Showing posts with label epidemic. Show all posts
Showing posts with label epidemic. Show all posts

Monday, December 19, 2016

Tropheryma whipplei as a Cause of Epidemic Fever Senegal 2010–2012 Volume 22 Number 7—July 2016 Emerging Infectious Disease journal CDC

Tropheryma whipplei as a Cause of Epidemic Fever Senegal 2010–2012 Volume 22 Number 7—July 2016 Emerging Infectious Disease journal CDC


Tropheryma whipplei as a Cause of Epidemic Fever, Senegal, 2010–2012 - Volume 22, Number 7—July 2016 - Emerging Infectious Disease journal - CDC



Volume 22, Number 7—July 2016

Research

Tropheryma whipplei as a Cause of Epidemic Fever, Senegal, 2010–2012

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  • Materials and Methods
  • Results
  • Discussion
  • Suggested Citation

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  • Figure

Tables

  • Table 1
  • Table 2

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Hubert Bassene, Oleg Mediannikov, Cristina Socolovschi, Pavel Ratmanov, Alpha K. Keita, Cheikh Sokhna, Didier Raoult, and Florence FenollarComments to Author 
Author affiliations: Aix-Marseille Université, Marseille, France and Dakar, Senegal (H. Bassene, O. Mediannikov, C. Socolovschi, P. Ratmanov, A.K. Keita, C. Sokhna, D. Raoult, F. Fenollar)Far Eastern State Medical University, Khabarovsk, Russia (P. Ratmanov)
Suggested citation for this article

Abstract

The bacterium Tropheryma whipplei, which causes Whipple disease in humans, is commonly detected in the feces of persons in Africa. It is also associated with acute infections. We investigated the role of T. whippleiin febrile patients from 2 rural villages in Senegal. During June 2010–March 2012, we collected whole-blood finger-prick samples from 786 febrile and 385 healthy villagers. T. whipplei was detected in blood specimens from 36 (4.6%) of the 786 febrile patients and in 1 (0.25%) of the 385 apparently healthy persons. Of the 37T. whipplei cases, 26 (70.2%) were detected in August 2010. Familial cases and a potential new genotype were observed. The patients’ symptoms were mainly headache (68.9%) and cough (36.1%). Our findings suggest that T. whipplei is a cause of epidemic fever in Senegal.
Determining the etiologic causes of febrile illness in tropical settings provides public health and local community benefits. In the context of a decline in malaria cases in many parts of sub-Saharan Africa, the few studies that have been conducted in recent years to analyze the burden of bacterial infections used traditional blood cultures and identified typhoid fever and Streptococcus pneumoniae as the leading documented causes of nonmalarial bloodstream infections (13). However, this method does not enable the identification of intracellular organisms, and most causes of fever remain unknown. In 2008, we initiated a study of the etiologies of fevers of unknown origin in Africa, particularly in Senegal. Our preliminary studies showed the presence of previously known pathogenic microorganisms, such as Borrelia crociduraeRickettsia felisR. conorii, and Coxiella burnetii, and the unexpected presence of Tropheryma whipplei (49).
T. whipplei was first considered to be an uncommon bacterium that causes Whipple disease, a rare chronic disease (10). However, T. whipplei is in fact a common bacterium associated with various conditions, such as acute infections (pneumonia and gastroenteritis) and chronic infections (classic Whipple disease and other infections without digestive involvement, including endocarditis and encephalitis) (1019). T. whipplei can also be carried in human feces and, less commonly, in the saliva (2023); carriage prevalence varies by the age and exposure of the population and by geographic area (2130).
T. whipplei is highly prevalent in rural Senegal, where carriage rates reach 75% among children <2 years of age, and overall seroprevalence is 72% (2126). In our preliminary study in Senegal, which was conducted in 2 villages (Dielmo and Ndiop) during December 2008–July 2009, we detected T. whippleibacteremia in 6.4% of the analyzed specimens (8). Bacteremia was significantly associated with cough, but no link to feces carriage was observed (8). However, our study had several limitations, such as a small number of febrile patients, no local control group of afebrile persons, and a short study period. In this same area, we recently showed that humans comprise the only source of T. whipplei among the populations in whom the bacterium is highly prevalent. Moreover, our findings showed that limited access to toilets and exposure to human feces was associated with the high prevalence of T. whipplei, suggesting that these conditions may facilitate fecal–oral transmission of the bacterium (31). To better characterize T. whipplei bacteremia, we extended our analysis, beginning in 2010, in this same area of rural Senegal to include the collection of >1,000 blood samples from healthy persons and ambulatory patients with acute fever.

