Cross Sectional Analysis of Major Viral Diseases in Shrimp Farms of Kerala, India

Cross Sectional Analysis of Major Viral Diseases in Shrimp Farms of Kerala, India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cross Sectional Analysis of Major Viral Diseases in Shrimp Farms of Kerala, India Saranya P, Ravikumar M. Chovatia, Gijo Ittoop, Rahul Krishnan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6564499/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Effective disease surveillance is crucial to predict, identify, and manage disease outbreaks in the shrimp aquaculture. A cross-sectional study was conducted to identify the major viral diseases occurrence in shrimp farms of Kerala. A two-stage random sampling was employed involving the selection of 31 shrimp farms in the first stage and 23 individual shrimp in the second stage. The surveillance was carried out from October 2023–2024, targeting major DNA viruses: WSSV, HPV, MBV, IHHNV and RNA viruses: IMNV, CMNV, TSV, DIV, LSNV, YHV-1 by using PCR and histopathological analysis. Among the 31 shrimp farms surveyed, WSSV was detected in 7 farms, while all other viral pathogen tested negative. The histopathological examination revealed characteristic Cowdry type A and basophilic inclusion bodies in the stomach, gill and pleopods consistent with WSSV infection. The overall prevalence of WSSV was observed as 22.6%. The relatively low prevalence is attributed due to the reduction in shrimp farming activity in Kerala as most farms operated only one production cycle per year. The risk factors analysis indicated that WSSV infection was significantly associated with specific variables including a culture duration exceeding 35 days, ammonia concentration > 0.5mg/L, stocking densities > 25 shrimps/m 2 , L. vannamei as the cultured species. This surveillance study provides valuable insights into the present status of shrimp viral diseases in Kerala and offers a foundation for improved prevention and control strategies in shrimp aquaculture. epitool surveillance shrimp viral diseases prevalence risk factor Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Shrimp farming plays a pivotal role in the aquaculture industry, experiencing significant growth in recent years. In India, the sector has expanded markedly, with annual production escalating from 35,500 tonnes in 1990–91 to 843,633 tonnes in 2020–2021 (MPEDA, 2022 ). Frozen shrimp remains a primary export commodity, representing 40.19% of total quantity and contributing 66.12% to export revenues in US dollars.​ Kerala holds a prominent position in India's shrimp aquaculture, employing both traditional and modern farming techniques. The state is endowed with abundant water resources, including 46,128.94 hectares of backwaters, of which 2,971.24 hectares are dedicated to shrimp farming, yielding a production of 1,549.83 tonnes (MPEDA, 2022 ).​ The most significant challenge in shrimp aquaculture is disease, leading to high mortality rates and considerable economic losses (Stentiford et al., 2012 ). Outbreaks result from a combination of factors such as suboptimal culture environments, inadequate management, high stocking densities, stress, compromised immune responses, and the presence of pathogens like viruses, bacteria, fungi and parasites (Kennedy et al ., 2016). Viral diseases are particularly concerning due to their high infectivity and lack of effective treatments.​ Globally, nearly 20 viruses have been identified in penaeid shrimp (Safeena et al., 2012 ). The World Organization for Animal Health (OIE) recognizes several significant viruses impacting shrimp aquaculture, including White Spot Syndrome Virus (WSSV), Infectious Hypodermal and Hematopoietic Necrosis Virus (IHHNV), Infectious Myonecrosis Virus (IMNV), Taura Syndrome Virus (TSV), and Yellow Head Virus Genotype 1 (YHV-1), all of which have substantial socio-economic implications.​While the shrimp aquaculture has been affected by viral diseases across various Indian states, there is a lack of comprehensive research focusing on Kerala. This study aims to systematically analyse the prevalence and impact of viral diseases on shrimp culture in Kerala.​ 2. Materials and methods 2.1 Study design and sampling technique This study focused on grow-out farms cultivating Penaeus vannamei and Penaeus monodon across five major shrimp-farming coastal districts in Kerala, India, which contribute significantly to the state’s overall shrimp production. The selected districts included Kannur, Thrissur, Ernakulam, Alappuzha, and Kollam. A two-stage random sampling method, as recommended by the World Organisation for Animal Health (OIE, 2009) was employed to ensure representative sampling across these districts. The sample size was calculated using FreeCalc: Calculate sample size for freedom testing with imperfect tests, available through the Epitools Epidemiological Calculators provided by Ausvet ( http://epitools.ausvet.com.au ) (Sergeant, 2018 ). This approach ensured statistically reliable insights into the status of viral diseases in shrimp. Baseline information on active shrimp farms in Kerala were obtained from the Department of Fisheries, Government of Kerala, and used for calculating the appropriate sample size. In the first stage, 31 shrimp farms were randomly selected from the five districts. In the second stage, 23 individual shrimp specimens were randomly collected from each selected farm. The surveillance study was conducted from October 2023 to 2024 to assess the presence of major viral diseases affecting shrimp aquaculture in the region. The input parameters and calculated. 2.2 Sample collection, processing and storage The total of 31 extensive and semi-intensive shrimp farms including 5 farms in Kannur district, 14 farms in Thrissur district, 5 farms in Ernakulam district, 2 farms in Alappuzha district, and 5 farms in Kollam district were selected randomly and 23 individual shrimps from each farm were collected for analysis. The samples were collected from various sides of the shrimp farms using cast nets or by retrieving shrimp from feeding trays. Live shrimp samples were collected for histopathological analysis and instantly fixed in Davidson’s fixative (37% Formaldehyde – 200 mL, Glacial acetic acid- 100 mL, 95% ethanol – 300 mL, Distilled water – 300 mL) at a ratio of 1:10 (sample: fixative). For DNA extraction, the samples were preserved in 95% ethanol while for RNA extraction the samples were stored in RNA later ® (Sigma-Aldrich, India). The collected samples were placed in ice packs during transportation to the Department of Aquatic Animal Health Management Laboratory in KUFOS. All samples were then stored at -20°C to maintain the viability for further analysis. Along with shrimp samples, water samples were also analysed in the pond site to determine the water quality parameters. The pH and salinity were measured with the help of pH meter (Eutech, U.K) and portable refractometer. The ammonia and alkalinity were tested using the commercial ammonia testing kit (HiMedia, India) and alkalinity testing kit (Borosil, India) following manufacturer’s protocol. Questionaries survey was also conducted, which was prepared with reference to the National Database on Aquatic Animal Disease (NSPAAD) and data collected from individual farmers during surveillance. 2.3 Nucleic acid extraction By using the method described by Otta et al. ( 1999 ) the viral DNA extraction was performed and RNA isolation was conducted using RNAiso Plus (Takara, India), according to the manufacturer’s protocol. The extracted nucleic acid were dissolved in nuclease-free water or 1X TE buffer and stored at − 20°C for future use. By using NanoDrop spectrophotometer (Thermo Fisher Scientific, USA) the purity and concentration of RNA samples were assessed. DNase treatment was carried out to further eliminate potential DNA contamination, by combining 1 µg of RNA with nuclease-free water to a final volume of 7 µL. A 2 µL master mix, containing an equal ratio of DNase I and buffer, was then added and incubated for 30 minutes at 37°C to allow DNase I activity. To inactivate the enzyme, 1 µL of EDTA was added, followed by heating the mixture at 65°C for 10 minutes. Subsequently, reverse transcription was carried out using the PrimeScript™ 1st Strand cDNA Synthesis Kit (Takara, India) in according to the manufacturer’s instructions. 2.4 Polymerase Chain Reaction (PCR) The primers used were custom synthesized by Eurofins, Bangalore. The details of primer sequences, amplicon size with reference used for the diagnostic PCR of all the DNA and RNA viruses were listed in the Table 2 . Table 1 Input and output value of sample size estimation Input Stage 1 Stage 2 Output Stage 1 Stage 2 Input value Output value Test sensitivity 0.95 0.95 Required sample size: 31 23 Test specificity 0.99 0.99 Cut-point number of positives: 1 1 Population size 350 100000 Type I error: 0.0436 0.0418 Design prevalence 0.14 0.2 Type II error: 0.0384 0.022 Diseased elements 50 20000 Population-level sensitivity: 0.9564 0.9582 Analysis method Modified hypergeometric exact Simple binomial (large population) Population-level specificity: 0.9616 0.978 Target Type I error 0.05 0.05 Interpretation: If a random sample of 31 units is taken from a population of 350 and 1 or fewer reactors are found, the probability that the population is diseased at a prevalence of 0.142857142857143 is 0.0436. If a random sample of 23 units is taken from a population of 100000 and 1 or fewer reactors are found, the probability that the population is diseased at a prevalence of 0.2 is 0.0418. Target Type II error 0.05 0.05 Method: Modified hypergeometric exact Simple binomial (large population) Population threshold for infinite probability formula 10000 10000 Maximum sample size 3200 3200 Table 2 Details of primer sequences and amplicon size used for diagnosis of shrimp viruses Pathogen Primer name Primer sequence Amplicon size Reference WSSV 146F1 ACTACTAACTTCAGCCTATCTAG 1447 bp (Lo et al., 1997 ) 146R1 TAATGCGGGTGTAATGTTCTTACGA 146F2 GTAACTGCCCCTTCCATCTCCA 941 bp 146R2 TACGGCAGCTGCTGCACCTTGT MBV MBV1.4F CGATTCCATATCGGCCGAATA 533 bp 361 bp (Belcher & Young, 1998 ) MBV1.4R TTGGCATGCACTCCCTGAGAT MBV1.4NF TCCAATCGCGTCTGCGATACT MBV1.4NR CGCTAATGGGGCACAAGTCTC IHHNV 309F TCCAACACTTAGTCAAAACCAA 309 bp (Tang et al., 2007 ) 309R TGTCTGCTACGATGATTATCCA HPV H441F GCATTACAAGAGCCAAGCAG 441 bp (Phromjai et al., 2002 ) H441R ACACTCAGCCTCTACCTTGT LSNV-1 LLV-F TTGCCTTCTCCCGAGTGGTC 200 bp (Sritunyalucksana et al ., 2005) LLV-R CCGGCTGAGGTAGCTGCTTG LSNVnF GCGCAAGAGTTCTCAGGCTT 140 bp (Prakasha et al., 2007 ) LSNVnR ATCACCGCAGGCTAATATAG IMNV 4587F CGACGCTGCTAACCATACAA 328 bp (Poulos and Lightner, 2006 ) 4914R ACTCGGCTGTTCGATCAAGT 4725NF GGCACATGCTCAGAGACA 139 bp 4863NR AGCGCTGAGTCCAGTCTTG DIV SHIV-F1 GGGCGGGAGATGGTGTTAGAT 457 bp (Qiu et al., 2017 ) SHIV-R1 TCGTTTCGGTACGAAGATGTA SHIV-F2 CGGGAAACGATTCGTATTGGG 129 bp SHIV-R2 TTGCTTGATCGGCATCCTTGA YHV- 1 10F: CCGCTAATTTCAAAAACTACG 135 bp (Wongteerasupaya et al., 1997 ) 144R AAGGTGTTATGTCGAGGAAGT TSV 9992F AAGTAGACAGCCGCGCTT 231 bp (Nunan et al ., 1998) 9195R TCAATGAGAGCTTGGTCC CMNV CMNV-7F1 AAATACGGCGATGACG 619 bp (Zhang et al., 2017 ) CMNV-7R1 ACGAAGTGCCCACAGAC CMNV-7F2 CACAACCGAGTCAAACC 165 bp CMNV-7R2 GCGTAAACAGCGAAGG The PCR was carried out using a Bio Rad T 100 Gradient 96 well thermal cycler (Bio Rad, USA) with specific primers. The master mix for a 25µL reaction volume contained 2.5 µL of 10X Taq buffer, 0.125µL of (5U/µL) Taq DNA polymerase, 0.5µL of 10mM dNTP mix, 1 µL of (10 pmole/µL) forward primer, 1 µL of (10 pmole/µL) reverse primer, (Origin, India), 1µL of template DNA and adjust to final volume using nuclease free water. The PCR conditions for detection of DNA and RNA virus were listed in Table 3 . the PCR products was analysed using Gel electrophoresis on a 2% agarose gel containing ethidium bromide, employing using a standard 100 bp or 1 kb Plus DNA ladder (Origin, India). The bands were visualized using a gel documentation system (Gel Doc™ EZ Imager, Bio-Rad, USA) to confirm the presence and size of PCR products. Table 3 Details of PCR condition used for diagnosis of shrimp viruses Pathogen Initial denaturation Denaturation Annealing Extension Final extension Cycles WSSV 94°C- 4min 55°C-1min 72 − 1 min 94°C- 1 min 55°C- 1 min 72°C- 2 min 72°C- 5 min 39 cycles 94°C- 4min 55°C- 1 min 72°C-1 min 94°C- 1 min 55°C- 1 min 72°C- 2 min 72°C- 5 min 39 cycles IHHNV 95°C- 5 min 95°C-30 sec 55°C- 30 sec 72°C- 30 sec 72°C-7 min 35 cycles HPV 94°C- 5 min 94°C- 30 sec 55°C-30 sec 72°C-30 sec 72°C-5min 30 cycles MBV 96°C- 30 sec 94°C-30 sec 65°C- 30 sec 72°C- 60 sec 72°C- 7 min 40 cycles 96°C- 5 min 94°C- 30 sec 60°C- 30 sec 72°C-60 sec 72°C-7 min 35 cycles IMNV 60°C- 30 min 95°C- 2 min 95°C-45 sec 60°C- 45 sec 60°C-7 min 39 cycles 95°C – 2 min 95°C- 30 sec 65°C- 30 sec 72°C- 30 sec 72°C-2 min 39 cycles LSNV- 1 94°C − 5 min 94°C- 1 min 60°C- 1 min 72°C- 1 min 72°C-10min 35 cycles 94°C − 5 min 94°C- 1 min 60°C- 1 min 72°C- 1 min 72°C-10 min 35 cycles YHV-1 50°C-30 min 94°C-2 min 94°C- 30 sec 58°C- 45 sec 68°C- 45 sec 68°C-7 min 40 cycles TSV 94°C- 3 min 94°C- 45 sec 60°C–45 sec 60°C- 45 sec 60°C- 7 min 40 cycles CMNV 94°C – 4 min 94°C-30 sec 45°C-30 sec 72°C-40 sec 72°C-7 min 35 cycles 94°C – 4 min 94°C-20 sec 50°C-20 sec 72°C-20 sec 72°C- 7 min 30 cycles DIV 95°C- 3 min 95°C- 30 sec 59°C-30 sec 72°C-30 sec 72°C- 2min 35 cycles 95°C- 3 min 95°C- 30 sec 59°C-30 sec 72°C-30 sec 72°C- 2min 35 cycles 2.5 Histopathological analysis For histological analysis, the fixed tissues were passed through a graded series of alcohol concentrations, ranging from 50–100%, to remove water from the samples. The tissues were then cleared by sequential immersion in xylene for two times and after clearing, the tissues were impregnated overnight in melted paraffin wax. Tissues embedding was performed using tissue embedder (Diapath, Italy). The tissue sections were cut at the thickness of 5 µm using a semi- automatic microtome (Leica RM2125 RTS, Germany) and stained with haematoxylin and eosin (H&E). The slides were coated with DPX mountant and examined under a microscope (Primostar 3, ZEISS, Germany). 