the 6 common viruses, KBV (Stoltz et al., 1995; Hung, 2000), ABPV, BQCV (Benjeddou et al., 2001), SBV (Grabensteiner et al., 2001), CBPV (Ribière et al., 2000), and DWV (Berényi et al., 2006; Ai et al., 2012; Ghorani et al., 2017). Pathogenic agents of bacterial diseases (AFB and EFB) in honey bees have been detected using PCR (Govan et al., 1998a; Govan et al., 1998b; Alippi et al., 2002; Alippi et al., 2004) and nested PCR assays (Djordjevic et al., 1998; Lauro et al., 2003). These methods allow rapid and specific detection of AFB and EFB in both agent cultures and directly infected bee hives.

A PCR variant named multiplex PCR, which uses several sets of specific primers instead of one, allows more rapid detection of target genes of pathogens. In honey bee disease detection, multiplex PCR has been applied to simultaneously detect and differentiate two or three pathogens (Grabensteiner et al., 2007; Garrido-Bailón et al., 2013; Arai et al., 2014; Guimarães-Cestaro et al., 2016). The efficiency of multiplex PCR or multiplex RT-PCR has been improved, and it can currently detect up to six viruses in honey bees simultaneously (Sguazza et al., 2013). This approach is very important in controlling diseases in honey bees, since in most cases, individual bees are commonly infected with multiple pathogens (Chen et al., 2004; de Miranda et al., 2010). However, multiplex PCR assays may fail to detect all the diseases in honey bees, owing to differences in gene expression of each disease, and are influenced by differences in primer annealing temperatures, concentrations of primers, and other parameters, all of which make it challenging to optimize such assays (Law et al., 2014).

PCR-based methods enable fast detection of honey bee diseases. However, conventional PCR yields results only after the final phase of the amplification, and does not allow accurate quantification, which is important to estimate pathogen load. Moreover, conventional PCR needs post-PCR handling such as gel electrophoresis, which increases labor time. Quantitative real-time PCR (qPCR) assays have been developed and validated for the detection of honey bee diseases. The first honey bees virus to be detected and quantified was DWV (Chen et al., 2005). Two common variants of qPCR have been developed based on amplicon detection methods: SYBR Green-based qPCR, and hydrolysis probe-based qPCR (TaqMan^®). Although both methods give accurate results, the TaqMan^® technology has higher specificity since it includes a specific probe in addition to specific primers (Mackay, 2004).

Owing to a number of advantages, including fast performance with high sensitivity, specificity, and reliability, real-time PCR has been used to detect various diseases in honey bees (Chantawannakul et al., 2006; Ward et al., 2007; Kukielka et al., 2008; Yoo et al., 2012a; Bradford et al., 2017; Schurr et al., 2017; Mataragka et al., 2020). Using qPCR, pathogens can be rapidly detected in a matter of minutes (Han et al., 2008; Truong et al., 2019). qPCR shows much higher sensitivity compared with conventional PCR and other methods, and it can detect as little as a few copies of target genes (Han et al., 2008; Kukielka and Sánchez-Vizcaíno, 2009; Blanchard et al., 2012; Yoo et al., 2012a; Bradford et al., 2017). Likewise, its variants, such as triplex qPCR, have been used to simultaneously detect honey bee diseases (Dainat et al., 2018); this method can also easily be combined with DNA chips (Kim et al., 2019b; Kim et al., 2020) or other methods (Kim et al., 2019a) for ultra-rapid and accurate detection.

Nevertheless, real-time PCR requires expensive instrumentation and special expertise to perform, and is hence not within reach of resource-constrained societies (Notomi et al., 2015).

Ultra-rapid real-time PCR is a newly introduced technique to quickly and accurately detect pathogens (Cho et al. 2006; Han et al. 2008; Kim et al. 2010; Kang et al. 2011). This method is similar in principle to real-time PCR, but uses a glass-silicone-based micro-chip, which improves temperature homogeneity and heating conductivity, instead of PCR tubes. It successfully detects several honey bee diseases in a short time. Paenibacillus larvae was the first honey bee pathogen to be detected using this method; all the steps of detection were performed within 7 min and 54 s in a GenSpector^® TMC-1000 system (Han et al., 2008). Following this, Yoo et al. reported that CBPV infection in honey bees could be detected in 16 min and 8 s (Yoo et al., 2010) and that 10³ copies of SBV-bearing plasmid could be successfully identified within 17 min (Yoo et al., 2012a). Subsequently, other honey bee viruses such as IAPV, DWV, CBPV, ABPV, and SBPV were also detected using this method (Kim et al., 2019; Kim et al., 2019; Truong et al., 2019). Ultra-rapid real-time PCR can be used independently or in combination with DNA chips to enhance its specificity (Kim et al., 2020). This method is currently well established as a valuable tool for distinguishing honey bee diseases.