Materials and Methods

We conducted the study during June 2010–March 2012 in Senegal’s rural Sine-Saloum area, a dry sahelian ecosystem with 2 typical seasons: dry (November–May) and rainy (June–October). We obtained written consent for every person included in the study. The National Ethics Committee of Senegal approved the study.
Participants
Study participants included 786 febrile patients at the healthcare center for the villages of Dielmo and Ndiop; 78% of the patients were <15 years of age, and the sex ratio was 1:1. For all patients with fever (defined as axillary temperature of >37.5°C), we conducted a medical examination, completed a questionnaire, and collected a whole-blood finger-prick sample (200-?L [4 drops]) (8). In parallel, we collected blood samples from a control group of 385 healthy, afebrile villagers; 62.5% of these study participants were <15 years of age, and the sex ratio was 1:1.
Molecular Analyses
DNA Extraction
For DNA extraction, we used a BioRobot EZ1 Workstation (QIAGEN, Courtaboeuf, France) according to the manufacturer’s instructions. Extraction was performed in Senegal, and specific quantitative real-time PCR (qPCR) was performed in France.
Specific qPCR
We used a 7900HT-thermocycler (Applied Biosystems, Foster City, CA, USA) with the QuantiTect-Probe PCR Kit (QIAGEN) to perform qPCR. First, we analyzed specimens for T. whipplei by using the primer pair Twhi3F (5?-TTG TGT ATT TGG TAT TAG ATG AAA CAG-3?)/Twhi3R (5?-CCC TAC AAT ATG AAA CAG CCT TTG-3?) and the specific Twhi3 probe (6-FAM-GGG ATA GAG CAG GAG GTG TCT GTC TGG-TAMRA). For specimens with positive results, we ran a second, confirmatory qPCR with the Twhi2F (5?-TGA GGA TGT ATC TGT GTA TGG GAC A-3?)/Twhi2R (5?-TCC TGT TAC AAG CAG TAC AAA ACA AA-3?) primer pair and the specific Twhi2 probe (6-FAM-GAG AGA TGG GGT GCA GGA CAG GG-TAMRA) (8,21). To validate the assays, we included positive (T. whipplei) and negative (PCR mix) controls in each run, as previously reported (8,21).
We considered samples to be T. whipplei–positive if qPCR results for the 2 specific genes were positive at a log-based fluorescence cycle threshold (Ct) of <38. We used qPCR for the ?-actin housekeeping gene, as previously described (7), to check the quality of DNA handling and blood specimen extraction; only positive samples were considered reliable.
Genotyping
We performed genotyping of T. whipplei as previously described (32). We attempted to amplify and sequence each of 4 multispacer sequences (TW133, ProS, SecA, and Pro184) from positive specimens. When sequences were obtained, we compared them with those available in the GenBank database and our internal laboratory database to determine their corresponding genotype.
Statistical Analyses
We performed statistical analyses by using Epi Info 6 software (http://www.cdc.gov/epiinfo/index.html); results with p<0.05 were considered statistically significant. The corrected ?2 test or the Fisher exact test was used where indicated.