2.6 Risk ratio analysis Data on water quality parameters, farm characteristics and farming practices were used to calculate the risk factors. The risk ratio was determined using Epi Info™ 7.2.6, by evaluating the correlation between exposure (farming practices and water quality) and outcome (disease). Single table analysis was conducted at a 95% confidence level, employing Taylor series approximation and two-tailed p- values using Fisher's exact test. 3. Results 2.1 Diagnosis of viral disease in shrimp Out of the 31 farm samples collected, all tested negative for tested DNA and RNA viruses, except for WSSV. The shrimp samples from 7 farms were confirmed as infected for WSSV by using nested PCR (Table 4 ). The overall WSSV prevalence was observed as 22.6%. In district-wise WSSV was found in Thrissur (5/14) and Kannur (2/5). No WSSV infections were detected in Ernakulam, Alappuzha or Kollam districts. The prevalence was notably higher during the month of monsoon season, which coincided with the time of surveillance. The affected shrimp shows the clinical signs include anorexic, lethargy, swimming near the surface of the pond, erratic movement, white spot-on carapace, soft shell, broken antennae, and significant colour variation with a reddish or pinkish abdomen. Table 4 Details of the farming practices and water quality parameters in shrimp farms No of farms No of farms positive for WSSV DOC Source of seed Area (acre) No of aerators Type of culture Extensive (E)/ Semi intensive (SI) Probiotics (yes/ no) Salinity (ppt) pH Alkalinity (ppm) Ammonia (ppm) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 - - - + + - - - - - - - + - + - + + + - - - - - - - - - - - - 60 75 56 31 55 60 23 35 35 16 31 35 70 30 43 34 75 47 47 81 54 32 65 70 30 41 43 85 59 60 45 SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF SPF WILD WILD WILD WILD WILD SPF SPF SPF SPF SPF SPF SPF 12 3 1 3 1 3 2.8 2.3 1 1 1 1.1 1.7 1 1.28 1 1 2 1.50 2 13 2 14 15 3 4 2.5 1 1.23 1.15 2.3 4 4 4 4 4 2 2 2 6 4 4 4 4 4 5 3 4 4 6 0 0 0 0 0 4 4 4 4 4 6 4 SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI SI E E E E E SI SI SI SI SI SI SI No No No No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes No No No No No Yes Yes Yes Yes Yes Yes Yes 20 19 20 20 10 18 10 16 15 17 26 15 13 15 15 16 18 17 16 18 17 18 17 14 19 19 15 11 15 16 25 8.5 7.5 7.8 7.8 7.1 7.8 7.6 7.6 7.2 7.3 7.3 7.2 7.6 7.7 7 7.3 7.2 7.6 7.7 7.8 7.7 7.4 7.1 7.4 7.5 7.5 7.8 7.3 7.1 6.9 7.4 110 90 100 160 130 100 90 150 110 140 90 80 120 90 150 80 160 130 150 100 100 90 90 100 100 130 90 130 170 120 90 0.25 0.15 0.1 2 0.75 0.1 0.1 1 0.75 0.25 0.2 0.5-1 1 0.75 1 0.5-1 0.85 1-1.5 0.9 0.1 0.1 0.1–0.2 0.1 0.2 0.1–0.2 0.25 0.1 0.25 0.25 0.25 0.25 Note: SPF – Specific Pathogen Free; ppt- parts per thousand; ppm- parts per million. 2.2 Histopathological analysis Histopathological analysis of healthy and WSSV - affected tissues of P. vannamei , stained with haematoxylin and eosin (H & E) was conducted at 40X magnification. The WSSV affected shrimp exhibited characteristic Cowdry- A type inclusion (red arrow) and basophilic inclusion (black arrow) in the cuticular epithelium of the stomach (Fig. 3 b), whereas the healthy shrimp did not show any inclusion bodies in the stomach (Fig. 3 a). Basophilic inclusion bodies were also observed in the pleopod of affected shrimp (Fig. 3 d). Figure 3 e shows the gill of a healthy shrimp while Fig. 3 f demonstrates gill from an affected shrimp with numerous basophilic inclusion bodies. 2.3 Data observation and risk factor analysis The sampled farms had days of culture (DOC) ranging from 16 to 85 days, with farmers typically extending the DOC of 90–120 days to achieve a marketable size. Pond preparation including drying, fertilization and liming was carried out in all 26 semi-intensive farms. Biosecurity measures such as bird fencing and the adoption of Specific Pathogen-Free (SPF) seeds were also implemented during the culture period. No water exchange occurred in the farms during the culture period. However, in some farms water exchange was performed after 2 months of stocking, with additional exchanges carried out only if necessary. The farms used 2–4 trays per acre for feeding and the salinity of the farms ranged between 10 and 26 ppt, and the pH varied from 6.9–8.5. Ammonia concentrations ranged from 0.1- 2 ppm and the alkalinity levels ranged between 80–170 ppm. Further details on the farm characteristics and farming practices were provided in Table 4 . To determine the risk factors associated with WSSV occurrence, single table analysis was performed for farming practices, water quality parameters and disease outcome. The results are summarized in Table 5 . It was found that culture duration greater than 35 days (Risk ratio (RR): 1.5385, (Confidence interval (CI): 1.1154–2.1221), ammonia concentration > 0.5mg/L (RR: 2.4000, (CI: 1.2288–4.6877), stocking density > 25/m 2 ( (RR): 1.7540, (CI: 1.0474–2.9373) and L. vannamei the cultured species (RR: 1.6364, (CI: 1.1320–2.3655) were associated with an increased risk of WSSV infection as RR values exceeds 1. Although the RR value for the increased number of crops per year was > 1, the confidence interval passed through 1 (CI: 0.9222–1.8290), and the p- value was > 0.05%, making it statistically insignificant. In contrast, a culture duration of 0–35 days (RR: 0.6500, CI: 0.4712–0.8966), ammonia concentration < 0.5mg/l (RR: 0.4167, CI: 0.2133–0.8138), stocking density < 25/m 2 (RR: 0.5701, CI: 0.3405–0.9548) were associated with a decrease in WSSV infection, as the RR values were below 1. The use of SPF seed, larger farm area and biosecurity measures (bird fencing, probiotics) found to decrease the risk of WSSV infection, though these factors showed insignificant associations (Fig. 4 ). Table 5 Result of Risk factor analysis at 95% confidence level and 2- tailed p value by fisher exact T test S.no Risk factors Risk ratio Confidence interval (95%) 2-tailed p value Lower Upper 1. Days of culture 0–35 0.6500 0.4712 0.8966 0.0331194204 2. Days of culture > 35 1.5385 1.1154 2.1221 0.0331194204 3. Disease at previous culture 1.6471 1.0305 2.6325 0.0281151897 4. Ammonia 0- 0.5 0.4167 0.2133 0.8138 0.0003011894 5. Ammonia > 0.5 2.4000 1.2288 4.6877 0.0003011894 6. Stocking density 25/m 2 1.7540 1.0474 2.9373 0.0123989618 8. Seed source SPF 0.7407 0.5926 0.9259 0.5497219132 9. Area of culture 0.9583 0.6084 1.5096 1.0000000000 10. Salinity 0–15 0.9167 0.6292 1.3355 1.0000000000 11. Salinity > 15 1.0909 0.7488 1.5894 1.0000000000 12. Bird fencing, probiotics 0.7566 0.4285 1.3359 0.3345890496 13. No of crops per year 1.2987 0.9222 1.8290 0.3717064545 14. Species culture L. vannamei 1.6364 1.1320 2.3655 0.0245012977 4. Discussion Continuous disease surveillance is crucial for the early identification, conformation of presence or absence, and monitoring the changes in incidence or prevalence of aquatic animal diseases. In the present study, a cross-sectional analysis was undertaken to assess the status of major shrimp viral diseases affecting shrimp farms in Kerala. A two-stage random sampling method was employed, involving the selection of 31 shrimp farms in the first stage a followed by sampling 23 individual shrimp per farm in second stage. This approach was based on the methodology described by Cameron and Baldock ( 1998 ) and is recommended by the World Organisation for Animal Health (OIE, 2009) for disease identifying and demonstrating disease freedom. A similar sampling strategy was adopted by Pinheiro et al. ( 2006 ) to determine the status of TSV and IMNV in P. vannamei cultured in Brazil. This study screened for the major shrimp viral pathogens, including DNA viruses- White Spot Syndrome Virus (WSSV), Hepatopancreatic Parvovirus (HPV), Monodon Baculovirus (MBV), and Infectious Hypodermal and Haematopoietic Necrosis Virus (IHHNV)—as well as RNA viruses such as Taura Syndrome Virus (TSV), Infectious Myonecrosis Virus (IMNV), Yellow Head Virus (YHV), Laem-Singh Virus (LSNV), Covert Mortality Nodavirus (CMNV), and Decapod Iridescent Virus 1 (DIV1) from 31 selected shrimp farms in Kerala. Among these, only WSSV was detected, with 7 out of 31 farm samples testing positive, resulting in an overall prevalence of 22.6% during the surveillance period The diagnosis was confirmed using highly sensitive techniques, including nested PCR and histopathological analysis. Nested PCR, reported to be more sensitive than conventional single-step PCR (Lo et al ., 1996), was instrumental in detecting low viral loads and early infections. The OIE (2019) also recognizes these techniques as among the most effective diagnostic tools for WSSV, further supporting the reliability of the findings. All seven WSSV positive samples were detected in the first-step PCR, reflects high viral load and severity of infection. An increased prevalence of WSSV was noted during the monsoon (June–September), particularly in the farms where the days of culture (DOC) exceeded 35 days. Extended culture duration, combined with environmental stressors such as fluctuations in water quality, may have increased the susceptibility to viral infections. In this study, WSSV was detected only in P. vannamei not in P. monodon . This finding aligns with previous reports by Babu et al. ( 2021 ) observed WSSV prevalence of 25.4% in P. vannamei across 130 shrimp farms along the east coast of India, while P. monodon remained unaffected. Histopathological analysis of the infected tissues revealed the presence of Cowdry type- A intranuclear inclusion bodies and denser basophilic inclusion bodies, which are characteristic of advanced WSSV infection stage and indicate severely affected nuclei. The risk ratio analysis highlighted several factors significantly associated with an increased likelihood of WSSV infection. This included ammonia concentration of > 0.5 ppm, culture duration exceeding 35 days, high stocking density (> 25 shrimp/m 2) , a history of disease in previous culture cycles, and the use of P. vannamei as the cultured species. Ammonia is a critical water quality parameter in shrimp aquaculture, as elevated level can significantly compromise shrimp health by inducing physiological stress. In shrimp ponds, ammonia accumulation is primarily attributed to the decomposition of organic matter, including uneaten feed and waste. According to CIBA ( 2006 ), the optimal concentration of ammonia for shrimp farming should be maintained below 0.01 ppm. In the present study, the affected farms affected by WSSV had ammonia concentration exceeding 0.5 ppm, which may have contributed to increased shrimp vulnerability to infection. Similarly, Babu et al. ( 2021 ) reported that 59.74% of WSSV-infected ponds had elevated ammonia levels, suggesting a strong correlation between ammonia buildup and increased susceptibility to WSSV outbreaks in shrimp populations. The average DOC in shrimp farming typically ranges from 90 to 120 days to attain marketable size (Kumaran et al ., 2017). Although, WSSV can infect shrimp at all the life stages, from eggs to broodstock, the most suitable stages for its detection are the late post-larval (PL) stages, juveniles and adult. This is because the probability of infection increases when subjected to stressors such as eye-stalk ablation, moulting, spawning, changes in salinity, temperature, pH, and plankton occurrence (OIE, 2019). In this study the DOC of sampled farms ranged from 16 to 85 days, and WSSV positive cases were found only in farms with DOC greater than 35 days. This aligns with findings by Tendencia et al. ( 2010 ), who reported that the increased incidence of WSSV infection occurred before DOC 51, with the final outbreak recorded at DOC115. These findings suggest that culture duration, particularly beyond 35 days, may increase the risk of WSSV outbreaks when combine with suboptimal environmental conditions. The accumulation of organic matter from excess feed and increased shrimp metabolism can disturb the shrimp immunity, making shrimp more vulnerable to disease. Improving environmental conditions through biosecurity measures such as probiotics usage and the installation of shrimp toilets