LAMP is a novel technique for nucleic acid amplification at isothermal conditions, yielding target DNA that can be amplified with inexpensive apparatus and unskilled personnel (Notomi et al., 2000; Sahoo et al., 2016). Moreover, this method requires at least 4 primers to recognize 6 different regions in the target DNA, which is expected to enhance specificity, efficiency, and rapidity compared with that of other DNA-based detection methods (Notomi et al., 2000). LAMP produces typical DNA products that have cauliflower-like structures and can be visualized directly or using suitable dyes, by naked eye (Iwamoto et al., 2003; Mori and Notomi, 2009; Yoo et al., 2012b). Successful LAMP can be observed under normal light as white turbidity in the reaction tube, owing to extensive accumulation of magnesium pyrophosphate (Seetang-Nun et al., 2013), a by-product (Tomita et al., 2008; Jinlong et al., 2010), or as a change in the color of a pH indicator such as phenol red or cresol red, due to a dramatic decrease in pH in the reaction solution (Govan et al., 1998b; Yoo et al., 2012b; Tanner et al., 2015), or under UV light, in the presence of fluorescence reagents such as SYBR Green or Genfinder (Nguyen et al., 2011; Nguyen et al., 2012; Yoo et al., 2012b; Almasi et al., 2013). Moreover, LAMP and its variants have been developed for the detection of a number of diseases in honey bees in a very short time with high sensitivity, as low as 1 pg or even 10³ copies of plasmid bearing the SBV target gene (Ma et al., 2011; Yoo et al., 2012b). In addition, LAMP can distinguish different viruses (Trzmiel and Hasiów-Jaroszewska, 2020; Dao et al., 2020). Therefore, this method is ideal for the development of a suitable tool for field-level diagnosis (Notomi et al., 2015; Wong et al., 2018). However, it should be considered that LAMP needs expertise in primer design, without which there is a risk of primer-primer interaction as well as cross contamination, and it is hence difficult to develop multiplex approaches (Higgins et al., 2018; Umesha and Manukumar, 2018; Wong et al., 2018; Trzmiel and Hasiów-Jaroszewska, 2020; Moehling et al., 2021).

Sanger sequencing technology has been used as the golden standard for nucleic acid sequencing, and has been widely applied to the diagnosis and molecular epidemiology of infectious diseases. It is expected to continue playing a vital role in disease prevention and control. This method generally targets either a single known pathogen or several individual pathogens. Investigating multiple infections in one or more hosts requires a variety of detection methods along with physical forces, and other modern equipment. Moreover, this method cannot be used to monitor and provide early warning of pathogens of other animal and viral diseases without appropriate sequencing.

NGS technologies have revolutionized research into infectious diseases. NGS has a wide range of applications, including research, clinical diagnosis, and epidemiology. Investigation of outbreaks and surveillance of infectious diseases have been greatly improved because the resolution of NGS is higher than that of other disease identification methods (Sikkema-Raddatz et al., 2013; Leopold et al., 2014; Hartman et al., 2019). The first application of NGS technology that demonstrated the potential of this approach for virus discovery was a metagenomic analysis of honey bees (Apis mellifera) adapted to shed light on the causes of colony collapse disorder (CCD), which strongly correlated with IAPV (Cox-Foster et al., 2007). More recently, NGS has been applied to investigate AFB epidemiology in honey bees, to determine when and from where the pathogen was introduced (Ågren et al., 2017). Excitingly, NGS has been used to identify a number of infections as well as reveal new viruses in honey bees (Ågren et al., 2017; Haddad et al., 2018; Kraberger et al., 2019).

However, NGS has several limitations, including a requirement for sophisticated operating system and bioinformatics software expertise in data analysis, and large data storage capabilities. In addition, expensive cost for NGS apparatus and library preparation are challenges for many developing countries. Therefore, this technology is still at the research level, and more time is needed to reduce its cost and simplify it for application in routine clinical practice (Yohe and Thyagarajan 2017; Gu, 2019).