Results

Prevalence of T. whipplei Bacteremia
A total of 786 febrile patients and 385 healthy controls were included in the study, among whom 36 (4.6%) and 1 (0.25%), respectively, were positive for T. whipplei DNA (p<0.00007). The positive control participant was a 13-year-old boy who had low concentrations of T. whipplei DNA (Ct of 36.85 and 37.99). The Ct for febrile patients ranged from 26.10 to 36.41 (mean ISD 33.40 ± 2.53).
Age Distribution
The prevalence of T. whipplei bacteremia was 4% (3/75) for febrile patients <12 months of age, 4.8% (12/250) for those 1–3 years of age, 4.2% (5/119) for those 4–6 years of age, 5.4% (9/167) for those 7–15 years of age, 2.7% (2/75) for those 16–29 years of age, and 5.2% (5/97) for those >30 years of age. Age data were not available for 3 patients. No significant differences in age distribution were observed.
Clinical Manifestations
Clinical data were available for 786 febrile patients (Table 1). The main symptoms in the 36 T. whipplei–positive febrile patients were headache (23 [68.9%]), cough (13 [36.1%]), rhinorrhea (8 [22.2%]), nausea (5 [13.9%]), vomiting (4 [11.1%]), and diarrhea (3 [8.3%]). No significant clinical differences were observed by Ct level.
Seasonality
Thumbnail of Monthly prevalence of Tropheryma whipplei bacteremia in Dielmo and Ndiop, Senegal, June 2010–March 2012. These 2 rural villages are located in the Sine-Saloum area, a dry sahelian ecosystem.
Figure. Monthly prevalence ofTropheryma whippleibacteremia in Dielmo and Ndiop, Senegal, June 2010–March 2012. These 2 rural villages are located in the Sine-Saloum area, a dry sahelian ecosystem.
All 36 T. whipplei cases detected among the 786 febrile patients were in the 466 patients tested during the June–October rainy season; no cases were detected among the 320 febrile patients sampled during the November–May dry season (p = 0.0000001). Moreover, 33 (92%) of these 36 cases were diagnosed during the 2010 rainy season, and the other 3 were diagnosed during August 2011 (2 cases) and October 2011 (1 case) (Figure). The highest prevalence of T. whipplei bacteremia cases was detected during August, when 28 (30%) of 93 febrile patients were found to be positive (19 [28%] of 73 patients in Dielmo and 9 [45%] of 20 patients in Ndiop). In fact, the data were affected by the high prevalence of cases observed in August 2010, which seemed to be indicative of an outbreak.
In July 2010, T. whipplei infection was detected in 2 febrile patients, an 18-year-old boy in Dielmo (case detected July 24) and a 15-year-old girl in Ndiop (case detected July 27). In August 2010, a total of 29 febrile patients from Dielmo were tested; 17 (58.5%) of the 29 patients had samples (18 total samples) positive for T. whippleibacteremia. During the same month in Ndiop, 9 (69%) of 13 febrile patients had positive samples. In September 2010, 2 patients were positive in Dielmo and 1 in Ndiop, and in October, 2 patients were positive in Dielmo and none in Ndiop. For almost 1 year, all specimens from febrile patients were negative for T. whipplei. Then, in August 2011, only 2 patients were positive in Dielmo, and in October 2011, only 1 patient was positive in Ndiop.
Treatment and Follow-Up
Data about antimicrobial drug therapy was available for 33 patients, 23 of whom benefited from treatment with amoxicillin (18 patients), metronidazole (3 patients), or cotrimoxazole (2 patients). In Dielmo, 24 specimens from 23 patients were positive for T. whipplei; 1 patient was sampled twice 15 days apart, and both specimens were positive. For 17 patients, blood specimens were also sampled during other febrile episodes. Nine specimens from 5 patients were sampled from 15 days to 13 months before the positive sample was detected, and 43 specimens from 17 patients were sampled from 3 weeks to 16 months after the positive sample was detected; all of these samples were negative. Moreover, our previously published data (8) included test results for a 4-year-old boy who was diagnosed with T. whipplei bacteremia in January 2009 (19 months before August 2010). Four other blood specimens from this patient were tested 1 month before (1 sample) or 4, 11, and 15 months after (3 samples) the positive specimen was detected, and all were negative for T. whipplei.
In Ndiop, 12 specimens from 12 patients were positive. For 8 of these patients, blood specimens were sampled during other febrile episodes. The specimen for 1 patient was sampled 1 month before the positive sample, and 9 specimens from 6 patients were sampled from 7 weeks to 18 months after the positive samples; all of these specimens were negative. No data were available for these patients about antibody response against T. whipplei.
Genotyping
Because of the lack of specimens available for genotyping and the low sensitivity of genotyping, we could obtain multispacer sequences for only 8 patients at the time of the 2010 peak in T. whipplei bacteremia cases (Table 2). The T. whipplei genotype corresponds to the concatenation of the 4 spacers (TW133-ProS-SecA-Pro184); however, TW133 sequencing was not successful, so the corresponding spacer was not available (NA) for any of the patients. ProS sequence was obtained for 5 patients, SecA for 6 patients, and Pro184 for all patients. For 4 patients, 3 spacers were available, enabling the detection of the same multispacer sequence combination (NA-7-2-1) for the 4 patients. For another 4 patients, 2 spacers were available, enabling the detection of the NA-7-NA-1 combination for 2 of the patients and the NA-NA-2-1 combination for the other 2 patients. None of the potential combinations has previously been sequenced in Senegal. Moreover, the NA-7-2-1 combination has also not previously been detected in any other area of the world and is thus a new genotype. Overall, our data suggest that the same genotype was detected in Dielmo and Ndiop during the summer of 2010. However, T. whipplei genotyping was performed (sometimes only partially) for only 8 of 36 patients, so we can only suspect, but not confirm, that an epidemic clone was present and that an outbreak was ongoing at that time.
Affected Households
In Dielmo during the peak of the August 2010 outbreak, multiple persons in several households were positive for T. whipplei bacteremia: 4 of 6 persons in household no. 19, 3 of 4 persons in household no. 39, 2 of 2 persons in household no. 9, and 2 of 3 persons in household no. 14. In Ndiop, 2 of 2 persons in household no. 3 and 2 of 3 persons in household no. 8 were positive for T. whipplei bacteremia. Of note, during this time, the family in household no. 39 had a furnace in which they baked bread that they marketed locally. In December 2010, most of the family left the village and the furnace was shut down; no additional T. whipplei bacteremia cases were subsequently observed.