can help mitigate these risks. Stocking density plays a crucial role in the severity and spread of infectious diseases in shrimp aquaculture. In the present study, a stocking density of more than 25 shrimp/m 2 was found as a risk factor for WSSV outbreaks. Among the 31 farms surveyed, five were extensive farms with low stocking densities, where no disease incidence was observed. However, disease occurrence in some extensive systems has been linked to the discharge of untreated effluent water from infected farms into natural water sources (Chakrabarty et al., 2014 ). High stocking densities can lead to increased organic loading in the culture environment, contributing to poor water quality and stress in shrimp—factors known to compromise immune function and increase susceptibility to WSSV (Martin et al., 1998 ). A recent study by Kim et al. ( 2024 ) demonstrated that elevated stocking densities with high water temperatures, significantly increased mortality in WSSV-infected shrimp. Similarly, Talukder et al. ( 2021 ) found that even a moderate stocking density (> 5/m 2 ) was significantly associated with WSSV outbreaks. In this study, most of the semi-intensive farms were culturing P. vannamei under high stocking densities, which may explain the observed association between the stocking density and increased WSSV risk. The present study also reports the history of disease in previous culture cycles is correlated with an increased risk of subsequent WSSV outbreaks. This is likely because of virus's ability to persist in sediments of pond for 21 to 32 days after harvesting (Bharathi et al ., 2019). These findings reinforce the importance of implementing best management practices (BMPs), such as sun-drying ponds for at least 3–5 weeks between crops, to ensure pathogen reduction and successful crop cycles. Talukder et al. ( 2021 ) similarly reported several significant risk factors for WSSV occurrence, including farm age, presence of nursery ponds, PL reservoirs, weed presence and control, stocking practices, ammonia, and oxygen concentrations. In this study, the use of SPF seed, larger farm area, and biosecurity measures (bird fencing, probiotics) were found to reduce risk, although associations were statistically insignificant. The continued risk despite the use of SPF seeds suggests that poor or inconsistent management practices may compromise the effectiveness of disease prevention strategies. Conversely, factors such as a Culture duration of 0–35 days, ammonia levels between 0–0.5 mg/L, and stocking density below 25/m 2 were statistically significant factors associated with reduced WSSV occurrence. Water quality parameters, including pH and salinity, did not show significant differences between WSSV-infected and uninfected farms. The pH ranged from 6.9 to 8.5, which is within the permissible range for shrimp culture (7.0–9.0; CIBA, 2006 ). Similarly, salinity levels ranged from 10 to 26 ppt, falling within or near the optimal range of 15–25 ppt (CIBA, 2006 ). Overall, the risk ratio analysis identified stocking density, ammonia concentration, extended culture duration (> 35 days), and culturing P. vannamei as significant risk factors for WSSV infection. These results underscore the need for maintaining optimal water quality and adopting better management practices to reduce the prevalence of viral disease in shrimp aquaculture systems. 5. Conclusion In conclusion, the findings of this study provide a snapshot of the status of major shrimp viral diseases in Kerala during the surveillance period from October 2023 to 2024. WSSV was identified as the only viral pathogen detected, with a prevalence of 22.6% across the sampled farms. Penaeus vannamei was found to be more susceptible to WSSV infection than P. monodon . Key risk factors associated with WSSV outbreaks included high stocking density, elevated ammonia levels, and extended culture duration. Reducing these risks through improved water quality management, controlled organic inputs, and strict adherence to Best Management Practices (BMPs) can significantly mitigate the impact of WSSV and support the sustainability of shrimp aquaculture in the region. Declarations Acknowledgement The authors thank the National Surveillance Programme on Aquatic Animal Diseases (NSPAAD), KUFOS, Kochi for the financial support provided during the research work. We would like to express our gratitude to the shrimp farmers of Kerala for providing the samples for analysis. Declaration of Competing Interests The authors have no conflict of interests to disclose. Author contributions Saranya P: investigation, methodology, original draft preparation; Ravikumar M. Chovatia: data analysis and validation; Rahul Krishnan: Histological analysis and validation; Gijo Ittoop: Supervision and validation; Muhammed P Safeena: conceptualization, formal analysis, supervision, and validation References Babu, B., Sathiyaraj, G., Mandal, A., Kandan, S., Biju, N., Palanisamy, S., You, S., Nisha, R. G., & Prabhu, N. M. (2021). Surveillance of disease incidence in shrimp farms located in the east coastal region of India and in vitro antibacterial efficacy of probiotics against Vibrio parahaemolyticus. Journal of Invertebrate Pathology , 179 , 107536. https://doi.org/10.1016/j.jip.2021.107536 Belcher, C. R., & Young, P. R. (1998). Colourimetric PCR-based detection of monodon baculovirus in whole Penaeus monodon postlarvae. Journal of Virological Methods , 74 (1), 21–29. https://doi.org/10.1016/s0166-0934(98)00067-6 Cameron, A.R. and Baldock, F.C. (1998). A new probability formula for surveys to substantiate freedom from disease. Preventive veterinary medicine , 34 (1),1-17. Chakrabarty, U., Mallik, A., Mondal, D., Dutta, S., & Mandal, N. (2014). Assessment of WSSV prevalence and distribution of disease-resistant shrimp among the wild population of Penaeus monodon along the west coast of India. Journal of Invertebrate Pathology , 119 , 12–18. https://doi.org/10.1016/j.jip.2014.03.005 CIBA. (2006). Handbook of Fisheries and Aquaculture . Central Institute of Brackishwater Aquaculture, India. (2006) . Kennedy, D. A., Kurath, G., Brito, I. L., Purcell, M. K., Read, A. F., Winton, J. R., & Wargo, A. R. (2015). Potential drivers of virulence evolution in aquaculture. Evolutionary Applications , 9 (2), 344–354. https://doi.org/10.1111/eva.12342 Kim, M. J., Shin, D., Jang, G. I., Kwon, M., & Kim, K. I. (2024). Influence of stocking density and interaction variability on disease progression of white spot syndrome virus-infected shrimp under different risk scenarios. Aquaculture , 595 , 741597. https://doi.org/10.1016/j.aquaculture.2024.741597 Kumaran, M., Anand, P., Kumar, J. A., Ravisankar, T., Paul, J., Vasagam, K. P. K., Vimala, D. D., & Raja, K. A. (2016). Is Pacific white shrimp (Penaeus vannamei) farming in India is technically efficient? — A comprehensive study. Aquaculture , 468 , 262–270. https://doi.org/10.1016/j.aquaculture.2016.10.019 Lo, C., Ho, C., Chen, C., Liu, K., Chiu, Y., Yeh, P., Peng, S., Hsu, H., Liu, H., Chang, C., Su, Wang, C., & Kou, G. (1997). Detection and tissue tropism of white spot syndrome baculovirus (WSBV) in captured brooders of Penaeus monodon with a special emphasis on reproductive organs. Diseases of Aquatic Organisms , 30 , 53–72. https://doi.org/10.3354/dao030053 Martin, J. M., Veran, Y., Guelorget, O., & Pham, D. (1998). Shrimp rearing: stocking density, growth, impact on sediment, waste output and their relationships studied through the nitrogen budget in rearing ponds. Aquaculture , 164 (1–4), 135–149. https://doi.org/10.1016/s0044-8486(98)00182-3 MPEDA, (2022). Handbook Fisheries Statistics. Nunan, L. M., & Lightner, D. V. (1997). Development of a non-radioactive gene probe by PCR for detection of white spot syndrome virus (WSSV). Journal of Virological Methods , 63 (1–2), 193–201. https://doi.org/10.1016/s0166-0934(96)02128-3 Otta, S., Shubha, G., Joseph, B., Chakraborty, A., Karunasagar, I., & Karunasagar, I. (1999). Polymerase chain reaction (PCR) detection of white spot syndrome virus (WSSV) in cultured and wild crustaceans in India. Diseases of Aquatic Organisms , 38 , 67–70. https://doi.org/10.3354/dao038067 Phromjai, J., Boonsaeng, V., Withyachumnarnkul, B., & Flegel, T. W. (2002). Detection of hepatopancreatic parvovirus in Thai shrimp Penaeus monodon by in situ hybridization, dot blot hybridization and PCR amplification. Diseases of Aquatic Organisms , 51 , 227–232. https://doi.org/10.3354/dao051227 Pinheiro, A. C., Lima, A. P., De Souza, M. E., Neto, E. C., Adrião, M., Gonçalves, V. S., & Coimbra, M. R. (2006). Epidemiological status of Taura syndrome and Infectious myonecrosis viruses in Penaeus vannamei reared in Pernambuco (Brazil). Aquaculture , 262 (1), 17–22. https://doi.org/10.1016/j.aquaculture.2006.10.006 Poulos, B. T., & Lightner, D. V. (2006). Detection of infectious myonecrosis virus (IMNV) of penaeid shrimp by reverse-transcriptase polymerase chain reaction (RT-PCR). Diseases of Aquatic Organisms , 73 , 69–72. https://doi.org/10.3354/dao073069 Prakasha, B., Ramakrishna, R., Karunasagar, I., & Karunasagar, I. (2007). Detection of Laem-Singh virus (LSNV) in cultured Penaeus monodon from India. Diseases of Aquatic Organisms , 77 , 83–86. https://doi.org/10.3354/dao01835 Qiu, L., Chen, M. M., Wan, X. Y., Li, C., Zhang, Q. L., Wang, R. Y., Cheng, D. Y., Dong, X., Yang, B., Wang, X. H., Xiang, J. H., & Huang, J. (2017). Characterization of a new member of Iridoviridae, Shrimp hemocyte iridescent virus (SHIV), found in white leg shrimp (Litopenaeus vannamei). Scientific Reports , 7 (1). https://doi.org/10.1038/s41598-017-10738-8 S, S. K., Bharathi, R. A., Rajan, J. J. S., Chitra, V., Muralidhar, M., & Alavandi, S. V. (2019). Viability of white spot syndrome virus (WSSV) in shrimp pond sediments with reference to physicochemical properties. Aquaculture International , 27 (5), 1369–1382. https://doi.org/10.1007/s10499-019-00394-2 Safeena, M. P., Rai, P., & Karunasagar, I. (2012). Molecular Biology and Epidemiology of Hepatopancreatic parvovirus of Penaeid Shrimp. Indian Journal of Virology , 23 (2), 191–202. https://doi.org/10.1007/s13337-012-0080-5 Sergeant, ESG, 2018. Epitools Epidemiological Calculators. Ausvet. Sritunyalucksana, K., Apisawetakan, S., Boon-Nat, A., Withyachumnarnkul, B., & Flegel, T. W. (2006). A new RNA virus found in black tiger shrimp Penaeus monodon from Thailand. Virus Research , 118 (1–2), 31–38. https://doi.org/10.1016/j.virusres.2005.11.005 Stentiford, G.D., Neil, D.M., Peeler, E.J., Shields, J.D., Small, H.J., Flegel, T.W., Vlak, J.M., Jones, B., Morado, F., Moss, S. and Lotz, J. (2012). Disease will limit future food supply from the global crustacean fishery and aquaculture sectors. Journal of invertebrate pathology , 110 (2), 141-157. Talukder, A. S., Punom, N. J., Eshik, M. M. E., Begum, M. K., Islam, H. R., Hossain, Z., & Rahman, M. S. (2021). Molecular identification of white spot syndrome virus (WSSV) and associated risk factors for white spot disease (WSD) prevalence in shrimp (Penaeus monodon) aquaculture in Bangladesh. Journal of Invertebrate Pathology , 179 , 107535. https://doi.org/10.1016/j.jip.2021.107535 Tang, K., Navarro, S., & Lightner, D. (2007). PCR assay for discriminating between infectious hypodermal and hematopoietic necrosis virus (IHHNV) and virus-related sequences in the genome of Penaeus monodon. Diseases of Aquatic Organisms , 74 (2), 165–170. https://doi.org/10.3354/dao074165 Tendencia, E. A., Bosma, R. H., & Verreth, J. A. (2010). WSSV risk factors related to water physico-chemical properties and microflora in semi-intensive Penaeus monodon culture ponds in the Philippines. Aquaculture , 302 (3–4), 164–168. https://doi.org/10.1016/j.aquaculture.2010.03.008 Wongteerasupaya, C., Tongcheua, W., Boonsaeng, V., Panyim, S., Tassanakajon, A., Withyachumnarnkul, B., & Flegel, T. (1997). Detection of yellow-head virus (YHV) of Penaeus monodon by RT-PCR amplification. Diseases of Aquatic Organisms , 31 , 181–186. https://doi.org/10.3354/dao031181 World Organisation for Animal Health (OIE) (2009) Guide for aquatic animal health surveillance . OIE, Paris. Available at: https://www.woah.org/en/document/guide-for-aquatic-animal-health-surveillance/ World Organisation for Animal Health (OIE) (2019) Manual of diagnostic tests for aquatic animals , 7th edn. OIE, Paris. Available at: https://www.woah.org/en/what-we-do/standards/codes-and-manuals/aquatic-manual/ Zhang, Q., Xu, T., Wan, X., Liu, S., Wang, X., Li, X., Dong, X., Yang, B., & Huang, J. (2017). Prevalence and distribution of covert mortality nodavirus (CMNV) in cultured crustacean. Virus Research , 233 , 113–119. https://doi.org/10.1016/j.virusres.2017.03.013 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6564499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452245838,"identity":"96c4847e-abf0-45f0-8292-1dac3dcae3b1","order_by":0,"name":"Saranya P","email":"","orcid":"","institution":"Kerala University of Fisheries and Ocean Studies","correspondingAuthor":false,"prefix":"","firstName":"Saranya","middleName":"","lastName":"P","suffix":""},{"id":452245840,"identity":"98c110f5-7907-4268-9a37-0a65a19e2f53","order_by":1,"name":"Ravikumar M. Chovatia","email":"","orcid":"","institution":"Kerala University of Fisheries and Ocean Studies","correspondingAuthor":false,"prefix":"","firstName":"Ravikumar","middleName":"M.","lastName":"Chovatia","suffix":""},{"id":452245842,"identity":"5e2f5817-14fc-4492-835f-6fecbe5354ac","order_by":2,"name":"Gijo Ittoop","email":"","orcid":"","institution":"Kerala University of Fisheries and Ocean Studies","correspondingAuthor":false,"prefix":"","firstName":"Gijo","middleName":"","lastName":"Ittoop","suffix":""},{"id":452245843,"identity":"cf25dfec-829a-43c2-a2c0-e74045e7edc8","order_by":3,"name":"Rahul Krishnan","email":"","orcid":"","institution":"Kerala University of Fisheries and Ocean Studies","correspondingAuthor":false,"prefix":"","firstName":"Rahul","middleName":"","lastName":"Krishnan","suffix":""},{"id":452245844,"identity":"9a88c06e-2995-4e09-a6c5-349189fc4e51","order_by":4,"name":"Muhammed P Safeena","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYFACxgdQBvNhBgYbKDsBrxZmAyiDLZmBIY00LTzGCC34gHz7YcYHH3fYMejO7vlszJNgY8/AfvgBw8MduLUYnElmNpx5JpnB7M7Zzck8CWmJDTxpBgyJZ/BoYcg/Js3bxsxgdiN382HeH4eBvshhYEhsw+Ow/sfsv/+21QO15Dw+zJNw2J6B/w1+LQw3ktmYGdsOg7QwAx12mLFBgoAtBjceM0v2th3nMbtzzNhwDtAvbRLPDA7gd1gy44efbdVyZrebH0u8AYYYP3/yw4c/8TkMCngYJKAsNiA+QFgDCEgQVjIKRsEoGAUjFAAARfJMsGjE3TgAAAAASUVORK5CYII=","orcid":"","institution":"Kerala University of Fisheries and Ocean Studies","correspondingAuthor":true,"prefix":"","firstName":"Muhammed","middleName":"P","lastName":"Safeena","suffix":""}],"badges":[],"createdAt":"2025-04-30 11:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6564499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6564499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82089931,"identity":"2bd135d5-f016-491a-9280-0ab27f9a9ad2","added_by":"auto","created_at":"2025-05-06 16:00:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142894,"visible":true,"origin":"","legend":"\u003cp\u003eThe geographical location map representing sampling districts in Kerala created using QGIS 3.34.1.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6564499/v1/e8025e5727f87175a7690350.png"},{"id":82089196,"identity":"006677b8-c2fa-4ddd-9615-a0a795ebc9c5","added_by":"auto","created_at":"2025-05-06 15:52:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218185,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6564499/v1/18a5525aa4ce5fef40f7c26c.png"},{"id":82089232,"identity":"cea7abca-ea7f-4e64-8965-c3ade01c9933","added_by":"auto","created_at":"2025-05-06 15:52:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1203920,"visible":true,"origin":"","legend":"\u003cp\u003ea- Histological sections of healthy shrimp stomach; b- Histological sections of WSSV affected shrimp showing Cowdry- A type inclusion (red arrow) and basophilic inclusion (black arrow) in the cuticular epithelium of stomach; c- Histological sections of healthy shrimp pleopod; d- Histological sections of WSSV affected shrimp pleopod; e- Histological sections of healthy shrimp gill; f- Histological sections of WSSV affected shrimp gill showing inclusion bodies.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6564499/v1/2925ca38e4f2d33ee52c6e59.png"},{"id":82089233,"identity":"45037271-4c0b-4f3c-b893-eb92fdd3864a","added_by":"auto","created_at":"2025-05-06 15:52:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32567,"visible":true,"origin":"","legend":"\u003cp\u003eA forest plot demonstrating the risk factor associated with WSSV in shrimp farms. The RR with confidence intervals \u0026gt;1 indicates a significant rise in disease, whereas \u0026lt;1 indicates a significant decrease in disease.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6564499/v1/ec1dd2fa580d32e90521f0a1.png"},{"id":88235749,"identity":"09e9b4c0-d475-4307-a6a7-94bcc69b698a","added_by":"auto","created_at":"2025-08-04 10:17:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2996675,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6564499/v1/bd60f438-5b8a-41a7-8f4e-5a2bfd9e5989.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCross Sectional Analysis of Major Viral Diseases in Shrimp Farms of Kerala, India\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eShrimp farming plays a pivotal role in the aquaculture industry, experiencing significant growth in recent years. In India, the sector has expanded markedly, with annual production escalating from 35,500 tonnes in 1990\u0026ndash;91 to 843,633 tonnes in 2020\u0026ndash;2021 (MPEDA, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Frozen shrimp remains a primary export commodity, representing 40.19% of total quantity and contributing 66.12% to export revenues in US dollars.​ Kerala holds a prominent position in India's shrimp aquaculture, employing both traditional and modern farming techniques. The state is endowed with abundant water resources, including 46,128.94 hectares of backwaters, of which 2,971.24 hectares are dedicated to shrimp farming, yielding a production of 1,549.83 tonnes (MPEDA, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).​\u003c/p\u003e \u003cp\u003eThe most significant challenge in shrimp aquaculture is disease, leading to high mortality rates and considerable economic losses (Stentiford et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Outbreaks result from a combination of factors such as suboptimal culture environments, inadequate management, high stocking densities, stress, compromised immune responses, and the presence of pathogens like viruses, bacteria, fungi and parasites (Kennedy \u003cem\u003eet al\u003c/em\u003e., 2016). Viral diseases are particularly concerning due to their high infectivity and lack of effective treatments.​\u003c/p\u003e \u003cp\u003eGlobally, nearly 20 viruses have been identified in penaeid shrimp (Safeena et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The World Organization for Animal Health (OIE) recognizes several significant viruses impacting shrimp aquaculture, including White Spot Syndrome Virus (WSSV), Infectious Hypodermal and Hematopoietic Necrosis Virus (IHHNV), Infectious Myonecrosis Virus (IMNV), Taura Syndrome Virus (TSV), and Yellow Head Virus Genotype 1 (YHV-1), all of which have substantial socio-economic implications.​While the shrimp aquaculture has been affected by viral diseases across various Indian states, there is a lack of comprehensive research focusing on Kerala. This study aims to systematically analyse the prevalence and impact of viral diseases on shrimp culture in Kerala.​\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and sampling technique\u003c/h2\u003e \u003cp\u003eThis study focused on grow-out farms cultivating \u003cem\u003ePenaeus vannamei\u003c/em\u003e and \u003cem\u003ePenaeus monodon\u003c/em\u003e across five major shrimp-farming coastal districts in Kerala, India, which contribute significantly to the state\u0026rsquo;s overall shrimp production. The selected districts included Kannur, Thrissur, Ernakulam, Alappuzha, and Kollam.\u003c/p\u003e \u003cp\u003eA two-stage random sampling method, as recommended by the World Organisation for Animal Health (OIE, 2009) was employed to ensure representative sampling across these districts. The sample size was calculated using FreeCalc: Calculate sample size for freedom testing with imperfect tests, available through the Epitools Epidemiological Calculators provided by Ausvet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://epitools.ausvet.com.au\u003c/span\u003e\u003cspan address=\"http://epitools.ausvet.com.au\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Sergeant, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This approach ensured statistically reliable insights into the status of viral diseases in shrimp.\u003c/p\u003e \u003cp\u003eBaseline information on active shrimp farms in Kerala were obtained from the Department of Fisheries, Government of Kerala, and used for calculating the appropriate sample size. In the first stage, 31 shrimp farms were randomly selected from the five districts. In the second stage, 23 individual shrimp specimens were randomly collected from each selected farm. The surveillance study was conducted from October 2023 to 2024 to assess the presence of major viral diseases affecting shrimp aquaculture in the region. The input parameters and calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample collection, processing and storage\u003c/h2\u003e \u003cp\u003eThe total of 31 extensive and semi-intensive shrimp farms including 5 farms in Kannur district, 14 farms in Thrissur district, 5 farms in Ernakulam district, 2 farms in Alappuzha district, and 5 farms in Kollam district were selected randomly and 23 individual shrimps from each farm were collected for analysis. The samples were collected from various sides of the shrimp farms using cast nets or by retrieving shrimp from feeding trays. Live shrimp samples were collected for histopathological analysis and instantly fixed in Davidson\u0026rsquo;s fixative (37% Formaldehyde \u0026ndash; 200 mL, Glacial acetic acid- 100 mL, 95% ethanol \u0026ndash; 300 mL, Distilled water \u0026ndash; 300 mL) at a ratio of 1:10 (sample: fixative). For DNA extraction, the samples were preserved in 95% ethanol while for RNA extraction the samples were stored in RNA\u003cem\u003elater\u003c/em\u003e\u003csup\u003e\u0026reg;\u003c/sup\u003e (Sigma-Aldrich, India). The collected samples were placed in ice packs during transportation to the Department of Aquatic Animal Health Management Laboratory in KUFOS. All samples were then stored at -20\u0026deg;C to maintain the viability for further analysis. Along with shrimp samples, water samples were also analysed in the pond site to determine the water quality parameters. The pH and salinity were measured with the help of pH meter (Eutech, U.K) and portable refractometer. The ammonia and alkalinity were tested using the commercial ammonia testing kit (HiMedia, India) and alkalinity testing kit (Borosil, India) following manufacturer\u0026rsquo;s protocol. Questionaries survey was also conducted, which was prepared with reference to the National Database on Aquatic Animal Disease (NSPAAD) and data collected from individual farmers during surveillance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Nucleic acid extraction\u003c/h2\u003e \u003cp\u003eBy using the method described by Otta et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) the viral DNA extraction was performed and RNA isolation was conducted using RNAiso Plus (Takara, India), according to the manufacturer\u0026rsquo;s protocol. The extracted nucleic acid were dissolved in nuclease-free water or 1X TE buffer and stored at \u0026minus;\u0026thinsp;20\u0026deg;C for future use. By using NanoDrop spectrophotometer (Thermo Fisher Scientific, USA) the purity and concentration of RNA samples were assessed. DNase treatment was carried out to further eliminate potential DNA contamination, by combining 1 \u0026micro;g of RNA with nuclease-free water to a final volume of 7 \u0026micro;L. A 2 \u0026micro;L master mix, containing an equal ratio of DNase I and buffer, was then added and incubated for 30 minutes at 37\u0026deg;C to allow DNase I activity. To inactivate the enzyme, 1 \u0026micro;L of EDTA was added, followed by heating the mixture at 65\u0026deg;C for 10 minutes. Subsequently, reverse transcription was carried out using the PrimeScript\u0026trade; 1st Strand cDNA Synthesis Kit (Takara, India) in according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Polymerase Chain Reaction (PCR)\u003c/h2\u003e \u003cp\u003eThe primers used were custom synthesized by Eurofins, Bangalore. The details of primer sequences, amplicon size with reference used for the diagnostic PCR of all the DNA and RNA viruses were listed in the Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInput and output value of sample size estimation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInput\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutput\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStage 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStage 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eInput value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eOutput value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest sensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRequired sample size:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest specificity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCut-point number of positives:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eType I error:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesign prevalence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eType II error:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiseased elements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation-level sensitivity:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModified hypergeometric exact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimple binomial (large population)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation-level specificity:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget Type I error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIf a random sample of\u0026nbsp;31\u0026nbsp;units is taken from a population of\u0026nbsp;350\u0026nbsp;and\u0026nbsp;1\u0026nbsp;or fewer reactors are found, the probability that the population is diseased at a prevalence of\u0026nbsp;0.142857142857143\u0026nbsp;is\u0026nbsp;0.0436.