Discussion

We report the detection of T. whipplei DNA in the blood of patients in Dielmo and Ndiop, Senegal. The validity of our data is based on strict experimental procedures and controls, including rigorous positive and negative controls, used to validate test results. In addition, we confirmed each positive PCR result by the successful amplification of an additional specific DNA sequence, and we performed T. whipplei genotyping on several specimens. We also showed that the presence of T. whipplei in blood is significantly linked to the presence of fever; T. whipplei DNA was detected (at a low level) in the blood of only 1 afebrile person in the study area. Moreover, we included a control group of afebrile persons from the same area, thereby reinforcing the validity of our data. Indeed, several well-known pathogens have been detected in recently analyzed specimens from healthy persons. For example, Plasmodium falciparum has been detected in 32% of blood specimens from healthy, afebrile persons in Senegal (33); respiratory viruses, including influenza virus, have been detected in 12% of nasopharyngeal samples from symptom-free Hajj pilgrims (34); and S. pneumoniae has been detected in 6.3% of blood specimens from afebrile children in Tanzania (35). Thus, because of the significantly higher prevalence of T. whipplei among febrile patients compared with healthy controls, we suspect that this microorganism is a pathogenic agent.
The overall prevalence of T. whipplei bacteremia is 4.6%. However, in August 2010, we observed a peak in T. whipplei bacteremia cases in Dielmo and Ndiop, where T. whipplei was involved in more than half of the observed cases of fever. This peak corresponds to a short outbreak of T. whipplei bacteremia with 1 potential genotype. A similar new genotype was observed for the patients from Dielmo and Ndiop for whom genotyping was available at the time of the outbreak. To date, 35 different T. whipplei genotypes have been detected in Senegal, but only 1 common genotype has been detected in Dielmo and Ndiop, even though the villages are 5 km apart (25). All of the other genotypes detected in the Sine-Saloum area were specific to each village, including the 2 that were more prevalent: genotype 52 was detected in 54% of feces samples in Dielmo, and genotype 49 was detected in 28% of feces samples from Ndiop (25).
Several familial cases also occurred during this outbreak. The family in household no. 39 in Dielmo was 1 of the most affected families: 3 of 4 persons living in the home had fever and T. whipplei bacteremia. Genotyping was available for 2 of these patients, both of whom exhibited the same potential genotype. The family in household no. 39 was involved in the management of a traditional oven for preparing bread, which was thoroughly cooked and sold directly to other residents. Since the departure of the baker and his family, no other outbreaks have been observed, and the prevalence of T. whipplei bacteremia has dramatically decreased. Thus, this family may have contributed to spread of the outbreak on a daily basis in Dielmo and possibly on a weekly basis at traditional markets, which served as the main contact between villagers from Dielmo and Ndiop. Also of note, no toilet facilities were present in household no. 39, and a link between a lack of toilet facilities and the high detection of T. whipplei, mainly in feces, has previously been reported (31). Thus, we hypothesize that T. whipplei was transmitted to customers who bought bread contaminated with infectious feces (31). Overall, all of our data confirm human-to-human transmission of the bacterium (22,23,26,31).
One of the main symptoms among febrile patients with T. whipplei bacteremia is cough (36.1%). In our preliminary study of T. whipplei bacteremia, cough was also the main manifestation observed (36). Thus, T. whipplei could be involved in respiratory infections (13,14,36,37). However, the presence of cough in ?36% of febrile patients who were either T. whipplei–positive or –negative may also suggest that this symptom was poorly specific.
Of note, a 4-year-old patient had 2 febrile episodes associated with T. whipplei bacteremia 18 months apart (8); however, it was not possible to make a distinction between relapse and reinfection beca