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIf a random sample of\u0026nbsp;23\u0026nbsp;units is taken from a population of\u0026nbsp;100000\u0026nbsp;and\u0026nbsp;1\u0026nbsp;or fewer reactors are found, the probability that the population is diseased at a prevalence of\u0026nbsp;0.2\u0026nbsp;is\u0026nbsp;0.0418.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget Type II error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMethod:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModified hypergeometric exact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSimple binomial (large population)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation threshold for infinite probability formula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c6\" namest=\"c4\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum sample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of primer sequences and amplicon size used for diagnosis of shrimp viruses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimer sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmplicon size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWSSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTACTAACTTCAGCCTATCTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1447 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Lo et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTAATGCGGGTGTAATGTTCTTACGA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTAACTGCCCCTTCCATCTCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e941 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTACGGCAGCTGCTGCACCTTGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMBV1.4F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGATTCCATATCGGCCGAATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e533 bp\u003c/p\u003e \u003cp\u003e361 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Belcher \u0026amp; Young, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMBV1.4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTGGCATGCACTCCCTGAGAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMBV1.4NF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCCAATCGCGTCTGCGATACT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMBV1.4NR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGCTAATGGGGCACAAGTCTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIHHNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCCAACACTTAGTCAAAACCAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e309 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Tang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGTCTGCTACGATGATTATCCA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH441F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCATTACAAGAGCCAAGCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e441 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Phromjai et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH441R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACACTCAGCCTCTACCTTGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eLSNV-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLLV-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTGCCTTCTCCCGAGTGGTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Sritunyalucksana \u003cem\u003eet al\u003c/em\u003e., 2005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLLV-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCGGCTGAGGTAGCTGCTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSNVnF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCGCAAGAGTTCTCAGGCTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Prakasha et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLSNVnR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATCACCGCAGGCTAATATAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIMNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4587F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGACGCTGCTAACCATACAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e328 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Poulos and Lightner, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4914R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTCGGCTGTTCGATCAAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4725NF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCACATGCTCAGAGACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4863NR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAGCGCTGAGTCCAGTCTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHIV-F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGGCGGGAGATGGTGTTAGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e457 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Qiu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHIV-R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCGTTTCGGTACGAAGATGTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHIV-F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCGGGAAACGATTCGTATTGGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e129 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSHIV-R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTGCTTGATCGGCATCCTTGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYHV- 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10F:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCGCTAATTTCAAAAACTACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e135 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Wongteerasupaya et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAGGTGTTATGTCGAGGAAGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9992F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAGTAGACAGCCGCGCTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e231 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Nunan \u003cem\u003eet al\u003c/em\u003e., 1998)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9195R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCAATGAGAGCTTGGTCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCMNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMNV-7F1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAATACGGCGATGACG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e619 bp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e(Zhang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMNV-7R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACGAAGTGCCCACAGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMNV-7F2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCACAACCGAGTCAAACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165 bp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMNV-7R2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCGTAAACAGCGAAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe PCR was carried out using a Bio Rad T 100 Gradient 96 well thermal cycler (Bio Rad, USA) with specific primers. The master mix for a 25\u0026micro;L reaction volume contained 2.5 \u0026micro;L of 10X Taq buffer, 0.125\u0026micro;L of (5U/\u0026micro;L) Taq DNA polymerase, 0.5\u0026micro;L of 10mM dNTP mix, 1 \u0026micro;L of (10 pmole/\u0026micro;L) forward primer, 1 \u0026micro;L of (10 pmole/\u0026micro;L) reverse primer, (Origin, India), 1\u0026micro;L of template DNA and adjust to final volume using nuclease free water. The PCR conditions for detection of DNA and RNA virus were listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. the PCR products was analysed using Gel electrophoresis on a 2% agarose gel containing ethidium bromide, employing using a standard 100 bp or 1 kb Plus DNA ladder (Origin, India). The bands were visualized using a gel documentation system (Gel Doc\u0026trade; EZ Imager, Bio-Rad, USA) to confirm the presence and size of PCR products.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of PCR condition used for diagnosis of shrimp viruses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathogen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eInitial denaturation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDenaturation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAnnealing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExtension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFinal extension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCycles\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWSSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94\u0026deg;C- 4min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e55\u0026deg;C-1min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u0026thinsp;\u0026minus;\u0026thinsp;1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C- 2 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C- 5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94\u0026deg;C- 4min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e55\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u0026deg;C-1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C- 2 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C- 5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHHNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e95\u0026deg;C- 5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C-7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e94\u0026deg;C- 5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C-5min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e96\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C- 60 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C- 7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e96\u0026deg;C- 5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C-60 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C-7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIMNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e60\u0026deg;C- 30 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u0026deg;C- 2 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95\u0026deg;C-45 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60\u0026deg;C- 45 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u0026deg;C-7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e95\u0026deg;C \u0026ndash; 2 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C-2 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLSNV- 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e94\u0026deg;C \u0026minus;\u0026thinsp;5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C-10min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e94\u0026deg;C \u0026minus;\u0026thinsp;5 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C- 1 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C-10 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYHV-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50\u0026deg;C-30 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e94\u0026deg;C-2 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58\u0026deg;C- 45 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68\u0026deg;C- 45 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68\u0026deg;C-7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e94\u0026deg;C- 3 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C- 45 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u0026deg;C\u0026ndash;45 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60\u0026deg;C- 45 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u0026deg;C- 7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e40 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCMNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e94\u0026deg;C \u0026ndash; 4 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C-40 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C-7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e94\u0026deg;C \u0026ndash; 4 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94\u0026deg;C-20 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50\u0026deg;C-20 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C-20 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C- 7 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e95\u0026deg;C- 3 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C- 2min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e95\u0026deg;C- 3 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u0026deg;C- 30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72\u0026deg;C-30 sec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u0026deg;C- 2min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35 cycles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Histopathological analysis\u003c/h2\u003e \u003cp\u003eFor histological analysis, the fixed tissues were passed through a graded series of alcohol concentrations, ranging from 50\u0026ndash;100%, to remove water from the samples. The tissues were then cleared by sequential immersion in xylene for two times and after clearing, the tissues were impregnated overnight in melted paraffin wax. Tissues embedding was performed using tissue embedder (Diapath, Italy). The tissue sections were cut at the thickness of 5 \u0026micro;m using a semi- automatic microtome (Leica RM2125 RTS, Germany) and stained with haematoxylin and eosin (H\u0026amp;E). The slides were coated with DPX mountant and examined under a microscope (Primostar 3, ZEISS, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Risk ratio analysis\u003c/h2\u003e \u003cp\u003eData on water quality parameters, farm characteristics and farming practices were used to calculate the risk factors. The risk ratio was determined using Epi Info\u0026trade; 7.2.6, by evaluating the correlation between exposure (farming practices and water quality) and outcome (disease). Single table analysis was conducted at a 95% confidence level, employing Taylor series approximation and two-tailed p- values using Fisher's exact test.