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Monday, November 21, 2016

African Swine Fever Epidemic Poland 2014–2015 Volume 22 Number 7—July 2016 Emerging Infectious Disease journal CDC

African Swine Fever Epidemic Poland 2014–2015 Volume 22 Number 7—July 2016 Emerging Infectious Disease journal CDC


African Swine Fever Epidemic, Poland, 2014–2015 - Volume 22, Number 7—July 2016 - Emerging Infectious Disease journal - CDC





Volume 22, Number 7—July 2016

Research

African Swine Fever Epidemic, Poland, 2014–2015

On This Page

  • Materials and Methods
  • Results
  • Discussion
  • Suggested Citation

Figures

  • Figure 1
  • Figure 2
  • Figure 3
  • Figure 4
  • Figure 5

Tables

  • Table

Downloads

  • PDF[2.32 MB - 7 pgs]
  • RIS[TXT - 2 KB]
Krzysztof ?mietankaComments to Author , Grzegorz Wo?niakowski, Edyta Kozak, Krzysztof Niemczuk, Magdalena Fr?czyk, ?ukasz Bocian, Andrzej Kowalczyk, and Zygmunt Pejsak
Author affiliations: National Veterinary Research Institute, Pu?awy, Poland
Suggested citation for this article

Abstract

In Poland, African swine fever (ASF) emerged in February 2014; by August 2015, the virus had been detected in >130 wild boar and in pigs in 3 backyard holdings. We evaluated ASF spread in Poland during these 18 months. Phylogenetic analysis indicated repeated incursions of genetically distinct ASF viruses of genotype II; the number of cases positively correlated wild boar density; and disease spread was very slow. More cases were reported during summer than autumn. The 18-month prevalence of ASF in areas under various animal movement restrictions was 18.6% among wild boar found dead or killed by vehicles and only 0.2% in hunted wild boar. Repeated introductions of the virus into the country, the primary role of wild boar in virus maintenance, and the slow spread of the disease indicate a need for enhanced biosecurity at pig holdings and continuous and intensive surveillance for fast detection of ASF.
African swine fever (ASF) is an infectious and notifiable disease of domestic and wild animals of the family Suidae (1,2). First described in Kenya in 1921, ASF was territorially restricted to Africa only until 1957, when it spread from Angola to Lisbon. From then on, ASF has been repeatedly detected in many countries of Europe, Central America, and South America. In some countries (e.g., France, Belgium, the Netherlands), ASF outbreaks were rapidly contained, but in others (e.g., Portugal and Spain) ASF virus (ASFV) persisted for >30 years. Another long-time infected region in Europe is Sardinia (Italy), where ASFV has been circulating since 1978 and where the disease has been maintained as endemic (3). In 2007, the most recent epidemic started in Georgia and thereafter moved to Armenia, Azerbaijan, and the Russian Federation (4,5). In 2012 and 2013, ASF occurred in Ukraine and Belarus, respectively, and in 2014, it crossed into the European Union. According to the World Organisation for Animal Health, >550 ASF cases among wild boar and outbreaks among domestic pigs were detected through 2015 in Estonia, Latvia, Lithuania, and Poland (5).
In Poland, the first cases of ASF were detected in wild boar in February 2014 in the northeastern part of the country, very near (<1 km) the border with Belarus (6). As of August 31, 2015, a total of 76 cases in wild boar and 3 outbreaks among domestic pigs had been found in 3 counties (basic administrative regions of Poland).
Extensive surveillance revealed a unique pattern of disease spread that did not fit the commonly perceived concept of ASF epidemiology. Our study objective was to describe the spatiotemporal spread of ASF in Poland during the first 18 months after detection of the first cases.

Materials and Methods

Surveillance Design and Diagnostic Tests
After the first cases of ASF in Poland were confirmed, the affected area was differentiated into 3 levels of risk: area I (regions free from ASF but located near areas where ASF had been occurring in wild boar), area II (ASF detected in wild boar only), and area III (established after detection of ASF in pigs) (7,8). Despite differences with regard to animal movement restrictions, the surveillance strategy applied to areas I–III was the same: all wild boar found dead and those killed in road accidents (passive surveillance) and hunted wild boar (active surveillance) from all areas were submitted for testing. Samples collected from dead wild boar were whole blood, serum, marrow bones, kidneys, liver, spleen, lymph nodes, and lungs; samples from hunted wild boar were whole blood and serum. Homogenates (10% wt/vol) of individual tissues were prepared in phosphate-buffered saline. Clarified material was stored at ?80°C or directly used for virus DNA extraction. Virus DNA was extracted directly from 200-?L aliquots of serum or tissue sample homogenates by using the commercial QIAamp DNA Mini Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s recommended procedures. We used a PCR with the ASF diagnosis primers and a commercial probe (Universal ProbeLibrary no. 162; Roche Applied Science, Branford, CT, USA), which generates an amplicon of 74 bp within viral protein 72, to confirm the presence of ASFV DNA. Specific primers and probes were added to a LightCycler 480 Probes Master Kit (Roche Applied Science), and reactions were performed in a Stratagene Mx3005P real-time PCR thermocycler (Agilent Technologies, Santa Clara, CA, USA) according to the protocol described by Fernández -Pinero et al. (9).
Altogether, from February 2014 through August 2015, samples from 609 dead/road accident wild boar and 12,253 hunted wild boar from areas I–III (7,8), as well as from ?35,000 domestic pigs, were tested by real-time PCR; detailed results and difficulties encountered during the diagnostic process are described elsewhere (10). According to terminology adopted in Poland, outbreaks were defined as the detection of DNA of ASFV in pigs (irrespective of the number of pigs in a holding), and cases were defined as the presence of viral DNA in >1 wild boar found at the same time and in the same place. Thus, the number of infected animals outnumbered the number of cases or outbreaks. However, for the purpose of prevalence calculations, we took into account individual animals. To calculate the annual prevalence of ASF in wild boar during the first year of the epidemic and to analyze potential seasonal variations, we established prevalence rates (with 95% CIs) separately for wild boar tested within the scope of active and passive surveillance in quarterly intervals: spring (March–May 2014), summer (June–August 2014), autumn (September–November 2014), and winter (December 2014–February 2015). In addition, we calculated prevalence in monthly intervals to encompass the period from the beginning of the epidemic in February 2014 through August 2015. We mapped the locations of ASF outbreaks and cases by using sampling location coordinates in ArcGIS for Desktop software (Esri Inc., Redlands, CA, USA).
DNA Sequencing and Phylogenetic Analysis
We used the DNA of ASFV representing 64 cases and 3 outbreaks for phylogenetic analysis. So far we have failed to produce proper-length readable sequences for samples from case nos. 20, 24, 26–28, 32, 51, 56, 57, and 68. The primers specific to the MGF505-2R gene ASFV sequence were designed on the basis of the complete genome sequence of the BA71V strain (GenBank accession no. U18466.2) by using online Primer 3 Plus software (http://www.bioinformatics.nl/primer3plus/). The primers were also 100% complementary to the Georgia 2007/1 sequence strain. The expected product length was estimated to be 1,173 bp. The primer sequences used for amplification and sequencing of the MGF505–2R fragment were LVR13F: 5?-GCAGAGGTATGATGTCCTTA-3? and LVR13F: 5?-TTCCTGTTGAACAAGTATCT-3?. The PCR products were separated in a 1.5% agarose gel (Invitrogen, Grand Island, NY, USA) and then purified according to the procedure for the QIAquick Gel Extraction Kit (QIAGEN). The amplicons were sequenced on a GS FLX/Titanium sequencer (Roche Applied Science) by Centrum Bada? DNA Service (Pozna?, Poland). Each product was sequenced in forward and backward directions and then assembled into a single contig by using Geneious R7 software (Biomatters Ltd., Auckland, New Zealand). The ClustalW alignment calculation parameters in MEGA6 (11) were as follows: gap opening penalty 15, gap extension penalty 6.66, transition weight 0.5, and delay divergent cutoff 30%. We plotted the phylogram by using the neighbor-joining algorithm in MEGA6 software and calculated the nucleotide similarity matrix providing the information about the sequence identity by using Geneious R7 software. The obtained nucleotide sequences of ASFV isolates were trimmed, assembled into contigs, and aligned by using Geneious R7 software. We also retrieved 2 sequences of ASFV representing genotype II (Georgia 2007/1 and Odintsovo/2014 Russia) from GenBank to use for comparison. The tree was rooted against ASFV strains Warmbaths South Africa and Malawi Lil 20/1, representing genotypes IV and VIII, respectively. We submitted the nucleotide sequences of ASFV successfully sequenced in Poland to GenBank under accession nos. KT366447–KT366459 and KT900042–KT900107.
Statistical Analyses
To evaluate correlations between the number of ASFV-positive wild boar and wild boar density in the forestry units in which ASF detections were notified during the first year after the beginning of the epidemic, we performed a Pearson and Spearman correlation analysis (significance level 0.05). The follow-up analysis was performed 3 months after detection of ASF in new areas, which led to enlargement of the infected zone in August 2015. To assess statistical differences between seasonal prevalence of ASF, we used the Fisher exact test with a Bonferroni correction for each single comparison (significance level 0.05).