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Diagnosis of viral disease in shrimp\u003c/h2\u003e \u003cp\u003eOut of the 31 farm samples collected, all tested negative for tested DNA and RNA viruses, except for WSSV. The shrimp samples from 7 farms were confirmed as infected for WSSV by using nested PCR (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The overall WSSV prevalence was observed as 22.6%. In district-wise WSSV was found in Thrissur (5/14) and Kannur (2/5). No WSSV infections were detected in Ernakulam, Alappuzha or Kollam districts. The prevalence was notably higher during the month of monsoon season, which coincided with the time of surveillance. The affected shrimp shows the clinical signs include anorexic, lethargy, swimming near the surface of the pond, erratic movement, white spot-on carapace, soft shell, broken antennae, and significant colour variation with a reddish or pinkish abdomen.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of the farming practices and water quality parameters in shrimp farms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo of farms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo of farms positive for WSSV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource of seed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (acre)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo of aerators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eType of culture Extensive (E)/ Semi intensive (SI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProbiotics\u003c/p\u003e \u003cp\u003e(yes/ no)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSalinity (ppt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eAlkalinity (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eAmmonia\u003c/p\u003e \u003cp\u003e(ppm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e8\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e21\u003c/p\u003e \u003cp\u003e22\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e26\u003c/p\u003e \u003cp\u003e27\u003c/p\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003cp\u003e75\u003c/p\u003e \u003cp\u003e56\u003c/p\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e55\u003c/p\u003e \u003cp\u003e60\u003c/p\u003e \u003cp\u003e23\u003c/p\u003e \u003cp\u003e35\u003c/p\u003e \u003cp\u003e35\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e31\u003c/p\u003e \u003cp\u003e35\u003c/p\u003e \u003cp\u003e70\u003c/p\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e43\u003c/p\u003e \u003cp\u003e34\u003c/p\u003e \u003cp\u003e75\u003c/p\u003e \u003cp\u003e47\u003c/p\u003e \u003cp\u003e47\u003c/p\u003e \u003cp\u003e81\u003c/p\u003e \u003cp\u003e54\u003c/p\u003e \u003cp\u003e32\u003c/p\u003e \u003cp\u003e65\u003c/p\u003e \u003cp\u003e70\u003c/p\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e41\u003c/p\u003e \u003cp\u003e43\u003c/p\u003e \u003cp\u003e85\u003c/p\u003e \u003cp\u003e59\u003c/p\u003e \u003cp\u003e60\u003c/p\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eWILD\u003c/p\u003e \u003cp\u003eWILD\u003c/p\u003e \u003cp\u003eWILD\u003c/p\u003e \u003cp\u003eWILD\u003c/p\u003e \u003cp\u003eWILD\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003cp\u003eSPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e2.8\u003c/p\u003e \u003cp\u003e2.3\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.1\u003c/p\u003e \u003cp\u003e1.7\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.28\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e1.50\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e2.5\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e1.23\u003c/p\u003e \u003cp\u003e1.15\u003c/p\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eE\u003c/p\u003e \u003cp\u003eE\u003c/p\u003e \u003cp\u003eE\u003c/p\u003e \u003cp\u003eE\u003c/p\u003e \u003cp\u003eE\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003cp\u003eSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e10\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e26\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e13\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e18\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e11\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003cp\u003e7.5\u003c/p\u003e \u003cp\u003e7.8\u003c/p\u003e \u003cp\u003e7.8\u003c/p\u003e \u003cp\u003e7.1\u003c/p\u003e \u003cp\u003e7.8\u003c/p\u003e \u003cp\u003e7.6\u003c/p\u003e \u003cp\u003e7.6\u003c/p\u003e \u003cp\u003e7.2\u003c/p\u003e \u003cp\u003e7.3\u003c/p\u003e \u003cp\u003e7.3\u003c/p\u003e \u003cp\u003e7.2\u003c/p\u003e \u003cp\u003e7.6\u003c/p\u003e \u003cp\u003e7.7\u003c/p\u003e \u003cp\u003e7\u003c/p\u003e \u003cp\u003e7.3\u003c/p\u003e \u003cp\u003e7.2\u003c/p\u003e \u003cp\u003e7.6\u003c/p\u003e \u003cp\u003e7.7\u003c/p\u003e \u003cp\u003e7.8\u003c/p\u003e \u003cp\u003e7.7\u003c/p\u003e \u003cp\u003e7.4\u003c/p\u003e \u003cp\u003e7.1\u003c/p\u003e \u003cp\u003e7.4\u003c/p\u003e \u003cp\u003e7.5\u003c/p\u003e \u003cp\u003e7.5\u003c/p\u003e \u003cp\u003e7.8\u003c/p\u003e \u003cp\u003e7.3\u003c/p\u003e \u003cp\u003e7.1\u003c/p\u003e \u003cp\u003e6.9\u003c/p\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e110\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e160\u003c/p\u003e \u003cp\u003e130\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e150\u003c/p\u003e \u003cp\u003e110\u003c/p\u003e \u003cp\u003e140\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e80\u003c/p\u003e \u003cp\u003e120\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e150\u003c/p\u003e \u003cp\u003e80\u003c/p\u003e \u003cp\u003e160\u003c/p\u003e \u003cp\u003e130\u003c/p\u003e \u003cp\u003e150\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e100\u003c/p\u003e \u003cp\u003e130\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003cp\u003e130\u003c/p\u003e \u003cp\u003e170\u003c/p\u003e \u003cp\u003e120\u003c/p\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e0.1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e0.75\u003c/p\u003e \u003cp\u003e0.1\u003c/p\u003e \u003cp\u003e0.1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.75\u003c/p\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e0.2\u003c/p\u003e \u003cp\u003e0.5-1\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.75\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e0.5-1\u003c/p\u003e \u003cp\u003e0.85\u003c/p\u003e \u003cp\u003e1-1.5\u003c/p\u003e \u003cp\u003e0.9\u003c/p\u003e \u003cp\u003e0.1\u003c/p\u003e \u003cp\u003e0.1\u003c/p\u003e \u003cp\u003e0.1\u0026ndash;0.2\u003c/p\u003e \u003cp\u003e0.1\u003c/p\u003e \u003cp\u003e0.2\u003c/p\u003e \u003cp\u003e0.1\u0026ndash;0.2\u003c/p\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e0.1\u003c/p\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e0.25\u003c/p\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eNote: SPF \u0026ndash; Specific Pathogen Free; ppt- parts per thousand; ppm- parts per million.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Histopathological analysis\u003c/h2\u003e \u003cp\u003eHistopathological analysis of healthy and WSSV - affected tissues of \u003cem\u003eP. vannamei\u003c/em\u003e, stained with haematoxylin and eosin (H \u0026amp; E) was conducted at 40X magnification. The WSSV affected shrimp exhibited characteristic Cowdry- A type inclusion (red arrow) and basophilic inclusion (black arrow) in the cuticular epithelium of the stomach (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), whereas the healthy shrimp did not show any inclusion bodies in the stomach (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Basophilic inclusion bodies were also observed in the pleopod of affected shrimp (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ee shows the gill of a healthy shrimp while Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ef demonstrates gill from an affected shrimp with numerous basophilic inclusion bodies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data observation and risk factor analysis\u003c/h2\u003e \u003cp\u003eThe sampled farms had days of culture (DOC) ranging from 16 to 85 days, with farmers typically extending the DOC of 90\u0026ndash;120 days to achieve a marketable size. Pond preparation including drying, fertilization and liming was carried out in all 26 semi-intensive farms. Biosecurity measures such as bird fencing and the adoption of Specific Pathogen-Free (SPF) seeds were also implemented during the culture period. No water exchange occurred in the farms during the culture period. However, in some farms water exchange was performed after 2 months of stocking, with additional exchanges carried out only if necessary.\u003c/p\u003e \u003cp\u003eThe farms used 2\u0026ndash;4 trays per acre for feeding and the salinity of the farms ranged between 10 and 26 ppt, and the pH varied from 6.9\u0026ndash;8.5. Ammonia concentrations ranged from 0.1- 2 ppm and the alkalinity levels ranged between 80\u0026ndash;170 ppm. Further details on the farm characteristics and farming practices were provided in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo determine the risk factors associated with WSSV occurrence, single table analysis was performed for farming practices, water quality parameters and disease outcome. The results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It was found that culture duration greater than 35 days (Risk ratio (RR): 1.5385, (Confidence interval (CI): 1.1154\u0026ndash;2.1221), ammonia concentration\u0026thinsp;\u0026gt;\u0026thinsp;0.5mg/L (RR: 2.4000, (CI: 1.2288\u0026ndash;4.6877), stocking density\u0026thinsp;\u0026gt;\u0026thinsp;25/m\u003csup\u003e2\u003c/sup\u003e ( (RR): 1.7540, (CI: 1.0474\u0026ndash;2.9373) and \u003cem\u003eL. vannamei\u003c/em\u003e the cultured species (RR: 1.6364, (CI: 1.1320\u0026ndash;2.3655) were associated with an increased risk of WSSV infection as RR values exceeds 1. Although the RR value for the increased number of crops per year was \u0026gt;\u0026thinsp;1, the confidence interval passed through 1 (CI: 0.9222\u0026ndash;1.8290), and the p- value was \u0026gt;\u0026thinsp;0.05%, making it statistically insignificant. In contrast, a culture duration of 0\u0026ndash;35 days (RR: 0.6500, CI: 0.4712\u0026ndash;0.8966), ammonia concentration\u0026thinsp;\u0026lt;\u0026thinsp;0.5mg/l (RR: 0.4167, CI: 0.2133\u0026ndash;0.8138), stocking density\u0026thinsp;\u0026lt;\u0026thinsp;25/m\u003csup\u003e2\u003c/sup\u003e (RR: 0.5701, CI: 0.3405\u0026ndash;0.9548) were associated with a decrease in WSSV infection, as the RR values were below 1. The use of SPF seed, larger farm area and biosecurity measures (bird fencing, probiotics) found to decrease the risk of WSSV infection, though these factors showed insignificant associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of Risk factor analysis at 95% confidence level and 2- tailed p value by fisher exact T test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS.no\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eConfidence interval (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2-tailed p value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLower\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eUpper\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays of culture 0\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0331194204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays of culture\u0026thinsp;\u0026gt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0331194204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisease at previous culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.6325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0281151897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmonia 0- 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003011894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmonia\u0026thinsp;\u0026gt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0003011894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStocking density\u0026thinsp;\u0026lt;\u0026thinsp;25/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0123989618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStocking density\u0026thinsp;\u0026gt;\u0026thinsp;25/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.9373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0123989618\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeed source SPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5497219132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea of culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalinity 0\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalinity\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0000000000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBird fencing, probiotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3345890496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo of crops per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3717064545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies culture \u003cem\u003eL. vannamei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0245012977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eContinuous disease surveillance is crucial for the early identification, conformation of presence or absence, and monitoring the changes in incidence or prevalence of aquatic animal diseases. In the present study, a cross-sectional analysis was undertaken to assess the status of major shrimp viral diseases affecting shrimp farms in Kerala.