Results

Thumbnail of Monthly prevalence of African swine fever in hunted wild boar, Poland, February 2014–August 2015. Error bars indicate 95% CIs.
Figure 1. Monthly prevalence of African swine fever in hunted wild boar, Poland, February 2014–August 2015. Error bars indicate 95% CIs.
Thumbnail of Monthly prevalence of African swine fever in dead (including road accident deaths) wild boar, Poland, February 2014–August 2015. Error bars indicate 95% CIs.
Figure 2. Monthly prevalence of African swine fever in dead (including road accident deaths) wild boar, Poland, February 2014–August 2015. Error bars indicate 95% CIs.
The average annual prevalence of ASFV (based on positive PCRs) among hunted wild boar was 0.12% (95% CI 0.1%–0.2%) (Table). Prevalence did not differ significantly by season. With regard to detection of ASFV in dead wild boar, the annual prevalence was 14.2% (95% CI 11.1%–17.9%) and ranged from 8.2% in spring to 24.3% in summer. The only significant difference (after taking the Bonferroni correction into account) was between summer and autumn (p<0.001). The monthly prevalence ranged from 0 to 0.7% among hunted wild boar and from 0 to 40.5% among dead wild boar (Figures 1, 2). The overall 18-month prevalence in areas under animal movement restrictions was 18.6% (95% CI 15.7%–21.8%) according to passive surveillance and 0.2% (95% CI 0.1%–0.2%) according to active surveillance.
Thumbnail of Locations of African swine fever (ASF) cases and outbreaks in Poland. Wild boar density data based on the National Forestry Service of Poland census.
Figure 3. Locations of African swine fever (ASF) cases and outbreaks in Poland. Wild boar density data based on the National Forestry Service of Poland census.
We found a correlation between the number of ASF notifications and the number of wild boar in the affected forestry units (Spearman rank correlation coefficient R = 0.90, p<0.05) in February 2015 (during the first year after detection of the first case). As of August 2015 (after detection of ASF in new areas in June 2015 and the enlargement of the infected zone), the correlation lost statistical significance (Figure 3).
Thumbnail of Phylogenetic analysis of African swine fever virus detected in pigs (outbreaks) and wild boar (cases) in Poland. Numbers on branches indicate bootstrap coefficient values. Scale bar indicates nucleotide substitutions per residue.
Figure 4. Phylogenetic analysis of African swine fever virus detected in pigs (outbreaks) and wild boar (cases) in Poland. Numbers on branches indicate bootstrap coefficient values. Scale bar indicates nucleotide substitutions per residue....
Thumbnail of Nucleotide alignment of the MGF505–2R gene variable sequence fragment (residues from 1,015 to 1,149 nt) showing point mutations and differences between isolate Georgia 2007/1 isolate and African swine fever virus field isolates from Poland. The graph was generated by using Bioedit version 7.2.5 software (Ibis Biosciences, Carlsbad, CA, USA). The dots indicate identical nucleotide residues. The variable residues are visible as a nucleotide symbol.
Figure 5. Nucleotide alignment of the MGF505–2R gene variable sequence fragment (residues from 1,015 to 1,149 nt) showing point mutations and differences between isolate Georgia 2007/1 isolate and African swine fever virus field...
The nucleotide and amino acid sequence identity of the MGF505-2R gene between ASFV isolates from Poland ranged from 99.47% to 100%. The largest cluster consisted of 42 sequences (41 from wild boar and 1 from pigs [outbreak 3]) exhibiting 100% homology between each other and indistinguishable from 2 references included for comparison: Georgia 2007/1 and Odintsovo 02/14 Russia (Figure 4). The second largest group containing 100% homologous sequences comprised 12 viruses (11 from wild boar and 1 from pigs [outbreak 2]) with 99.9% similarity to viruses of the previous group. The DNA fragment of the virus recovered from pigs identified as from the first outbreak differed slightly from those mentioned above, and the only identical sequence was from the virus from case no. 4. Sequences representing case nos. 15, 17, 41, 45, 55, and 72 formed a clearly separate and diverse cluster (within-group genetic diversity 99.5%–99.9%) (Figure 5).

Discussion

After the emergence of ASF in Poland, the preliminary forecasts had predicted that the virus would deplete the population of wild boar in the region or would spread quickly to new areas because it is inherently so highly contagious. These predicted events, however, did not occur. Nor did the concept that ASFV cannot be sustained among wild boar without spillover from domestic pigs (12,13) apply to the situation in Poland. So far, the number of cases in wild boar in Poland has greatly outnumbered outbreaks among domestic pigs. The virus has been found almost exclusively in wild boar, which seem to be the sole mediator for virus dissemination. The total area of the infected region is only ?1,500 km2. The slow spatial spread of ASF may be associated with the social behavior of wild boar, which has been studied quite extensively in Bia?owie?a Primeval Forest, straddling the Poland–Belarus border (14). Wild boar show strong site fidelity, and most (?70%) stay within 1–2 km of the center of their natal home ranges; only a relatively small percentage (5%–10%) of the population disperses from their natal range but not farther than 20–30 km. Spatial overlap of family groups is limited (15), which hampers transmission of the virus between groups by either direct contact between susceptible and sick animals or indirect contact with infected carcasses. In addition, the high virulence of the virus, which leads to the high case-fatality rate, prevents infected wild boar from long-distance movements. Therefore, long-distance dispersal of the virus by wild boar as carriers is assumed to be unlikely and mostly requires human involvement. However, specific socio-agricultural conditions in the affected region (i.e., low pig density, very few commercial farms, and small-scale national and international trade) create favorable barriers hitherto preventing the spread of the virus over long distances. It seems that the overall effect on the population was not significant and that, despite a high lethality rate of genotype II for wild boar (no. deaths/no. infected animals) (16,17), the mortality rate (no. deaths/no. animals in the affected population) seems to not be very high. Therefore, the virus does not seem to be highly contagious, which can also be explained on one side by the inherent epidemiologic properties of ASF (no airborne transmission and required contact with blood or excretions of infected animals) and on the other side from the specific behavior of wild boar described above. The results obtained in our study provide grounds for redefining the role of wild boar, which after 18-months of observation can be considered as a reservoir host for ASFV.
The complete genetic identity between a large cluster of Poland ASFV isolates with Georgia 2007/1 isolates clearly shows that the examined region, although relatively variable, can remain highly conservative for a long time (8 years). On the other hand, the genetic divergence of up to 0.5% in viruses from Poland highlighted by the presence of separate clusters on the phylogenetic tree clearly indicates that Poland has experienced a few incursions of genetically distinct ASFVs of genotype II. This finding is also supported by epidemiologic observations: 29 of 76 cases were located no farther than 5 km from the border with Belarus. With respect to outbreaks among pigs, the phylogenetic analysis clearly indicates no direct link between the 3 outbreaks. Epidemiologic investigations showed that wild boar were the most likely source of infection for domestic pigs (mainly poor biosecurity of pig holdings, enabling contact with wild animals). Overall, results of phylogenetic studies demonstrate the dynamic nature of the ASF epidemic in eastern Europe and raise serious concerns for control of ASF. We emphasize that without close and transparent collaboration between ASF-affected countries, eradication goals will be difficult to achieve.
The statistical relationship between wild boar density and the number of ASF cases was found after the 12 months after the beginning of the epidemic. The correlation was not statistically significant a few months after the virus spread to new forestry units with high wild boar density, apparently because of substantial changes in the population size in areas II and III as a result of introduced control measures (according to the most current census, the population in the aforementioned areas decreased by ?25%). Moreover, the analysis of combined data for Poland and the Baltic States, conducted by a panel of European Food Safety Authority experts, found no correlation between wild boar density and ASF case notifications (18). This issue requires clarification, and the analysis will be continuously updated. However, during the first year, all cases in wild boar were detected in areas with a wild boar density of >1 animal/km2 (Figure 3); currently, in areas where substantial efforts have been undertaken to reduce wild boar populations, the number of ASF notifications has been reduced considerably. This finding raises potential implications for ASF control strategies (i.e., maintaining the wild boar population in the affected region at the level of ?0.5–0.7 animals/km2) and can be taken into account as a control option for reducing the number of cases among wild boa

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