\u003c/p\u003e \u003cp\u003e A two-stage random sampling method was employed, involving the selection of 31 shrimp farms in the first stage a followed by sampling 23 individual shrimp per farm in second stage. This approach was based on the methodology described by Cameron and Baldock (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and is recommended by the World Organisation for Animal Health (OIE, 2009) for disease identifying and demonstrating disease freedom. A similar sampling strategy was adopted by Pinheiro et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) to determine the status of TSV and IMNV in \u003cem\u003eP. vannamei\u003c/em\u003e cultured in Brazil.\u003c/p\u003e \u003cp\u003eThis study screened for the major shrimp viral pathogens, including DNA viruses- White Spot Syndrome Virus (WSSV), Hepatopancreatic Parvovirus (HPV), Monodon Baculovirus (MBV), and Infectious Hypodermal and Haematopoietic Necrosis Virus (IHHNV)\u0026mdash;as well as RNA viruses such as Taura Syndrome Virus (TSV), Infectious Myonecrosis Virus (IMNV), Yellow Head Virus (YHV), Laem-Singh Virus (LSNV), Covert Mortality Nodavirus (CMNV), and Decapod Iridescent Virus 1 (DIV1) from 31 selected shrimp farms in Kerala. Among these, only WSSV was detected, with 7 out of 31 farm samples testing positive, resulting in an overall prevalence of 22.6% during the surveillance period The diagnosis was confirmed using highly sensitive techniques, including nested PCR and histopathological analysis. Nested PCR, reported to be more sensitive than conventional single-step PCR (Lo \u003cem\u003eet al\u003c/em\u003e., 1996), was instrumental in detecting low viral loads and early infections. The OIE (2019) also recognizes these techniques as among the most effective diagnostic tools for WSSV, further supporting the reliability of the findings.\u003c/p\u003e \u003cp\u003eAll seven WSSV positive samples were detected in the first-step PCR, reflects high viral load and severity of infection. An increased prevalence of WSSV was noted during the monsoon (June\u0026ndash;September), particularly in the farms where the days of culture (DOC) exceeded 35 days. Extended culture duration, combined with environmental stressors such as fluctuations in water quality, may have increased the susceptibility to viral infections. In this study, WSSV was detected only in \u003cem\u003eP. vannamei\u003c/em\u003e not in \u003cem\u003eP. monodon\u003c/em\u003e. This finding aligns with previous reports by Babu et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) observed WSSV prevalence of 25.4% in \u003cem\u003eP. vannamei\u003c/em\u003e across 130 shrimp farms along the east coast of India, while \u003cem\u003eP. monodon\u003c/em\u003e remained unaffected. Histopathological analysis of the infected tissues revealed the presence of Cowdry type- A intranuclear inclusion bodies and denser basophilic inclusion bodies, which are characteristic of advanced WSSV infection stage and indicate severely affected nuclei.\u003c/p\u003e \u003cp\u003eThe risk ratio analysis highlighted several factors significantly associated with an increased likelihood of WSSV infection. This included ammonia concentration of \u0026gt;\u0026thinsp;0.5 ppm, culture duration exceeding 35 days, high stocking density (\u0026gt;\u0026thinsp;25 shrimp/m\u003csup\u003e2)\u003c/sup\u003e, a history of disease in previous culture cycles, and the use of \u003cem\u003eP. vannamei\u003c/em\u003e as the cultured species.\u003c/p\u003e \u003cp\u003eAmmonia is a critical water quality parameter in shrimp aquaculture, as elevated level can significantly compromise shrimp health by inducing physiological stress. In shrimp ponds, ammonia accumulation is primarily attributed to the decomposition of organic matter, including uneaten feed and waste. According to CIBA (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the optimal concentration of ammonia for shrimp farming should be maintained below 0.01 ppm.\u003c/p\u003e \u003cp\u003eIn the present study, the affected farms affected by WSSV had ammonia concentration exceeding 0.5 ppm, which may have contributed to increased shrimp vulnerability to infection. Similarly, Babu et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that 59.74% of WSSV-infected ponds had elevated ammonia levels, suggesting a strong correlation between ammonia buildup and increased susceptibility to WSSV outbreaks in shrimp populations.\u003c/p\u003e \u003cp\u003eThe average DOC in shrimp farming typically ranges from 90 to 120 days to attain marketable size (Kumaran \u003cem\u003eet al\u003c/em\u003e., 2017). Although, WSSV can infect shrimp at all the life stages, from eggs to broodstock, the most suitable stages for its detection are the late post-larval (PL) stages, juveniles and adult. This is because the probability of infection increases when subjected to stressors such as eye-stalk ablation, moulting, spawning, changes in salinity, temperature, pH, and plankton occurrence (OIE, 2019).\u003c/p\u003e \u003cp\u003eIn this study the DOC of sampled farms ranged from 16 to 85 days, and WSSV positive cases were found only in farms with DOC greater than 35 days. This aligns with findings by Tendencia et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), who reported that the increased incidence of WSSV infection occurred before DOC 51, with the final outbreak recorded at DOC115. These findings suggest that culture duration, particularly beyond 35 days, may increase the risk of WSSV outbreaks when combine with suboptimal environmental conditions. The accumulation of organic matter from excess feed and increased shrimp metabolism can disturb the shrimp immunity, making shrimp more vulnerable to disease. Improving environmental conditions through biosecurity measures such as probiotics usage and the installation of shrimp toilets can help mitigate these risks.\u003c/p\u003e \u003cp\u003eStocking density plays a crucial role in the severity and spread of infectious diseases in shrimp aquaculture. In the present study, a stocking density of more than 25 shrimp/m\u003csup\u003e2\u003c/sup\u003e was found as a risk factor for WSSV outbreaks. Among the 31 farms surveyed, five were extensive farms with low stocking densities, where no disease incidence was observed. However, disease occurrence in some extensive systems has been linked to the discharge of untreated effluent water from infected farms into natural water sources (Chakrabarty et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh stocking densities can lead to increased organic loading in the culture environment, contributing to poor water quality and stress in shrimp\u0026mdash;factors known to compromise immune function and increase susceptibility to WSSV (Martin et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). A recent study by Kim et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrated that elevated stocking densities with high water temperatures, significantly increased mortality in WSSV-infected shrimp. Similarly, Talukder et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that even a moderate stocking density (\u0026gt;\u0026thinsp;5/m\u003csup\u003e2\u003c/sup\u003e) was significantly associated with WSSV outbreaks. In this study, most of the semi-intensive farms were culturing \u003cem\u003eP. vannamei\u003c/em\u003e under high stocking densities, which may explain the observed association between the stocking density and increased WSSV risk.\u003c/p\u003e \u003cp\u003eThe present study also reports the history of disease in previous culture cycles is correlated with an increased risk of subsequent WSSV outbreaks. This is likely because of virus's ability to persist in sediments of pond for 21 to 32 days after harvesting (Bharathi \u003cem\u003eet al\u003c/em\u003e., 2019). These findings reinforce the importance of implementing best management practices (BMPs), such as sun-drying ponds for at least 3\u0026ndash;5 weeks between crops, to ensure pathogen reduction and successful crop cycles. Talukder et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) similarly reported several significant risk factors for WSSV occurrence, including farm age, presence of nursery ponds, PL reservoirs, weed presence and control, stocking practices, ammonia, and oxygen concentrations.\u003c/p\u003e \u003cp\u003eIn this study, the use of SPF seed, larger farm area, and biosecurity measures (bird fencing, probiotics) were found to reduce risk, although associations were statistically insignificant. The continued risk despite the use of SPF seeds suggests that poor or inconsistent management practices may compromise the effectiveness of disease prevention strategies. Conversely, factors such as a Culture duration of 0\u0026ndash;35 days, ammonia levels between 0\u0026ndash;0.5 mg/L, and stocking density below 25/m\u003csup\u003e2\u003c/sup\u003e were statistically significant factors associated with reduced WSSV occurrence.\u003c/p\u003e \u003cp\u003eWater quality parameters, including pH and salinity, did not show significant differences between WSSV-infected and uninfected farms. The pH ranged from 6.9 to 8.5, which is within the permissible range for shrimp culture (7.0\u0026ndash;9.0; CIBA, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Similarly, salinity levels ranged from 10 to 26 ppt, falling within or near the optimal range of 15\u0026ndash;25 ppt (CIBA, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the risk ratio analysis identified stocking density, ammonia concentration, extended culture duration (\u0026gt;\u0026thinsp;35 days), and culturing \u003cem\u003eP. vannamei\u003c/em\u003e as significant risk factors for WSSV infection. These results underscore the need for maintaining optimal water quality and adopting better management practices to reduce the prevalence of viral disease in shrimp aquaculture systems.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the findings of this study provide a snapshot of the status of major shrimp viral diseases in Kerala during the surveillance period from October 2023 to 2024. WSSV was identified as the only viral pathogen detected, with a prevalence of 22.6% across the sampled farms. \u003cem\u003ePenaeus vannamei\u003c/em\u003e was found to be more susceptible to WSSV infection than \u003cem\u003eP. monodon\u003c/em\u003e. Key risk factors associated with WSSV outbreaks included high stocking density, elevated ammonia levels, and extended culture duration. Reducing these risks through improved water quality management, controlled organic inputs, and strict adherence to Best Management Practices (BMPs) can significantly mitigate the impact of WSSV and support the sustainability of shrimp aquaculture in the region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the National Surveillance Programme on Aquatic Animal Diseases (NSPAAD), KUFOS, Kochi for the financial support provided during the research work. We would like to express our gratitude to the shrimp farmers of Kerala for providing the samples for analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration\u0026nbsp;of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSaranya P: investigation, methodology, original draft preparation; Ravikumar M. Chovatia: data analysis and validation; Rahul Krishnan: Histological analysis and validation; Gijo Ittoop: Supervision and validation; Muhammed P Safeena: conceptualization, formal analysis, supervision, and validation \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBabu, B., Sathiyaraj, G., Mandal, A., Kandan, S., Biju, N., Palanisamy, S., You, S., Nisha, R. G., \u0026amp; Prabhu, N. M. (2021). Surveillance of disease incidence in shrimp farms located in the east coastal region of India and in vitro antibacterial efficacy of probiotics against Vibrio parahaemolyticus. \u003cem\u003eJournal of Invertebrate Pathology\u003c/em\u003e, \u003cem\u003e179\u003c/em\u003e, 107536. https://doi.org/10.1016/j.jip.2021.107536\u003c/li\u003e\n\u003cli\u003eBelcher, C. R., \u0026amp; Young, P. R. (1998). Colourimetric PCR-based detection of monodon baculovirus in whole Penaeus monodon postlarvae. \u003cem\u003eJournal of Virological Methods\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(1), 21\u0026ndash;29. https://doi.org/10.1016/s0166-0934(98)00067-6\u003c/li\u003e\n\u003cli\u003eCameron, A.R. and Baldock, F.C. (1998). A new probability formula for surveys to substantiate freedom from disease. \u003cem\u003ePreventive veterinary medicine\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1),1-17.\u003c/li\u003e\n\u003cli\u003eChakrabarty, U., Mallik, A., Mondal, D., Dutta, S., \u0026amp; Mandal, N. (2014). Assessment of WSSV prevalence and distribution of disease-resistant shrimp among the wild population of \u003cem\u003ePenaeus monodon\u003c/em\u003e along the west coast of India. \u003cem\u003eJournal of Invertebrate Pathology\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e, 12\u0026ndash;18. https://doi.org/10.1016/j.jip.2014.03.005 \u003c/li\u003e\n\u003cli\u003eCIBA. (2006). \u003cem\u003eHandbook of Fisheries and Aquaculture\u003c/em\u003e. Central Institute of Brackishwater Aquaculture, India. (2006)\u003cem\u003e.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eKennedy, D. A., Kurath, G., Brito, I. L., Purcell, M. K., Read, A. F., Winton, J. R., \u0026amp; Wargo, A. R. (2015). Potential drivers of virulence evolution in aquaculture. \u003cem\u003eEvolutionary Applications\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 344\u0026ndash;354. https://doi.org/10.1111/eva.12342 \u003c/li\u003e\n\u003cli\u003eKim, M. J., Shin, D., Jang, G. I., Kwon, M., \u0026amp; Kim, K. I. (2024). Influence of stocking density and interaction variability on disease progression of white spot syndrome virus-infected shrimp under different risk scenarios. \u003cem\u003eAquaculture\u003c/em\u003e, \u003cem\u003e595\u003c/em\u003e, 741597. https://doi.org/10.1016/j.aquaculture.2024.741597\u003c/li\u003e\n\u003cli\u003eKumaran, M., Anand, P., Kumar, J. A., Ravisankar, T., Paul, J., Vasagam, K. P. K., Vimala, D. D., \u0026amp; Raja, K. A. (2016). Is Pacific white shrimp (Penaeus vannamei) farming in India is technically efficient? \u0026mdash; A comprehensive study. \u003cem\u003eAquaculture\u003c/em\u003e, \u003cem\u003e468\u003c/em\u003e, 262\u0026ndash;270. https://doi.org/10.1016/j.aquaculture.2016.10.019\u003c/li\u003e\n\u003cli\u003eLo, C., Ho, C., Chen, C., Liu, K., Chiu, Y., Yeh, P., Peng, S., Hsu, H., Liu, H., Chang, C., Su, Wang, C., \u0026amp; Kou, G. (1997). Detection and tissue tropism of white spot syndrome baculovirus (WSBV) in captured brooders of Penaeus monodon with a special emphasis on reproductive organs. \u003cem\u003eDiseases of Aquatic Organisms\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e, 53\u0026ndash;72. https://doi.org/10.3354/dao030053 \u003c/li\u003e\n\u003cli\u003eMartin, J. M., Veran, Y., Guelorget, O., \u0026amp; Pham, D. (1998). Shrimp rearing: stocking density, growth, impact on sediment, waste output and their relationships studied through the nitrogen budget in rearing ponds. \u003cem\u003eAquaculture\u003c/em\u003e, \u003cem\u003e164\u003c/em\u003e(1\u0026ndash;4), 135\u0026ndash;149. https://doi.org/10.1016/s0044-8486(98)00182-3 \u003c/li\u003e\n\u003cli\u003eMPEDA, (2022). Handbook Fisheries Statistics.\u003c/li\u003e\n\u003cli\u003eNunan, L. M., \u0026amp; Lightner, D. V. (1997). Development of a non-radioactive gene probe by PCR for detection of white spot syndrome virus (WSSV). \u003cem\u003eJournal of Virological Methods\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(1\u0026ndash;2), 193\u0026ndash;201. https://doi.org/10.1016/s0166-0934(96)02128-3 \u003c/li\u003e\n\u003cli\u003eOtta, S., Shubha, G., Joseph, B., Chakraborty, A., Karunasagar, I., \u0026amp; Karunasagar, I. (1999). Polymerase chain reaction (PCR) detection of white spot syndrome virus (WSSV) in cultured and wild crustaceans in India. \u003cem\u003eDiseases of Aquatic Organisms\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e, 67\u0026ndash;70. https://doi.org/10.3354/dao038067 \u003c/li\u003e\n\u003cli\u003ePhromjai, J., Boonsaeng, V., Withyachumnarnkul, B., \u0026amp; Flegel, T. W. (2002). Detection of hepatopancreatic parvovirus in Thai shrimp Penaeus monodon by in situ hybridization, dot blot hybridization and PCR amplification. \u003cem\u003eDiseases of Aquatic Organisms\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e, 227\u0026ndash;232. https://doi.org/10.3354/dao051227 \u003c/li\u003e\n\u003cli\u003ePinheiro, A. C., Lima, A. P., De Souza, M. E., Neto, E. C., Adri\u0026atilde;o, M., Gon\u0026ccedil;alves, V. S., \u0026amp; Coimbra, M. R. (2006). Epidemiological status of Taura syndrome and Infectious myonecrosis viruses in Penaeus vannamei reared in Pernambuco (Brazil). \u003cem\u003eAquaculture\u003c/em\u003e, \u003cem\u003e262\u003c/em\u003e(1), 17\u0026ndash;22. https://doi.org/10.1016/j.aquaculture.2006.10.006 \u003c/li\u003e\n\u003cli\u003ePoulos, B. T., \u0026amp; Lightner, D. V. (2006). Detection of infectious myonecrosis virus (IMNV) of penaeid shrimp by reverse-transcriptase polymerase chain reaction (RT-PCR). \u003cem\u003eDiseases of Aquatic Organisms\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e, 69\u0026ndash;72. https://doi.org/10.3354/dao073069\u003c/li\u003e\n\u003cli\u003ePrakasha, B., Ramakrishna, R., Karunasagar, I., \u0026amp; Karunasagar, I. (2007). Detection of Laem-Singh virus (LSNV) in cultured Penaeus monodon from India. \u003cem\u003eDiseases of Aquatic Organisms\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e, 83\u0026ndash;86. https://doi.org/10.3354/dao01835 \u003c/li\u003e\n\u003cli\u003eQiu, L., Chen, M. M., Wan, X. Y., Li, C., Zhang, Q. L., Wang, R. Y., Cheng, D. Y., Dong, X., Yang, B., Wang, X. H., Xiang, J. H., \u0026amp; Huang, J. (2017). Characterization of a new member of Iridoviridae, Shrimp hemocyte iridescent virus (SHIV), found in white leg shrimp (Litopenaeus vannamei). \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1). https://doi.org/10.1038/s41598-017-10738-8\u003c/li\u003e\n\u003cli\u003eS, S. K., Bharathi, R. A., Rajan, J. J. S., Chitra, V., Muralidhar, M., \u0026amp; Alavandi, S. V. (2019). Viability of white spot syndrome virus (WSSV) in shrimp pond sediments with reference to physicochemical properties. \u003cem\u003eAquaculture International\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(5), 1369\u0026ndash;1382. https://doi.org/10.1007/s10499-019-00394-2\u003c/li\u003e\n\u003cli\u003eSafeena, M. P., Rai, P., \u0026amp; Karunasagar, I. (2012). Molecular Biology and Epidemiology of Hepatopancreatic parvovirus of Penaeid Shrimp. \u003cem\u003eIndian Journal of Virology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(2), 191\u0026ndash;202.\u003cu\u003e \u003c/u\u003ehttps://doi.org/10.1007/s13337-012-0080-5\u003c/li\u003e\n\u003cli\u003eSergeant, ESG, 2018. Epitools Epidemiological Calculators. Ausvet.\u003c/li\u003e\n\u003cli\u003eSritunyalucksana, K., Apisawetakan, S., Boon-Nat, A., Withyachumnarnkul, B., \u0026amp; Flegel, T. W. (2006). A new RNA virus found in black tiger shrimp Penaeus monodon from Thailand. \u003cem\u003eVirus Research\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e(1\u0026ndash;2), 31\u0026ndash;38. https://doi.org/10.1016/j.virusres.2005.11.005 \u003c/li\u003e\n\u003cli\u003eStentiford, G.D., Neil, D.M., Peeler, E.J., Shields, J.D., Small, H.J., Flegel, T.W., Vlak, J.M., Jones, B., Morado, F., Moss, S. and Lotz, J. (2012). Disease will limit future food supply from the global crustacean fishery and aquaculture sectors. \u003cem\u003eJournal of invertebrate pathology\u003c/em\u003e, \u003cem\u003e110\u003c/em\u003e(2), 141-157.\u003c/li\u003e\n\u003cli\u003eTalukder, A. S., Punom, N. J., Eshik, M. M. E., Begum, M. K., Islam, H. R., Hossain, Z., \u0026amp; Rahman, M. S. (2021). Molecular identification of white spot syndrome virus (WSSV) and associated risk factors for white spot disease (WSD) prevalence in shrimp (Penaeus monodon) aquaculture in Bangladesh. \u003cem\u003eJournal of Invertebrate Pathology\u003c/em\u003e, \u003cem\u003e179\u003c/em\u003e, 107535. https://doi.org/10.1016/j.jip.2021.107535\u003c/li\u003e\n\u003cli\u003eTang, K., Navarro, S., \u0026amp; Lightner, D. (2007). PCR assay for discriminating between infectious hypodermal and hematopoietic necrosis virus (IHHNV) and virus-related sequences in the genome of Penaeus monodon. \u003cem\u003eDiseases of Aquatic Organisms\u003c/em\u003e, \u003cem\u003e74\u003c/em\u003e(2), 165\u0026ndash;170. https://doi.org/10.3354/dao074165\u003c/li\u003e\n\u003cli\u003eTendencia, E. A., Bosma, R. H., \u0026amp; Verreth, J. A. (2010). WSSV risk factors related to water physico-chemical properties and microflora in semi-intensive Penaeus monodon culture ponds in the Philippines. \u003cem\u003eAquaculture\u003c/em\u003e, \u003cem\u003e302\u003c/em\u003e(3\u0026ndash;4), 164\u0026ndash;168. https://doi.org/10.1016/j.aquaculture.2010.03.008\u003c/li\u003e\n\u003cli\u003eWongteerasupaya, C., Tongcheua, W., Boonsaeng, V., Panyim, S., Tassanakajon, A., Withyachumnarnkul, B., \u0026amp; Flegel, T. (1997). Detection of yellow-head virus (YHV) of Penaeus monodon by RT-PCR amplification. \u003cem\u003eDiseases of Aquatic Organisms\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e, 181\u0026ndash;186. https://doi.org/10.3354/dao031181 \u003c/li\u003e\n\u003cli\u003eWorld Organisation for Animal Health (OIE) (2009) \u003cem\u003eGuide for aquatic animal health surveillance\u003c/em\u003e. OIE, Paris. Available at: https://www.woah.org/en/document/guide-for-aquatic-animal-health-surveillance/\u003c/li\u003e\n\u003cli\u003eWorld Organisation for Animal Health (OIE) (2019) \u003cem\u003eManual of diagnostic tests for aquatic animals\u003c/em\u003e, 7th edn. OIE, Paris. Available at: https://www.woah.org/en/what-we-do/standards/codes-and-manuals/aquatic-manual/\u003c/li\u003e\n\u003cli\u003eZhang, Q., Xu, T., Wan, X., Liu, S., Wang, X., Li, X., Dong, X., Yang, B., \u0026amp; Huang, J. (2017). Prevalence and distribution of covert mortality nodavirus (CMNV) in cultured crustacean. \u003cem\u003eVirus Research\u003c/em\u003e, \u003cem\u003e233\u003c/em\u003e, 113\u0026ndash;119. https://doi.org/10.1016/j.virusres.2017.03.013 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"epitool, surveillance, shrimp viral diseases, prevalence, risk factor ","lastPublishedDoi":"10.21203/rs.3.rs-6564499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6564499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEffective disease surveillance is crucial to predict, identify, and manage disease outbreaks in the shrimp aquaculture. A cross-sectional study was conducted to identify the major viral diseases occurrence in shrimp farms of Kerala. A two-stage random sampling was employed involving the selection of 31 shrimp farms in the first stage and 23 individual shrimp in the second stage. The surveillance was carried out from October 2023\u0026ndash;2024, targeting major DNA viruses: WSSV, HPV, MBV, IHHNV and RNA viruses: IMNV, CMNV, TSV, DIV, LSNV, YHV-1 by using PCR and histopathological analysis. Among the 31 shrimp farms surveyed, WSSV was detected in 7 farms, while all other viral pathogen tested negative. The histopathological examination revealed characteristic Cowdry type A and basophilic inclusion bodies in the stomach, gill and pleopods consistent with WSSV infection. The overall prevalence of WSSV was observed as 22.6%. The relatively low prevalence is attributed due to the reduction in shrimp farming activity in Kerala as most farms operated only one production cycle per year. The risk factors analysis indicated that WSSV infection was significantly associated with specific variables including a culture duration exceeding 35 days, ammonia concentration\u0026thinsp;\u0026gt;\u0026thinsp;0.5mg/L, stocking densities\u0026thinsp;\u0026gt;\u0026thinsp;25 shrimps/m\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003eL. vannamei\u003c/em\u003e as the cultured species. This surveillance study provides valuable insights into the present status of shrimp viral diseases in Kerala and offers a foundation for improved prevention and control strategies in shrimp aquaculture.\u003c/p\u003e","manuscriptTitle":"Cross Sectional Analysis of Major Viral Diseases in Shrimp Farms of Kerala, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 15:51:56","doi":"10.21203/rs.3.rs-6564499/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e13a578-bea2-4f36-94cf-3108d3a59709","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T03:03:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 15:51:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6564499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6564499","identity":"rs-6564499","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}